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nu.pattern.opencv.osx.x86_64.libopencv_java2411.dylib Maven / Gradle / Ivy

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????>N2cv6detail7BlenderEN2cv6detail14FeatherBlenderEN2cv6detail16MultiBandBlenderEN2cv6detail21BlocksGainCompensatorEN2cv6detail15GainCompensatorEN2cv6detail19ExposureCompensatorEN2cv6detail21NoExposureCompensatorEN2cv6detail18SurfFeaturesFinderEN2cv6detail17OrbFeaturesFinderEN2cv6detail21SurfFeaturesFinderGpuEN2cv6detail21BestOf2NearestMatcherEN12_GLOBAL__N_114MatchPairsBodyEN12_GLOBAL__N_110GpuMatcherEN12_GLOBAL__N_110CpuMatcherEN2cv6detail14FeaturesFinderEN2cv6detail15FeaturesMatcherE-C??6?-C??6*?????MbP?????Mb`?N2cv6detail24HomographyBasedEstimatorEN2cv6detail18BundleAdjusterBaseEN2cv6detail20BundleAdjusterReprojEN2cv6detail17BundleAdjusterRayEN2cv6detail9EstimatorE????
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N12_GLOBAL__N_19BTVL1_OCLEN12_GLOBAL__N_114BTVL1_OCL_BaseEN2cv8superres11FrameSourceEN12_GLOBAL__N_116EmptyFrameSourceEN12_GLOBAL__N_116VideoFrameSourceEN12_GLOBAL__N_118CaptureFrameSourceEN12_GLOBAL__N_117CameraFrameSourceEN12_GLOBAL__N_120VideoFrameSource_GPUE????????????????????????????????	????????????????????????????????????
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Stack trace:
also_run_disabled_testsbreak_on_failurecatch_exceptionscolorfilterparam_filteroutputprint_timerandom_seedrepeatshufflestack_trace_depthstream_result_tothrow_on_failuretest_detail.xml*GTEST_TOTAL_SHARDSGTEST_SHARD_INDEXTypeParam*DeathTest:*DeathTest/*GTEST_SHARD_STATUS_FILEDISABLED_*:*/DISABLED_*GetParam()death_test_styledeath_test_use_forkinternal_run_death_testfast./unknown fileN7testing8internal18OsStackTraceGetterEN7testing32ScopedFakeTestPartResultReporterEN7testing8internal35DefaultGlobalTestPartResultReporterEN7testing8internal38DefaultPerThreadTestPartResultReporterEN7testing4TestEN7testing8TestCaseEN7testing8internal27PrettyUnitTestResultPrinterEN7testing8internal17TestEventRepeaterEN7testing8internal24XmlUnitTestResultPrinterEN7testing8UnitTestEN7testing8internal23DefaultDeathTestFactoryEN7testing8internal12UnitTestImplEN7testing8internal13DeathTestImplEN7testing8internal16ForkingDeathTestEN7testing8internal15NoExecDeathTestEN7testing8internal13ExecDeathTestEN7testing8internal24HasNewFatalFailureHelperElist_testsThis program contains tests written using Google Test. You can use the
following command line flags to control its behavior:

Test Selection:
  @G--gtest_list_tests@D
      List the names of all tests instead of running them. The name of
      TEST(Foo, Bar) is "Foo.Bar".
  @G--gtest_filter=@YPOSTIVE_PATTERNS[@G-@YNEGATIVE_PATTERNS]@D
      Run only the tests whose name matches one of the positive patterns but
      none of the negative patterns. '?' matches any single character; '*'
      matches any substring; ':' separates two patterns.
  @G--gtest_param_filter=@YPOSITIVE_PATTERNS[@G-@YNEGATIVE_PATTERNS]@D
      Like @G--gtest_filter@D, but applies to the test's parameter. If a
      test is not parameterized, its parameter is considered to be the
      empty string.
  @G--gtest_also_run_disabled_tests@D
      Run all disabled tests too.

Test Execution:
  @G--gtest_repeat=@Y[COUNT]@D
      Run the tests repeatedly; use a negative count to repeat forever.
  @G--gtest_shuffle@D
      Randomize tests' orders on every iteration.
  @G--gtest_random_seed=@Y[NUMBER]@D
      Random number seed to use for shuffling test orders (between 1 and
      99999, or 0 to use a seed based on the current time).

Test Output:
  @G--gtest_color=@Y(@Gyes@Y|@Gno@Y|@Gauto@Y)@D
      Enable/disable colored output. The default is @Gauto@D.
  -@G-gtest_print_time=0@D
      Don't print the elapsed time of each test.
  @G--gtest_output=xml@Y[@G:@YDIRECTORY_PATH@G/@Y|@G:@YFILE_PATH]@D
      Generate an XML report in the given directory or with the given file
      name. @YFILE_PATH@D defaults to @Gtest_details.xml@D.

Assertion Behavior:
  @G--gtest_death_test_style=@Y(@Gfast@Y|@Gthreadsafe@Y)@D
      Set the default death test style.
  @G--gtest_break_on_failure@D
      Turn assertion failures into debugger break-points.
  @G--gtest_throw_on_failure@D
      Turn assertion failures into C++ exceptions.
  @G--gtest_catch_exceptions=0@D
      Do not report exceptions as test failures. Instead, allow them
      to crash the program or throw a pop-up (on Windows).

Except for @G--gtest_list_tests@D, you can alternatively set the corresponding
environment variable of a flag (all letters in upper-case). For example, to
disable colored text output, you can either specify @G--gtest_color=no@D or set
the @GGTEST_COLOR@D environment variable to @Gno@D.

For more information, please read the Google Test documentation at
@Ghttp://code.google.com/p/googletest/@D. If you find a bug in Google Test
(not one in your own code or tests), please report it to
@G@D.
N7testing8internal26GoogleTestFailureExceptionEN7testing8internal27OsStackTraceGetterInterfaceEN7testing31TestPartResultReporterInterfaceEN7testing17TestEventListenerEN7testing22EmptyTestEventListenerEN7testing8internal16DeathTestFactoryEN7testing8internal9DeathTestEN7testing8internal11ThreadLocalINSt3__16vectorINS0_9TraceInfoENS2_9allocatorIS4_EEEEE11ValueHolderEN7testing8internal26ThreadLocalValueHolderBaseEN7testing8internal11ThreadLocalIPNS_31TestPartResultReporterInterfaceEE11ValueHolderE>@?????@22PerfEarlyExitExceptionN4perf8TestBase21PerfSkipTestExceptionEN7testing11EnvironmentEN4perf8TestBaseEN12_GLOBAL__N_115PerfEnvironmentEZN4perf8TestBase10_calibrateEvE7_helper=
??G?=N2cv9videostab17WeightingDeblurerEN2cv9videostab12DeblurerBaseEN2cv9videostab15VideoFileSourceEN2cv9videostab12IFrameSourceE??p}?N2cv9videostab26PyrLkRobustMotionEstimatorEN2cv9videostab22IGlobalMotionEstimatorEN2cv9videostab14ColorInpainterEN2cv9videostab25ConsistentMosaicInpainterEN2cv9videostab15MotionInpainterEN2cv9videostab18InpaintingPipelineEN2cv9videostab21ColorAverageInpainterEN2cv9videostab13InpainterBaseE????????

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 0@`unknown exceptionstd::exceptioncv::Exceptionorg/opencv/core/CvExceptionjava/lang/Exceptiond.cols == 1 || d.rows == 1diag/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/include/opencv2/core/mat.hppUnknown exception in JNI code {core::minMaxLoc()}Unknown exception in JNI code {core::getTextSize()}DynamicPyramidGridFASTSTARSIFTSURFORBMSERGFTTHARRISSimpleBlobDenseBRISKSpecified feature detector type is not supported.create/Users/jason/Projects/opencv/opencv-2.4.11/modules/java/generator/src/cpp/features2d_manual.hppFlannBasedBruteForceBruteForce-L1BruteForce-HammingBruteForce-HammingLUTBruteForce-SL2Specified descriptor matcher type is not supported.OpponentBRIEFFREAKSpecified descriptor extractor type is not supported.ONEWAYFERNSpecified generic descriptor matcher type is not supported.unknown/unsupported type of '%s' parameter == %dwrite/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/algorithm.cpp!nestedAlgo.empty()readNo parameter '%s' is foundsetParameter '%s' is readonlyWrong argument type in the setterWrong parameter type in the setterUnknown/unsupported parameter typegetWrong argument typeparamTypeparamHelpargType == Param::INT || argType == Param::BOOLEAN || argType == Param::REAL || argType == Param::STRING || argType == Param::MAT || argType == Param::MAT_VECTOR || argType == Param::ALGORITHM || argType == Param::SHORT || argType == Param::FLOAT || argType == Param::UNSIGNED_INT || argType == Param::UINT64 || argType == Param::UCHARaddParam_Argument error: the setter method was called for the parameter '' of the algorithm '', the parameter has  type, so it should be set by integer, unsigned integer, uint64, unsigned char, boolean, float or double value, so it should be set by integer value, but the setter was called with  valueintegershortbooleandoublestringcv::Matstd::vectoralgorithmunsigned intunsigned int64unsigned chargetNameOfTypeArgument error: the getterso it should be get as integer, unsigned integer, uint64, boolean, unsigned char, float or double value, so it should be get as integer, unsigned integer, uint64, unsigned char, float or double value, so it should be get as integer value, so it should be get as float or double value, but the getter was called to get a i == 0 || vec[i].first != vec[i-1].firstaddCustom memory allocator is not supportedcvSetMemoryManager/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/alloc.cppFailed to allocate %lu bytesOutOfMemoryErrorscn == 1convertAndUnrollScalar/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/arithm.cppop == CMP_LT || op == CMP_LE || op == CMP_EQ || op == CMP_NE || op == CMP_GE || op == CMP_GTcompareThe operation is neither 'array op array' (where arrays have the same size and the same type), nor 'array op scalar', nor 'scalar op array'The lower bounary is neither an array of the same size and same type as src, nor a scalarinRangeThe upper bounary is neither an array of the same size and same type as src, nor a scalar((int)lbScalar ^ (int)ubScalar) == 0lb.type() == ub.type()src.size == dst.size && src.type() == dst.type()cvNotsrc1.size == dst.size && src1.type() == dst.type()cvAndcvOrcvXorcvAndScvOrScvXorSsrc1.size == dst.size && src1.channels() == dst.channels()cvAddcvSubcvAddScvSubRScvMulsrc2.size == dst.size && src2.channels() == dst.channels()cvDivcvAddWeightedcvAbsDiffcvAbsDiffSsrc1.size == dst.size && dst.type() == CV_8UcvInRangecvInRangeScvCmpcvCmpScvMincvMaxcvMinScvMaxSThe operation is neither 'array op array' (where arrays have the same size and type), nor 'array op scalar', nor 'scalar op array'binary_op(mask.type() == CV_8UC1 || mask.type() == CV_8SC1)mask.size == src1.sizeThe operation is neither 'array op array' (where arrays have the same size and the same number of channels), nor 'array op scalar', nor 'scalar op array'arithm_opsrc2.type() == CV_64F && (src2.rows == 4 || src2.rows == 1)When the input arrays in add/subtract/multiply/divide functions have different types, the output array type must be explicitly specifiedEither all the pointers should be null or they all should be non-nullcvSetIPLAllocators/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/array.cppNon-positive width or heightcvCreateMatHeaderInvalid matrix typecvInitMatHeaderNon-positive cols or rowscvReleaseMatBad CvMat headercvCloneMatNULL matrix header pointercvInitMatNDHeaderinvalid array data typeNULL  pointernon-positive or too large number of dimensionsone of dimesion sizes is non-positiveThe array is too bigcvCreateMatNDHeaderBad CvMatND headercvCloneMatNDsrc->dims <= CV_MAX_DIM_dst.data == data0Incorrect number of arrayscvInitNArrayIteratorSome of required array pointers is NULLIterator pointer is NULLCOI set is not allowed hereNumber of dimensions is the same for all arraysData type is not the same for all arraysNumber of channels is not the same for all arraysDepth is not the same for all arraysMask should have 8uC1 or 8sC1 data typeDimension sizes are the same for all arrayscvCreateSparseMatbad number of dimensionscvReleaseSparseMatInvalid sparse array headercvCloneSparseMatInvalid sparse matrix headercvInitSparseMatIteratorNULL iterator pointerData is already allocatedcvCreateDataunrecognized or unsupported array typecvSetDataFor multidimensional array only CV_AUTOSTEP is allowed herecvReleaseDataOnly continuous nD arrays are supported herecvGetRawDatacvGetElemTypecvGetDimsbad dimension indexcvGetDimSizeArray should be CvMat or IplImagecvGetSizecvGetSubRectcvGetRowscvGetColscvGetDiagThe number of channels must be 1, 2, 3 or 4cvScalarToRawDatacvRawDataToScalarindex is out of rangecvPtr1DcvPtr2DCOI must be non-null in case of planar imagescvPtr3DNULL pointer to indicescvPtrNDcvGet1DcvGet2DcvGetReal1DcvGetReal* support only single-channel arrayscvGetReal2DcvGetReal3DcvGetRealNDcvSet1DcvSet2DcvSetReal1DcvSetReal* support only single-channel arrayscvSetReal2DcvSetReal3DcvSetRealNDNULL array pointer is passedcvGetMatThe matrix has NULL data pointerThe image has NULL data pointerImages with planar data layout should be used with COI selectedThe image is interleaved and has over CV_CN_MAX channelsPixel order should be used with coi == 0Input array has NULL data pointerUnrecognized or unsupported array typeNULL pointer to array or destination headercvReshapeMatNDNone of array parameters is changed: dummy call?Non-positive or too large number of dimensionsNew dimension sizes are not specifiedThe output header should be CvMat or CvMatNDThe matrix is not continuous so the number of rows can not be changedThe total number of matrix elements is not divisible by the new number of rowsThe total matrix width is not divisible by the new number of columnsThe output header should be CvMatNDThe input array must be CvMatNDThe last dimension full size is not divisible by new number of channelsSimultaneous change of shape and number of channels is not supported. Do it by 2 separate callsNon-continuous nD arrays are not supportedOne of new dimension sizes is non-positiveNumber of elements in the original and reshaped array is differentCOI is not supported by this operationcvReshapeCOI is not supportedThe matrix is not continuous, thus its number of rows can not be changedcvGetImagenull pointer to headercvInitImageHeaderBad input roiUnsupported formatBad input originBad input aligncvReleaseImageHeadercvReleaseImagecvSetImageROIrect.width >= 0 && rect.height >= 0 && rect.x < image->width && rect.y < image->height && rect.x + rect.width >= (int)(rect.width > 0) && rect.y + rect.height >= (int)(rect.height > 0)cvResetImageROINull pointer to imagecvGetImageROIcvSetImageCOIcvGetImageCOIBad image headercvCloneImageUnknown type of term criteriacvCheckTermCriteriaIterations flag is set and maximum number of iterations is <= 0Accuracy flag is set and epsilon is < 0Neither accuracy nor maximum iterations number flags are set in criteria typecvGetMatNDOne of indices is out of rangeicvGetNodePtricvDeleteNodeGRAYRGBBGRBGRA-false  , --=[]func != 0split/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/convert.cpp!_mv.fixedType() || CV_MAT_TYPE(_mv.flags) == m.depth()mv && n > 0mergemv[i].size == mv[0].size && mv[i].depth() == depth0 < cn && cn <= CV_CN_MAXsrc && nsrcs > 0 && dst && ndsts > 0 && fromTo && npairs > 0mixChannelsj < nsrcs && src[j].depth() == depthi1 >= 0 && j < ndsts && dst[j].depth() == depthfromTo.size()%2 == 0 && nsrc > 0 && ndst > 00 <= coi && coi < src.channels()extractChannelsrc.size == dst.size && src.depth() == dst.depth()insertChannel0 <= coi && coi < dst.channels() && src.channels() == 1convertScaleAbsconvertTointerpolation == 0LUT(lutcn == cn || lutcn == 1) && lut.total() == 256 && lut.isContinuous() && (src.depth() == CV_8U || src.depth() == CV_8S)Unknown/unsupported norm typenormalizenz > 0cvSplitdvec[j].size() == src.size()dvec[j].depth() == src.depth()dvec[j].channels() == 1i < src.channels()cvMergesvec[j].size == dst.size && svec[j].depth() == dst.depth() && svec[j].channels() == 1 && i < dst.channels()src.size == dst.size && dst.type() == CV_8UC(src.channels())cvConvertScaleAbssrc.size == dst.size && src.channels() == dst.channels()cvConvertScaledst.size() == src.size() && dst.type() == CV_MAKETYPE(lut.depth(), src.channels())cvLUTdst.size() == src.size() && src.channels() == dst.channels()cvNormalizechannels() == CV_MAT_CN(dtype)copyTo/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/copy.cppmask.depth() == CV_8U && (mcn == 1 || mcn == cn)size() == mask.size()checkScalar(value, type(), _value.kind(), _InputArray::MAT )mask.empty() || mask.type() == CV_8Usrc.dims <= 2fliprepeatny > 0 && nx > 0maskarr == 0cvCopysrc.depth() == dst.depth() && src.size == dst.size(coi1 != 0 || src.channels() == 1) && (coi2 != 0 || dst.channels() == 1)src.channels() == dst.channels()src.type() == dst.type() && src.size() == dst.size()cvFlipsrc.type() == dst.type() && dst.rows % src.rows == 0 && dst.cols % src.cols == 0cvRepeatcvCreateChildMemStorage/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/datastructs.cppcvReleaseMemStoragecvClearMemStoragecvSaveMemStoragePoscvRestoreMemStoragePosNULL storage pointercvMemStorageAllocToo large memory block is requestedrequested size is negative or too bigcvCreateSeqSpecified element size doesn't match to the size of the specified element type (try to use 0 for element type)cvSetSeqBlockSizeStorage block size is too small to fit the sequence elementscvSeqElemIdxcvCvtSeqToArraycvMakeSeqHeaderForArrayElement size doesn't match to the size of predefined element type (try to use 0 for sequence element type)cvStartAppendToSeqcvStartWriteSeqcvFlushSeqWritercvEndWriteSeqcvCreateSeqBlockcvStartReadSeqcvChangeSeqBlockcvGetSeqReaderPoscvSetSeqReaderPoscvSeqPushcvSeqPopcvSeqPushFrontcvSeqPopFrontcvSeqInsertcvSeqRemoveInvalid indexNULL sequence pointercvSeqPushMultinumber of removed elements is negativecvSeqPopMulticvClearSeqInvalid sequence headercvSeqSliceBad sequence slicecvSeqRemoveSlicestart slice index is out of rangeInvalid destination sequence headercvSeqInsertSliceSource is not a sequence nor matrixThe source array must be 1d coninuous vectorSource and destination sequence element sizes are different.Bad input sequencecvSeqSortNull compare functioncvSeqSearchNull element pointercvSeqPartitioncvCreateSetcvSetAddcvCreateGraphcvClearGraphcvGraphAddVtxcvGraphRemoveVtxByPtrThe vertex does not belong to the graphcvGraphRemoveVtxThe vertex is not foundcvFindGraphEdgeByPtrgraph pointer is NULLcvFindGraphEdgecvGraphAddEdgeByPtrvertex pointers coinside (or set to NULL)cvGraphAddEdgecvGraphRemoveEdgeByPtrcvGraphRemoveEdgecvGraphVtxDegreeByPtrcvGraphVtxDegreeNull graph pointercvCreateGraphScannergraph->storage != 0Null double pointer to graph scannercvReleaseGraphScannerNull graph scannercvNextGraphItemInvalid graph pointercvCloneGraphcvTreeToNodeSeqcvInsertNodeIntoTreecvRemoveNodeFromTreeframe node could not be deletedcvInitTreeNodeIteratorcvNextTreeNodecvPrevTreeNode_points.type() == CV_32F && !_points.empty()buildnlabels == nvecmat.isContinuous() && vecmat.type() == CV_32F && vecmat.total() == (size_t)points.colsfindNearestK > 0 && (normType == NORM_L2 || normType == NORM_L1)lowerBound.size == upperBound.size && lowerBound.isContinuous() && upperBound.isContinuous() && lowerBound.type() == upperBound.type() && lowerBound.type() == CV_32F && lowerBound.total() == (size_t)ptdimsfindOrthoRangeidxmat.isContinuous() && idxmat.type() == CV_32S && (idxmat.cols == 1 || idxmat.rows == 1)getPointslabelsmat.isContinuous()(unsigned)k < (unsigned)points.rows(unsigned)ptidx < (unsigned)points.rowsgetPointicvInitMemStorageicvDestroyMemStorageicvGoNextMemBlockicvGrowSeqThe sequence has NULL storage pointericvSeqElemsClearFlagsicvSeqFindNextElemvals[ofs[k]] <= pivotmedianPartitionvals[ofs[k]] >= pivotstd::abs(more - less) <= 1connectivity == 8 || connectivity == 4LineIterator/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/drawing.cpp0 <= thickness && thickness <= 255line0 <= shift && shift <= XY_SHIFTthickness <= 255rectangleradius >= 0 && thickness <= 255 && 0 <= shift && shift <= XY_SHIFTcircleaxes.width >= 0 && axes.height >= 0 && thickness <= 255 && 0 <= shift && shift <= XY_SHIFTellipsebox.size.width >= 0 && box.size.height >= 0 && thickness <= 255fillConvexPolypts && npts && ncontours >= 0 && 0 <= shift && shift <= XY_SHIFTfillPolypts && npts && ncontours >= 0 && 0 <= thickness && thickness <= 255 && 0 <= shift && shift <= XY_SHIFTpolylinespoints.checkVector(2, CV_32S) >= 0p.checkVector(2, CV_32S) >= 0cvDrawContourselem_type == CV_32SC2pt1 && pt2cvClipLineiterator != 0cvInitLineIteratortext != 0 && _font != 0cvPutTextfont != 0 && hscale > 0 && vscale > 0 && thickness >= 0cvInitFontcvGetTextSize0 <= shift && shift <= XY_SHIFT && thickness >= 0PolyLineUnknown font typegetFontDatatype == CV_32FC1 || type == CV_32FC2 || type == CV_64FC1 || type == CV_64FC2dft/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/dxt.cppThis mode (using nonzero_rows with a single-column matrix) breaks the function's logic, so it is prohibited.
For fast convolution/correlation use 2-column matrix or single-row matrix insteadtype == srcB.type() && srcA.size() == srcB.size()mulSpectrumstype == CV_32FC1 || type == CV_64FC1dctOdd-size DCT's are not implementedsrc.size == dst.sizecvDFTdst.data == dst0.datasrcA.size == dst.size && srcA.type() == dst.type()cvMulSpectrumscvDCTfactors[0] == factors[nf-1]DFTThe library is compiled without OpenGL supportIntGetProcAddress/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/gl_core_3_1.cpp.could not open directory: %sglob_rec/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/glob.cpp0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rowsGpuMat/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/gpumat.cpp0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rowsreshapeBad new number of rowsThe total width is not divisible by the new number of channelsmask.empty() || mask.type() == CV_8UC1setTounknown functionOpenCV Error: () in , file sharedMemPerBlock/Users/jason/Projects/opencv/opencv-2.4.11/modules/dynamicuda/include/opencv2/dynamicuda/dynamicuda.hppqueryMemoryfreeMemorytotalMemorysupportsisCompatiblemajorVersionminorVersionmultiProcessorCountsetDevicegetDeviceresetDevicedeviceSupportsbuiltWithhashasPtxhasBinhasEqualOrLessPtxhasEqualOrGreaterhasEqualOrGreaterPtxhasEqualOrGreaterBincopycopyWithMaskconvertmallocPitchThe library is compiled without CUDA supportmat.rows == mat.cols && (type == CV_32F || type == CV_64F)determinant/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/lapack.cpptype == CV_32F || type == CV_64Finvertm == nmethod == DECOMP_LU || method == DECOMP_CHOLESKYtype == _src2.type() && (type == CV_32F || type == CV_64F)solve(method != DECOMP_LU && method != DECOMP_CHOLESKY) || is_normal || src.rows == src.colsThe function can not solve under-determined linear systemssrc.rows == src.colseigenw.type() == u.type() && u.type() == vt.type() && u.data && vt.data && w.databackSubstu.cols >= nm && vt.rows >= nm && (w.size() == Size(nm, 1) || w.size() == Size(1, nm) || w.size() == Size(vt.rows, u.cols))rhs.data == 0 || (rhs.type() == type && rhs.rows == m)rows == mat->colscvDetsrc.type() == dst.type() && src.rows == dst.cols && src.cols == dst.rowscvInvertA.type() == x.type() && A.cols == x.rows && x.cols == b.colscvSolvep == evects0.datacvEigenVVp == evals0.dataw.type() == type && (w.size() == cv::Size(nm,1) || w.size() == cv::Size(1, nm) || w.size() == cv::Size(nm, nm) || w.size() == cv::Size(n, m))cvSVDu.type() == typev.type() == typeu.size() == svd.u.size()v.size() == svd.vt.size()cvSVBkSb_SVDcomputeX.size == Y.size && type == Y.type() && (depth == CV_32F || depth == CV_64F)magnitude/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/mathfuncs.cppphasecartToPolarMag.empty() || (Angle.size == Mag.size && type == Mag.type() && (depth == CV_32F || depth == CV_64F))polarToCartdepth == CV_32F || depth == CV_64Fexplogpowthe value at (%d, %d)=%g is out of rangecheckRangea.depth() == CV_32FpatchNaNsMag.size() == X.size() && Mag.type() == X.type()cvCartToPolarAngle.size() == X.size() && Angle.type() == X.type()Mag.size() == Angle.size() && Mag.type() == Angle.type()cvPolarToCartX.size() == Angle.size() && X.type() == Angle.type()Y.size() == Angle.size() && Y.type() == Angle.type()src.type() == dst.type() && src.size == dst.sizecvExpcvLogcvPowctype == CV_32F || ctype == CV_64FsolveCubic(coeffs.size() == Size(n0, 1) || coeffs.size() == Size(n0+1, 1) || coeffs.size() == Size(1, n0) || coeffs.size() == Size(1, n0+1))CV_MAT_DEPTH(ctype) >= CV_32F && CV_MAT_CN(ctype) <= 2solvePolycoeffs0.rows == 1 || coeffs0.cols == 1_roots.data == _roots0.datacvSolveCubic_r.data == _r0.datacvSolvePolytype == B.type() && (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2)gemm/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/matmul.cppa_size.width == lena_size.height == lenC.type() == type && (((flags&GEMM_3_T) == 0 && C.rows == d_size.height && C.cols == d_size.width) || ((flags&GEMM_3_T) != 0 && C.rows == d_size.width && C.cols == d_size.height))type == CV_64FC2scn == m.cols || scn + 1 == m.colstransformscn + 1 == m.cols && (depth == CV_32F || depth == CV_64F)perspectiveTransformsrc1.type() == src2.type()scaleAdddata && nsamples > 0calcCovarMatrix_mean.size() == sizedata[i].size() == size && data[i].type() == typesrc.size() > 0(*each).size() == size && (*each).type() == type((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0)nsamples > 0mean.size() == sizetype == v2.type() && type == icovar.type() && sz == v2.size() && len == icovar.rows && len == icovar.colsMahalanobissrc.channels() == 1mulTransposeddelta.channels() == 1 && (delta.rows == src.rows || delta.rows == 1) && (delta.cols == src.cols || delta.cols == 1)mat.type() == type() && mat.size == size && func != 0dotdata.channels() == 1operator()_mean.size() == mean_szcomputeVarretainedVariance > 0 && retainedVariance <= 1mean.data && eigenvectors.data && ((mean.rows == 1 && mean.cols == data.cols) || (mean.cols == 1 && mean.rows == data.rows))projectmean.data && eigenvectors.data && ((mean.rows == 1 && eigenvectors.rows == data.cols) || (mean.cols == 1 && eigenvectors.rows == data.rows))backProject(D.rows == ((flags & CV_GEMM_A_T) == 0 ? A.rows : A.cols)) && (D.cols == ((flags & CV_GEMM_B_T) == 0 ? B.cols : B.rows)) && D.type() == A.type()cvGEMMdst.depth() == src.depth() && dst.channels() == m.rowscvTransformdst.type() == src.type() && dst.channels() == m.rows-1cvPerspectiveTransformcvScaleAddvecarr != 0 && count >= 1cvCalcCovarMatrix(evals0.cols == 1 || evals0.rows == 1) && ecount0 <= ecount && evects0.cols == evects.cols && evects0.rows == ecount0cvCalcPCAmean0.data == mean.datadst.cols <= evects.rows && dst.rows == data.rowscvProjectPCAdst.rows <= evects.rows && dst.cols == data.colsdst0.data == dst.datadata.cols <= evects.rows && dst.rows == data.rowscvBackProjectPCAdata.rows <= evects.rows && dst.cols == data.colsCV_MAT_CN(_type) == e.a.channels()assign/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/matop.cppUnknown operationInvalid matrix initializer type0 <= d && d <= CV_MAX_DIM && _sizes/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/matrix.cppstep[dims-1] == (size_t)CV_ELEM_SIZE(flags)m.dims >= 2Matm.dims <= 2rangesr == Range::all() || (0 <= r.start && r.start < r.end && r.end <= m.size[i])dims <= 2img->dataOrder == IPL_DATA_ORDER_PIXELimg->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0operator _IplImagenelems <= (size_t)size.p[0]pop_back(int)nelems >= 0reserveresizepush_backCOI is not supported by the functioncvarrToMatseq->total > 0 && CV_ELEM_SIZE(seq->flags) == seq->elem_sizedims <= 2 && step[0] > 0locateROIadjustROICV_IS_IMAGE(arr)extractImageCOI0 <= coi && coi < mat.channels()insertImageCOIch.size == mat.size && ch.depth() == mat.depth() && 0 <= coi && coi < mat.channels()cn <= 4scalarToRawDatai < 0getMat0 <= i && i < (int)vv.size()This method is not implemented for oclMat yetk == STD_VECTOR_MAT0 <= i && i < (int)v.size()getMatVectorThis function in deprecated, do not use itgetGlBuffergetGlTexturek == GPU_MATgetGpuMatk == OPENGL_BUFFERgetOGlBufferk == OPENGL_TEXTUREgetOGlTexture2Dsizei < (int)vv.size()totaltypeempty!fixedSize() || ((Mat*)obj)->size.operator()() == _sz!fixedType() || ((Mat*)obj)->type() == mtype!fixedSize() || ((gpu::GpuMat*)obj)->size() == _sz!fixedType() || ((gpu::GpuMat*)obj)->type() == mtype!fixedSize() || ((ogl::Buffer*)obj)->size() == _sz!fixedType() || ((ogl::Buffer*)obj)->type() == mtype!fixedSize() || ((Mat*)obj)->size.operator()() == Size(cols, rows)!fixedSize() || ((gpu::GpuMat*)obj)->size() == Size(cols, rows)!fixedSize() || ((ogl::Buffer*)obj)->size() == Size(cols, rows)!fixedType() && !fixedSize()CV_MAT_TYPE(mtype) == m.type()m.dims == dimsm.size[j] == sizes[j]mtype == type0 || (CV_MAT_CN(mtype) == 1 && ((1 << type0) & fixedDepthMask) != 0)dims == 2 && ((sizes[0] == sz.height && sizes[1] == sz.width) || (allowTransposed && sizes[0] == sz.width && sizes[1] == sz.height))dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0)!fixedSize() || len == vv.size()mtype == type0 || (CV_MAT_CN(mtype) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0)!fixedSize() || len == ((vector*)v)->size() / eszVectors with element size %d are not supported. Please, modify OutputArray::create()
create() called for the missing output array!fixedSize() || len == len0v[j].empty()i < (int)v.size()!fixedType() || (CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0)!fixedSize()releasecleark == MATgetMatRefgetGpuMatRefgetOGlBufferRefgetOGlTexture2DRef!src[i].empty() && src[i].dims <= 2 && src[i].rows == src[0].rows && src[i].type() == src[0].type()hconcat!src[i].empty() && src[i].dims <= 2 && src[i].cols == src[0].cols && src[i].type() == src[0].type()vconcatsetIdentitytracesrc.dims <= 2 && esz <= (size_t)32transposesrc.size() == dst.size() && (src.cols == 1 || src.rows == 1)m.dims <= 2 && m.rows == m.colscompleteSymmdims <= 2 && m.dims <= 2 && size() == m.size() && tp == m.type() && ((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1))crossreduceop == CV_REDUCE_SUM || op == CV_REDUCE_MAX || op == CV_REDUCE_MIN || op == CV_REDUCE_AVGUnsupported combination of input and output array formatssrc.dims <= 2 && src.channels() == 1 && func != 0sortsortIdxdata0.dims <= 2 && type == CV_32F && K > 0kmeansN >= K(best_labels.cols == 1 || best_labels.rows == 1) && best_labels.cols*best_labels.rows == N && best_labels.type() == CV_32S && best_labels.isContinuous()(unsigned)labels[i] < (unsigned)Kcounters[k] != 0src.rows == dst.cols && src.cols == dst.rows && src.type() == dst.type()cvTransposesrcA.size() == dst.size() && srcA.type() == dst.type()cvCrossProductThe reduced dimensionality index is out of rangecvReduceThe output array size is incorrectInput and output arrays must have the same number of channelsThe function only supports 32sC1 and 32fC1 datatypescvRangesrc.size() == idx.size() && idx.type() == CV_32S && src.data != idx.datacvSortidx0.data == idx.datasrc.size() == dst.size() && src.type() == dst.type()!centers.empty()cvKMeans2centers.rows == cluster_countcenters.cols == data.colscenters.depth() == data.depth()labels.isContinuous() && labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1) && labels.cols + labels.rows - 1 == data.rows_arrays && (_ptrs || _planes)initnarrays <= 1000arrays[i] != 0A.size == arrays[i0]->sizeA.step[d-1] == A.elemSize()m != 0 && _idxposgetConvertElemgetConvertScaleElemmSparseMat_sizes && 0 < d && d <= CV_MAX_DIM_sizes[i] > 0hdrhdr != 0hdr && hdr->dims == 1ptrhdr && hdr->dims == 2hdr && hdr->dims == 3erasenormType == NORM_INF || normType == NORM_L1 || normType == NORM_L2normOnly 32f and 64f are supportedminMaxLoc0 <= _dims && _dims <= CV_MAX_DIMsetSizes >= 0src.data != dst.datasortIdx_cn == 2 || cn == 3 || cn == 4setVertexArray/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/opengl_interop.cppdepth == CV_16S || depth == CV_32S || depth == CV_32F || depth == CV_64Fcn == 3 || cn == 4setColorArraycn == 3setNormalArraydepth == CV_8S || depth == CV_16S || depth == CV_32S || depth == CV_32F || depth == CV_64Fcn >= 1 && cn <= 4setTexCoordArraythrow_noglicvSetOpenGlFuncTab/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/opengl_interop_deprecated.cppGlBuffercopyFrombindunbindmapHostunmapHostmapDeviceunmapDeviceGlTextureGlFontdrawrenderGlCameralookAtsetCameraPossetScalesetProjectionMatrixsetPerspectiveProjectionsetOrthoProjectionsetupProjectionMatrixsetupModelViewMatrixicvCheckGlErrorMATLABCSVPYTHONNUMPYUnknown formatter/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/out.cpp[writeMat, 
  writeElemsuint8int8uint16int16int32float32float64uint64array([], type=''){}gcd.gzInvalid filenamegetDefaultObjectName/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/persistence.cpp_NULL double pointer to file storagecvReleaseFileStorageInvalid pointer to file storagecvGetFileNodeNull key elementThe node is neither a map nor an empty collectionDuplicated keycvGetFileNodeByNameNull element namecvGetRootFileNodeNULL or empty filenameNULL or empty buffercvOpenFileStorageCV_STORAGE_APPEND and CV_STORAGE_MEMORY are not currently compatibleAppending data to compressed file is not implementedrtwta+t.xml.XML.XmlUTF-16utf-16Utf-16UTF-16 XML encoding is not supported! Use 8-bit encoding
strlen(encoding) < 1000


Could not find  in the end of file.
r+t 
%YAML:1.0
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%YAML:cvStartWriteStructThe file storage is opened for readingcvEndWriteStructcvWriteIntcvWriteRealcvWriteStringcvWriteCommentcvStartNextStreamcvWriteRawDataNegative number of elementsNull data pointercvStartReadRawDataNull pointer to source file node or readerThe file node should be a numerical scalar or a sequencecvReadRawDataSliceNull pointer to reader or destination arrayThe readed sequence is a scalar, thus len must be 1The sequence element is not a numerical scalarThe sequence slice does not fit an integer number of recordsNull pointers to source file node or destination arraycvReadRawDatacvWriteFileNodeopencv-sequenceopencv-sequence-treeopencv-graphopencv-sparse-matrixopencv-imageopencv-matrixopencv-nd-matrixInvalid type infocvRegisterTypeSome of required function pointers (is_instance, release, read or write) are NULLType name should start with a letter or _Type name should contain only letters, digits, - and _NULL double pointercvReleaseUnknown object typerelease function pointer is NULLNULL structure pointercvCloneclone function pointer is NULLcvReadThe node does not represent a user object (unknown type?)cvWriteNull pointer to the written objectUnknown objectThe object does not have write functionNULL object pointercvSaveCould not open the file storage. Check the path and permissionsCould not find the/an object in file storagecvLoadNULL memory storage is passed - the loaded dynamic structure can not be storedExtra closing '%c'operator<<The closing '%c' does not match the opening '%c'Incorrect element name %sInvalid fs.statelen % elemSize == 0writeRawelem_size > 0readRawUnknown array typeCV_IS_SPARSE_MAT(m)icvClose
%s(%d): %sThe storage is not openedicvPutsicvGetsSome collection type: CV_NODE_SEQ or CV_NODE_MAP must be specifiedicvXMLStartWriteStructtype_idAn attempt to add element without a key to a map, or add element with key to sequenceicvXMLWriteTagA single _ is a reserved tag nameClosing tag should not include any attributesKey should start with a letter or _Key name may only contain alphanumeric characters [a-zA-Z0-9], '-' and '_'An extra closing tagicvXMLEndWriteStructNull string pointericvXMLWriteStringThe written string is too longltgtampaposquot#x%02xNull commenticvXMLWriteCommentDouble hyphen '--' is not allowed in the comments
Some collection type - CV_NODE_SEQ or CV_NODE_MAP, must be specifiedicvYMLStartWriteStruct!!%s %c!!%sEndWriteStruct w/o matching StartWriteStructicvYMLEndWriteStructicvYMLWriteStringx%02xicvYMLWriteComment...
---
'opencv_storage tag is missing tag is missingicvXMLSkipSpacesComments are not allowed hereInvalid character in the streamToo long string or a last string w/o newlineicvXMLParseTagPreliminary end of the streamTag should start with '<'Unknown tag typeName should start with a letter or underscoreClosing tag should not contain any attributesAttribute name should be followed by '='Attribute value should be put into single or double quotesInvalid closing tag for )Mismatched closing tagThere should be space between literalsInvalid numeric value (inconsistent explicit type specification?)Literal " is not allowed within a string. Use "Closing " is expectedLiteral ' or > are not allowed. Use ' or >Invalid numeric value in the stringInvalid character in the symbol entity nameToo long string literalThe actual type is different from the specified typeicvFSCreateCollectionicvProcessSpecialDoubleBad format of floating-point constant%YAML:1.icvYMLParseUnsupported YAML version (it must be 1.x)---The YAML streams must start with '---', except the first oneInvalid or unsupported syntax...Only collections as YAML streams are supported by this parsericvYMLSkipSpacesIncorrect indentationTabs are prohibited in YAML!Invalid charactericvYMLParseValueEmpty type nameintfloatThe wrong closing bracketMissing , between the elementsComplex keys are not supportedMulti-line text literals are not supportedBlock sequence elements must be preceded with '-'icvYMLParseKeyKey may not start with '-'Missing ':'An empty keyInvalid data type specificationicvDecodeFormatToo long data type specification%d.%.8e.Nan-.Inf.Inf%.16eelements with keys can not be written to sequenceicvXMLWriteScalaricvYMLWriteThe key is an emptyThe key is too longKey must start with a letter or _Key names may only contain alphanumeric characters [a-zA-Z0-9], '-', '_' and ' 'Unknown type of file nodeicvWriteFileNodeicvReleaseSeqflagscountdtSome of essential sequence attributes are absenticvReadSeqThe sequence flags are invalidcurveclosedholeuntypedheader_dtheader_user_dataOne of "header_dt" and "header_user_data" is there, while the other is notrectoriginOnly one of "header_user_data", "rect" and "origin" tags may occurxywidthheightcolordataThe image data is not found in file storageThe number of stored elements does not match to "count"Too complex format for the matrixicvDecodeSimpleFormatrecursive0FalseFALSEsequenceslevel hole curve untypedThe size of element calculated from "dt" and the elem_size do not matchicvGetFormatSize of sequence element (elem_size) is inconsistent with seq->flags%ui%uu%d%cThe size of header calculated from "header_dt" is greater than header_sizeicvWriteHeaderDataopencv-sequence-tree instance should contain a field "sequences" that should be a sequenceicvReadSeqTreeAll the sequence tree nodes should contain "level" fieldicvReleaseGraphvertex_dtedge_dtvertex_countedge_countSome of essential graph attributes are absenticvReadGraphorientedGraph edges should start with 2 integers and a float%df%sverticesedgesNo edges dataNo vertices dataSome of stored vertex indices are out of rangeDuplicated edge has occured2if%ssizesSome of essential matrix attributes are absenticvReadSparseMatCould not determine sparse matrix dimensionalityiThe matrix data is not found in file storageSparse matrix data is corruptedSome of essential image attributes are absenticvReadImagelayoutinterleavedOnly interleaved images can be readThe matrix size does not match to the number of stored elementsroicoiImages with planar data layout are not supportedicvWriteImagetop-leftbottom-leftplanarrowscolsicvReadMaticvReadMatNDCould not determine the matrix dimensionality_param1.channels() == 1 && (_param1.rows == 1 || _param1.cols == 1) && (_param1.rows + _param1.cols - 1 == cn || _param1.rows + _param1.cols - 1 == 1 || (_param1.size() == Size(1, 4) && _param1.type() == CV_64F && cn <= 4))fill/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/rand.cpp_param2.channels() == 1 && (((_param2.rows == 1 || _param2.cols == 1) && (_param2.rows + _param2.cols - 1 == cn || _param2.rows + _param2.cols - 1 == 1 || (_param1.size() == Size(1, 4) && _param1.type() == CV_64F && cn <= 4))) || (_param2.rows == cn && _param2.cols == cn && disttype == NORMAL))scaleFunc != 0Unknown distribution typedst.elemSize() <= 32randShuffleerrcode == 0makeRNGKeycn <= 4 && func != 0sum/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/stat.cppsrc.channels() == 1 && func != 0countNonZeromeanmeanStdDevdst.type() == CV_64F && dst.isContinuous() && (dst.cols == 1 || dst.rows == 1) && dcn >= cn(cn == 1 && (mask.empty() || mask.type() == CV_8U)) || (cn >= 1 && mask.empty() && !minIdx && !maxIdx)minMaxIdximg.dims <= 2bad cell size (not 1, 2 or 4) in normHammingnormHammingnormType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR || ((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src.type() == CV_8U)src1.size == src2.size && src1.type() == src2.type()normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR || ((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src1.type() == CV_8U)type == src2.type() && src1.cols == src2.cols && (type == CV_32F || type == CV_8U)batchDistance_nidx.needed() == (K > 0)(type == CV_8U && dtype == CV_32S) || dtype == CV_32FK == 1 && update == 0 && mask.empty()The combination of type=%d, dtype=%d and normType=%d is not supportedsrc.type() == CV_8UC1findNonZeroidx.isContinuous()0 < coi && coi <= 4cvSumcvAvgcvAvgSdv%s:%d: error: (%d) %s in function %s
%s:%d: error: (%d) %s

General configuration for OpenCV 2.4.11 =====================================
  Version control:               unknown

  Platform:
    Host:                        Darwin 14.5.0 x86_64
    CMake:                       3.2.2
    CMake generator:             Unix Makefiles
    CMake build tool:            /usr/bin/make
    Configuration:               Release

  C/C++:
    Built as dynamic libs?:      NO
    C++ Compiler:                /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++  (ver 7.0.0.7000072)
    C++ flags (Release):         -fPIC   -fsigned-char -W -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-unnamed-type-template-args -fdiagnostics-show-option -Wno-long-long -Wno-semicolon-before-method-body -fno-omit-frame-pointer -msse -msse2 -msse3 -O3 -DNDEBUG  -DNDEBUG
    C++ flags (Debug):           -fPIC   -fsigned-char -W -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-unnamed-type-template-args -fdiagnostics-show-option -Wno-long-long -Wno-semicolon-before-method-body -fno-omit-frame-pointer -msse -msse2 -msse3 -g  -O0 -DDEBUG -D_DEBUG
    C Compiler:                  /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/cc
    C flags (Release):           -fPIC   -fsigned-char -W -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-unnamed-type-template-args -fdiagnostics-show-option -Wno-long-long -Wno-semicolon-before-method-body -fno-omit-frame-pointer -msse -msse2 -msse3 -O3 -DNDEBUG  -DNDEBUG
    C flags (Debug):             -fPIC   -fsigned-char -W -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-unnamed-type-template-args -fdiagnostics-show-option -Wno-long-long -Wno-semicolon-before-method-body -fno-omit-frame-pointer -msse -msse2 -msse3 -g  -O0 -DDEBUG -D_DEBUG
    Linker flags (Release):
    Linker flags (Debug):
    Precompiled headers:         NO

  OpenCV modules:
    To be built:                 core flann imgproc highgui features2d calib3d ml video legacy objdetect photo gpu ocl nonfree contrib java python stitching superres ts videostab
    Disabled:                    world
    Disabled by dependency:      -
    Unavailable:                 androidcamera dynamicuda viz

  GUI: 
    QT:                          NO
    Cocoa:                       YES
    OpenGL support:              NO
    VTK support:                 NO

  Media I/O: 
    ZLib:                        build (ver 1.2.7)
    JPEG:                        build (ver 62)
    PNG:                         build (ver 1.5.12)
    TIFF:                        build (ver 42 - 4.0.2)
    JPEG 2000:                   build (ver 1.900.1)
    OpenEXR:                     build (ver 1.7.1)

  Video I/O:
    DC1394 1.x:                  NO
    DC1394 2.x:                  NO
    FFMPEG:                      NO
      codec:                     NO
      format:                    NO
      util:                      NO
      swscale:                   NO
      gentoo-style:              NO
    GStreamer:                   NO
    OpenNI:                      NO
    OpenNI PrimeSensor Modules:  NO
    PvAPI:                       NO
    GigEVisionSDK:               NO
    QuickTime:                   NO
    QTKit:                       YES
    V4L/V4L2:                    NO/NO
    XIMEA:                       NO

  Other third-party libraries:
    Use IPP:                     NO
    Use Eigen:                   YES (ver 3.2.4)
    Use TBB:                     NO
    Use OpenMP:                  NO
    Use GCD                      YES
    Use Concurrency              NO
    Use C=:                      NO
    Use Cuda:                    NO
    Use OpenCL:                  YES

  OpenCL:
    Version:                     static
    libraries:                   -framework OpenCL
    Use AMD FFT:                 NO
    Use AMD BLAS:                NO

  Python:
    Interpreter:                 /usr/local/bin/python2 (ver 2.7.9)
    Libraries:                   /usr/lib/libpython2.7.dylib (ver 2.7.10)
    numpy:                       /usr/local/lib/python2.7/site-packages/numpy/core/include (ver 1.7.1)
    packages path:               lib/python2.7/site-packages

  Java:
    ant:                         /usr/local/bin/ant (ver 1.9.4)
    JNI:                         /Library/Java/JavaVirtualMachines/jdk1.7.0_67.jdk/Contents/Home/include /Library/Java/JavaVirtualMachines/jdk1.7.0_67.jdk/Contents/Home/include/darwin /Library/Java/JavaVirtualMachines/jdk1.7.0_67.jdk/Contents/Home/include
    Java tests:                  YES

  Documentation:
    Build Documentation:         NO
    Sphinx:                      NO
    PdfLaTeX compiler:           NO
    Doxygen:                     YES (/Applications/doxygen.app/Contents/MacOS/Doxywizard)

  Tests and samples:
    Tests:                       YES
    Performance tests:           YES
    C/C++ Examples:              NO

  Install path:                  /usr/local

  cvconfig.h is in:              /Users/jason/Projects/opencv/opencv-2.4.11/target
-----------------------------------------------------------------

OPENCV_TEMP_PATH/tmp/__opencv_temp.XXXXXX/__opencv_temp.XXXXXXOpenCV Error: %s (%s) in %s, file %s, line %d%s
No ErrorBacktraceUnspecified errorInternal errorInsufficient memoryBad argumentIterations do not convergeAutotrace callIncorrect size of input arrayNull pointerDivision by zero occuredImage step is wrongInplace operation is not supportedRequested object was not foundInput image depth is not supported by functionFormats of input arguments do not matchSizes of input arguments do not matchOne of arguments' values is out of rangeUnsupported format or combination of formatsInput COI is not supportedBad number of channelsBad flag (parameter or structure field)Bad parameter of type CvPointBad type of mask argumentParsing errorThe function/feature is not implementedMemory block has been corruptedAssertion failedNo GPU supportGpu API callNo OpenGL supportOpenGL API callUnknown %s code %dstatuserrormodule != 0 && module->name != 0 && module->version != 0cvRegisterModule/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/src/system.cppThe module is not foundcvGetModuleInfo%s: %s%skey_ >= 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VHUQTUSXRZP]M`JbJ[TFSGUQTR TFTR TFUGSQTR OIPIXOYO OIYO OIOJYNYO YIXIPOOO YIOO YIYJONOOMWUFTGRM UGRM UFVGRMH\PFOGMM PGMM PFQGMM ZFYGWM ZGWM ZF[GWMKZSFQGPIPKQMSNUNWMXKXIWGUFSF SFPIQMUNXKWGSF UFQGPKSNWMXIUFG^SFKbLb SFTFLb YFQbRb YFZFRb KP\P\Q KPKQ\Q IWZWZX IWIXZXE^^F\GXHUHQGOFMFKGJIJKLMNMPLQJQHOF ^FE[ ^FF[ ^FG[ XTVTTUSWSYU[W[YZZXZVXT PGMFJIKLNMQJPG OFKGJK LMPLQH YUVTSWTZW[ZXYU XTTUSY U[YZZV E[G[E`UQUNTLRKPKNLMMLPLSMUOVQVSUTTUQ OLNMMPMSNU RKPLOMNPNSOUPV VKUQUSVUXVZV\U]R]O\L[JYHWGTFQFNGLHJJILHOHRIUJWLYNZQ[T[WZYYXYWZ WKVQVSWU VKXKWQWSXUZV[empty_any][WARNING] The cvflann::set_distance_type function is deperecated, use cv::flann::GenericIndex instead.[ERROR] cvflann::set_distance_type only provides backwards compatibility for the L1 and L2 distances. For other distance types you must use cv::flann::GenericIndextreesbranchingiterationscenters_initcb_indextarget_precisionbuild_weightmemory_weightsample_fractionleaf_sizetable_numberkey_sizemulti_probe_levelfilenamechecksepssortedUnknown/unsupported distance type/Users/jason/Projects/opencv/opencv-2.4.11/modules/flann/src/miniflann.cppknnSearchmaxResults > 0radiusSearchLSH index does not support radiusSearch operationwbCan not open file %s for writing FLANN index
saveReading FLANN index error: the saved data size (%d, %d) or type (%d) is different from the passed one (%d, %d), %d
Reading FLANN index error: unsupported feature type %d for the index type %d
Reading FLANN index error: unsupported distance type %d
Invalid index file, cannot readFLANN_INDEXInvalid index file, wrong signaturetype=%d
buildIndex_Only continuous arrays are supportedI can only search one feature at a time for range search
Missing parameter '' in the parameters givenDatatype of saved index is different than of the one to be created.The index saved belongs to a different datasetUnknown index typeUnknown algorithm for choosing initial centers.Branching factor must be at least 2Failed to allocate memory.
Cannot read from fileInvalid key_size (=). Valid values for your system are  <= key_size < initialize/Users/jason/Projects/opencv/opencv-2.4.11/modules/flann/include/opencv2/flann/lsh_table.hdimleaf_max_sizereorderIt doesn't make any sense to use more than one tree for exact searchBuilding kmeans tree...
Building kdtree tree...
----------------------------------------------------
Autotuned parameters:
Search parameters:
Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g
Choosing linear, dataset too small
Computing ground truth... 
Autotuning parameters...
KMEANS, Step 1: Exploring parameter space
KMeansTree using params: max_iterations=%d, branching=%d
KMeansTree buildTime=%g, searchTime=%g, build_weight=%g
  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist
---------------------------------------------------------
Got as close as I can
Start linear estimation
No need for linear estimation
matches.cols=%d, nn=%d
Ground truth is not computed for as many neighbors as requested%8d %10.4g %10.5g %10.5g %10.5g
KD-TREE, Step 1: Exploring parameter space
KDTree using params: trees=%d
KDTree buildTime=%g, searchTime=%g
 : Computing ground truth
Estimating number of checks
KMeans algorithm, estimating cluster border factor
Optimum cb_index: %g
Required number of checks: %d 
LSH is not implemented for that typequery.type() == type && indices.type() == CV_32S && dists.type() == dtyperunKnnSearch_query.isContinuous() && indices.isContinuous() && dists.isContinuous()runRadiusSearch_1.6.10DataType::type == data.type() && data.isContinuous()loadIndex_dst.size == src.size && dst.channels() == cnaccumulate/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/accum.cppmask.empty() || (mask.size == src.size && mask.type() == CV_8U)accumulateSquaresrc2.size && src1.size && src2.type() == src1.type()accumulateProductdst.size == src1.size && dst.channels() == cnmask.empty() || (mask.size == src1.size && mask.type() == CV_8U)accumulateWeightedCV_IS_SEQ_CHAIN_CONTOUR( chain )icvApproximateChainTC89/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/approx.cppheader_size >= (int)sizeof(CvContour)cvApproxChainsUnsupported sequence typecvApproxPolyNULL storage pointer header_size is negative. Pass 0 to make the destination header_size == input header_sizeInput curves are not polygonal. Use cvApproxChains firstInput curves have uknown typeNew header size must be non-less than sizeof(CvContour)Unknown approximation methodAccuracy must be non-negativeInvalid approximation methodsrc.depth() == CV_8UCanny/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/canny.cppsrc.size == dst.size && src.depth() == CV_8U && dst.type() == CV_8UcvCannyCLAHEclipLimittilesXtilesYapply/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/clahe.cppdepth == CV_8U || depth == CV_16U || depth == CV_32FcvtColor/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/color.cppscn == 3 || scn == 4(scn == 3 || scn == 4) && depth == CV_8U(dcn == 3 || dcn == 4) && scn == 2 && depth == CV_8Uscn == 2 && depth == CV_8Uscn == 1 && (dcn == 3 || dcn == 4)scn == 1 && depth == CV_8Uscn == 3 && (dcn == 3 || dcn == 4)(scn == 3 || scn == 4) && (depth == CV_8U || depth == CV_32F)scn == 3 && (dcn == 3 || dcn == 4) && (depth == CV_8U || depth == CV_32F)scn == 1 && dcn == 1Bayer->Gray demosaicing only supports 8u and 16u typesscn == 1 && dcn == 3Bayer->RGB demosaicing only supports 8u and 16u typesdepth == CV_8Udcn == 3 || dcn == 4sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8UUnknown/unsupported color conversion codedcn == 1sz.width % 2 == 0 && sz.height % 2 == 0scn == 4 && dcn == 4Unsupported image depthsrc.depth() == dst.depth()cvCvtColorhrange == 180 || hrange == 256RGB2HSV_bcoeffs[i] >= 0 && coeffs[i*3+1] >= 0 && coeffs[i*3+2] >= 0 && coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 2*(1 << lab_shift)RGB2Lab_bcoeffs[j] >= 0 && coeffs[j + 1] >= 0 && coeffs[j + 2] >= 0 && coeffs[j] + coeffs[j + 1] + coeffs[j + 2] < 1.5f*LabCbrtTabScaleRGB2Lab_fcoeffs[i*3] >= 0 && coeffs[i*3+1] >= 0 && coeffs[i*3+2] >= 0 && coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 1.5fRGB2Luv_fwhitept[1] == 1.fLuv2RGB_fcvStartReadChainPoints/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/contours.cppcvReadChainPointcvStartFindContours[Start]FindContours support only 8uC1 and 32sC1 imagescvSubstituteContourcvFindNextContourcvEndFindContoursNULL double CvSeq pointercvFindContoursNonzero offset is not supported in CV_LINK_RUNS yetci.isContinuous()findContours0 <= contourIdx && contourIdx < (int)lastdrawContoursnpoints > 0hierarchy.total() == ncontours && hierarchy.type() == CV_32SC4npoints >= 0 && (depth == CV_32S || depth == CV_32F)approxPolyDPcurve.checkVector(2) >= 0 && (curve.depth() == CV_32F || curve.depth() == CV_32S)arcLengthpoints.checkVector(2) >= 0 && (points.depth() == CV_32F || points.depth() == CV_32S)boundingRectcontour.checkVector(2) >= 0 && (contour.depth() == CV_32F || contour.depth() == CV_32S)contourAreaminAreaRectminEnclosingCirclecontour1.checkVector(2) >= 0 && contour2.checkVector(2) >= 0 && (contour1.depth() == CV_32F || contour1.depth() == CV_32S) && contour1.depth() == contour2.depth()matchShapesnelems >= 0 && (depth == CV_32F || depth == CV_32S)convexHullptnum > 3convexityDefectshull.checkVector(1, CV_32S) > 20 <= idx0 && idx0 < ptnum0 <= idx1 && idx1 < ptnum0 <= idx2 && idx2 < ptnumd.depth >= 0isContourConvexfitEllipse(is2d || is3d) && (points.depth() == CV_32F || points.depth() == CV_32S)fitLinel.isContinuous()pointPolygonTesticvFindContoursInIntervalContour header size must be >= sizeof(CvContour)Input array must be 8uC1 or 8sC1!_seq || _seq->elem_size == sizeof(_Tp)SeqcvConvexHull2/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/convhull.cppDestination must be valid memory storage or matrixThe hull matrix should be continuous and have a single row or a single columnThe hull matrix size might be not enough to fit the hullThe hull matrix must have the same type as input or 32sC1 (integers)Point sequence can not be empty if the output is matrixInput sequence is not a sequence of pointscvConvexityDefectsFloating-point coordinates are not supported hereConvex hull must represented as a sequence of indices or sequence of pointersConvex hull is neither sequence nor matrixThe matrix should be 1-dimensional and continuous array of int'sConvex hull is larger than the point sequenceInput sequence must be polygon (closed 2d curve)cvCheckContourConvexityCV_SEQ_ELTYPE(contour) == CV_32FC2icvCalcAndWritePtIndicessrc.type() == CV_8UC1 || src.type() == CV_32FC1preCornerDetect/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/corner.cppsrc.size() == dst.size() && dst.type() == CV_32FC1cvCornerMinEigenValcvCornerHarrissrc.rows == dst.rows && src.cols*6 == dst.cols*dst.channels() && dst.depth() == CV_32FcvCornerEigenValsAndVecscvPreCornerDetectcornerEigenValsVecsThe source image must be 8-bit single-channel (CV_8UC1)cvFindCornerSubPix/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/cornersubpix.cpp(icvGetRectSubPix_8u32f_C1R( (uchar*)src->data.ptr, src->step, size, src_buffer, (win_w + 2) * sizeof( src_buffer[0] ), cvSize( win_w + 2, win_h + 2 ), cI )) >= 0ncorners >= 0 && corners.depth() == CV_32FcornerSubPixsrc.size() == dst.size() && src.channels() == dst.channels()cvSobel/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/deriv.cppcvLaplacektype == CV_32F || ktype == CV_64FgetScharrKernelsdx >= 0 && dy >= 0 && dx+dy == 1getSobelKernelsThe kernel size must be odd and not larger than 31dx >= 0 && dy >= 0 && dx+dy > 0ksize > ordersource image must be 8uC1 and the distance map must be 32fC1 (or 8uC1 in case of simple L1 distance transform)cvDistTransform/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/distransform.cppthe source and the destination images must be of the same sizeMask size should be 3 or 5 or 0 (presize)the output array of labels must be 32sC1the array of labels has a different size3x3 mask can not be used for "labeled" distance transform. Use 5x5 maskicvTrueDistTransThe input image must have 8uC1 type and the output one must have 32fC1 typeCV_IS_MASK_ARR( src ) && CV_MAT_TYPE( dst->type ) == CV_8UC1icvDistanceATS_L1_8uCV_ARE_SIZES_EQ( src, dst )The arrays must have equal number of columns (which is number of dimensions but 1)cvCalcEMD2/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/emd.cppThe array must have equal typesThe signatures must be 32fC1The flow matrix size does not match to the signatures' sizesThe flow matrix must be 32fC1Only one of cost matrix or distance function should be non-NULL in case of user-defined distanceThe lower boundary can not be calculated if the cost matrix is usedThe cost matrix size does not match to the signatures' sizesThe cost matrix must be 32fC1In case of user-defined distance Distance function is undefinedNumber of dimensions can be 0 only if a user-defined metric is usedBad or unsupported metric typeicvInitEMDqualityLevel > 0 && minDistance >= 0 && maxCorners >= 0goodFeaturesToTrack/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/featureselect.cppmask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size())_corners && _corner_countcvGoodFeaturesToTrackUnknown/unsupported border typeborderInterpolate/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/filter.cppcolumnBorderType != BORDER_WRAP!rowFilter.empty() && !columnFilter.empty()bufType == srcType0 <= anchor.x && anchor.x < ksize.width && 0 <= anchor.y && anchor.y < ksize.heightroi.x >= 0 && roi.y >= 0 && roi.width >= 0 && roi.height >= 0 && roi.x + roi.width <= wholeSize.width && roi.y + roi.height <= wholeSize.heightstartsrcRoi.x >= 0 && srcRoi.y >= 0 && srcRoi.width >= 0 && srcRoi.height >= 0 && srcRoi.x + srcRoi.width <= src.cols && srcRoi.y + srcRoi.height <= src.rowswholeSize.width > 0 && wholeSize.height > 0proceedsrc && dst && count > 0srcY >= startYdstY <= roi.heightsrc.type() == srcType && dst.type() == dstTypedstOfs.x >= 0 && dstOfs.y >= 0 && dstOfs.x + srcRoi.width <= dst.cols && dstOfs.y + srcRoi.height <= dst.rows_kernel.channels() == 1getKernelTypecn == CV_MAT_CN(bufType) && ddepth >= std::max(sdepth, CV_32S) && kernel.type() == ddepthgetLinearRowFilterUnsupported combination of source format (=%d), and buffer format (=%d)cn == CV_MAT_CN(bufType) && sdepth >= std::max(ddepth, CV_32S) && kernel.type() == sdepthgetLinearColumnFilterUnsupported combination of buffer format (=%d), and destination format (=%d)cn == CV_MAT_CN(_dstType)createSeparableLinearFilterktype == CV_8U || ktype == CV_32S || ktype == CV_32F || ktype == CV_64Fpreprocess2DKernelcn == CV_MAT_CN(dstType) && ddepth >= sdepthgetLinearFilterUnsupported combination of source format (=%d), and destination format (=%d)createLinearFiltercvFilter2D(symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0SymmColumnVec_32s8uSymmColumnSmallVec_32s16sSymmColumnSmallVec_32fSymmColumnVec_32f16sSymmColumnVec_32fanchor.inside(Rect(0, 0, ksize.width, ksize.height))normalizeAnchor/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/precomp.hpp(symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0 && this->ksize <= 5SymmRowSmallFilterkernel.type() == DataType
::type && (kernel.rows == 1 || kernel.cols == 1)RowFilterkernel.type() == DataType::type && (kernel.rows == 1 || kernel.cols == 1)ColumnFilterthis->ksize == 3SymmColumnSmallFilterSymmColumnFilter_kernel.type() == DataType::typeFilter2DNumber of channels in input image must be 1 or 3cvFloodFill/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/floodfill.cppConnectivity must be 4, 0(=4) or 8lo_diff and up_diff must be non-negativeSeed point is outside of imagemask must be 2 pixel wider and 2 pixel taller than filled imagegetGaborKernel/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/gabor.cpp!GHT_Ballard_Pos_info_auto.name().empty()/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/generalized_hough.cpp!GHT_Ballard_PosScale_info_auto.name().empty()!GHT_Ballard_PosRotation_info_auto.name().empty()!GHT_Guil_Full_info_auto.name().empty()Unsupported methodtempl.type() == CV_8UC1setTemplatecannyThreshold > 0image.type() == CV_8UC1detectGeneralizedHough.POSITIONGeneralizedHough.POSITION_SCALEGeneralizedHough.POSITION_ROTATIONGeneralizedHough.POSITION_SCALE_ROTATIONminDistMinimum distance between the centers of the detected objects.levelsR-Table levels.votesThresholdThe accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.dpInverse ratio of the accumulator resolution to the image resolution.!hasVotes || voteOutBuf.size() == oldSizefilterMinDist!hasVotes || voteOutBuf.size() == posOutBuf.size()templEdges.type() == CV_8UC1processTempltemplDx.type() == CV_32FC1 && templDx.size() == templSizetemplDy.type() == templDx.type() && templDy.size() == templSizelevels > 0imageEdges.type() == CV_8UC1calcHistimageDx.type() == CV_32FC1 && imageDx.size() == imageSizeimageDy.type() == imageDx.type() && imageDy.size() == imageSizelevels > 0 && r_table.size() == static_cast(levels + 1)dp > 0.0votesThreshold > 0findPosInHistMinimal scale to detect.maxScaleMaximal scale to detect.scaleStepScale step.minScale > 0.0 && minScale < maxScalescaleStep > 0.0minAngleMinimal rotation angle to detect in degrees.maxAngleMaximal rotation angle to detect in degrees.angleStepAngle step in degrees.minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0angleStep > 0.0 && angleStep < 360.0maxSizeMaximal size of inner buffers.xiAngle difference in degrees between two points in feature.Feature table levels.angleEpsilonMaximal difference between angles that treated as equal.angleThreshAngle threshold.scaleThreshScale threshold.posThreshPosition threshold.buildFeatureListedges.type() == CV_8UC1getContourPointsdx.type() == CV_32FC1 && dx.size == edges.sizedy.type() == dx.type() && dy.size == edges.sizecalcOrientationtemplFeatures.size() == static_cast(levels + 1)imageFeatures.size() == templFeatures.size()angleThresh > 0calcScalescaleThresh > 0calcPositionposThresh > 0NULL vertex array pointercvBoxPoints/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/geometry.cppChains are not supported. Convert them to polygonal representation using cvApproxChains()cvPointPolygonTestInput contour is neither a valid sequence nor a matrixp1.depth() == CV_32S || p1.depth() == CV_32FintersectConvexConvexp2.depth() == CV_32S || p2.depth() == CV_32Fn >= 0 && m >= 0temp.ptr() == dst_model must have CV_64FC1 type, rows == 1 and cols == 13*componentsCountGMM/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/grabcut.cppcovDeterms[ci] > std::numeric_limits::epsilon()dtrm > std::numeric_limits::epsilon()calcInverseCovAndDetermimage is emptygrabCutimage mush have CV_8UC3 typemask is emptycheckMaskmask must have CV_8UC1 typemask must have as many rows and cols as imgmask element value must be equelGC_BGD or GC_FGD or GC_PR_BGD or GC_PR_FGD!bgdSamples.empty() && !fgdSamples.empty()initGMMsi>=0 && i<(int)vtcs.size()addTermWeights/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/gcgraph.hppaddEdgesj>=0 && j<(int)vtcs.size()w>=0 && revw>=0i != jinSourceSegmentdims > 0 && histSize/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/histogram.cpp!mask.data || mask.type() == CV_8UC1nimages > 0 && dims > 0rsz == dims*2 || (rsz == 0 && images.depth(0) == CV_8U)csz == 0 || csz == dimsdims > 0 && hist.datacalcBackProjectdims > 0H0.isContinuous()nimages > 0rsz == dims*2 || (rsz == 2 && _1d) || (rsz == 0 && images.depth(0) == CV_8U)csz == 0 || csz == dims || (csz == 1 && _1d)H1.type() == H2.type() && H1.type() == CV_32FcompareHistit.planes[0].isContinuous() && it.planes[1].isContinuous()Unknown comparison methoddims > 0 && dims == H2.dims() && H1.type() == H2.type() && H1.type() == CV_32FH1.size(i) == H2.size(i)Number of dimensions is out of rangecvCreateHistNull  pointerInvalid histogram typeNull histogram header pointercvMakeHistHeaderForArrayOnly uniform bin ranges can be used here (to avoid memory allocation)cvReleaseHistInvalid histogram headercvClearHistcvThreshHistcvNormalizeHistcvGetMinMaxHistValueInvalid histogram header[s]cvCompareHistOne of histograms is sparse and other is notThe histograms have different numbers of dimensionsThe histograms have different sizesDestination double pointer is NULLcvCopyHistNULL ranges pointercvSetHistBinRangesOne of  elements is NULLBin ranges should go in ascenting orderBad histogram pointercvCalcArrHistNull double array pointercvCalcArrBackProject_dst.size() == images[0].size() && _dst.depth() == images[0].depth()cvCalcArrBackProjectPatchBad normalization factor (set it to 1.0 if unsure)The patch width and height must be positiveResultant image must have 32fC1 typeThe output map must be (W-w+1 x H-h+1), where the input images are (W x H) each and the patch is (w x h)NULL histogram array pointercvCalcBayesianProbToo small number of histogramsThe function supports dense histograms onlyscale must be positivecvCalcProbDensityInvalid histogram pointer[s]All histograms must have 32fC1 typeequalizeHistopencv-histchannels != 0 || nimages == dimshistPrepareImagesimages[j].channels() == 1c >= 0j < nimagesimages[j].size() == imsize && images[j].depth() == depthmask.size() == imsize && mask.channels() == 1ranges[i] && ranges[i][0] < ranges[i][1]ranges[i][k] < ranges[i][k+1]is_uniformhave_rangesmatExpected CvMatNDicvReadHistbinsUnknown Histogram typethresh'thresh' node is missingfThe source image must be 8-bit, single-channelcvHoughLines2/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/hough.cppNULL destinationrho, theta and threshold must be positiveThe destination matrix should be continuous and have a single row or a single columnThe destination matrix data type is inappropriate, see the manualDestination is not CvMemStorage* nor CvMat*Unrecognized method idcvHoughCirclesdp, min_dist, canny_threshold and acc_threshold must be all positive numbersCV_IS_MAT(img) && CV_MAT_TYPE(img->type) == CV_8UC1icvHoughLinesStandardicvHoughLinesSDivlinesMax > 0 && rho > 0 && theta > 0CV_IS_MAT(image) && CV_MAT_TYPE(image->type) == CV_8UC1icvHoughLinesProbabilisticssize.area() > 0/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/imgwarp.cppdsize.area() || (inv_scale_x > 0 && inv_scale_y > 0)dsize.area()func != 0 && cn <= 4Unknown interpolation methodmap1.size().area() > 0remap!map2.data || (map2.size() == map1.size())nnfunc != 0ifunc != 0((map1.type() == CV_32FC2 || map1.type() == CV_16SC2) && !map2.data) || (map1.type() == CV_32FC1 && map2.type() == CV_32FC1)(m1type == CV_16SC2 && (nninterpolate || m2type == CV_16UC1 || m2type == CV_16SC1)) || (m2type == CV_16SC2 && (nninterpolate || m1type == CV_16UC1 || m1type == CV_16SC1)) || (m1type == CV_32FC1 && m2type == CV_32FC1) || (m1type == CV_32FC2 && !m2->data)convertMapsdstm1type == CV_16SC2 || dstm1type == CV_32FC1 || dstm1type == CV_32FC2Unsupported combination of input/output matricessrc.cols > 0 && src.rows > 0warpAffine(M0.type() == CV_32F || M0.type() == CV_64F) && M0.rows == 2 && M0.cols == 3warpPerspective(M0.type() == CV_32F || M0.type() == CV_64F) && M0.rows == 3 && M0.cols == 3matM.rows == 2 && matM.cols == 3invertAffineTransformsrc.checkVector(2, CV_32F) == 4 && dst.checkVector(2, CV_32F) == 4getPerspectiveTransformsrc.checkVector(2, CV_32F) == 3 && dst.checkVector(2, CV_32F) == 3getAffineTransformsrc.type() == dst.type()cvResizecvWarpAffinecvWarpPerspectivesrc.type() == dst.type() && dst.size() == mapx.size()cvRemapM.size() == M0.size()cv2DRotationMatrixcvGetPerspectiveTransformcvGetAffineTransformcvLogPolarM should be >0cvLinearPolarUnknown/unsupported interpolation typeinitInterTab2DinitInterTab1Dksize <= MAX_ESIZEresizeGeneric_Invokercn <= 4 && ssize.area() > 0remapBilinearNULL pointer to line parameterscvFitLine/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/linefit.cppThe sequence has no pointsInput sequence must consist of 2d points or 3d pointsInput array is not a sequence nor matrixInput array must be 1d continuous array of 2d or 3d pointsBoth reps and aeps must be non-negativeUser-defined distance is not allowed(icvFitLine2D( (CvPoint2D32f*)points, ptseq->total, dist, (float)param, (float)reps, (float)aeps, line )) >= 0(icvFitLine3D( (CvPoint3D32f*)points, ptseq->total, dist, (float)param, (float)reps, (float)aeps, line )) >= 0cvMatchShapes/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/matchcontours.cppThe passed sequence is not a valid contourcvMoments/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/moments.cppInvalid image typecvGetHuMomentscvGetSpatialMomentcvGetCentralMomenthu.isContinuous()HuMomentsop == MORPH_ERODE || op == MORPH_DILATEgetMorphologyRowFilter/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/morph.cppUnsupported data type (=%d)getMorphologyColumnFiltergetMorphologyFilterdepth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F || depth == CV_64FcreateMorphologyFiltershape == MORPH_RECT || shape == MORPH_CROSS || shape == MORPH_ELLIPSEgetStructuringElementunknown morphological operationmorphologyExcols > 0 && rows > 0 && anchor.inside(cv::Rect(0,0,cols,rows)) && (shape != CV_SHAPE_CUSTOM || values != 0)cvCreateStructuringElementExcvReleaseStructuringElementcvErodecvDilatecvMorphologyExmorphOp((size_t)src[i] & 15) == 0((size_t)_src[i] & 15) == 0_kernel.type() == CV_8UMorphFilterphaseCorrelateRes/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/phasecorr.cppsrc1.type() == CV_32FC1 || src1.type() == CV_64FC1src1.size == src2.sizesrc1.type() == window.type()src1.size == window.sizecreateHanningWindowmagSpectrumsdivSpectrumsweightedCentroidborderType != BORDER_CONSTANTpyrDown/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/pyramids.cppborderType == BORDER_DEFAULTpyrUpbuildPyramid_filter == CV_GAUSSIAN_5x5 && src.type() == dst.type()cvPyrDowncvPyrUpcvReleasePyramidThe number of extra layers must be non negativecvCreatePyramidThe buffer is too small to fit the pyramid!_src.empty()pyrDown_std::abs(dsize.width*2 - ssize.width) <= 2 && std::abs(dsize.height*2 - ssize.height) <= 2std::abs(dsize.width - ssize.width*2) == dsize.width % 2 && std::abs(dsize.height - ssize.height*2) == dsize.height % 2pyrUp_Input sequence must consist of 2d points or pointers to 2d pointscvMinAreaRect2/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/rotcalipers.cppcvSampleLine/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/samplers.cppcvGetRectSubPix(func( src->data.ptr, src_step, src_size, dst->data.ptr, dst_step, dst_size, center )) >= 0map matrix is not validcvGetQuadrangleSubPixTransformation matrix must be 2x3The transformation matrix should have 32fC1 or 64fC1 type(func( src->data.ptr, src->step, src_size, dst->data.ptr, dst->step, dst_size, m )) >= 0Only 8-bit, 3-channel input images are supportedcvWatershed/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/segmentation.cppOnly 32-bit, 1-channel output images are supportedThe input and output images must have the same sizeThe number of pyramid levels is too large or negativecvPyrMeanShiftFilteringOnly 8-bit, 3-channel images are supportedThe input and output images must have the same typecvArcLength/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/shapedescr.cppNull center or radius pointerscvMinEnclosingCirclecvContourAreaOnly curves with integer coordinates are supported in case of contour slice(icvContourSecArea( contour, slice, &area )) >= 0cvFitEllipse2Number of points should be >= 5cvBoundingRectThe image/matrix format is not supported by the functionCV_MAT_CN(sumType) == CV_MAT_CN(srcType)getRowSumFilter/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/smooth.cppCV_MAT_CN(sumType) == CV_MAT_CN(dstType)getColumnSumFilterUnsupported combination of sum format (=%d), and destination format (=%d)getGaussianKernelksize.width > 0 && ksize.width % 2 == 1 && ksize.height > 0 && ksize.height % 2 == 1createGaussianFilterksize % 2 == 1medianBlursrc.depth() == CV_8U && (cn == 1 || cn == 3 || cn == 4)Bilateral filtering is only implemented for 8u and 32f imagesbilateralFiltersrc.type() == CV_8UC1 || src.type() == CV_8UC3adaptiveBilateralFilterAdaptive Bilateral filtering is only implemented for 8u imagesdst.size() == src.size() && (smooth_type == CV_BLUR_NO_SCALE || dst.type() == src.type())cvSmoothThe destination image does not have the proper typesumCount == ksize-1(src.type() == CV_8UC1 || src.type() == CV_8UC3) && src.type() == dst.type() && src.size() == dst.size() && src.data != dst.databilateralFilter_8u(src.type() == CV_32FC1 || src.type() == CV_32FC3) && src.type() == dst.type() && src.size() == dst.size() && src.data != dst.databilateralFilter_32fadaptiveBilateralFilter_8u(ksize.width & 1) && (ksize.height & 1)adaptiveBilateralFilter_8u_InvokerSubdivision is emptylocate/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/subdivision2d.cppedge > 0insertSubdiv2D::locate returned invalid location = %dedgeDst(edge, &t) > 0edgeOrg( edge, &t ) > 0edgeOrg(e) == edgeOrg(o_next)checkSubdivedgeOrg(e) == edgeOrg(o_prev)edgeDst(e) == edgeDst(d_next)edgeDst(e) == edgeDst(d_prev)edgeDst(o_next) == edgeOrg(d_prev)edgeDst(o_prev) == edgeOrg(d_next)getEdge(getEdge(getEdge(e,NEXT_AROUND_LEFT),NEXT_AROUND_LEFT),NEXT_AROUND_LEFT) == egetEdge(getEdge(getEdge(e,NEXT_AROUND_RIGHT),NEXT_AROUND_RIGHT),NEXT_AROUND_RIGHT) == eintegral/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/sumpixels.cppsum.data == sum0.data && sqsum.data == sqsum0.data && tilted.data == tilted0.datacvIntegralimg.dims <= 2 && templ.dims <= 2 && corr.dims <= 2crossCorr/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/templmatch.cppdepth == tdepth || tdepth == CV_32Fcorrsize.height <= img.rows + templ.rows - 1 && corrsize.width <= img.cols + templ.cols - 1ccn == 1 || delta == 0the input arrays are too bigCV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMEDmatchTemplate(img.depth() == CV_8U || img.depth() == CV_32F) && img.type() == templ.type()img.rows >= templ.rows && img.cols >= templ.colsresult.size() == cv::Size(std::abs(img.cols - templ.cols) + 1, std::abs(img.rows - templ.rows) + 1) && result.type() == CV_32FcvMatchTemplatethreshold/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/thresh.cppadaptiveThresholdblockSize % 2 == 1 && blockSize > 1Unknown/unsupported adaptive threshold methodUnknown/unsupported threshold typesrc.size == dst.size && src.channels() == dst.channels() && (src.depth() == dst.depth() || dst.depth() == CV_8U)cvThresholdcvAdaptiveThresholdUnknown threshold typethresh_8uthresh_16sthresh_32fm1type == CV_16SC2 || m1type == CV_32FC1 || m1type == CV_32FC2initUndistortRectifyMap/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/undistort.cppA.size() == Size(3,3) && A.size() == R.size()Ar.size() == Size(3,3) || Ar.size() == Size(4, 3)distCoeffs.size() == Size(1, 4) || distCoeffs.size() == Size(4, 1) || distCoeffs.size() == Size(1, 5) || distCoeffs.size() == Size(5, 1) || distCoeffs.size() == Size(1, 8) || distCoeffs.size() == Size(8, 1)dst.data != src.dataundistortcvUndistort2mapx0.data == mapx.data && mapy0.data == mapy.datacvInitUndistortMapcvInitUndistortRectifyMapCV_IS_MAT(_src) && CV_IS_MAT(_dst) && (_src->rows == 1 || _src->cols == 1) && (_dst->rows == 1 || _dst->cols == 1) && _src->cols + _src->rows - 1 == _dst->rows + _dst->cols - 1 && (CV_MAT_TYPE(_src->type) == CV_32FC2 || CV_MAT_TYPE(_src->type) == CV_64FC2) && (CV_MAT_TYPE(_dst->type) == CV_32FC2 || CV_MAT_TYPE(_dst->type) == CV_64FC2)cvUndistortPointsCV_IS_MAT(_cameraMatrix) && _cameraMatrix->rows == 3 && _cameraMatrix->cols == 3CV_IS_MAT(_distCoeffs) && (_distCoeffs->rows == 1 || _distCoeffs->cols == 1) && (_distCoeffs->rows*_distCoeffs->cols == 4 || _distCoeffs->rows*_distCoeffs->cols == 5 || _distCoeffs->rows*_distCoeffs->cols == 8)CV_IS_MAT(matR) && matR->rows == 3 && matR->cols == 3CV_IS_MAT(matP) && matP->rows == 3 && (matP->cols == 3 || matP->cols == 4)src.isContinuous() && (src.depth() == CV_32F || src.depth() == CV_64F) && ((src.rows == 1 && src.channels() == 2) || src.cols*src.channels() == 2)undistortPoints(distCoeffs0.cols == 1 || distCoeffs0.rows == 1) && (ndcoeffs == 4 || ndcoeffs == 5 || ndcoeffs == 8)initWideAngleProjMapcameraMatrix0.size() == Size(3,3)Unknown projection typemapPointSphericalavg iters = %g
arr != 0 && contour_header != 0 && block != 0cvPointSeqFromMat/Users/jason/Projects/opencv/opencv-2.4.11/modules/imgproc/src/utils.cppInput array is not a valid matrixThe matrix can not be converted to point sequence because of inappropriate element typeThe matrix converted to point sequence must be 1-dimensional and continuoustop >= 0 && bottom >= 0 && left >= 0 && right >= 0copyMakeBordervalue[0] == value[1] && value[0] == value[2] && value[0] == value[3]src1.depth() == CV_8UPSNRdst.type() == src.type()cvCopyMakeBorder%ud%u%0%ddchannels == 1 || channels == 3 || channels == 4imencode/Users/jason/Projects/opencv/opencv-2.4.11/modules/highgui/src/loadsave.cppcould not find encoder for the specified extensionencoder->isFormatSupported(CV_8U)codef != 0_buf && CV_IS_MAT_CONT(_buf->type)cvDecodeImagecvDecodeImageMimage.channels() == 1 || image.channels() == 3 || image.channels() == 4imwrite_could not find a writer for the specified extensionbuf.data && buf.isContinuous()imdecode_Inner function failed./Users/jason/Projects/opencv/opencv-2.4.11/modules/highgui/src/utils.cppSource image must have 1, 3 or 4 channelsDestination image must be 8uDestination image must have 1 or 3 channelsUnsupported combination of input/output formatssize.width>0 && size.height>0imshow/Users/jason/Projects/opencv/opencv-2.4.11/modules/highgui/src/window.cpppointCloudShowcvSetOpenGlDrawCallbackcvSetOpenGlContextcvUpdateWindowThe library is compiled without QT supportfontQtaddTextdisplayStatusBardisplayOverlaystartLoopstopLoopsaveWindowParametersloadWindowParameterscreateButtonCNULL window name/Users/jason/Projects/opencv/opencv-2.4.11/modules/highgui/src/window_cocoa.mmNULL trackbar or window nameBad trackbar maximal valueNULL name stringNULL windowmouseCallbackT^?,VmouseCallbackmouseParamT^v,VmouseParamautosizeTc,VautosizefirstContentTc,VfirstContentslidersT@"NSMutableDictionary",&,VslidersTi,VstatusimageT@"NSImage",&,VimagesliderT@"NSSlider",&,VsliderT@"NSTextField",&,VnamevalueT^i,VvalueuserDataT^v,VuserDatacallbackT^?,Vcallbackcallback2T^?,Vcallback2Warning, camera failed to properly initialize!Cleaned up camera.QTKit didn't find any attached Video Input Devices!QTKit failed to open a Video Capture DeviceQTKit failed to start capture session with opened Capture DeviceQTKit failed to add Output to Capture SessionCamera dropped frame!WARNING: Couldn't read movie file WARNING: Didn't properly encode FourCC. Expected  but got WARNING: Failed to remove existing file WARNING: Could not create empty movie file container.Didn't successfully update movie file.Raw image encoder error: throwOnEror/Users/jason/Projects/opencv/opencv-2.4.11/modules/highgui/src/grfmt_base.cppWindows bitmap (*.bmp;*.dib)BMv/1
RGBYRYBYOpenEXR Image files (*.exr)???JPEG files (*.jpeg;*.jpg;*.jpe)jP  
?
JPEG 2000 LOADER ERROR: cannot convert colorspace
JPEG 2000 LOADER ERROR: unable to create colorspace
JPEG2000 LOADER ERROR: colorspace conversion failed
JPEG-2000 files (*.jp2)jp2?PNG

decoderreadDataFromBuf/Users/jason/Projects/opencv/opencv-2.4.11/modules/highgui/src/grfmt_png.cppPNG input buffer is incomplete1.5.12Portable Network Graphics files (*.png)encoder && encoder->m_bufwriteDataToBufPortable image format (*.pbm;*.pgm;*.ppm;*.pxm;*.pnm)P%c
%d %d
%d
% 4d% 6dSun raster files (*.sr;*.ras)Y?j?TIFF Files (*.tiff;*.tif)r+bbuf.isContinuous()open/Users/jason/Projects/opencv/opencv-2.4.11/modules/highgui/src/bitstrm.cpp!_descriptors.empty()/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/bagofwords.cppdescriptors[0].cols == _descriptors.colsdescriptors[0].type() == _descriptors.type()!descriptors.empty()clusterqueryIdx == (int)icomputethresholdStepminThresholdmaxThresholdminRepeatabilityminDistBetweenBlobsfilterByColorblobColorfilterByAreaminAreamaxAreafilterByCircularityminCircularitymaxCircularityfilterByInertiaminInertiaRatiomaxInertiaRatiofilterByConvexityminConvexitymaxConvexityBlob detector only supports 8-bit images!detectImpl/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/blobdetector.cppbytes must be 16, 32, or 64BriefDescriptorExtractor/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/brief.cppdescriptorSizedescriptorSize must be 16, 32, or 64imageCollection.size() == pointCollection.size()/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/descriptors.cppFeature2D.!descriptorExtractor.empty()OpponentColorDescriptorExtractorinput image must be an BGR image of type CV_8UC3convertBGRImageToOpponentColorSpace/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/detectors.cppuseHarrisDetectorIncorrect type of input image.
drawKeypoints/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/draw.cpp!outImage.empty()matchesMask must have the same size as matches1to2drawMatchesi1 >= 0 && i1 < static_cast(keypoints1.size())i2 >= 0 && i2 < static_cast(keypoints2.size())!img.empty()_drawKeypointoutImg has size less than need to draw img1 and img2 together_prepareImgAndDrawKeypointshessianThresholdfdetector must not be empty when keypoints1 or keypoints2 is emptyevaluateFeatureDetector/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/evaluation.cppmatches1to2.size() == correctMatches1to2Mask.size()computeRecallPrecisionCurvekeypoints1 must not be emptyevaluateGenericDescriptorMatcherdmatch must not be empty when matches1to2 is emptyminy < maxydr > FLT_EPSILONpixel && offsetsmakeOffsets/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/fast_score.cppthresoctavesbytesnonmaxSuppressionresponseThresholdlineThresholdProjectedlineThresholdBinarizedsuppressNonmaxSizedeltamaxVariationminDiversitymaxEvolutionareaThresholdminMarginedgeBlurSizenFeaturesscaleFactornLevelsfirstLeveledgeThresholdpatchSizeWTA_KscoreTypeorientationNormalizedscaleNormalizedpatternScalenbOctavenfeaturesqualityLevelminDistancekinitFeatureScalefeatureScaleLevelsfeatureScaleMulinitXyStepinitImgBoundvaryXyStepWithScalevaryImgBoundWithScaledetectormaxTotalKeypointsgridRowsgridColsnormTypecrossCheckFeature2D.BRISKFeature2D.BRIEFFeature2D.FASTFeature2D.FASTXFeature2D.STARFeature2D.MSERFeature2D.ORBFeature2D.FREAKFeature2D.GFTTFeature2D.SimpleBlobFeature2D.HARRISFeature2D.DenseFeature2D.GridDescriptorMatcher.BFMatcherDescriptorMatcher.FlannBasedMatcherInput vector does not match the required sizebuildPattern/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/freak.cppNumber of images: number of keypoints: :correlation threshold too small (restrictive)selectPairsbitset set argument out of rangekeypointIndexes has element < 0. TODO: process this case/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/keypoint.cppminSize >= 0runByKeypointSizemaxSize >= 0minSize <= maxSizeimageCount > 0/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/matchers.cppdescriptors[i].cols == dim && descriptors[i].type() == typeimgIdx < (int)startIdxs.size()getDescriptorglobalIdx < (int)size()globalDescIdx < size()(globalDescIdx>=0) && (globalDescIdx < size())getLocalIdxmasks.size() == imageCountcheckMasksmasks[i].rows == queryDescriptorsCount && masks[i].cols == trainDescCollection[i].rows && masks[i].type() == CV_8UC1knn > 0knnMatchmaxDistance > std::numeric_limits::epsilon()radiusMatchqueryDescriptors.type() == trainDescCollection[0].type()knnMatchImpl(int64)imgCount*IMGIDX_ONE < INT_MAXtrainDescCollection[iIdx].rows < IMGIDX_ONEradiusMatchImplBruteForce-Hamming(2)Unknown matcher name!_indexParams.empty()FlannBasedMatcher!_searchParams.empty()indexParamsip.type() == FileNode::SEQip[i].type() == FileNode::MAPsearchParamssp.type() == FileNode::SEQsp[i].type() == FileNode::MAPtypenamedeep clone functionality is not implemented, because Flann::Index has not copy constructor or clone method clone!_images.empty()_images.size() == _points.size()imgIdx < (int)imageCount()getKeypointsimgIdx < (int)images.size()getKeyPointlocalPointIdx < (int)keypoints[imgIdx].size()globalPointIdx < (int)keypointCount()getImage!images.empty()images.size() == keypoints.size()!images[i].empty()DescriptorMatcher.!extractor.empty() && !matcher.empty()VectorDescriptorMatcherknnMatches[i].size() <= 1convertMatchessrc != 0extractMSER/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/mser.cppCV_MAT_TYPE(src->type) == CV_8UC1 || CV_MAT_TYPE(src->type) == CV_8UC3mask == 0 || (CV_ARE_SIZES_EQ(src, mask) && CV_MAT_TYPE(mask->type) == CV_8UC1)storage != 0patchSize >= 2/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/orb.cppWTA_K == 2 || WTA_K == 3 || WTA_K == 4img.type() == CV_8UC1 && blockSize*blockSize <= 2048HarrisResponsescomputeDescriptorsWrong WTA_K. It can be only 2, 3 or 4.computeOrbDescriptorimg.type() == CV_8UC1StarDetectorComputeResponses/Users/jason/Projects/opencv/opencv-2.4.11/modules/features2d/src/stardetector.cppmatI.type() == CV_8UcomputeIntegralImagesOnly 8-bit grayscale or color images are supportedcvFindChessboardCorners/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/calibinit.cppBoth width and height of the pattern should have bigger than 2Null pointer to cornersNumber of channels must be 1, 3 or 4cvDrawChessboardCornersOnly 8-bit, 16-bit or floating-point 32-bit images are supportednelems >= 0drawChessboardCornersisAsymmetricGrid ^ isSymmetricGridfindCirclesGridUnkown pattern typeerr.empty()updateAlt/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/calibration.cppCV_IS_MAT(A) && CV_IS_MAT(B)cvCalcMatMulDerivCV_ARE_TYPES_EQ(A, B) && (CV_MAT_TYPE(A->type) == CV_32F || CV_MAT_TYPE(A->type) == CV_64F)A->cols == B->rowsCV_ARE_TYPES_EQ(A, dABdA) && dABdA->rows == A->rows*B->cols && dABdA->cols == A->rows*A->colsCV_ARE_TYPES_EQ(A, dABdB) && dABdB->rows == A->rows*B->cols && dABdB->cols == B->rows*B->colsCV_IS_MAT(_rvec1) && CV_IS_MAT(_rvec2)cvComposeRTCV_MAT_TYPE(_rvec1->type) == CV_32F || CV_MAT_TYPE(_rvec1->type) == CV_64F_rvec1->rows == 3 && _rvec1->cols == 1 && CV_ARE_SIZES_EQ(_rvec1, _rvec2)CV_IS_MAT(_tvec1) && CV_IS_MAT(_tvec2)CV_ARE_SIZES_EQ(_tvec1, _tvec2) && CV_ARE_SIZES_EQ(_tvec1, _rvec1)Input argument is not a valid matrixcvRodrigues2The first output argument is not a valid matrixThe matrices must have 32f or 64f data typeAll the matrices must have the same data typeJacobian is not a valid matrixJacobian must have 32fC1 or 64fC1 datatypeJacobian must be 3x9 or 9x3Input matrix must be 1x3, 3x1 or 3x3Output matrix must be 3x3, single-channel floating point matrixOutput matrix must be 1x3 or 3x1One of required arguments is not a valid matrixcvProjectPoints2Homogeneous coordinates are not supportedRotation must be represented by 1x3 or 3x1 floating-point rotation vector, or 3x3 rotation matrixTranslation vector must be 1x3 or 3x1 floating-point vectorInstrinsic parameters must be 3x3 floating-point matrixdp/drot must be 2Nx3 floating-point matrixdp/dT must be 2Nx3 floating-point matrixdp/df must be 2Nx2 floating-point matrixdp/dc must be 2Nx2 floating-point matrixdp/df must be 2Nx8, 2Nx5, 2Nx4 or 2Nx2 floating-point matrixdistCoeffs is NULL while dpdk is notCV_IS_MAT(objectPoints) && CV_IS_MAT(imagePoints) && CV_IS_MAT(A) && CV_IS_MAT(rvec) && CV_IS_MAT(tvec)cvFindExtrinsicCameraParams2(CV_MAT_DEPTH(rvec->type) == CV_64F || CV_MAT_DEPTH(rvec->type) == CV_32F) && (rvec->rows == 1 || rvec->cols == 1) && rvec->rows*rvec->cols*CV_MAT_CN(rvec->type) == 3(CV_MAT_DEPTH(tvec->type) == CV_64F || CV_MAT_DEPTH(tvec->type) == CV_32F) && (tvec->rows == 1 || tvec->cols == 1) && tvec->rows*tvec->cols*CV_MAT_CN(tvec->type) == 3Both object points and image points must be 2DcvInitIntrinsicParams2Dobject points and image points must be a single-row matricesOne of required vector arguments is not a valid matrixcvCalibrateCamera2image width and height must be positivethe array of point counters must be 1-dimensional integer vectorthe output array of rotation vectors must be 3-channel 1xn or nx1 array or 1-channel nx3 or nx9 array, where n is the number of viewsthe output array of translation vectors must be 3-channel 1xn or nx1 array or 1-channel nx3 array, where n is the number of viewsIntrinsic parameters must be 3x3 floating-point matrixThe number of points in the view #%d is < 4Focal length (fx and fy) must be positivePrincipal point must be within the imageNon-zero skew is not supported by the functionThe intrinsic matrix must have [fx 0 cx; 0 fy cy; 0 0 1] shapeThe specified aspect ratio (= cameraMatrix[0][0] / cameraMatrix[1][1]) is incorrectFor non-planar calibration rigs the initial intrinsic matrix must be specifiedSome of parameters is a NULL pointer!cvCalibrationMatrixValuesInput parameters must be a matrices!Size of matrices must be 3x3!CV_IS_MAT(_imagePoints1) && CV_IS_MAT(_imagePoints2) && CV_IS_MAT(_objectPoints) && CV_IS_MAT(_npoints) && CV_IS_MAT(matR) && CV_IS_MAT(matT)cvStereoCalibrateCV_ARE_TYPES_EQ(_imagePoints1, _imagePoints2) && CV_ARE_DEPTHS_EQ(_imagePoints1, _objectPoints)(_npoints->cols == 1 || _npoints->rows == 1) && CV_MAT_TYPE(_npoints->type) == CV_32SC1(CV_MAT_DEPTH(_imagePoints1->type) == CV_32F || CV_MAT_DEPTH(_imagePoints1->type) == CV_64F) && ((_imagePoints1->rows == pointsTotal && _imagePoints1->cols*cn == 2) || (_imagePoints1->rows == 1 && _imagePoints1->cols == pointsTotal && cn == 2))CV_IS_MAT(_points1) && CV_IS_MAT(_points2) && (_points1->rows == 1 || _points1->cols == 1) && (_points2->rows == 1 || _points2->cols == 1) && CV_ARE_SIZES_EQ(_points1, _points2)cvStereoRectifyUncalibratedstype == CV_8UC1 || stype == CV_16SC1 || stype == CV_32SC1 || stype == CV_32FC1reprojectImageTo3DQ.size() == Size(4,4)dtype == CV_16SC3 || dtype == CV_32SC3 || dtype == CV_32FC3cols >= 0disp.size() == _3dimg.size()cvReprojectImageTo3DCV_IS_MAT(matrixM) && CV_IS_MAT(matrixR) && CV_IS_MAT(matrixQ) && matrixM->cols == 3 && matrixM->rows == 3 && CV_ARE_SIZES_EQ(matrixM, matrixR) && CV_ARE_SIZES_EQ(matrixM, matrixQ)cvRQDecomp3x3cvDecomposeProjectionMatrixSize of projection matrix must be 3x4!Size of calibration and rotation matrices must be 3x3!Size of position vector must be 4x1!cvDet(&tmpMatrixM) != 0.0npoints >= 0 && (depth == CV_32F || depth == CV_64F)projectPointscalibrateCameraDistortion coefficients must be 1x4, 4x1, 1x5, 5x1, 1x8 or 8x1 floating-point vectornimages > 0 && nimages == (int)imagePoints1.total() && (!imgPtMat2 || nimages == (int)imagePoints2.total())collectCalibrationDatani >= 0ni > 0 && ni == ni1ni == ni2ni1 > 0 && ni1 == ni3adjust3rdMatrixcvCheckChessboardsupports single-channel images only/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/checkchessboard.cppsupports depth=8 images onlysortedIndices.type() == CV_32SC1findCorners/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/circlesgrid.cppsortedIndices.cols == 1_patternSize.height >= 0 && _patternSize.width >= 0CirclesGridFinderfindHolesUnknown pattern typeisDetectionCorrectCenters are not newinsertWinnersamples is emptyfilterOutliersByDensityfilteredSamples is emptyBasis size is not 2findBasisdegenerate basisNumber of basis graphs is not 2maxLoc.x >= 0 && maxLoc.y >= 0findLongestPathlargeHoles != 0 && smallHoles != 0getAsymmetricHolesh >= 2 && w >= 2getCornerSegmentslargeSegments.size() == cornersCountgetFirstCornerobjectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/fisheye.cpp_rvec.total() * _rvec.channels() == 3 && (_rvec.depth() == CV_32F || _rvec.depth() == CV_64F)_tvec.total() * _tvec.channels() == 3 && (_tvec.depth() == CV_32F || _tvec.depth() == CV_64F)_tvec.getMat().isContinuous() && _rvec.getMat().isContinuous()_K.size() == Size(3,3) && (_K.type() == CV_32F || _K.type() == CV_64F) && _D.type() == _K.type() && _D.total() == 4undistorted.type() == CV_32FC2 || undistorted.type() == CV_64FC2distortPointsK.size() == Size(3,3) && (K.type() == CV_32F || K.type() == CV_64F) && D.total() == 4distorted.type() == CV_32FC2 || distorted.type() == CV_64FC2P.empty() || P.size() == Size(3, 3) || P.size() == Size(4, 3)R.empty() || R.size() == Size(3, 3) || R.total() * R.channels() == 3D.total() == 4 && K.size() == Size(3, 3) && (K.depth() == CV_32F || K.depth() == CV_64F)m1type == CV_16SC2 || m1type == CV_32F || m1type <=0(K.depth() == CV_32F || K.depth() == CV_64F) && (D.depth() == CV_32F || D.depth() == CV_64F)(P.depth() == CV_32F || P.depth() == CV_64F) && (R.depth() == CV_32F || R.depth() == CV_64F)K.size() == Size(3, 3) && (D.empty() || D.total() == 4)K.size() == Size(3, 3) && (K.depth() == CV_32F || K.depth() == CV_64F)estimateNewCameraMatrixForUndistortRectify(D.empty() || D.total() == 4) && (D.depth() == CV_32F || D.depth() == CV_64F || D.empty())(_R.size() == Size(3, 3) || _R.total() * _R.channels() == 3) && (_R.depth() == CV_32F || _R.depth() == CV_64F)stereoRectify!objectPoints.empty() && !imagePoints.empty() && objectPoints.total() == imagePoints.total()calibrateimagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2(!K.empty() && K.size() == Size(3,3)) || K.empty()(!D.empty() && D.total() == 4) || D.empty()(!rvecs.empty() && rvecs.channels() == 3) || rvecs.empty()(!tvecs.empty() && tvecs.channels() == 3) || tvecs.empty()((flags & CALIB_USE_INTRINSIC_GUESS) && !K.empty() && !D.empty()) || !(flags & CALIB_USE_INTRINSIC_GUESS)!objectPoints.empty() && !imagePoints1.empty() && !imagePoints2.empty()stereoCalibrateobjectPoints.total() == imagePoints1.total() || imagePoints1.total() == imagePoints2.total()imagePoints1.type() == CV_32FC2 || imagePoints1.type() == CV_64FC2imagePoints2.type() == CV_32FC2 || imagePoints2.type() == CV_64FC2(!K1.empty() && K1.size() == Size(3,3)) || K1.empty()(!D1.empty() && D1.total() == 4) || D1.empty()(!K2.empty() && K1.size() == Size(3,3)) || K2.empty()(!D2.empty() && D1.total() == 4) || D2.empty()((flags & CALIB_FIX_INTRINSIC) && !K1.empty() && !K2.empty() && !D1.empty() && !D2.empty()) || !(flags & CALIB_FIX_INTRINSIC)abs_max < thresholda.type() == CV_64FC1operator+operator=!objectPoints.empty() && objectPoints.type() == CV_64FC3ComputeExtrinsicRefine!imagePoints.empty() && imagePoints.type() == CV_64FC2NormalizePixels!_objectPoints.empty() && _objectPoints.type() == CV_64FC3InitExtrinsics!_imagePoints.empty() && _imagePoints.type() == CV_64FC2!objectPoints.empty() && (objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3)CalibrateExtrinsics!imagePoints.empty() && (imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2)omc.type() == CV_64FC3 || Tc.type() == CV_64FC3svd.w.at(0) / svd.w.at((int)svd.w.total() - 1) < thresh_condComputeJacobians!omc.empty() && omc.type() == CV_64FC3!Tc.empty() && Tc.type() == CV_64FC3svd.w.at(0) / svd.w.at(svd.w.rows - 1) < thresh_condEstimateUncertaintiesA.getMat().cols == B.getMat().rowsdABA.type() == CV_64FC1 && B.type() == CV_64FC1row.type() == CV_64FC1median!row.empty() && row.rows == 1m.depth() == CV_64F && m.getMat().rows == 1median3dsrc.type() == CV_64FC1subMatrixdata && dims <= 2 && rows == m && cols == n && channels() == 1operator Matxdata && dims <= 2 && (rows == 1 || cols == 1) && rows + cols - 1 == n && channels() == 1operator VecCV_IS_MAT(imagePoints) && CV_IS_MAT(objectPoints)cvFindHomography/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/fundam.cppcount >= 4CV_IS_MASK_ARR(mask) && CV_IS_MAT_CONT(mask->type) && (mask->rows == 1 || mask->cols == 1) && mask->rows*mask->cols == count(_m1->cols == 1 || _m1->rows == 1) && CV_ARE_SIZES_EQ(_m1, _m2)run8PointCV_IS_MAT(points1) && CV_IS_MAT(points2) && CV_ARE_SIZES_EQ(points1, points2)cvFindFundamentalMatCV_IS_MAT(fmatrix) && fmatrix->cols == 3 && (fmatrix->rows == 3 || (fmatrix->rows == 9 && method == CV_FM_7POINT))points parameter is not a valid matrixcvComputeCorrespondEpilinesThe format of point matrix is unsupportedpoints->rows == 1 || points->cols == 1The point matrix does not have a proper layout (2xn, 3xn, nx2 or nx3)The dimensionality of points must be 2 or 3fmatrix is not a valid matrixfundamental matrix must have 32fC1 or 64fC1 typefundamental matrix must be 3x3lines parameter is not a valid matrixThe format of the matrix of lines is unsupportedlines->rows == 1 || lines->cols == 1The lines matrix does not have a proper layout (3xn or nx3)The numbers of points and lines are differentThe input parameter is not a valid matrixcvConvertPointsHomogeneousThe output parameter is not a valid matrixInvalid inplace operationEither the number of channels or columns or rows must be =1Either the number of channels or columns or rows in the input matrix must be =1Either the number of channels or columns or rows in the output matrix must be =1Both matrices must have the same number of pointsBoth matrices must be floating-point (single or double precision)Both input and output point dimensionality must be 2, 3 or 4The dimensionalities of input and output point sets differ too muchnpoints >= 0 && points2.checkVector(2) == npoints && points1.type() == points2.type()findHomographyfindFundamentalMatnpoints >= 0 && (points.depth() == CV_32F || points.depth() == CV_32S)computeCorrespondEpilinesnpoints >= 0 && (src.depth() == CV_32F || src.depth() == CV_32S)convertPointsFromHomogeneousconvertPointsToHomogeneous_dst.fixedType()convertPointsHomogeneousthe number of model points should be positivecvRANSACUpdateNumIters/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/modelest.cppCV_ARE_SIZES_EQ(m1, m2) && CV_ARE_SIZES_EQ(m1, mask)runRANSACrunLMeDSCV_MAT_TYPE(ms1->type) == CV_64FC3checkSubsetcount >= 0 && to.checkVector(3) == countestimateAffine3D(icvCreatePOSITObject( points, numPoints, &pObject )) >= 0cvCreatePOSITObject/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/posit.cpp(icvPOSIT( pObject, imagePoints,(float) focalLength, criteria, rotation, translation )) >= 0cvPOSITncorners >= 0find4QuadCornerSubpix/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/quadsubpix.cppnpoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F))solvePnP/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/solvepnp.cppnpoints == 4The flags argument must be one of CV_ITERATIVE or CV_EPNPopoints.isContinuous()solvePnPRansacopoints.depth() == CV_32F || opoints.depth() == CV_64F(opoints.rows == 1 && opoints.channels() == 3) || opoints.cols*opoints.channels() == 3ipoints.isContinuous()ipoints.depth() == CV_32F || ipoints.depth() == CV_64F(ipoints.rows == 1 && ipoints.channels() == 2) || ipoints.cols*ipoints.channels() == 2objectPoints.depth() == CV_64F || objectPoints.depth() == CV_32FpnpTaskimagePoints.depth() == CV_64F || imagePoints.depth() == CV_32FcvReleaseStereoBMState/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/stereobm.cppdisptype == CV_16S || disptype == CV_32FAll the images must have the same sizefindStereoCorrespondenceBMBoth input images must have CV_8UC1Disparity image must have CV_16SC1 or CV_32FC1 formatStereo BM state is NULL.preFilterType must be = CV_STEREO_BM_NORMALIZED_RESPONSEpreFilterSize must be odd and be within 5..255preFilterCap must be within 1..63SADWindowSize must be odd, be within 5..255 and be not larger than image width or heightnumberOfDisparities must be positive and divisble by 16texture threshold must be non-negativeuniqueness ratio must be non-negativeleft.size() == right.size() && left.type() == right.type() && left.depth() == DataType::depth/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/stereosgbm.cppimg.type() == CV_8UC1 || img.type() == CV_16SC1filterSpecklesnumberOfDisparities > 0 && disp.type() == CV_16S && (costType == CV_16S || costType == CV_32S) && disp.size() == cost.size()validateDisparityD % 16 == 0computeDisparitySGBMSome of parameters is a NULL pointercvTriangulatePoints/Users/jason/Projects/opencv/opencv-2.4.11/modules/calib3d/src/triangulate.cppInput parameters must be matricesNumber of points must be more than zeroNumber of points must be the sameNumber of proj points coordinates must be == 2Number of world points coordinates must be == 4Size of projection matrices must be 3x4cvCorrectMatchesThe fundamental matrix must be a 3x3 matrixThe fundamental matrix must be a single-channel matrixThe point-matrices must have one row, and an equal number of columnsThe first set of points must contain two channels; one for x and one for yThe second set of points must contain two channels; one for x and one for yCV_IS_MAT(new_points1)The first output matrix must have the same dimensions as the input matricesThe first output matrix must have two channels; one for x and one for yCV_IS_MAT(new_points2)The second output matrix must have the same dimensions as the input matricesThe second output matrix must have two channels; one for x and one for ymy_nnUnknown activation function/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/ann_mlp.cppThe array of layer neuron counters must be an integer vectorthere should be at least one input and one output and every hidden layer must have more than 1 neuronThe network has not been initializedpredictBoth input and output must be floating-point matrices of the same type and have the same number of rowsinput matrix must have the same number of columns as the number of neurons in the input layeroutput matrix must have the same number of columns as the number of neurons in the output layerSome of new output training vector components run exceed the original range too muchThe network has not been created. Use method create or the appropriate constructorinput training data should be a floating-point matrix withthe number of rows equal to the number of training samples and the number of columns equal to the size of 0-th (input) layeroutput training data should be a floating-point matrix withthe number of rows equal to the number of training samples and the number of columns equal to the size of last (output) layerThe numbers of input and output samples do not matchsample_weights (if passed) must be a valid matrixsample_weights must be 1d floating-point vector containing weights of all or selected training samplessome of sample weights are negativeIDENTITYSIGMOID_SYMGAUSSIANactivation_functionf_param1f_param2min_valmax_valmin_val1max_val1training_paramstrain_methodBACKPROPdw_scalemoment_scaleRPROPdw0dw_plusdw_minusdw_mindw_maxterm_criteriaepsilonopencv-ml-ann-mlplayer_sizesinput_scaledoutput_scaleinv_output_scaleweightsinput_scale tag is not found or is invalidoutput_scale tag is not found or is invalidinv_output_scale tag is not found or is invalidweights tag is not found or is invalidlayer_sizes != 0my_boost_treeUnknown/unsupported boosting type/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/boost.cppBoosted trees can only be used for 2-class classification.The boosted tree ensemble has not been trained yetthe input sample must be 1d floating-point vector with the same number of elements as the total number of variables or as the number of variables used for trainingthe missing data mask must be 8-bit vector of the same size as input sampleThe output matrix of weak classifier responses must be valid floating-point vector of the same number of components as the length of input sliceone of input categorical variable is not an integerIn raw mode the input vectors must be continuousDiscreteAdaboostRealAdaboostLogitBoostGentleAdaboostDefaultGiniMisclassificationSquaredErrboosting_typesplitting_criteriantreesweight_trimming_rateUnknown boosting type tag is missingThe number of trees stored does not match  tag valueopencv-ml-boost-treeThe classifier has not been trained yetdata is empty/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/data.cpp
delimited, miss_character and flt_separator must be differentidx value is not correctvar_idx is not correcttype is not correctit`s impossible to assign CV_VAR_ORDERED type to categorical variableordcattypes string is not correcttrain samples count is not correcttest_sample_count > 0set_train_test_splitvariable index is not correctisTrained()/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/em.cpp!sample.empty()!covs.empty()decomposeCovs!covs[clusterIndex].empty()meansFlt.type() == CV_32FC1clusterTrainSamples!clusterSamples.empty()!covsEigenValues.empty()computeLogWeightDivDetweights.empty()doTraininvCovsEigenValues.empty()!means.empty()computeProbabilitiessample.type() == CV_64FC1sample.rows == 1sample.cols == means.cols!samples.empty()checkTrainDatasamples.channels() == 1nclusters > 0nclusters <= nsamplesstartStep == EM::START_AUTO_STEP || startStep == EM::START_E_STEP || startStep == EM::START_M_STEPcovMatType == EM::COV_MAT_GENERIC || covMatType == EM::COV_MAT_DIAGONAL || covMatType == EM::COV_MAT_SPHERICAL!probs || (!probs->empty() && probs->rows == nsamples && probs->cols == nclusters && (probs->type() == CV_32FC1 || probs->type() == CV_64FC1))!weights || (!weights->empty() && (weights->cols == 1 || weights->rows == 1) && static_cast(weights->total()) == nclusters && (weights->type() == CV_32FC1 || weights->type() == CV_64FC1))!means || (!means->empty() && means->rows == nclusters && means->cols == dim && means->channels() == 1)!covs || (!covs->empty() && static_cast(covs->size()) == nclusters)!m.empty() && m.size() == covSize && (m.channels() == 1)meansprobsCvERTrees do not support surrogate splits/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/ertrees.cppCvERTrees do not support data updateThe array of _responses must be an integer or floating-point vector containing as many elements as the total number of samples in the training data matrixThe memory buffer cannot be allocated since its size exceeds integer fields limitset_dataThe many folds in cross-validation for such a small dataset%d-th value of %d-th (categorical) variable is not an integer%d-th value of %d-th (categorical) variable is too large%d-th value of %d-th (ordered) variable (=%g) is too largeEvery class weight should be positiveNo training data has been set_subsample_idx must be null for extra-treesopencv-ml-extremely-randomized-trees must be non-negative(active_var_mask->cols >= 1) && (params.nactive_vars > 0) && (params.nactive_vars <= active_var_mask->cols)trainResponse should be a 32fC1 or 32sC1 vector./Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/gbt.cpp_sample_idx should be a 32sC1, 8sC1 or 8uC1 vector.SquaredLossAbsoluteLossHuberLossDevianceLossloss_functionensemble_lengthshrinkagesubsample_portionclass_labelsUnknown loss functionclass_labels must stored as a matrixopencv-ml-gradient-boosting-treesThe model has not been trained yetbase_valueclass_counttrees_ tag is missingmy_stat_model/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/inner_functions.cppCvStatModel::writeCvStatModel::readInvalid index arraythe index array must be 1-dimensionalComponent mask should contain as many elements as the total number of input variablesNo components/input_variables is selected!index array may not contain more elements than the total number of input variablesUnsupported index array data type (it should be 8uC1, 8sC1 or 32sC1)the index array elements are out of rangeThere are duplicated index array elementsInvalid or absent var_type arrayvar_type array must be 1-dimensionaltype mask must be 8uC1 or 8sC1 arraytype mask must be of  + 1 sizevar index array should be continuous 1-dimensional integer vectorvar index array is too largeInvalid response arrayResponse array must be 1-dimensionalResponse array must contain as many elements as the total number of samplesUnsupported response typesample index array should be continuous 1-dimensional integer vectorsample index array is too largeout_response_map pointer is NULLresponse #%d is not integralThere is only a single classInvalid or NULL training data matrixtrain data must be floating-point matrixmissing value mask must be 8-bit matrix of the same size as training dataUnknown training data layout (must be CV_ROW_SAMPLE or CV_COL_SAMPLE)output pointer to train samples is NULLoutput response pointer is NULLINTERNAL ERROR: some of the args are NULL pointersclasses array must be a single row of integersThe sample is not a valid vectorInput sample must have 32fC1 typeInput sample must be 1-dimensional vectorThe sample size is different from what has been used for trainingINTERNAL ERROR: The row_sample pointer is NULLINTERNAL ERROR: invalid comp_idxThe output matrix of probabilities is invalidThe matrix of probabilities must be 1-dimensional vector of 32fC1 typeThe vector of probabilities must contain as many elements as the number of classes in the training setArray of output labels is not a valid matrixArray of cluster centers is not a valid matrixProbability matrix is not validAssertion: sample_idx->rows == 1 && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 failedAssertion: comp_idx->rows == 1 && CV_MAT_TYPE(comp_idx->type) == CV_32SC1 failedNULL labelsAssertion: labels->rows == 1 failedArray of output labels should be 1d vectorSize of vector of output labels is not equal to the total number of input samplesAssertion: labels->cols == samples_selected failedNULL centersInvalid number of rows in matrix of output centersNumber of columns in matrix of output centers is not equal to the total number of components in the input samplesAssertion: centers->cols == dims_selected failedNULL probsInvalid number of columns in output probability matrixNumber of rows in output probability matrix is not equal to the total number of input samplesAssertion: probs->rows == samples_selected failedSome of input arguments is not the CvMatAssertion: free_response == out_n failedAssertion: ICV_IS_MAT_OF_TYPE(probs, CV_64FC1) failedAssertion: ICV_IS_MAT_OF_TYPE(labels, CV_32SC1) failedAssertion: nsamples == labels->cols failedAssertion: (int)cvSum(counts).val[0] == nsamples failedAssertion: maxprob_loc >= 0 failedThe function supports only single-channel arraysUnsupported combination of input and output vectorsAssertion: prob_vector failedCvKNearest::train/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/knearest.cppCould not allocate memory for responsesThe newly added data have different dimensionalitymax_k must be a positive numberThe search tree must be constructed first using train methodfind_nearestInput samples must be floating-point matrix (x)The results must be 1d vector containing as much elements as the number of samplesThe results must be floating-point or integer (in case of classification) vectork must be within 1..max_k rangeThe neighbor responses (if present) must be floating-point matrix of  x  sizeThe distances from the neighbors (if present) must be floating-point matrix of  x  sizenclusterscovMatTypemaxIterscovsStatModel.EMmy_nb/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/nbayes.cppThe new training data is inconsistent with the original training dataIn the current implementation the new training data must have absolutely the same set of class labels as used in the original training dataThe input samples must be 32f matrix with the number of columns = var_allWhen the number of input samples is >1, the output vector of results must be passedThe output array must be integer or floating-point vector with the number of elements = number of rows in the input matrixopencv-ml-bayesianvar_countvar_allvar_idxcls_labelsproductsumavginv_eigen_valuescov_rotate_matscNo "cls_labels" in NBayes classifierNumber of classes is less 1The field "var_count" of NBayes classifier is missingvar_count == data->var_countfind_best_split/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/rtrees.cppmy_random_treesopencv-ml-random-treesThis method is not supported for regression problemsget_train_error0 <= class_idx && class_idx < nclassespredict_probThis function works for binary classification problems only...Invalid CvRTrees objectnclassesnsamplesnactive_varsoob_errorvar_importanceSome , , , , ,  of tags are missing is not equal to the number of trees saved in fileLower bound of the grid must be less then the upper one/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/svm.cppLower bound of the grid must be positiveGrid step must greater then 1CvSVM::get_default_gridInvalid type of parameter (use one of CvSVM::C, CvSVM::GAMMA et al.)Too small storage block sizemy_svmUnknown/unsupported kernel typegamma parameter of the kernel must be positiveThe kernel parameter  must be positive or zeroThe kernel parameter  must be positiveUnknown/unsupported SVM typeThe parameter C must be positiveThe parameter nu must be between 0 and 1The parameter p must be positivesv_count != 0do_trainparams.class_weights must be 1d floating-point vector containing as many elements as the number of classesWhile cross-validation one or more of the classes have been fell out of the sample. Try to enlarge CvSVM::trainParameter  must be > 1CvSVM::train_autoINTERNAL ERROR: Unknown SVM type, the SVM structure is probably corruptedThe SVM should be trained firstC_SVCNU_SVCONE_CLASSEPS_SVRNU_SVRLINEARPOLYRBFSIGMOIDsvm_typekerneldegreegammacoef0nupSVM model data is invalid, check sv_count, var_* and class_count tagsopencv-ml-svmclass_weightssv_totalsupport_vectorsdecision_functionssv_countrhoalphaindexAssertion: sv_count == sv_total failedsvm_type tag is not foundMissing of invalid SVM typeSVM kernel tag is not foundSVM kernel type tag is not foundMissing of invalid SVM kernel typeThe requested element is not foundArray of class labels is missing or invalidvar_idx array is missing or invalidMissing or invalid sequence of support vectorsAssertion: var_count == 1 || (CV_NODE_IS_SEQ(sv_elem->tag) && sv_elem->data.seq->total == var_count) faileddecision_functions is missing or is not a collection or has a wrong number of elementssv_count is missing or non-positiverho is missingalpha is missing in the decision functionAssertion: sv_count == 1 || (CV_NODE_IS_SEQ(alpha_node->tag) && alpha_node->data.seq->total == sv_count) failedindex is missing in the decision functionAssertion: sv_count == 1 || (CV_NODE_IS_SEQ(index_node->tag) && index_node->data.seq->total == sv_count) failedoptimize_linearInvalid type parameter/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/testset.cppsamples parameter must be not NULLresponses parameter must be not NULLnum_samples parameter must be positivenum_features parameter must be positivenum_classes parameter must be positiveparams.max_categories should be >= 2/Users/jason/Projects/opencv/opencv-2.4.11/modules/ml/src/tree.cppparams.max_depth should be >= 0params.cv_folds should be =0 (the tree is not pruned) or n>0 (tree is pruned using n-fold cross-validation)params.regression_accuracy should be >= 0The new training data must have the same types and the input and output variables and the same categories for categorical variablesis_classifierord_var_countcat_var_countuse_surrogatesmax_categoriesregression_accuracymax_depthmin_sample_countcross_validation_foldsuse_1se_ruletruncate_pruned_treepriorsvar_typecat_countcat_mappriors must stored as a matrixvar_idx (if exist) must be valid 1d integer vector containing  elementssome of var_idx elements are out of rangevar_type must exist and be a sequence of 0's and 1'sBoth cat_count and cat_map must exist and be valid 1d integer vectors of an appropriate sizesome of cat_count elements are out of rangecat_map vector length is not equal to the total number of categories in all categorical varsmy_treeroot->leftroot->rightThe tree has not been trained yetthe input sample must be 1d floating-point vector with the same number of elements as the total number of variables used for trainingvarqualityinnot_inledepthsample_countnorm_class_idxTncomplexitynode_risktree_risktree_errorsplitsopencv-ml-treebest_tree_idxnodessome of the splits are not stored properlySplit variable index is out of rangeEither 'in' or 'not_in' tags should be inside a categorical split datasome of in/not_in elements are out of rangesome of the tree elements are not stored properlyincorrect node depthsplits tag must stored as a sequencenodes tag is missingCV_MAT_DEPTH(frameType) == CV_8U/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/bgfg_gaussmix.cppimage.depth() == CV_8UlearningRate >= 0Only 1- and 3-channel 8-bit images are supported in BackgroundSubtractorMOGnchannels <= CV_CN_MAX/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/bgfg_gaussmix2.cppnchannels == 1 || nchannels == 3getBackgroundImagemin < max/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/bgfg_gmg.cppmaxFeatures > 0learningRate >= 0.0 && learningRate <= 1.0numInitializationFrames >= 1quantizationLevels >= 1 && quantizationLevels <= 255backgroundPrior >= 0.0 && backgroundPrior <= 1.0frame.depth() == CV_8U || frame.depth() == CV_16U || frame.depth() == CV_32Fframe.channels() == 1 || frame.channels() == 3 || frame.channels() == 4newLearningRate >= 0.0 && newLearningRate <= 1.0cvMeanShift/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/camshift.cppInput window has non-positive sizesstate and measurement vectors must have positive number of dimensionscvCreateKalman/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/kalman.cppcvReleaseKalmancvKalmanPredictcvKalmanCorrectDP > 0 && MP > 0img.depth() == CV_8U && winSize.width > 2 && winSize.height > 2buildOpticalFlowPyramid/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/lkpyramid.cppmaxLevel >= 0 && winSize.width > 2 && winSize.height > 2calcOpticalFlowPyrLK(npoints = prevPtsMat.checkVector(2, CV_32F, true)) >= 0nextPtsMat.checkVector(2, CV_32F, true) == npointsstatusMat.isContinuous()errMat.isContinuous()levels1 >= 0ofs.x >= winSize.width && ofs.y >= winSize.height && ofs.x + prevPyr[lvlStep1].cols + winSize.width <= fullSize.width && ofs.y + prevPyr[lvlStep1].rows + winSize.height <= fullSize.heightlevels2 >= 0ofs.x >= winSize.width && ofs.y >= winSize.height && ofs.x + nextPyr[lvlStep2].cols + winSize.width <= fullSize.width && ofs.y + nextPyr[lvlStep2].rows + winSize.height <= fullSize.heightprevPyr[level * lvlStep1].size() == nextPyr[level * lvlStep2].size()prevPyr[level * lvlStep1].type() == nextPyr[level * lvlStep2].type()featuresA && featuresBcvCalcOpticalFlowPyrLKcvCalcAffineFlowPyrLKimgA and imgB must have equal stepspyramid A has insufficient sizepyramid B has insufficient sizethe search window is too smallOutput parameter M is not a valid matrixcvEstimateRigidTransformBoth input images must have the same sizeBoth input images must have the same data typeBoth input images must have either 8uC1 or 8uC3 typecalcSharrDerivSome of the precomputed pyramids are missingicvInitPyramidalAlgorithmThe number of pyramid levels is negativeInvalid termination criteriacvUpdateMotionHistory/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/motempl.cppcvCalcMotionGradientaperture_size must be 3, 5 or 7both delta's must be positiveMHI and orientation must be single-channel floating-point imagesorientation image must be different from MHIcvCalcGlobalOrientationMHI duration must be positiveNULL memory storagecvSegmentMotionBoth MHI and the destination maskprev0.size() == next0.size() && prev0.channels() == next0.channels() && prev0.channels() == 1 && pyr_scale < 1calcOpticalFlowFarneback/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/optflowgf.cppflow.size() == prev.size() && flow.type() == CV_32FC2cvCalcOpticalFlowFarneback(int)pyr_from_images.size() == layers && (int)pyr_to_images.size() == layerscalcOpticalFlowSF/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/simpleflow.cppconfidence.at(r0, c0) >= 0calcConfidenceDenseOpticalFlow.DualTVL1tauTime step of the numerical schemelambdaWeight parameter for the data term, attachment parameterthetaWeight parameter for (u - v)^2, tightness parameternscalesNumber of scales used to create the pyramid of imageswarpsNumber of warpings per scaleStopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running timeStopping criterion iterations number used in the numerical schemeuseInitialFlowI0.type() == CV_8UC1 || I0.type() == CV_32FC1calc/Users/jason/Projects/opencv/opencv-2.4.11/modules/video/src/tvl1flow.cppI0.size() == I1.size()I0.type() == I1.type()!useInitialFlow || (_flow.size() == I0.size() && _flow.type() == CV_32FC2)nscales > 0historynmixturesbackgroundRationoiseSigmavarThresholddetectShadowsvarThresholdGenfVarInitfVarMinfVarMaxfCTnShadowDetectionfTaumaxFeaturesMaximum number of features to store in histogram. Harsh enforcement of sparsity constraint.learningRateAdaptation rate of histogram. Close to 1, slow adaptation. Close to 0, fast adaptation, features forgotten quickly.initializationFramesNumber of frames to use to initialize histograms of pixels.quantizationLevelsNumber of discrete colors to be used in histograms. Up-front quantization.backgroundPriorPrior probability that each individual pixel is a background pixel.smoothingRadiusRadius of smoothing kernel to filter noise from FG mask image.decisionThresholdThreshold for FG decision rule. Pixel is FG if posterior probability exceeds threshold.updateBackgroundModelPerform background model update.BackgroundSubtractor.MOGBackgroundSubtractor.MOG2BackgroundSubtractor.GMGChess board size is invalid/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/3dtracker.cppNull imageUnsupported image formatNull image dataInvalid ROIChannel depth of source image must be 8Source image must have 1 or 3 channelsInvalid or NULL first_frame parameter/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/bgfg_acmmm2003.cppfirst_frame must have 3 color channelsAssertion: model && CV_MAT_TYPE(image->type) == CV_8UC3 && (!mask || (CV_IS_MASK_ARR(mask) && CV_ARE_SIZES_EQ(image, mask))) failed/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/bgfg_codebook.cppAssertion: (unsigned)roi.x < (unsigned)image->cols && (unsigned)roi.y < (unsigned)image->rows && roi.width >= 0 && roi.height >= 0 && roi.x + roi.width <= image->cols && roi.y + roi.height <= image->rows failedAssertion: nblocks > 0 failedAssertion: model && CV_MAT_TYPE(image->type) == CV_8UC3 && image->cols == model->size.width && image->rows == model->size.height && CV_IS_MASK_ARR(mask) && CV_ARE_SIZES_EQ(image, mask) failedAssertion: model && (!mask || (CV_IS_MASK_ARR(mask) && mask->cols == model->size.width && mask->rows == model->size.height)) failedAssertion: (unsigned)roi.x < (unsigned)mask->cols && (unsigned)roi.y < (unsigned)mask->rows && roi.width >= 0 && roi.height >= 0 && roi.x + roi.width <= mask->cols && roi.y + roi.height <= mask->rows failedLCalpha1alpha2alpha3N1cN2cN1ccN2ccSaveNameLoadNameObjWithoutHolesMorphologyFGDNGMOGCV_IS_IMAGE(first_frame)cvCreateGaussianBGModel/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/bgfg_gaussmix.cppMaxumum number of channels in the image is excedded (change CV_BGFG_MOG2_MAXBANDS constant)!icvReleaseGaussianBGModelmog != 0icvUpdateGaussianBGModelthe image not the same size as the reserved GMM background modelicvUpdateGaussianBGModel2DebugWndunknownWARNING!!! Can't read structure %s
WARNING!!! Structure %s is not sequence and can not be read
FGDetectorBlobTrackerffffiListBlobTrackAnalysisPPVPVSBlobTrackFVGenAbnormalThresholdIf trajectory histogram value is lesst then  then trajectory is abnormalSmoothRadiusRadius (in bins) for histogram smoothingLSmoothKernelL - Linear, G - GaussianBinNumNumber of bin for each dimention of feature vectorHistBlobNumBlobListBlobLastFrameStateWARNING!!! Can't load %s matrix
abnormal.ymlSSHISTIORCan not add track analyzer %s! (not more that %d analyzers)
DebugAVIName of AVI file to save images from debug windowTraceLenLength (in frames) of trajectory part that is used for comparisonIf trajectory is equal with less then  tracks then trajectory is abnormalPosThresholdMinimal allowd distance in blob width that is allowedVelThresholdMinimal allowed relative difference between blob speedTrackDistGen1BlobTrackGenatWarning! Cannot open %s file for track output
%d, %.1f, %.1f, %.2fYMLWARNING!!! Cannot open %s file for trajectory output.%s_obj%dFrameBeginVideoObjPosffSizeTimesFileBlobTrackerRealAuto1- %sFrame: %d ALL_TIME - %f
ForFrame Frame: %d %s %f on %f blobs
ForBlob  Frame: %d %s - %f
PostProcessingBlobDeleterBlobTrackerUpdateBlobDetectorTrajectoryGenerationTrackAnalysisBlobTrackerAutoFrameCountNextBlobIDCollisionIf 1 then collision cases are processed in special wayAlphaSizeSize update speed (0..1)AlphaPosPosition update speed (0..1)AlphaCoefficient for model histogram updating (0 - hist is not updated)NearestBlobConfidenceTypeType of calculated Confidence (NearestBlob, AverFG, BC)CCAverFGBCWARNING!!! Invalid Predictor in CC trackerMSPFCCCRSizeVarIDBlobPredictBlobPrevBlobHypPredictorResolverDataNoisePosif 1 then collision cases are processed in special wayBGImageUsingWeight of using BG image in update hist model (0 - BG dies not use 1 - use)FGWeightWeight of FG mask using (0 - mask will not be used for tracking)Coefficient for model histogram updating (0 - hist is not upated)IterNumMaximal number of iteration in meanshift operationMSFGHistVolumeMSParticleNumNumber of particlesUseVelPercent of particles which use velocity featureSize variation (in object size)PosVarPosition variation (in object size)ParticlesPredictedffffiffdParticlesResampledsizeof(DefParticle) is %d
Coefficient for model histogramm updating (0 - hist is not upated)MSFGSModelNoiseDataNoiseSizeKalmanBlobTrackPostOneBlobTrackPredictorTimeAverBlobTrackPostProccvCalcImageHomography/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/calcimagehomography.cpp%d

%15.10f 

%15.10lf %fvalues_read == 1LoadCameraParams/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/calibfilter.cpp%f %lf [WARNING] %s:%i: square CS matrix (-> no reduction)
/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/calonder.cppPHI_DISTR_TYPE not implemented.
[OK] NO compression to leaves applied, dim=%i

[OK] compressing leaves with phi %i x %i
clamp_mode == %i is not valid (%s:%i).
%.10eINVALID PARAMS in RTreeClassifier::train: reduced_num_dim{%i} > base_set.size(){%i}
[OK] Training trees: base size=%i, reduced size=%i
[OK] Trained 0 / %i trees
[OK] Trained %i / %i trees[WARNING] RTC: suspicious value num_quant_bits_=%i found; setting to %i.
[OK] Loaded RTC, quantization=%i bits
RTreeClassifier::write: num_quant_bits_=%i
[DEBUG] writing all float posteriors to %s...
[DEBUG] writing all byte posteriors to %s...
[EXPERIMENTAL] read entire tree from '%s'
[OK] RTC: overall %i/%i (%.3f%%) zeros in float leaves
          overall %i/%i (%.3f%%) zeros in uint8 leaves
[OK] signature quantization is now %i bits (before: %i)
a[WARNING] RandomizedTree::compressLeaves:  not compressing because reduced_dim == classes()WARNING: Cannot write float posteriors (posteriors_ = NULL).[DEBUG] done[OK] RTC: discarded float posteriors of all treescvRandSetRangeNull pointer to RNG statecvcompat.hindex is not in -1..3cvRandInitcvRandcvStartScanGraphNull scanner pointercvEndScanGraphcvCreateConDensation/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/condens.cppcvReleaseConDensationcvConDensUpdateByTimecvConDensInitSampleSetsource  has not appropriate formatsource  has not appropriate size(icvCreateContourTree( contour, storage, &tree, threshold )) >= 0cvCreateContourTree/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/contourtree.cppcvContourFromContourTreecvMatchContourTreesUnknown/unsupported comparison methodThe first argument is not a valid contour treeThe second argument is not a valid contour tree(icvDynamicCorrespondMulti( lines, first, first_runs, second, second_runs, first_corr, second_corr )) >= 0cvDynamicCorrespondMulti/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/correspond.cpp/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/createhandmask.cppoutput image have wrong number of channels(icvCreateHandMask8uC1R( numbers, img_mask_data, img_mask_step, img_mask_size, roi )) >= 0cvCreateHandMaskcvFindDominantPoints/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/dominants.cpp(icvFindDominantPointsIPAN( contour, storage, &corners, dmin*dmin, dmax*dmax, dneigh*dneigh, (float)amax )) >= 0/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/dpstereo.cppAll images must be single-channel and have 8uparameter /maxDisparity/ is out of rangeUnsupported mode of function/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/eigenobjects.cppDifferent sizes of objectsDifferent steps of objectsUnsupported i/o flagdifferent sizes of imagesDifferent sizes of projectsprbs.size == prbs0.size/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/em.cpp_sample_idx == 0labels0.data == labels.data_sample_idx.empty()Bad start type of EM algorithmSimpleLatencyHMinWMinMinDistToBorderMinimal allowed distance from blob center to image border in blob sizesClasteringROIScaleDetermines the size of search window around a blobOnlyROIShows the whole debug image (0) or only ROIs where the detector was applied (1)/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/epilines.cppFeature2D.SURFcvExtractSURF/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/features2d.cppnOctavesnOctaveLayersuprightextended(icvFindHandRegion( points, count, indexs, line, size, -flag, center, storage, numbers )) >= 0cvFindHandRegion/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/findhandregion.cpp(icvFindHandRegionA( points, count, indexs, line, size, flag, center, storage, numbers )) >= 0(icvFindHandRegionA( points, count, indexs, line, size, jc, center, storage, numbers )) >= 0cvFindHandRegionA/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/hmm.cpp(icvUniformImgSegm( obs_info, hmm )) >= 0cvUniformImgSegm(icvEstimateObsProb( obs_info, hmm )) >= 0cvEstimateObsProbNull pointer.cvEViterbi/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/hmmobs.cpp(icvImgToObs_DCT_8u32f_C1R( mat->data.ptr, mat->step, cvGetMatSize(mat), obs, dctSize, obsSize, delta )) >= 0cvImgToObs_DCT(icvImgToObs_DCT_32f_C1R( mat->data.fl, mat->step, cvGetMatSize(mat), obs, dctSize, obsSize, delta )) >= 0RGB<->Grayscale conversion is not implemented for images stored in XML/YAMLload/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/image.cppRGB<->Grayscale conversion is not implemented for matrices stored in XML/YAML.Yml.YML.yaml.Yaml.YAMLThe object is neither an image, nor a matrixicvRetrieveImageicvRetrieveMatrixdescriptors must be either CV_32FC1 or CV_64FC1cvCreateKDTree/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/kdtree.cppdesc columns be equal feature dimensionsFindFeaturesresults and desc must be same heightdist and desc must be same heightresults must be CV_32SC1dist must be CV_64FC1bounds_{min,max} must 1 x dims or dims x 1FindOrthoRangebounds_{min,max} must have same typeVoronoi Diagram is not defined/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/lcm.cppTreshold parameter must be non negativeCan't operate with multiconnected domainsCan't operate with large domainsContour sequence is empty/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/lee.cppStorage is not initializedUnsupported parameter: typeUnsupported parameter: orientationContour sequence is not initializedContour sequence is not emptyImage is not initializedUnsupported presision valueUnsupported regularisation methodNumber of images must be more than one/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/levmarprojbandle.cppNumber of points4D must be more than zeroPoints must have 4 cordinatesNumber of images must be more than zeroMaximum number of iteration must be in [1..1000]Epsilon parameter must be >= 0resultPoints4D must be a matrix 4 x NumPntresultPoints4D must have 4 cordinateseach of initial projMatrs must be a matrix 3 x 4each of observProjPoints must be a matrix 2 x NumPntseach of pointsPres must be a matrix 1 x NumPnteach of resultProjMatrs must be a matrix 3 x 4d:\test\bundle.txtbegin

========================================
Iter=0
Error = %20.15lf
Change = %20.15lf
projection errors

Image=%d
%40.35lf, %40.35lf

----- test 13,01 currImage=%d currNorm=%lf -----
numPoints=%d
Iter=%d

Change inside newVal change = %20.15lf
   normAll1= %20.15lf
   normAll2= %20.15lf

Best error = %40.35lf
points4D must be a matrix 4xNNumber of coordinates of points4D must be 4projMatr must be a matrix 3x4Size of projection matrix (projMatr) must be 3x4Status must be a matrix 1xNSize of status of points must be 1xNderivProj must be a matrix VisN x 12derivProj must be a matrix 2VisN x 12points4D must be a matrix N x 4Size of presPoints status must be 1xNderivPoint must be a matrix 2 x 4VisNumNumber of points must more than zeromatrW must be a matrix 12NumIm x 4NumPntjacProjErr must be a matrix 12NumIm x 1jacPointErr must be a matrix 4NumPnt x 1Source image must have 3 channel./Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/lines.cppChannel depth of source image must be 8.Channel depth of image must be 8.vectors must be either CV_32FC1 or CV_64FC1cvCreateLSH/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/lsh.cppdata must be n x d, where d is what was used to construct LSHcvLSHAddtype of data and constructed LSH must agreeindices must be CV_32SC1indices must be n x 1 or 1 x n for n x d datacvLSHRemovek must be positivecvLSHQuerydist must be n x k for n x d datadist and indices must be same size(icvMorphEpilines8uC3Multi( lines, first_pix, first_num, second_pix, second_num, dst_pix, dst_num, alpha, first, first_runs, second, second_runs, first_corr, second_corr )) >= 0cvMorphEpilinesMulti/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/morphing.cpp%s/patch_%04d.pngImage depth %d is not supported
%s/%spca_descriptors.yml_lr_hrposeCountpatchWidthpatchHeightpyrLevelspcaDimHighpcaDimLowminScalestepScaleCreated descriptor for PCA component %d
File %s not found...
Successfully read %d pca components
affine_posesaffine posespca_components_numberpca components numberdescriptor_for_pca_component_%ddescriptor for pca component %davg_%seigenvectors_%sCannot open images list file %s
%shrlrCalculating %d PCA descriptors (you can grab a coffee, this will take a while)...
The first component is the average Vector, so the total number of components is  + 1patch_widthpatch_heightCannot open file %s! Exiting!eigenvectorsknn == 1/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/oneway.cppdeep clone functionality is not implemented, because OneWayDescriptorBase has not copy constructor or clone method pca.ymlOpenCV was built without SURF supportextractPatchesNo element name has been given/Users/jason/Projects/opencv/opencv-2.4.11/modules/core/include/opencv2/core/operations.hppInitializing the descriptors...Source images have different formatscvCalcOpticalFlowBM/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/optflowbm.cppDestination images have different formatsSource images must have 8uC1 type and destination images must have 32fC1 typetwo source or two destination images have different stepscvCalcOpticalFlowHS/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/optflowhs.cppsource and destination images have different step(icvCalcOpticalFlowHS_8u32fR( (uchar*)srcA->data.ptr, (uchar*)srcB->data.ptr, srcA->step, cvGetMatSize( srcA ), usePrevious, velx->data.fl, vely->data.fl, velx->step, (float)lambda, criteria )) >= 0cvCalcOpticalFlowLK/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/optflowlk.cpp(icvCalcOpticalFlowLK_8u32fR( (uchar*)srcA->data.ptr, (uchar*)srcB->data.ptr, srcA->step, cvGetMatSize( srcA ), winSize, velx->data.fl, vely->data.fl, velx->step )) >= 0The histogram header is invalid cvCalcPGH/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/pgh.cppSparse histogram are not supportedThe histogram must be two-dimensionalThe contour is not valid or the point type is not supported(icvCalcPGH( contour, ((CvMatND*)(hist->bins))->data.fl, size[0], size[1] )) >= 0fhcircle0.size() == (size_t)(radius+1) && circle0.size() % 2 == 0/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/planardetect.cppradiusnoctavesnviewsbase-feature-sizeclustering-distancenstructsstruct-sizesignature-sizecompression-methodpatch-sizefeaturesposteriors_nclasses > 1 && _patchSize >= 5 && _nstructs > 0 && _nviews > 0 && _structSize > 0 && (_compressionMethod == COMPRESSION_NONE || _compressionMethod == COMPRESSION_RANDOM_PROJ || _compressionMethod == COMPRESSION_PCA)preparepatchSize.width <= 256 && patchSize.height <= 256points.size() == refimgs.size()labels.empty() || labels.size() == points.size()0 <= classId && classId < nclassesmatM.type() == CV_64FtrainFromSingleViewThe descriptor has not been trained or the floating-point posteriors have been deletedpatch.size() == patchSizesignatureSizestructSizecompressionMethoddeep clone dunctionality is not implemented, because FernClassifier has not copy constructor or clone method model-roifern-classifiermodel-pointsmodelPoints.size() == (size_t)fernClassifier.getClassCount()0 <= kpt.octave && kpt.octave < (int)pyr.size()n8 > 0getDiscreteCirclecvPyrSegmentation/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/pyrsegmentation.cppsrc and dst have different formatssrc and dst have different ROIs(icvPyrSegmentation8uC1R( src_data, src_step, dst_data, dst_step, src_size, CV_GAUSSIAN_5x5, comp, storage, level, thresh1, thresh2 )) >= 0(icvPyrSegmentation8uC3R( src_data, src_step, dst_data, dst_step, src_size, CV_GAUSSIAN_5x5, comp, storage, level, thresh1, thresh2 )) >= 0/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/segment.cppinput image has more than one channelcvSnakeImage/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/snakes.cpp(icvSnake8uC1R( data, step, size, points, length, alpha, beta, gamma, coeffUsage, win, criteria, calcGradient ? 2 : 1 )) >= 0state != 0cvFindStereoCorrespondenceGC/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/stereogc.cppCV_ARE_SIZES_EQ(left, right) && CV_ARE_TYPES_EQ(left, right) && CV_MAT_TYPE(left->type) == CV_8UC1!dispLeft || (CV_ARE_SIZES_EQ(dispLeft, left) && CV_MAT_CN(dispLeft->type) == 1)!dispRight || (CV_ARE_SIZES_EQ(dispRight, left) && CV_MAT_CN(dispRight->type) == 1)dispLeft && dispRightcvCreateSubdiv2D/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/subdiv2.cppcvSubdiv2DLocateedge != 0cvSubdivDelaunay2DInsertcvSubdiv2DLocate returned invalid location = %dcvInitSubdivDelaunay2DcvClearSubdivVoronoi2DcvCalcSubdivVoronoi2DcvFindNearestPoint2Dsubdiv != 0icvSubdiv2DCheckcvSubdiv2DDeleteEdgecvSubdiv2DMakeEdgecvSubdiv2DSetEdgePointsWARNING!!! - Video %s does not exist!
Read node %s
DurWARNING in parsing %s record!!! Cannot read array element
Videonoise_gaussiannoise_normalnoise_uniformnoise_specklenoise_salt_and_pepperFile.aviWARNING!!! Cannot open avi file %s
TransShiftBGMaskFrameNumframeanglessxsydxdyIGNNoiseAmpWARNING!!! Can not open avi file %s
/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/texture.cppNumber of channels must be 1Depth must be equal IPL_DEPTH_8UHistogram-based method is not implementedMatrices are not allocateddescriptorOptimizationType different from CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST
is not supportedstep is not in 0 .. GLCM->numMatrices - 1descriptor is not in 0 .. GLCM->numDescriptors - 1Descriptors are not calculatedDescriptor index is out of rangeThe step index is out of range/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/trifocal.cppInput parameters must be a matricesNumber of points must be same and == 6Number of points coordinates must be 2Size of project matrix must be 3x4 or 9x4 (for 3 matrices)Number points must be more than 6number of points must be the sameProbability must be >=0 and <=1Threshold for good points must be at least >= 0Number of coordinates of points must be 2points and resultT must be a matricesNumber of points must be 4. And they must have 2 coordinatessize of matrix resultT must be 3x3Number of points1 must be 3 and and have 3 coordinatesNumber of points2 must be 3 and and have 3 coordinatesSize of fundReduceCoef1 must be 1x5Size of fundReduceCoef2 must be 1x5Size of resFundReduceCoef must be 1x5Size of fundReduceCoefs must be 1x5Size of projMatrCoefs must be 1x4Size of projMatr must be 3x4Number of points must be at least 6Number of points must be sameObject points must have 4 coordinatesProjected points must have 2 or 3 coordinatesd:\test\recProjMatr.txt%d (%lf %lf %lf %lf) - (%lf %lf %lf)
Number of points must be at least 5Size of transMatr must be 4x4Number of coordinates of points must be 4srcMatr and dstMatr must be a matricesNumber of rows of matrices must be the sameNumber of coordinates of projected points must be 2Number of coordinates of 4D points must be 4Size of projection matrix must be 3x4/Users/jason/Projects/opencv/opencv-2.4.11/modules/legacy/src/video.cppAll the input images must have the same typeUncorrelated sizes of the input image and output fieldsrectstiltedimg.type() == CV_8U || img.type() == CV_8UC3integralHistogram/Users/jason/Projects/opencv/opencv-2.4.11/modules/objdetect/src/cascadedetect.cppoldCascade.empty()runAtscaleFactor > 1 && image.depth() == CV_8UdetectMultiScalestageTypeBOOSTfeatureTypeHAARLBPHOGorigWinSize.height > 0 && origWinSize.width > 0stageParamsmaxDepthfeatureParamsmaxCatCountstagesstageThresholdweakClassifiersinternalNodesleafValuescorners.depth() == CV_32S && corners.cols*corners.channels() == 8 && n == (int)codes.size()drawDataMatrixCodes/Users/jason/Projects/opencv/opencv-2.4.11/modules/objdetect/src/datamatrix.cppInvalid classifier pointercvSetImagesForHaarClassifierCascade/Users/jason/Projects/opencv/opencv-2.4.11/modules/objdetect/src/haar.cppScale must be positiveAll integral images must have the same sizeOnly (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowedSum and tilted_sum must have the same stride (step, widthStep)Invalid classifier cascadecvHaarDetectObjectsForROCNull storage pointerOnly 8-bit images are supportedscale factor must be > 1Null path is passedcvLoadHaarClassifierCascade%s%s%d/AdaBoostCARTHaarClassifier.txtInvalid pathCould not allocate memory for input_cascade%s/%d/AdaBoostCARTHaarClassifier.txtelements_read == (size_t)(size)opencv-haar-classifiericvCreateHidHaarClassifierCascadehid_cascade has been already createdNegative number of cascade stagesheader of the stage classifier #%d is invalid (has null pointers or non-positive classfier count)rectangle #%d of the classifier #%d of the stage classifier #%d is not inside the reference (original) cascade windowInvalid cascade pointercvRunHaarClassifierCascadeSumHidden cascade has not been created.
Use cvSetImagesForHaarClassifierCascade%d%n%d%d%d%d%d%f%n%s%n%f%d%d%n%f%n%d%d%nNumber of stages should be positiveicvCreateHaarClassifierCascadeInvalid stages nodeicvReadHaarClassifiersize node is not a valid sequence.Invalid size node: width must be positive integerInvalid size node: height must be positive integerInvalid stage %dTrees node is not a valid sequence. (stage %d)Tree node is not a valid sequence. (stage %d, tree %d)Tree node %d is not a valid map. (stage %d, tree %d)featureFeature node is not a valid map. (stage %d, tree %d, node %d)Rects node is not a valid sequence. (stage %d, tree %d, node %d)Rect %d is not a valid sequence. (stage %d, tree %d, node %d)x coordinate must be non-negative integer. (stage %d, tree %d, node %d, rect %d)y coordinate must be non-negative integer. (stage %d, tree %d, node %d, rect %d)width must be positive integer and (x + width) must not exceed window width. (stage %d, tree %d, node %d, rect %d)height must be positive integer and (y + height) must not exceed window height. (stage %d, tree %d, node %d, rect %d)weight must be real number. (stage %d, tree %d, node %d, rect %d)tilted must be 0 or 1. (stage %d, tree %d, node %d)threshold must be real number. (stage %d, tree %d, node %d)left_nodeleft node must be valid node number. (stage %d, tree %d, node %d)left_valleft node or left value must be specified. (stage %d, tree %d, node %d)left value must be real number. (stage %d, tree %d, node %d)Tree structure is broken: too many values. (stage %d, tree %d, node %d)right_noderight node must be valid node number. (stage %d, tree %d, node %d)right_valright node or right value must be specified. (stage %d, tree %d, node %d)right value must be real number. (stage %d, tree %d, node %d)Tree structure is broken: too few values. (stage %d, tree %d)stage_thresholdstage threshold must be real number. (stage %d)parentparent must be integer number. (stage %d)nextnext must be integer number. (stage %d)stage %dtree %dnode %droot nodeblockSize.width % cellSize.width == 0 && blockSize.height % cellSize.height == 0getDescriptorSize/Users/jason/Projects/opencv/opencv-2.4.11/modules/objdetect/src/hog.cpp(winSize.width - blockSize.width) % blockStride.width == 0 && (winSize.height - blockSize.height) % blockStride.height == 0checkDetectorSize()setSVMDetectorwinSizeblockSizeblockStridecellSizenbinsderivAperturewinSigmahistogramNormTypeL2HysThresholdgammaCorrectionnlevelsSVMDetector{opencv-object-detector-hogcomputeGradient(unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) && (unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height)getBlockpt.x % cacheStride.width == 0 && pt.y % cacheStride.height == 0blockHist != 0pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0opencv-object-detector-hogfile not existreadALTModelversion?V6.01version doesnot matchrectList.size() == weights.size()groupRectanglesInitial image_classNames.empty() || _classNames.size() == filenames.size()/Users/jason/Projects/opencv/opencv-2.4.11/modules/objdetect/src/latentsvmdetector.cpp[:pyramid_leveltype == CG_NAME/Users/jason/Projects/opencv/opencv-2.4.11/modules/objdetect/src/linemod.cppweak_thresholdnum_featuresstrong_thresholdtype == DN_NAMEdistance_thresholddifference_thresholdextract_thresholdi != class_templates.end()getTemplatesi->second.size() > size_t(template_id)pyramid_levelsTmodalitiesmod_fn.size() == modalities.size()readClassmodalities[i]->name() == (std::string)(*mod_it)(int)fn["pyramid_levels"] == pyramid_levelsclass_idclass_templates.find(class_id_tmp) == class_templates.end()template_pyramidstemplate_idtemplate_id == expected_idtemplatesit != class_templates.end()writeClassInvalid value of quantized parametergetLabel(src.rows * src.cols) % 16 == 0computeResponseMapsresponse_map.rows % T == 0linearizeresponse_map.cols % T == 0templ.features.size() <= 63similaritysimilarityLocalrb
elements_read == (size_t)(p * sizeX * sizeY)parserRFilter/Users/jason/Projects/opencv/opencv-2.4.11/modules/objdetect/src/lsvmparser.cppparserPFilterUnsupported image format! Only CV_8UC1, CV_8UC2 and CV_8UC3 are supportedfastNlMeansDenoising/Users/jason/Projects/opencv/opencv-2.4.11/modules/photo/src/denoising.cppType of input image should be CV_8UC3!fastNlMeansDenoisingColoredUnsupported matrix format! Only uchar, Vec2b, Vec3b are supportedfastNlMeansDenoisingMultiType of input images should be CV_8UC3!fastNlMeansDenoisingColoredMultiInput images vector should not be empty!fastNlMeansDenoisingMultiCheckPreconditionsAll windows sizes should be odd!imgToDenoiseIndex and temporalWindowSize should be chosen corresponding srcImgs size!Input images should have the same size and type!src.channels() == sizeof(T)FastNlMeansDenoisingInvoker/Users/jason/Projects/opencv/opencv-2.4.11/modules/photo/src/fast_nlmeans_denoising_invoker.hpptemplate_window_size_ <= 46340almost_dist2weight_[0] == fixed_point_mult_srcImgs.size() > 0FastNlMeansMultiDenoisingInvoker/Users/jason/Projects/opencv/opencv-2.4.11/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hppsrcImgs[0].channels() == sizeof(T)almost_dist2weight[0] == fixed_point_mult_All the input and output images must have the same sizecvInpaint/Users/jason/Projects/opencv/opencv-2.4.11/modules/photo/src/inpaint.cppOnly 8-bit 1-channel and 3-channel input/output images are supportedThe mask must be 8-bit 1-channel imageThe flags argument must be one of CV_INPAINT_TELEA or CV_INPAINT_NSThe library is compiled without GPU supportthrow_nogpu/Users/jason/Projects/opencv/opencv-2.4.11/modules/gpu/src/precomp.hppimg.type() == CV_8U/Users/jason/Projects/opencv/opencv-2.4.11/modules/gpu/src/speckle_filtering.cppSelected device doesn't support double/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/arithm.cppsrc1.type() == src2.type() && src1.channels() == 1cmpOp >= CMP_EQ && cmpOp <= CMP_NEarithm_compareabsSumsqrSumminMaxsrc.size() == mask.size() || mask.empty()src.step % src.elemSize1() == 0(normType & NORM_RELATIVE) == 0src2.empty() || (src1.type() == src2.type() && src1.size() == src2.size())arithm_flip_rowsarithm_flip_colsarithm_flip_rows_colsdepth == CV_8U || depth == CV_8Slut.channels() == 1 || lut.channels() == src.channels()lut.rows == 1 && lut.cols == 256arithm_exparithm_logsrc1.type() == src2.type() && src1.size() == src2.size() && (src1.depth() == CV_32F || src1.depth() == CV_64F)arithm_magnitudex.type() == y.type() && x.size() == y.size() && (x.depth() == CV_32F || x.depth() == CV_64F)x.step % x.elemSize() == 0 && y.step % y.elemSize() == 0arithm_phase_indegreesarithm_phase_inradiansarithm_cartToPolarangle.depth() == CV_32F || angle.depth() == CV_64Fmagnitude.size() == angle.size() && magnitude.type() == angle.type()arithm_polarToCart_magarithm_polarToCartsrc.step % src.elemSize() == 0selected device doesn't support doublegroupnum != 0transpose_inplaceaddWeightedsrc1.size() == src2.size() && src1.type() == src2.type()ucharcharushort-D T=%s -D WT=%s -D convertToT=convert_%s%s_sat_rtex.depth() == CV_32F || x.depth() == CV_64Farithm_pow4-D T=%s%snx > 0 && ny > 0arithmetic_run_genericsrc2.empty() || (!src2.empty() && src1.type() == src2.type() && src1.size() == src2.size())mask.empty() || (!mask.empty() && mask.type() == CV_8UC1 && mask.size() == src1.size())op_type >= ADD && op_type <= MAXarithm_binary_opFUNC_ADDFUNC_SUBFUNC_MULFUNC_DIVFUNC_ABSFUNC_ABS_DIFFFUNC_MINFUNC_MAX-D T=%s%s -D WT=%s%s -D convertToT=convert_%s%s%s -D %s -D convertToWT=convert_%s%s_rte_mat -D HAVE_SCALAR_scalar_maskocn == cn || (ocn == 4 && cn == 3)scalarToVector==>>=<<=!=-D T=%s -D Operation=%sarithm_absdiff_nonsaturate_runsrc1.step % src1.elemSize() == 0 && (src2.empty() || src2.step % src2.elemSize() == 0)diff.step % diff.elemSize() == 0arithm_absdiff_nonsaturate-D srcT=%s%s -D dstT=%s%s -D convertToDstT=convert_%s%s_binary -D BINARY-D srcT=%s -D dstT=%sarithmetic_exp_log_runsrc.depth() == CV_32F || src.depth() == CV_64F-D srcT=%s-D DEGREE-D RADIAN8-D srcT=%s%s -D dstT=int%s -D convertToDstT=convert_int%sarithm_op_nonzerooperationType >= AND && operationType <= NOTbitwise_runmask.empty() || (mask.type() == CV_8UC1 && mask.size() == src1.size())ulong16-D Operation=%c -D T=%s%s -D OP_BINARY -D HAVE_MASKarithm_bitwise-D T=%s%c-D VT=%s%s -D T=%s0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.wholecols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.wholerowsoclMat/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/include/opencv2/ocl/matrix_operations.hpparithmetic_sumFUNC_SUMFUNC_ABS_SUMFUNC_SQR_SUM-D srcT=%s%s -D dstT=%s%s -D convertToDstT=convert_%s%s -D %sarithm_op_sumarithmetic_minMax-D T= -D MAX_VAL= -D MIN_VAL= -D vlen= -D WITH_MASKarithm_op_minMax -D DEPTH_src.oclchannels() == 1arithmetic_minMaxLoc-D DEPTH_%d -D REPEAT_S%d -D REPEAT_E%darithm_op_minMaxLoc-D DEPTH_%d -D REPEAT_E%darithm_op_minMaxLoc_maskframeType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/bgfg_mog.cppframe.depth() == CV_8UlearningRate >= 0.0f-D CN1 -D NMIXTURES=%d-D NMIXTURES=%dgetBackgroundImage_kernelmog2_kernelgetBackgroundImage2_kernelmog_withLearning_kernelmog_withoutLearning_kernelsrc1.depth() <= CV_32FblendLinear/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/blend.cppweights1.size() == weights2.size() && weights1.size() == src1.size() && weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1-D T=%s%s -D convertToT=convert_%s%s%s -D FT=float%s -D convertToFT=convert_float%squery.channels() == 1 && query.depth() < CV_64FmatchSingle/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/brute_force_matcher.cpptrain.cols == query.cols && train.type() == query.type()trainIdx.type() == CV_32SC1matchConvertdistance.type() == CV_32FC1 && distance.cols == trainIdx.colsmasks.size() == trainDescCollection.size()makeGpuCollectionmask.empty() || (mask.type() == CV_8UC1 && mask.cols == train.rows)matchCollectionimgIdx.type() == CV_32SC1 && imgIdx.cols == trainIdx.colsknnMatchSingletrain.type() == query.type() && train.cols == query.colstrainIdx.type() == CV_32SC2 || trainIdx.type() == CV_32SC1knnMatchConvertdistance.type() == CV_32FC2 || distance.type() == CV_32FC1distance.size() == trainIdx.size()trainIdx.isContinuous() && distance.isContinuous()knnMatch2CollectiontrainIdx.type() == CV_32SC2knnMatch2ConvertimgIdx.type() == CV_32SC2 && imgIdx.cols == trainIdx.colsdistance.type() == CV_32FC2 && distance.cols == trainIdx.colsradiusMatchSingletrainIdx.empty() || (trainIdx.rows == query.rows && trainIdx.size() == distance.size())radiusMatchConvertdistance.type() == CV_32FC1 && distance.size() == trainIdx.size()nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rowsradiusMatchCollectiontrainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size() && trainIdx.size() == imgIdx.size())imgIdx.type() == CV_32SC1 && imgIdx.size() == trainIdx.size()-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%dBruteForceMatch_UnrollMatchfloat -D T_FLOAT-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%dBruteForceMatch_MatchBruteForceMatch_knnUnrollMatchBruteForceMatch_knnMatchBruteForceMatch_calcDistanceUnrolledBruteForceMatch_calcDistanceBruteForceMatch_findBestMatchBruteForceMatch_RadiusUnrollMatchBruteForceMatch_RadiusMatchK.size() == Size(3, 3) && K.type() == CV_32FbuildWarpPlaneMaps/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/build_warps.cppR.size() == Size(3, 3) && R.type() == CV_32F(T.size() == Size(3, 1) || T.size() == Size(1, 3)) && T.type() == CV_32F && T.isContinuous()K_Rinv.isContinuous()buildWarpCylindricalMapsbuildWarpSphericalMapsM.rows == 2 && M.cols == 3buildWarpAffineMapsM.rows == 3 && M.cols == 3buildWarpPerspectiveMapsdsize.area() > 0dx_.type() == CV_32SC1 && dy_.type() == CV_32SC1 && dx_.size() == dy_.size()CannyBuf/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/canny.cppdx.type() == CV_32SC1 && dy.type() == CV_32SC1 && dx.size() == dy.size()calcSobelRowPasscalcMagnitude_buf-D L2GRADcalcMagnitudecalcMapedgesHysteresisLocalcount >= 0edgesHysteresisGlobal_gpuedgesHysteresisGlobalgetEdgesOpenCL not availablegetContext/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/cl_context.cppCan't select OpenCL devicecurrentContext != NULLInvalid feature typesupportsFeaturedeviceInfo->_id >= 0setContextdeviceInfo->_id < (int)global_devices.size()deviceInfo == &infoImplpClPlatform != NULLinitializeContextpClContext != NULLpClDevice != NULLinitializeOpenCLDevicesOPENCV_OPENCL_DEVICEdeviceID >= 0selectOpenCLDeviceERROR: Can't find OpenCL platform by name: GPUCPUALLACCELERATORERROR: Unsupported device type for OpenCL device (GPU, CPU, ACCELERATOR): Skip unsupported version of OpenCL device: ERROR: Can't select OpenCL device: ERROR: Required OpenCL device not found, check configuration:     Platform: any    Device types:     Device name: ERROR: Invalid configuration string for OpenCL devicethis != currentContext~ContextImplContextImplplatform_id == NULLdevice_id == NULLcl_khr_fp64-D DOUBLE_SUPPORTIntel -D INTEL_DEVICEkernel != NULLqueryWaveFrontSize/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/cl_operations.cppopenCLReadBufferopenCLCreateBufferopenCLMallocPitchExopenCLMemcpy2DopenCLCopyBuffer2DopenCLFreeProgramCache::getProgramCache() != NULLopenCLGetKernelFromSourceprogram != NULLopenCLVerifyKernellocalThreads[0] <= ctx->getDeviceInfo().maxWorkItemSizes[0]localThreads[1] <= ctx->getDeviceInfo().maxWorkItemSizes[1]localThreads[2] <= ctx->getDeviceInfo().maxWorkItemSizes[2]localThreads[0] * localThreads[1] * localThreads[2] <= kernelWorkGroupSizelocalThreads[0] * localThreads[1] * localThreads[2] <= ctx->getDeviceInfo().maxWorkGroupSize_C_DopenCLExecuteKernelcustom_globalThreads != NULLopenCLExecuteKernelInteropload_constant/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/cl_programcache.cppreleaseProgram.clbgetOrBuildProgramCan't write data to file: BUILD LOG: dynamic programInvalid file (empty): hashLength > 0readConfigurationFromFilenumberOfEntries > 0Invalid file: f.is_open()writeConfigurationToFilememcmp(&foptions, options.c_str(), entry.optionsLength) != 0cvtColor_caller/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/color.cppRGB2RGB5x5RGB5x52RGBBGR5x52GrayGray2BGR5x5RGB2GrayGray2RGBRGB2YCrCbYCrCb2RGBRGB2XYZXYZ2RGBRGB2HSVHLS -D hrange=%d -D hscale=%f2RGB -D hrange=%d -D hscale=%fscn == 4 && depth == CV_8URGBA2mRGBA-D DEPTH_%d -D dcn=%d -D scn=%d -D %sREVERSEORDER-D DEPTH_%d -D greenbits=%d -D scn=%d -D bidx=%d-D DEPTH_%d -D greenbits=%d -D dcn=%d -D bidx=%d-D DEPTH_%d -D scn=%d -D bidx=%d -D pixels_per_work_item=%d-D DEPTH_%d -D dcn=%d -D bidx=%d-D DEPTH_%d -D dcn=%d -D bidx=%d -D pixels_per_work_item=%d-D DEPTH_%d -D scn=%d -D bidx=%dsrc.type() == CV_32FC1columnSum/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/columnsum.cppCL_DEVICE_NOT_FOUNDCL_DEVICE_NOT_AVAILABLECL_COMPILER_NOT_AVAILABLECL_MEM_OBJECT_ALLOCATION_FAILURECL_OUT_OF_RESOURCESCL_OUT_OF_HOST_MEMORYCL_PROFILING_INFO_NOT_AVAILABLECL_MEM_COPY_OVERLAPCL_IMAGE_FORMAT_MISMATCHCL_IMAGE_FORMAT_NOT_SUPPORTEDCL_BUILD_PROGRAM_FAILURECL_MAP_FAILURECL_MISALIGNED_SUB_BUFFER_OFFSETCL_EXEC_STATUS_ERROR_FOR_EVENTS_IN_WAIT_LISTCL_INVALID_VALUECL_INVALID_DEVICE_TYPECL_INVALID_PLATFORMCL_INVALID_DEVICECL_INVALID_CONTEXTCL_INVALID_QUEUE_PROPERTIESCL_INVALID_COMMAND_QUEUECL_INVALID_HOST_PTRCL_INVALID_MEM_OBJECTCL_INVALID_IMAGE_FORMAT_DESCRIPTORCL_INVALID_IMAGE_SIZECL_INVALID_SAMPLERCL_INVALID_BINARYCL_INVALID_BUILD_OPTIONSCL_INVALID_PROGRAMCL_INVALID_PROGRAM_EXECUTABLECL_INVALID_KERNEL_NAMECL_INVALID_KERNEL_DEFINITIONCL_INVALID_KERNELCL_INVALID_ARG_INDEXCL_INVALID_ARG_VALUECL_INVALID_ARG_SIZECL_INVALID_KERNEL_ARGSCL_INVALID_WORK_DIMENSIONCL_INVALID_WORK_GROUP_SIZECL_INVALID_WORK_ITEM_SIZECL_INVALID_GLOBAL_OFFSETCL_INVALID_EVENT_WAIT_LISTCL_INVALID_EVENTCL_INVALID_OPERATIONCL_INVALID_GL_OBJECTCL_INVALID_BUFFER_SIZECL_INVALID_MIP_LEVELCL_INVALID_GLOBAL_WORK_SIZEOpenCL DFT is not implemented/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/fft.cppgetMorphologyFilter_GPU/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/filtering.cpptype == CV_8UC1 || type == CV_8UC3 || type == CV_8UC4 || type == CV_32FC1 || type == CV_32FC3 || type == CV_32FC4iterations > 0createMorphologyFilter_GPUdelta == 0filter2Dunsupported border typesepFilter2DLaplacianksize == 1 || ksize == 3createGaussianFilter_GPUGaussianBlurBORDER_CONSTANTBORDER_REPLICATEBORDER_REFLECTBORDER_WRAPBORDER_REFLECT_101This border type is not supported-D VAR_PER_CHANNEL=1 -D CALCVAR=1 -D FIXED_WEIGHT=0 -D EXTRA=%d -D MAX_VAR_VAL=%f -D ABF_GAUSSIAN=%d -D THREADS=%d -D anX=%d -D anY=%d -D ksX=%d -D ksY=%d -D %sksize.height > 0 && ksize.width > 0 && ((ksize.height & 1) == 1) && ((ksize.width & 1) == 1)normalizeROIroi.x >= 0 && roi.y >= 0 && roi.width <= src_size.width && roi.height <= src_size.heightsrc.clCxt == dst.clCxtGPUErode(src.cols == dst.cols) && (src.rows == dst.rows)(src.oclchannels() == dst.oclchannels())morph_C1_D0localThreads[0]*localThreads[1] * 8 >= (localThreads[0] * 4 + ksize.width - 1) * (localThreads[1] + ksize.height - 1)morphlocalThreads[0]*localThreads[1] * 2 >= (localThreads[0] + ksize.width - 1) * (localThreads[1] + ksize.height - 1)-D VAL=255-D VAL=255 -D GENTYPE=uchar4-D VAL=FLT_MAX -D GENTYPE=float-D VAL=FLT_MAX -D GENTYPE=float4unsupported type-D RADIUSX=%d -D RADIUSY=%d -D LSIZE0=%d -D LSIZE1=%d -D ERODE %s %s-D RECTKERNELGPUDilate-D VAL=0-D VAL=0 -D GENTYPE=uchar4-D VAL=-FLT_MAX -D GENTYPE=float-D VAL=-FLT_MAX -D GENTYPE=float4-D RADIUSX=%d -D RADIUSY=%d -D LSIZE0=%d -D LSIZE1=%d -D DILATE %s %sGPUFilter2Dsrc.oclchannels() == dst.oclchannels()kernel.cols == ksize.width && kernel.rows == ksize.heightkernel.channels() == 1anchor.x >= 0 && anchor.x < kernel.colsanchor.y >= 0 && anchor.y < kernel.rows(size_t)ksize.width <= BLOCK_SIZE(int)(offsetX * src.elemSize()) == offsetXBytes(int)(_offsetX * dst.elemSize()) == _offsetXBytesBORDER_WRAP is not supported!-D LOCAL_SIZE=%d -D BLOCK_SIZE_Y=%d -D DATA_DEPTH=%d -D DATA_CHAN=%d -D USE_DOUBLE=%d -D ANCHOR_X=%d -D ANCHOR_Y=%d -D KERNEL_SIZE_X=%d -D KERNEL_SIZE_Y=%d -D KERNEL_SIZE_Y2_ALIGNED=%d -D %s -D %s -D %sEXTRA_EXTRAPOLATIONNO_EXTRA_EXTRAPOLATIONBORDER_ISOLATEDNO_BORDER_ISOLATEDBLOCK_SIZE > kernelWorkGroupSizeGPUFilterBoxw >= ksize.width && h >= ksize.height-D LOCAL_SIZE=%d -D BLOCK_SIZE_Y=%d -D DATA_DEPTH=%d -D DATA_CHAN=%d -D USE_DOUBLE=%d -D ANCHOR_X=%d -D ANCHOR_Y=%d -D KERNEL_SIZE_X=%d -D KERNEL_SIZE_Y=%d -D %s -D %s -D %sboxFilter0 <= anchor && anchor < ksize-D BLK_X=%d -D BLK_Y=%d -D RADIUSX=%d -D RADIUSY=%d -D KERNEL_MATRIX_X=DIG(0x%x)0x0 -D KERNEL_MATRIX_Y= -D SRCTYPE=uchar -D CONVERT_SRCTYPE=convert_float -D WORKTYPE=float -D SRCTYPE=float -D CONVERT_SRCTYPE= -D WORKTYPE=float -D SRCTYPE=uchar2 -D CONVERT_SRCTYPE=convert_float2 -D WORKTYPE=float2 -D SRCTYPE=float2 -D CONVERT_SRCTYPE= -D WORKTYPE=float2 -D SRCTYPE=uchar3 -D CONVERT_SRCTYPE=convert_float3 -D WORKTYPE=float3 -D SRCTYPE=float3 -D CONVERT_SRCTYPE= -D WORKTYPE=float3 -D SRCTYPE=uchar4 -D CONVERT_SRCTYPE=convert_float4 -D WORKTYPE=float4 -D SRCTYPE=float4 -D CONVERT_SRCTYPE= -D WORKTYPE=float4Image type is not supported!sepFilter2D_SinglePass -D DSTTYPE=uchar -D CONVERT_DSTTYPE=convert_uchar_sat -D DSTTYPE=uchar2 -D CONVERT_DSTTYPE=convert_uchar2_sat -D DSTTYPE=uchar3 -D CONVERT_DSTTYPE=convert_uchar3_sat -D DSTTYPE=uchar4 -D CONVERT_DSTTYPE=convert_uchar4_sat -D DSTTYPE=float -D CONVERT_DSTTYPE= -D DSTTYPE=float2 -D CONVERT_DSTTYPE= -D DSTTYPE=float3 -D CONVERT_DSTTYPE= -D DSTTYPE=float4 -D CONVERT_DSTTYPE= -D BORDER_CONSTANT -D BORDER_REPLICATE -D BORDER_REFLECT -D BORDER_REFLECT_101 -D BORDER_WRAPBORDER type is not supported!sep_filter_singlepassbordertype <= BORDER_REFLECT_101linearRowFilter_gpuksize == (anchor << 1) + 1-D RADIUSX=%d -D LSIZE0=%d -D LSIZE1=%d -D CN=%d -D %srow_filtercol_filter-D RADIUSY=%d -D LSIZE0=%d -D LSIZE1=%d -D CN=%d -D %s -D GENTYPE_SRC=%s -D GENTYPE_DST=%s -D convert_to_DST=%sconvert_uchar_satfloat2uchar2convert_uchar2_satfloat4uchar4convert_uchar4_satconvert_int_satint4convert_int4_satclCxt == dst.clCxtlinearColumnFilter_gpusrc.cols == dst.colsOpenCL BLAS is not implemented/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/gemm.cppclBlasSetup/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/gftt.cppdownloadPointsminMaxEig_caller-D T=float -D DEPTH_5 -D vlen=1arithm_op_minMax_final -D WITH_MASK=1oclHaarDetectObjects/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/haar.cppImage too small-D PACKED_CLASSIFIER -D NODE_SIZE=%d -D WND_SIZE_X=%d -D WND_SIZE_Y=%d -D STUMP_BASED=%d -D SPLITNODE=%d -D SPLITSTAGE=%d -D OUTPUTSZ=%d -D PIXEL_STEP=%d -D WGSTART=%d -D LSx=%d -D LSy=%dgpuRunHaarClassifierCascadePacked-D STUMP_BASED=1-D STUMP_BASED=0gpuRunHaarClassifierCascadegpuscaleclassifiergpuRunHaarClassifierCascade_scaled2InitCreateBaseBufsCreateFactorRelatedBufsgpuCreateHidHaarClassifierCascadegpuSetImagesForHaarClassifierCascadegpuSetHaarClassifierCascade(win_size.width - block_size.width ) % block_stride.width == 0 && (win_size.height - block_size.height) % block_stride.height == 0HOGDescriptor/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/hog.cppblock_size.width % cell_size.width == 0 && block_size.height % cell_size.height == 0block_stride == cell_sizecell_size == Size(8, 8)cells_per_block == Size(2, 2)img.type() == CV_8UC1 || img.type() == CV_8UC4win_stride.width % block_stride.width == 0 && win_stride.height % block_stride.height == 0getDescriptorsUnknown descriptor formatpadding == Size(0, 0)scale0 > 1compute_hists_lut_kernel-D CPU-D WAVE_SIZE=%dnormalize_hists_36_kernelnormalize_hists_kernelnormalize_hists: histogram's size is too small or too bignormalize_histsclassify_hists_180_kernelclassify_hists_252_kernelclassify_hists_kernelextract_descrs_by_rows_kernelextract_descrs_by_cols_kernelcompute_gradients_8UC1_kernelcompute_gradients_8UC4_kernelthresholdType == THRESH_BINARY || thresholdType == THRESH_BINARY_INV || thresholdType == THRESH_TRUNC || thresholdType == THRESH_TOZERO || thresholdType == THRESH_TOZERO_INV/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/imgproc.cppSelected device does not support doubleinterpolation == INTER_LINEAR || interpolation == INTER_NEAREST(map1.type() == CV_16SC2 && (map2.empty() || (map2.type() == CV_16UC1 || map2.type() == CV_16SC1)) ) || (map1.type() == CV_32FC2 && !map2.data) || (map1.type() == CV_32FC1 && map2.type() == CV_32FC1)!map2.data || map2.size() == map1.size()borderType == BORDER_CONSTANT || borderType == BORDER_REPLICATE || borderType == BORDER_WRAP || borderType == BORDER_REFLECT_101 || borderType == BORDER_REFLECTINTER_NEARESTINTER_LINEARINTER_CUBICINTER_LANCZOSBORDER_TRANSPARENT_32FC2_16SC2_16UC1_2_32FC1Unsupported map types-D %s -D %s -D T=%s%s -D WT=%s%s -D convertToT=convert_%s%s%s -D convertToWT=convert_%s%s -D convertToWT2=convert_%s2 -D WT2=%s2src.type() == CV_8UC1 || src.type() == CV_8UC3 || src.type() == CV_8UC4 || src.type() == CV_32FC1 || src.type() == CV_32FC3 || src.type() == CV_32FC4dsize.area() > 0 || (fx > 0 && fy > 0)interpolation == INTER_LINEAR || interpolation == INTER_NEAREST || (interpolation == INTER_AREA && inv_fx >= 1 && inv_fy >= 1)m % 2 == 1 && m > 1medianFilter(src.depth() == CV_8U || src.depth() == CV_32F) && (src.channels() == 1 || src.channels() == 4)medianFilter3medianFilter5Non-supported filter lengthUnsupported border type-D GENTYPE=%s%s -D %scopymakeborder(src.depth() == CV_8U || src.depth() == CV_32F) && src.oclchannels() != 2 && src.oclchannels() != 3interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBICSelect device doesn't support doubleintegral_colsintegral_rowsintegral_sum_colsintegral_sum_rowscornerHarris_dxdyborderType == cv::BORDER_CONSTANT || borderType == cv::BORDER_REFLECT101 || borderType == cv::BORDER_REPLICATE || borderType == cv::BORDER_REFLECTcalcHarriscornerMinEigenVal_dxdycalcMinEigenValThe input image is emptymeanShiftFilteringOnly 8-bit, 4-channel images are supportedmeanShiftProcmat_src.type() == CV_8UC1calLUTBilateral filtering is only implemented for CV_8U imagesx.depth() == CV_32F && t.depth() == CV_32Fconvolvet.cols <= 17 && t.rows <= 17THRESH_BINARYTHRESH_BINARY_INVTHRESH_TRUNCTHRESH_TOZEROTHRESH_TOZERO_INV-D T=%s%s -D %s -D VECTORIZED -D VT=%s%d -D VLOADN=vload%d -D VECSIZE=%d -D VSTOREN=vstore%d -D DST_ALIGNED -D SRC_ALIGNEDNNLNCUBICAREALAN4-D %s -D T=%s%s -D WT=%s -D WTV=%s%s -D convertToWTV=convert_%s%s -D convertToT=convert_%s%s%s_FAST -D WT2V=%s%s -D convertToWT2V=convert_%s%s -D AREA_FAST -D XSCALE=%d -D YSCALE=%d -D SCALE=%fwarpAffine_gpuLinearCubicwarpPerspective_gpuextractCovDataDx.rows == Dy.rows && Dx.cols == Dy.cols-D BLK_X=%d -D BLK_Y=%d -D SRCTYPE=uchar -D SRCTYPE=float -D SCHARRsobel3sobel5sobel7Kernel size is not supported!Dx.offset == 0 && Dy.offset == 0BORDER_REFLECT101corner_ocl-D anX=%d -D anY=%d -D ksX=%d -D ksY=%d -D %smeanShiftFiltering_gpu!(dst.step & 0x3)meanshift_kernel(src.cols == dstr.cols) && (src.rows == dstr.rows) && (src.rows == dstsp.rows) && (src.cols == dstsp.cols)meanShiftProc_gpu!(dstsp.step & 0x3)meanshiftproc_kernelcalc_sub_histcalc_sub_hist_bordermerge_histCLAHE_OCLcalcLut(src.channels() == 1 || src.channels() == 3) && src.type() == dst.type() && src.size() == dst.size() && src.data != dst.dataoclbilateralFilter_8ubilateralbilateral2frame0.type() == CV_32FC1interpolateFrames/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/interpolate_frames.cppframe1.size() == frame0.size() && frame1.type() == frame0.type()fu.size() == frame0.size() && fu.type() == frame0.type()fv.size() == frame0.size() && fv.type() == frame0.type()bu.size() == frame0.size() && bu.type() == frame0.type()bv.size() == frame0.size() && bv.type() == frame0.type()frame1.step == step && fu.step == step && fv.step == step && bu.step == step && bv.step == step && newFrame.step == step && buf.step == stepmemsetKernelnormalizeKernelforwardWarpKernelblendFramesKernel/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/kalman.cppmeasurement.empty() == falsecorrectsrc.cols * src.channels() == centers.cols * centers.channels()distanceToCenters/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/kmeans.cppsrc.depth() == CV_32F && centers.depth() == CV_32FdistType == NORM_L1 || distType == NORM_L2SQR-D L1_DIST-D L2SQR_DISTtype == CV_32F && K > 0(_bestLabels.cols == 1 || _bestLabels.rows == 1) && _bestLabels.cols * _bestLabels.rows == N && _bestLabels.type() == CV_32S!samples_ocl.empty()/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/knearest.cppsamples.cols == CvKNearest::var_countsamples.type() == CV_32FC1k >= 1 && k <= max_kknn_find_nearestmatchTemplate_Prepared_SQDIFF_NORMED(image.depth() == CV_8U && templ.depth() == CV_8U ) || ((image.depth() == CV_32F && templ.depth() == CV_32F) && result.depth() == CV_32F)matchTemplateNaive_SQDIFF/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/match_template.cppimage.oclchannels() == templ.oclchannels() && (image.oclchannels() == 1 || image.oclchannels() == 4) && result.oclchannels() == 1result.rows == image.rows - templ.rows + 1 && result.cols == image.cols - templ.cols + 1matchTemplate_Naive_SQDIFFconvolve is not fully implemented yetconvolve_32FmatchTemplateNaive_CCORRmatchTemplate_Naive_CCORRimage.depth() == CV_8U && templ.depth() == CV_8UmatchTemplate_CCOFFmatchTemplate_Prepared_CCOFFmatchTemplate: unsupported number of channelsmatchTemplate_Prepared_CCOFF_NORMEDmatchTemplate_CCOFF_NORMEDextractFirstChannelimage.type() == templ.type()image.cols >= templ.cols && image.rows >= templ.rowsupload/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/matrix_operations.cppsrc.flags & cv::_InputArray::OCL_MATgetOclMatRefdownloadcopy_to_with_maskmask.type() == CV_8UC1this->depth() >= 0 && this->depth() <= 6set_to_without_mask_C1_D0set_to_without_maskset_to_with_maskoclMat's number of rows can not be changed for current version-D GENTYPE4=%s4convertC3C4convertC4C3char2ushort2short2int2double2uchar3char3ushort3short3int3float3double3char4ushort4short4double4-D GENTYPE=%sconvert_toconvert_%s_sat_rte-D srcT=%s -D dstT=%s -D convertToDstType=%s-D GENTYPE=%s%cImage forma is not supportedbindTexture/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/mcwutil.cppImage format is not supportedopenCLExecuteKernel_2ocl_moments/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/moments.cppMoments - double is not supported by your GPU!-D CV_8UC1-D CV_16UC1-D CV_16SC1-D CV_32FC1-D CV_64FC1CvMomentscontouricvContourMomentssrc.type() == CV_8UC4meanShiftSegmentation/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/mssegmentation.cppgaussianBlurdst.size() == src.size()gaussianBlurOcl/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/optical_flow_farneback.cpppolynomialExpansion-D polyN=%dgaussianBlur5frame0.channels() == 1 && frame1.channels() == 1frame0.size() == frame1.size()polyN == 5 || polyN == 7!fastPyramids || std::abs(pyrScale - 0.5) < 1e-6boxFilter5updateFlowupdateMatricesdepth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/pyrdown.cpppyrdown_runsrc.depth() != CV_8SderivLambda >= 0sparse/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/pyrlk.cppprevImg.size() == nextImg.size() && prevImg.type() == nextImg.type()patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6prevPts.rows == 1 && prevPts.type() == CV_32FC2nextPts.size() == prevPts.size() && nextPts.type() == CV_32FC2prevImg.type() == CV_8UC1densemaxLevel >= 0winSize.width > 2 && winSize.height > 2lkSparse -D CPUlkSparse_runlkDense/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/pyrup.cppconvert_%s%s_sat_rte-D Type=%s%s -D floatType=float%s -D convertToType=%s -D convertToFloat=%sconvert_float4(float)keys.rows == 1sortByKey/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/sort_by_key.cppkeys.channels() == 1vecSize <= static_cast(keys.cols)keys.size() == vals.size()isSizePowerOf2(vecSize)bitonicSort-D IS_GT=%d -D K_T=%s -D V_T=%sThis function is incorrect at the moment.selectionSortLocalselectionSortFinalkeys.depth() == CV_32S || keys.depth() == CV_32F -D K_%sFLTINThistogramRadixNpermuteRadixNblockInsertionSortmat_src/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/split_merge.cppn > 0depth == mat_src[i].depth()size == mat_src[i].size()total_channels <= 4merge_vector_runmerge_vectormat_dstsplit_vector_runsplit_vector-D VEC_SIZE=%d -D DATA_DEPTH=%d -D DATA_CHAN=%d -D DST0_ALIGNED -D DST1_ALIGNED -D DST2_ALIGNED -D DST3_ALIGNEDBuild 56860Build 76921Build 78712 -D BYPASS_VSTORE=truemsg_type_ == CV_32F || msg_type_ == CV_16SStereoConstantSpaceBP/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/stereo_csbp.cppmsg_type == CV_32F || msg_type == CV_16Srthis.levels <= 8 && (left.type() == CV_8UC1 || left.type() == CV_8UC3)csbp_operatorinit_data_cost_init_data_cost_caller1init_data_cost_reduce_init_data_cost_reduce_callerget_first_k_initial_local_get_first_initial_local_callerget_first_k_initial_global_get_first_initial_global_callercompute_data_cost_compute_data_cost_callercompute_data_cost_reduce_compute_data_cost_reduce_callerinit_message_init_messagecompute_message_calc_all_iterations_callercompute_disp_compute_disp0 < ndisp && ndisp <= max_supported_ndispStereoBM_OCL/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/stereobm.cppndisp % 8 == 0winSize % 2 == 1prefilter_xsobelstereoKernel-D radius=%dtextureness_kernel0 < rthis.ndisp && 0 < rthis.iters && 0 < rthis.levelsStereoBeliefPropagationImpl/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/stereobp.cpprthis.msg_type == CV_32F || rthis.msg_type == CV_16Srthis.msg_type == CV_32F || (1 << (rthis.levels - 1)) * scale * rthis.max_data_term < numeric_limits::max()left.size() == right.size() && left.type() == right.type()left.type() == CV_8UC1 || left.type() == CV_8UC3 || left.type() == CV_8UC4min(lowest_cols, lowest_rows) > min_image_dim_sizecomp_data%s -D CN=%d-D T_SHORT-D T_FLOATdata_step_downlevel_up_messageone_iteration-D %s -D CNDISP=%dT_SHORTT_FLOAToutput(data.type() == rthis.msg_type) && (data.rows % rthis.ndisp == 0)/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/svm.cppsvm_linearsvm_sigmodsvm_poly-D ADDPOWsvm_rbf-D ADDEXP/Users/jason/Projects/opencv/opencv-2.4.11/modules/ocl/src/tvl1flow.cpp!useInitialFlow || (flowx.size() == I0.size() && flowx.type() == CV_32FC1 && flowy.size() == flowx.size() && flowy.type() == flowx.type())centeredGradientKernelestimateDualVariablesKernelestimateUKernelwarpBackwardKernelarithm_LUT#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void LUT_C1( __global const srcT * src, __global const dstT *lut,
__global dstT *dst,
int cols1, int rows,
int src_offset1,
int lut_offset1,
int dst_offset1,
int src_step1, int dst_step1)
{
int x1 = get_global_id(0);
int y = get_global_id(1);
if (x1 < cols1 && y < rows)
{
int src_index = mad24(y, src_step1, src_offset1 + x1);
int dst_index = mad24(y, dst_step1, dst_offset1 + x1);
dst[dst_index] = lut[lut_offset1 + src[src_index]];
}
}
__kernel void LUT_C2( __global const srcT * src, __global const dstT *lut,
__global dstT *dst,
int cols1, int rows,
int src_offset1,
int lut_offset1,
int dst_offset1,
int src_step1, int dst_step1)
{
int x1 = get_global_id(0) << 1;
int y = get_global_id(1);
if (x1 < cols1 && y < rows)
{
int src_index = mad24(y, src_step1, src_offset1 + x1);
int dst_index = mad24(y, dst_step1, dst_offset1 + x1);
dst[dst_index    ] =                  lut[lut_offset1 + (src[src_index    ] << 1)    ];
dst[dst_index + 1] = x1 + 1 < cols1 ? lut[lut_offset1 + (src[src_index + 1] << 1) + 1] : dst[dst_index + 1];
}
}
__kernel void LUT_C4( __global const srcT * src, __global const dstT *lut,
__global dstT *dst,
int cols1, int rows,
int src_offset1,
int lut_offset1,
int dst_offset1,
int src_step1, int dst_step1)
{
int x1 = get_global_id(0) << 2;
int y = get_global_id(1);
if (x1 < cols1 && y < rows)
{
int src_index = mad24(y, src_step1, src_offset1 + x1);
int dst_index = mad24(y, dst_step1, dst_offset1 + x1);
dst[dst_index    ] =                  lut[lut_offset1 + (src[src_index    ] << 2)    ];
dst[dst_index + 1] = x1 + 1 < cols1 ? lut[lut_offset1 + (src[src_index + 1] << 2) + 1] : dst[dst_index + 1];
dst[dst_index + 2] = x1 + 2 < cols1 ? lut[lut_offset1 + (src[src_index + 2] << 2) + 2] : dst[dst_index + 2];
dst[dst_index + 3] = x1 + 3 < cols1 ? lut[lut_offset1 + (src[src_index + 3] << 2) + 3] : dst[dst_index + 3];
}
}
afd02da735fe96c1be560841a0616208#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#ifdef BINARY
__kernel void arithm_absdiff_nonsaturate_binary(__global srcT *src1, int src1_step, int src1_offset,
__global srcT *src2, int src2_step, int src2_offset,
__global dstT *dst, int dst_step, int dst_offset,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst_index  = mad24(y, dst_step, x + dst_offset);
#ifdef INTEL_DEVICE
if(src1_index >= 0 && src2_index >= 0)
#endif
{
dstT t0 = convertToDstT(src1[src1_index]);
dstT t1 = convertToDstT(src2[src2_index]);
dstT t2 = t0 - t1;
dst[dst_index] = t2 >= (dstT)(0) ? t2 : -t2;
}
}
}
#else
__kernel void arithm_absdiff_nonsaturate(__global srcT *src1, int src1_step, int src1_offset,
__global dstT *dst, int dst_step, int dst_offset,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int dst_index  = mad24(y, dst_step, x + dst_offset);
#ifdef INTEL_DEVICE
if(src1_index >= 0)
#endif
{
dstT t0 = convertToDstT(src1[src1_index]);
dst[dst_index] = t0 >= (dstT)(0) ? t0 : -t0;
}
}
}
#endif
3f15259dde8b84d419a8748dead572f0arithm_add#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#if defined (FUNC_ADD)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) + convertToWT(src2[src2_index]));
#endif
#if defined (FUNC_SUB)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) - convertToWT(src2[src2_index]));
#endif
#if defined (FUNC_MUL)
#if defined (HAVE_SCALAR)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) * scalar * convertToWT(src2[src2_index]));
#else
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) * convertToWT(src2[src2_index]));
#endif
#endif
#if defined (FUNC_DIV)
#if defined (HAVE_SCALAR)
#define EXPRESSION T zero = (T)(0); \
dst[dst_index] = src2[src2_index] == zero ? zero : \
convertToT(convertToWT(src1[src1_index]) * scalar / convertToWT(src2[src2_index]));
#else
#define EXPRESSION T zero = (T)(0); \
dst[dst_index] = src2[src2_index] == zero ? zero : \
convertToT(convertToWT(src1[src1_index]) / convertToWT(src2[src2_index]));
#endif
#endif
#if defined (FUNC_ABS_DIFF)
#define EXPRESSION WT value = convertToWT(src1[src1_index]) - convertToWT(src2[src2_index]); \
value = value > (WT)(0) ? value : -value; \
dst[dst_index] = convertToT(value);
#endif
#if defined (FUNC_MIN)
#define EXPRESSION dst[dst_index] = min( src1[src1_index], src2[src2_index] );
#endif
#if defined (FUNC_MAX)
#define EXPRESSION dst[dst_index] = max( src1[src1_index], src2[src2_index] );
#endif
#ifndef HAVE_SCALAR
__kernel void arithm_binary_op_mat(__global T *src1, int src1_step, int src1_offset,
__global T *src2, int src2_step, int src2_offset,
__global T *dst, int dst_step, int dst_offset,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst_index  = mad24(y, dst_step, x + dst_offset);
EXPRESSION
}
}
#else
__kernel void arithm_binary_op_mat_scalar(__global T *src1, int src1_step, int src1_offset,
__global T *src2, int src2_step, int src2_offset,
WT scalar,
__global T *dst, int dst_step,  int dst_offset,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst_index = mad24(y, dst_step, x + dst_offset);
EXPRESSION
}
}
#endif
6fd43f6cf03580fec25aef52bea680a2arithm_addWeighted#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void addWeighted(__global T * src1, int src1_step1, int src1_offset1,
__global T * src2, int src2_step1, int src2_offset1,
__global T * dst, int dst_step1, int dst_offset1,
WT alpha, WT beta, WT gama,
int cols1, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols1 && y < rows)
{
int src1_index = mad24(y, src1_step1, x + src1_offset1);
int src2_index = mad24(y, src2_step1, x + src2_offset1);
int dst_index = mad24(y, dst_step1, x + dst_offset1);
dst[dst_index] = convertToT(src1[src1_index]*alpha + src2[src2_index]*beta + gama);
}
}
8ef56ec47d035ad8ba7181337797571earithm_add_mask#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#if defined (FUNC_ADD)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) + convertToWT(src2[src2_index]));
#endif
#if defined (FUNC_SUB)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) - convertToWT(src2[src2_index]));
#endif
#if defined (FUNC_MUL)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) * convertToWT(src2[src2_index]));
#endif
#if defined (FUNC_DIV)
#define EXPRESSION T zero = (T)(0); \
dst[dst_index] = src2[src2_index] == zero ? zero : \
convertToT(convertToWT(src1[src1_index]) / convertToWT(src2[src2_index]));
#endif
__kernel void arithm_binary_op_mat_mask(__global T * src1, int src1_step, int src1_offset,
__global T * src2, int src2_step, int src2_offset,
__global uchar * mask, int mask_step, int mask_offset,
__global T * dst, int dst_step, int dst_offset,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int mask_index = mad24(y, mask_step, x + mask_offset);
if (mask[mask_index])
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst_index  = mad24(y, dst_step, dst_offset + x);
EXPRESSION
}
}
}
6f293dea131291749c2be083eafe6839arithm_add_scalar#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#if defined (FUNC_ADD)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) + scalar);
#endif
#if defined (FUNC_SUB)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) - scalar);
#endif
#if defined (FUNC_MUL)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) * scalar);
#endif
#if defined (FUNC_DIV)
#define EXPRESSION T zero = (T)(0); \
dst[dst_index] = src1[src1_index] == zero ? zero : convertToT(scalar / convertToWT(src1[src1_index]));
#endif
#if defined (FUNC_ABS)
#define EXPRESSION \
T value = src1[src1_index] > (T)(0) ? src1[src1_index] : -src1[src1_index]; \
dst[dst_index] = value;
#endif
#if defined (FUNC_ABS_DIFF)
#define EXPRESSION WT value = convertToWT(src1[src1_index]) - scalar; \
value = value > (WT)(0) ? value : -value; \
dst[dst_index] = convertToT(value);
#endif
__kernel void arithm_binary_op_scalar (__global T *src1, int src1_step, int src1_offset,
WT scalar,
__global T *dst,  int dst_step,  int dst_offset,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int dst_index = mad24(y, dst_step, x + dst_offset);
EXPRESSION
}
}
247f201b03d2ae30ab341fefb9d8bf9farithm_add_scalar_mask#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#if defined (FUNC_ADD)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) + scalar);
#endif
#if defined (FUNC_SUB)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) - scalar);
#endif
#if defined (FUNC_MUL)
#define EXPRESSION dst[dst_index] = convertToT(convertToWT(src1[src1_index]) * scalar);
#endif
#if defined (FUNC_DIV)
#define EXPRESSION T zero = (T)(0); \
dst[dst_index] = src2[src2_index] == zero ? zero : \
convertToT(convertToWT(src1[src1_index]) / scalar[0]);
#endif
__kernel void arithm_binary_op_scalar_mask(__global T *src1, int src1_step, int src1_offset,
WT scalar,
__global uchar *mask, int mask_step, int mask_offset,
__global T *dst,  int dst_step,  int dst_offset,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int mask_index = mad24(y, mask_step, x + mask_offset);
if (mask[mask_index])
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int dst_index = mad24(y, dst_step, dst_offset + x);
EXPRESSION
}
}
}
8288be1429ee6b66e7cf0c2a3afa9028__kernel void arithm_bitwise(__global uchar * src1ptr, int src1_step, int src1_offset,
#ifdef OP_BINARY
__global uchar * src2ptr, int src2_step, int src2_offset,
#elif defined HAVE_SCALAR
T scalar,
#endif
#ifdef HAVE_MASK
__global uchar * mask, int mask_step, int mask_offset,
#endif
__global uchar * dstptr, int dst_step, int dst_offset, int dst_rows, int dst_cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
#ifdef HAVE_MASK
mask += mad24(y, mask_step, x + mask_offset);
if (mask[0])
#endif
{
int src1_index = mad24(y, src1_step, mad24(x, (int)sizeof(T), src1_offset));
#ifdef OP_BINARY
int src2_index = mad24(y, src2_step, mad24(x, (int)sizeof(T), src2_offset));
#endif
int dst_index = mad24(y, dst_step, mad24(x, (int)sizeof(T), dst_offset));
__global const T * src1 = (__global const T *)(src1ptr + src1_index);
#ifdef OP_BINARY
__global const T * src2 = (__global const T *)(src2ptr + src2_index);
#endif
__global T * dst = (__global T *)(dstptr + dst_index);
#ifdef OP_BINARY
dst[0] = src1[0] Operation src2[0];
#elif defined HAVE_SCALAR
dst[0] = src1[0] Operation scalar;
#else
dst[0] = Operation src1[0];
#endif
}
}
}
7dfc3e052a5289cb3de59d38f374d10d#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define CV_PI M_PI
#else
#define CV_PI M_PI_F
#endif
__kernel void arithm_cartToPolar_D5 (__global float *src1, int src1_step, int src1_offset,
__global float *src2, int src2_step, int src2_offset,
__global float *dst1, int dst1_step, int dst1_offset,
__global float *dst2, int dst2_step, int dst2_offset,
int rows, int cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst1_index = mad24(y, dst1_step, x + dst1_offset);
int dst2_index = mad24(y, dst2_step, x + dst2_offset);
float x = src1[src1_index];
float y = src2[src2_index];
float x2 = x * x;
float y2 = y * y;
float magnitude = sqrt(x2 + y2);
float tmp = y >= 0 ? 0 : CV_PI*2;
tmp = x < 0 ? CV_PI : tmp;
float tmp1 = y >= 0 ? CV_PI*0.5f : CV_PI*1.5f;
float cartToPolar = y2 <= x2 ? x*y/(x2 + 0.28f*y2 + FLT_EPSILON) + tmp :
tmp1 - x*y/(y2 + 0.28f*x2 + FLT_EPSILON);
#ifdef DEGREE
cartToPolar *= (180/CV_PI);
#endif
dst1[dst1_index] = magnitude;
dst2[dst2_index] = cartToPolar;
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void arithm_cartToPolar_D6 (__global double *src1, int src1_step, int src1_offset,
__global double *src2, int src2_step, int src2_offset,
__global double *dst1, int dst1_step, int dst1_offset,
__global double *dst2, int dst2_step, int dst2_offset,
int rows, int cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst1_index = mad24(y, dst1_step, x + dst1_offset);
int dst2_index = mad24(y, dst2_step, x + dst2_offset);
double x = src1[src1_index];
double y = src2[src2_index];
double x2 = x * x;
double y2 = y * y;
double magnitude = sqrt(x2 + y2);
float tmp = y >= 0 ? 0 : CV_PI*2;
tmp = x < 0 ? CV_PI : tmp;
float tmp1 = y >= 0 ? CV_PI*0.5 : CV_PI*1.5;
double cartToPolar = y2 <= x2 ? x*y/(x2 + 0.28f*y2 + DBL_EPSILON)  + tmp :
tmp1 - x*y/(y2 + 0.28f*x2 + DBL_EPSILON);
#ifdef DEGREE
cartToPolar *= (180/CV_PI);
#endif
dst1[dst1_index] = magnitude;
dst2[dst2_index] = cartToPolar;
}
}
#endif
a43471e45195c432df267f55f46bba02#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void arithm_compare(__global T * src1, int src1_step1, int src1_offset1,
__global T * src2, int src2_step1, int src2_offset1,
__global uchar * dst, int dst_step1, int dst_offset1,
int cols1, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols1 && y < rows)
{
int src1_index = mad24(y, src1_step1, x + src1_offset1);
int src2_index = mad24(y, src2_step1, x + src2_offset1);
int dst_index = mad24(y, dst_step1, x + dst_offset1);
dst[dst_index] = convert_uchar(src1[src1_index] Operation src2[src2_index] ? 255 : 0);
}
}
3bf172a1e9d43fc31ca2a20d4c48b567#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void arithm_exp_C1(__global srcT *src, __global srcT *dst,
int cols1, int rows,
int srcOffset1, int dstOffset1,
int srcStep1, int dstStep1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < cols1 && y < rows)
{
int srcIdx = mad24(y, srcStep1, x + srcOffset1);
int dstIdx = mad24(y, dstStep1, x + dstOffset1);
dst[dstIdx] = exp(src[srcIdx]);
}
}
__kernel void arithm_exp_C2(__global srcT *src, __global srcT *dst,
int cols1, int rows,
int srcOffset1, int dstOffset1,
int srcStep1, int dstStep1)
{
int x1 = get_global_id(0) << 1;
int y = get_global_id(1);
if(x1 < cols1 && y < rows)
{
int srcIdx = mad24(y, srcStep1, x1 + srcOffset1);
int dstIdx = mad24(y, dstStep1, x1 + dstOffset1);
dst[dstIdx] =                      exp(src[srcIdx]);
dst[dstIdx + 1] = x1 + 1 < cols1 ? exp(src[srcIdx + 1]) : dst[dstIdx + 1];
}
}
__kernel void arithm_exp_C4(__global srcT *src, __global srcT *dst,
int cols1, int rows,
int srcOffset1, int dstOffset1,
int srcStep1, int dstStep1)
{
int x1 = get_global_id(0) << 2;
int y = get_global_id(1);
if(x1 < cols1 && y < rows)
{
int srcIdx = mad24(y, srcStep1, x1 + srcOffset1);
int dstIdx = mad24(y, dstStep1, x1 + dstOffset1);
dst[dstIdx] =                      exp(src[srcIdx]);
dst[dstIdx + 1] = x1 + 1 < cols1 ? exp(src[srcIdx + 1]) : dst[dstIdx + 1];
dst[dstIdx + 2] = x1 + 2 < cols1 ? exp(src[srcIdx + 2]) : dst[dstIdx + 2];
dst[dstIdx + 3] = x1 + 3 < cols1 ? exp(src[srcIdx + 3]) : dst[dstIdx + 3];
}
}
606622bd16248ee57812ed0da18de6f8arithm_flip#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void arithm_flip_rows(__global T * src, int src_step, int src_offset,
__global T * dst, int dst_step, int dst_offset,
int rows, int cols, int thread_rows, int thread_cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < thread_rows)
{
int src_index_0 = mad24(y,            src_step, x + src_offset);
int src_index_1 = mad24(rows - y - 1, src_step, x + src_offset);
int dst_index_0 = mad24(y,            dst_step, x + dst_offset);
int dst_index_1 = mad24(rows - y - 1, dst_step, x + dst_offset);
T data0 = src[src_index_0], data1 = src[src_index_1];
dst[dst_index_0] = data1;
dst[dst_index_1] = data0;
}
}
__kernel void arithm_flip_rows_cols(__global T * src, int src_step, int src_offset,
__global T * dst, int dst_step, int dst_offset,
int rows, int cols, int thread_rows, int thread_cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < thread_rows)
{
int src_index_0 = mad24(y,            src_step, x            + src_offset);
int dst_index_0 = mad24(rows - y - 1, dst_step, cols - x - 1 + dst_offset);
int src_index_1 = mad24(rows - y - 1, src_step, cols - x - 1 + src_offset);
int dst_index_1 = mad24(y,            dst_step, x            + dst_offset);
T data0 = src[src_index_0], data1 = src[src_index_1];
dst[dst_index_0] = data0;
dst[dst_index_1] = data1;
}
}
__kernel void arithm_flip_cols(__global T * src, int src_step, int src_offset,
__global T * dst, int dst_step, int dst_offset,
int rows, int cols, int thread_rows, int thread_cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < thread_cols && y < rows)
{
int src_index_0 = mad24(y, src_step, x            + src_offset);
int dst_index_0 = mad24(y, dst_step, cols - x - 1 + dst_offset);
int src_index_1 = mad24(y, src_step, cols - x - 1 + src_offset);
int dst_index_1 = mad24(y, dst_step, x            + dst_offset);
T data0 = src[src_index_0], data1 = src[src_index_1];
dst[dst_index_1] = data1;
dst[dst_index_0] = data0;
}
}
1a13aef3910e62cabcd190131cc9fe56#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void arithm_log_C1(__global srcT *src, __global srcT *dst,
int cols1, int rows,
int srcOffset1, int dstOffset1,
int srcStep1, int dstStep1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < cols1 && y < rows)
{
int srcIdx = mad24(y, srcStep1, x + srcOffset1);
int dstIdx = mad24(y, dstStep1, x + dstOffset1);
dst[dstIdx] = log(src[srcIdx]);
}
}
__kernel void arithm_log_C2(__global srcT *src, __global srcT *dst,
int cols1, int rows,
int srcOffset1, int dstOffset1,
int srcStep1, int dstStep1)
{
int x1 = get_global_id(0) << 1;
int y = get_global_id(1);
if(x1 < cols1 && y < rows)
{
int srcIdx = mad24(y, srcStep1, x1 + srcOffset1);
int dstIdx = mad24(y, dstStep1, x1 + dstOffset1);
dst[dstIdx] =                      log(src[srcIdx]);
dst[dstIdx + 1] = x1 + 1 < cols1 ? log(src[srcIdx + 1]) : dst[dstIdx + 1];
}
}
__kernel void arithm_log_C4(__global srcT *src, __global srcT *dst,
int cols1, int rows,
int srcOffset1, int dstOffset1,
int srcStep1, int dstStep1)
{
int x1 = get_global_id(0) << 2;
int y = get_global_id(1);
if(x1 < cols1 && y < rows)
{
int srcIdx = mad24(y, srcStep1, x1 + srcOffset1);
int dstIdx = mad24(y, dstStep1, x1 + dstOffset1);
dst[dstIdx] =                      log(src[srcIdx]);
dst[dstIdx + 1] = x1 + 1 < cols1 ? log(src[srcIdx + 1]) : dst[dstIdx + 1];
dst[dstIdx + 2] = x1 + 2 < cols1 ? log(src[srcIdx + 2]) : dst[dstIdx + 2];
dst[dstIdx + 3] = x1 + 3 < cols1 ? log(src[srcIdx + 3]) : dst[dstIdx + 3];
}
}
576799ef2a4fec6719bfe812cfbeb270#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void arithm_magnitude(__global T *src1, int src1_step, int src1_offset,
__global T *src2, int src2_step, int src2_offset,
__global T *dst,  int dst_step,  int dst_offset,
int rows, int cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst_index  = mad24(y, dst_step,  x + dst_offset);
T data1 = src1[src1_index];
T data2 = src2[src2_index];
T tmp = hypot(data1, data2);
dst[dst_index] = tmp;
}
}
1e7ac19c4313a240ab431d2249537d09arithm_minMax#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#ifdef DEPTH_5
#define MIN_VAL (-FLT_MAX)
#define MAX_VAL FLT_MAX
#elif defined DEPTH_6
#define MIN_VAL (-DBL_MAX)
#define MAX_VAL DBL_MAX
#endif
__kernel void arithm_op_minMax(__global const T * src, int src_step, int src_offset, int src_rows, int src_cols,
int total, int groupnum, __global T * dst
#ifdef WITH_MASK
, __global const uchar * mask, int mask_step, int mask_offset
#endif
)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int id = get_global_id(0);
__local T localmem_max[128], localmem_min[128];
T minval = (T)(MAX_VAL), maxval = (T)(MIN_VAL), temp;
int y, x;
for (int grainSize = groupnum << 8; id < total; id += grainSize)
{
y = id / src_cols;
x = id % src_cols;
#ifdef WITH_MASK
if (mask[mad24(y, mask_step, x + mask_offset)])
#endif
{
temp = src[mad24(y, src_step, x + src_offset)];
minval = min(minval, temp);
maxval = max(maxval, temp);
}
}
if (lid > 127)
{
localmem_min[lid - 128] = minval;
localmem_max[lid - 128] = maxval;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (lid < 128)
{
localmem_min[lid] = min(minval, localmem_min[lid]);
localmem_max[lid] = max(maxval, localmem_max[lid]);
}
barrier(CLK_LOCAL_MEM_FENCE);
for (int lsize = 64; lsize > 0; lsize >>= 1)
{
if (lid < lsize)
{
int lid2 = lsize + lid;
localmem_min[lid] = min(localmem_min[lid], localmem_min[lid2]);
localmem_max[lid] = max(localmem_max[lid], localmem_max[lid2]);
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (lid == 0)
{
dst[gid] = localmem_min[0];
dst[gid + groupnum] = localmem_max[0];
}
}
d62572e14800b115334df5c2e2937529arithm_minMaxLoc#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define RES_TYPE double4
#define CONVERT_RES_TYPE convert_double4
#else
#define RES_TYPE float4
#define CONVERT_RES_TYPE convert_float4
#endif
#if defined (DEPTH_0)
#define VEC_TYPE uchar4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_uchar4
#define CONDITION_FUNC(a,b,c) (convert_int4(a) ? b : c)
#define MIN_VAL 0
#define MAX_VAL 255
#endif
#if defined (DEPTH_1)
#define VEC_TYPE char4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_char4
#define CONDITION_FUNC(a,b,c) (convert_int4(a) ? b : c)
#define MIN_VAL -128
#define MAX_VAL 127
#endif
#if defined (DEPTH_2)
#define VEC_TYPE ushort4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_ushort4
#define CONDITION_FUNC(a,b,c) (convert_int4(a) ? b : c)
#define MIN_VAL 0
#define MAX_VAL 65535
#endif
#if defined (DEPTH_3)
#define VEC_TYPE short4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_short4
#define CONDITION_FUNC(a,b,c) (convert_int4(a) ? b : c)
#define MIN_VAL -32768
#define MAX_VAL 32767
#endif
#if defined (DEPTH_4)
#define VEC_TYPE int4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_int4
#define CONDITION_FUNC(a,b,c) ((a) ? b : c)
#define MIN_VAL INT_MIN
#define MAX_VAL INT_MAX
#endif
#if defined (DEPTH_5)
#define VEC_TYPE float4
#define VEC_TYPE_LOC float4
#define CONVERT_TYPE convert_float4
#define CONDITION_FUNC(a,b,c) ((a) ? b : c)
#define MIN_VAL (-FLT_MAX)
#define MAX_VAL FLT_MAX
#endif
#if defined (DEPTH_6)
#define VEC_TYPE double4
#define VEC_TYPE_LOC double4
#define CONVERT_TYPE convert_double4
#define CONDITION_FUNC(a,b,c) ((a) ? b : c)
#define MIN_VAL (-DBL_MAX)
#define MAX_VAL DBL_MAX
#endif
#if defined (REPEAT_S0)
#define repeat_s(a) a=a;
#endif
#if defined (REPEAT_S1)
#define repeat_s(a) a.s0 = a.s1;
#endif
#if defined (REPEAT_S2)
#define repeat_s(a) a.s0 = a.s2;a.s1 = a.s2;
#endif
#if defined (REPEAT_S3)
#define repeat_s(a) a.s0 = a.s3;a.s1 = a.s3;a.s2 = a.s3;
#endif
#if defined (REPEAT_E0)
#define repeat_e(a) a=a;
#endif
#if defined (REPEAT_E1)
#define repeat_e(a) a.s3 = a.s2;
#endif
#if defined (REPEAT_E2)
#define repeat_e(a) a.s3 = a.s1;a.s2 = a.s1;
#endif
#if defined (REPEAT_E3)
#define repeat_e(a) a.s3 = a.s0;a.s2 = a.s0;a.s1 = a.s0;
#endif
__kernel void arithm_op_minMaxLoc(int cols, int invalid_cols, int offset, int elemnum, int groupnum,
__global VEC_TYPE *src, __global RES_TYPE *dst)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int  id = get_global_id(0);
int idx = offset + id + (id / cols) * invalid_cols;
__local VEC_TYPE localmem_max[128], localmem_min[128];
VEC_TYPE minval, maxval, temp;
__local VEC_TYPE_LOC localmem_maxloc[128], localmem_minloc[128];
VEC_TYPE_LOC minloc, maxloc, temploc, negative = -1;
int idx_c;
if (id < elemnum)
{
temp = src[idx];
idx_c = idx << 2;
temploc = (VEC_TYPE_LOC)(idx_c, idx_c + 1, idx_c + 2, idx_c + 3);
if (id % cols == 0 )
{
repeat_s(temp);
repeat_s(temploc);
}
if (id % cols == cols - 1)
{
repeat_e(temp);
repeat_e(temploc);
}
minval = temp;
maxval = temp;
minloc = temploc;
maxloc = temploc;
}
else
{
minval = MAX_VAL;
maxval = MIN_VAL;
minloc = negative;
maxloc = negative;
}
int grainSize = (groupnum << 8);
for (id = id + grainSize; id < elemnum; id = id + grainSize)
{
idx = offset + id + (id / cols) * invalid_cols;
temp = src[idx];
idx_c = idx << 2;
temploc = (VEC_TYPE_LOC)(idx_c, idx_c+1, idx_c+2, idx_c+3);
if (id % cols == 0 )
{
repeat_s(temp);
repeat_s(temploc);
}
if (id % cols == cols - 1)
{
repeat_e(temp);
repeat_e(temploc);
}
minval = min(minval, temp);
maxval = max(maxval, temp);
minloc = CONDITION_FUNC(minval == temp, temploc, minloc);
maxloc = CONDITION_FUNC(maxval == temp, temploc, maxloc);
}
if (lid > 127)
{
localmem_min[lid - 128] = minval;
localmem_max[lid - 128] = maxval;
localmem_minloc[lid - 128] = minloc;
localmem_maxloc[lid - 128] = maxloc;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (lid < 128)
{
localmem_min[lid] = min(minval,localmem_min[lid]);
localmem_max[lid] = max(maxval,localmem_max[lid]);
VEC_TYPE minVal = localmem_min[lid], maxVal = localmem_max[lid];
localmem_minloc[lid] = CONDITION_FUNC(minVal == minval, minloc, localmem_minloc[lid]);
localmem_maxloc[lid] = CONDITION_FUNC(maxVal == maxval, maxloc, localmem_maxloc[lid]);
}
barrier(CLK_LOCAL_MEM_FENCE);
for (int lsize = 64; lsize > 0; lsize >>= 1)
{
if (lid < lsize)
{
int lid2 = lsize + lid;
localmem_min[lid] = min(localmem_min[lid], localmem_min[lid2]);
localmem_max[lid] = max(localmem_max[lid], localmem_max[lid2]);
VEC_TYPE min1 = localmem_min[lid], min2 = localmem_min[lid2];
localmem_minloc[lid] = CONDITION_FUNC(min1 == min2, localmem_minloc[lid2], localmem_minloc[lid]);
VEC_TYPE max1 = localmem_max[lid], max2 = localmem_max[lid2];
localmem_maxloc[lid] = CONDITION_FUNC(max1 == max2, localmem_maxloc[lid2], localmem_maxloc[lid]);
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if ( lid == 0)
{
dst[gid] = CONVERT_RES_TYPE(localmem_min[0]);
dst[gid + groupnum] = CONVERT_RES_TYPE(localmem_max[0]);
dst[gid + 2 * groupnum] = CONVERT_RES_TYPE(localmem_minloc[0]);
dst[gid + 3 * groupnum] = CONVERT_RES_TYPE(localmem_maxloc[0]);
}
}
a59ab29ff6ebd4dcab6304a7547f4cf3arithm_minMaxLoc_mask#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define RES_TYPE double4
#define CONVERT_RES_TYPE convert_double4
#else
#define RES_TYPE float4
#define CONVERT_RES_TYPE convert_float4
#endif
#if defined (DEPTH_0)
#define TYPE uchar
#define VEC_TYPE uchar4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_uchar4
#define CONDITION_FUNC(a,b,c) (convert_int4(a) ? b : c)
#define MIN_VAL 0
#define MAX_VAL 255
#endif
#if defined (DEPTH_1)
#define TYPE char
#define VEC_TYPE char4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_char4
#define CONDITION_FUNC(a,b,c) (convert_int4(a) ? b : c)
#define MIN_VAL -128
#define MAX_VAL 127
#endif
#if defined (DEPTH_2)
#define TYPE ushort
#define VEC_TYPE ushort4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_ushort4
#define CONDITION_FUNC(a,b,c) (convert_int4(a) ? b : c)
#define MIN_VAL 0
#define MAX_VAL 65535
#endif
#if defined (DEPTH_3)
#define TYPE short
#define VEC_TYPE short4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_short4
#define CONDITION_FUNC(a,b,c) (convert_int4(a) ? b : c)
#define MIN_VAL -32768
#define MAX_VAL 32767
#endif
#if defined (DEPTH_4)
#define TYPE int
#define VEC_TYPE int4
#define VEC_TYPE_LOC int4
#define CONVERT_TYPE convert_int4
#define CONDITION_FUNC(a,b,c) ((a) ? b : c)
#define MIN_VAL INT_MIN
#define MAX_VAL INT_MAX
#endif
#if defined (DEPTH_5)
#define TYPE float
#define VEC_TYPE float4
#define VEC_TYPE_LOC float4
#define CONVERT_TYPE convert_float4
#define CONDITION_FUNC(a,b,c) ((a) ? b : c)
#define MIN_VAL (-FLT_MAX)
#define MAX_VAL FLT_MAX
#endif
#if defined (DEPTH_6)
#define TYPE double
#define VEC_TYPE double4
#define VEC_TYPE_LOC double4
#define CONVERT_TYPE convert_double4
#define CONDITION_FUNC(a,b,c) ((a) ? b : c)
#define MIN_VAL (-DBL_MAX)
#define MAX_VAL DBL_MAX
#endif
#if defined (REPEAT_E0)
#define repeat_e(a) a=a;
#endif
#if defined (REPEAT_E1)
#define repeat_e(a) a.s3 = a.s2;
#endif
#if defined (REPEAT_E2)
#define repeat_e(a) a.s3 = a.s1;a.s2 = a.s1;
#endif
#if defined (REPEAT_E3)
#define repeat_e(a) a.s3 = a.s0;a.s2 = a.s0;a.s1 = a.s0;
#endif
#if defined (REPEAT_E0)
#define repeat_me(a) a = a;
#endif
#if defined (REPEAT_E1)
#define repeat_me(a) a.s3 = 0;
#endif
#if defined (REPEAT_E2)
#define repeat_me(a) a.s3 = 0;a.s2 = 0;
#endif
#if defined (REPEAT_E3)
#define repeat_me(a) a.s3 = 0;a.s2 = 0;a.s1 = 0;
#endif
__kernel void arithm_op_minMaxLoc_mask (int cols,int invalid_cols,int offset,int elemnum,int groupnum,__global TYPE *src,
int minvalid_cols,int moffset,__global uchar *mask,__global RES_TYPE  *dst)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int  id = get_global_id(0);
int idx = id + (id / cols) * invalid_cols;
int midx = id + (id / cols) * minvalid_cols;
__local VEC_TYPE lm_max[128],lm_min[128];
VEC_TYPE minval, maxval, temp, m_temp, zeroVal = (VEC_TYPE)(0);
__local VEC_TYPE_LOC lm_maxloc[128], lm_minloc[128];
VEC_TYPE_LOC minloc, maxloc, temploc, negative = -1, one = 1, zero = 0;
if(id < elemnum)
{
temp = vload4(idx, &src[offset]);
m_temp = CONVERT_TYPE(vload4(midx,&mask[moffset]));
int idx_c = (idx << 2) + offset;
temploc = (VEC_TYPE_LOC)(idx_c,idx_c+1,idx_c+2,idx_c+3);
if (id % cols == cols - 1)
{
repeat_me(m_temp);
repeat_e(temploc);
}
minval = m_temp != zeroVal ? temp : (VEC_TYPE)MAX_VAL;
maxval = m_temp != zeroVal ? temp : (VEC_TYPE)MIN_VAL;
minloc = CONDITION_FUNC(m_temp != zeroVal, temploc , negative);
maxloc = minloc;
}
else
{
minval = MAX_VAL;
maxval = MIN_VAL;
minloc = negative;
maxloc = negative;
}
for(id=id + (groupnum << 8); id < elemnum;id = id + (groupnum << 8))
{
idx = id + (id / cols) * invalid_cols;
midx = id + (id / cols) * minvalid_cols;
temp = vload4(idx, &src[offset]);
m_temp = CONVERT_TYPE(vload4(midx,&mask[moffset]));
int idx_c = (idx << 2) + offset;
temploc = (VEC_TYPE_LOC)(idx_c,idx_c+1,idx_c+2,idx_c+3);
if (id % cols == cols - 1)
{
repeat_me(m_temp);
repeat_e(temploc);
}
minval = min(minval, m_temp != zeroVal ? temp : minval);
maxval = max(maxval, m_temp != zeroVal ? temp : maxval);
minloc = CONDITION_FUNC(minval == temp && m_temp != zeroVal, temploc , minloc);
maxloc = CONDITION_FUNC(maxval == temp && m_temp != zeroVal, temploc , maxloc);
}
if(lid > 127)
{
lm_min[lid - 128] = minval;
lm_max[lid - 128] = maxval;
lm_minloc[lid - 128] = minloc;
lm_maxloc[lid - 128] = maxloc;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 128)
{
lm_min[lid] = min(minval, lm_min[lid]);
lm_max[lid] = max(maxval, lm_max[lid]);
VEC_TYPE con_min = CONVERT_TYPE(minloc != negative ? one : zero);
VEC_TYPE con_max = CONVERT_TYPE(maxloc != negative ? one : zero);
VEC_TYPE lmMinVal = lm_min[lid], lmMaxVal = lm_max[lid];
lm_minloc[lid] = CONDITION_FUNC(lmMinVal == minval && con_min != zeroVal, minloc , lm_minloc[lid]);
lm_maxloc[lid] = CONDITION_FUNC(lmMaxVal == maxval && con_max != zeroVal, maxloc , lm_maxloc[lid]);
}
barrier(CLK_LOCAL_MEM_FENCE);
for(int lsize = 64; lsize > 0; lsize >>= 1)
{
if(lid < lsize)
{
int lid2 = lsize + lid;
lm_min[lid] = min(lm_min[lid], lm_min[lid2]);
lm_max[lid] = max(lm_max[lid], lm_max[lid2]);
VEC_TYPE con_min = CONVERT_TYPE(lm_minloc[lid2] != negative ? one : zero);
VEC_TYPE con_max = CONVERT_TYPE(lm_maxloc[lid2] != negative ? one : zero);
VEC_TYPE lmMinVal1 = lm_min[lid], lmMinVal2 = lm_min[lid2];
VEC_TYPE lmMaxVal1 = lm_max[lid], lmMaxVal2 = lm_max[lid2];
lm_minloc[lid] = CONDITION_FUNC(lmMinVal1 == lmMinVal2 && con_min != zeroVal, lm_minloc[lid2] , lm_minloc[lid]);
lm_maxloc[lid] = CONDITION_FUNC(lmMaxVal1 == lmMaxVal2 && con_max != zeroVal, lm_maxloc[lid2] , lm_maxloc[lid]);
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if( lid == 0)
{
dst[gid] = CONVERT_RES_TYPE(lm_min[0]);
dst[gid + groupnum] = CONVERT_RES_TYPE(lm_max[0]);
dst[gid + 2 * groupnum] = CONVERT_RES_TYPE(lm_minloc[0]);
dst[gid + 3 * groupnum] = CONVERT_RES_TYPE(lm_maxloc[0]);
}
}
a2ac711cf5b963637b561e0c868b7f04arithm_nonzero#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void arithm_op_nonzero(__global srcT * src, int src_step, int src_offset, int src_cols,
int total, int groupnum, __global dstT * dst)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int  id = get_global_id(0);
__local dstT localmem_nonzero[128];
dstT nonzero = (dstT)(0);
for (int grain = groupnum << 8; id < total; id += grain)
nonzero += convertToDstT(src[mad24(id / src_cols, src_step, id % src_cols + src_offset)] == (srcT)(0)) ? (dstT)(0) : (dstT)(1);
if (lid > 127)
localmem_nonzero[lid - 128] = nonzero;
barrier(CLK_LOCAL_MEM_FENCE);
if (lid < 128)
localmem_nonzero[lid] = nonzero + localmem_nonzero[lid];
barrier(CLK_LOCAL_MEM_FENCE);
for (int lsize = 64; lsize > 0; lsize >>= 1)
{
if (lid < lsize)
{
int lid2 = lsize + lid;
localmem_nonzero[lid] = localmem_nonzero[lid] + localmem_nonzero[lid2];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (lid == 0)
dst[gid] = localmem_nonzero[0];
}
650573f3600f3c0f0a6a87ab3d29e803arithm_phase#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define CV_PI M_PI
#define CV_2PI (2 * CV_PI)
#else
#define CV_PI M_PI_F
#define CV_2PI (2 * CV_PI)
#endif
__kernel void arithm_phase_inradians_D5(__global float *src1, int src1_step1, int src1_offset1,
__global float *src2, int src2_step1, int src2_offset1,
__global float *dst,  int dst_step1,  int dst_offset1,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step1, x + src1_offset1);
int src2_index = mad24(y, src2_step1, x + src2_offset1);
int dst_index  = mad24(y, dst_step1, x + dst_offset1);
float data1 = src1[src1_index];
float data2 = src2[src2_index];
float tmp = atan2(data2, data1);
if (tmp < 0)
tmp += CV_2PI;
dst[dst_index] = tmp;
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void arithm_phase_inradians_D6(__global double *src1, int src1_step1, int src1_offset1,
__global double *src2, int src2_step1, int src2_offset1,
__global double *dst,  int dst_step1,  int dst_offset1,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step1, x + src1_offset1);
int src2_index = mad24(y, src2_step1, x + src2_offset1);
int dst_index  = mad24(y, dst_step1, x + dst_offset1);
double data1 = src1[src1_index];
double data2 = src2[src2_index];
double tmp = atan2(data2, data1);
if (tmp < 0)
tmp += CV_2PI;
dst[dst_index] = tmp;
}
}
#endif
__kernel void arithm_phase_indegrees_D5(__global float *src1, int src1_step1, int src1_offset1,
__global float *src2, int src2_step1, int src2_offset1,
__global float *dst,  int dst_step1,  int dst_offset1,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step1, x + src1_offset1);
int src2_index = mad24(y, src2_step1, x + src2_offset1);
int dst_index  = mad24(y, dst_step1, x + dst_offset1);
float data1 = src1[src1_index];
float data2 = src2[src2_index];
float tmp = atan2(data2, data1);
tmp = 180 * tmp / CV_PI;
if (tmp < 0)
tmp += 360;
dst[dst_index] = tmp;
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void arithm_phase_indegrees_D6 (__global double *src1, int src1_step1, int src1_offset1,
__global double *src2, int src2_step1, int src2_offset1,
__global double *dst,  int dst_step1,  int dst_offset1,
int cols, int rows)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step1, x + src1_offset1);
int src2_index = mad24(y, src2_step1, x + src2_offset1);
int dst_index  = mad24(y, dst_step1, x + dst_offset1);
double data1 = src1[src1_index];
double data2 = src2[src2_index];
double tmp = atan2(data2, data1);
tmp = 180 * tmp / CV_PI;
if (tmp < 0)
tmp += 360;
dst[dst_index] = tmp;
}
}
#endif
74e75b6750558dabe4856b6557b6ae41#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define CV_PI M_PI
#else
#define CV_PI M_PI_F
#endif
__kernel void arithm_polarToCart_mag_D5 (__global float *src1, int src1_step, int src1_offset,
__global float *src2, int src2_step, int src2_offset,
__global float *dst1, int dst1_step, int dst1_offset,
__global float *dst2, int dst2_step, int dst2_offset,
int rows, int cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst1_index = mad24(y, dst1_step, x + dst1_offset);
int dst2_index = mad24(y, dst2_step, x + dst2_offset);
float x = src1[src1_index];
float y = src2[src2_index];
#ifdef DEGREE
float ascale = CV_PI/180.0f;
float alpha = y * ascale;
#else
float alpha = y;
#endif
float a = cos(alpha) * x;
float b = sin(alpha) * x;
dst1[dst1_index] = a;
dst2[dst2_index] = b;
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void arithm_polarToCart_mag_D6 (__global double *src1, int src1_step, int src1_offset,
__global double *src2, int src2_step, int src2_offset,
__global double *dst1, int dst1_step, int dst1_offset,
__global double *dst2, int dst2_step, int dst2_offset,
int rows, int cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, x + src1_offset);
int src2_index = mad24(y, src2_step, x + src2_offset);
int dst1_index = mad24(y, dst1_step, x + dst1_offset);
int dst2_index = mad24(y, dst2_step, x + dst2_offset);
double x = src1[src1_index];
double y = src2[src2_index];
#ifdef DEGREE
float ascale = CV_PI/180.0;
float alpha = y * ascale;
#else
float alpha = y;
#endif
double a = cos(alpha) * x;
double b = sin(alpha) * x;
dst1[dst1_index] = a;
dst2[dst2_index] = b;
}
}
#endif
__kernel void arithm_polarToCart_D5 (__global float *src,  int src_step,  int src_offset,
__global float *dst1, int dst1_step, int dst1_offset,
__global float *dst2, int dst2_step, int dst2_offset,
int rows, int cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src_index  = mad24(y, src_step,  x + src_offset);
int dst1_index = mad24(y, dst1_step, x + dst1_offset);
int dst2_index = mad24(y, dst2_step, x + dst2_offset);
float y = src[src_index];
#ifdef DEGREE
float ascale = CV_PI/180.0f;
float alpha = y * ascale;
#else
float alpha = y;
#endif
float a = cos(alpha);
float b = sin(alpha);
dst1[dst1_index] = a;
dst2[dst2_index] = b;
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void arithm_polarToCart_D6 (__global float *src,  int src_step,  int src_offset,
__global float *dst1, int dst1_step, int dst1_offset,
__global float *dst2, int dst2_step, int dst2_offset,
int rows, int cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src_index  = mad24(y, src_step,  x + src_offset);
int dst1_index = mad24(y, dst1_step, x + dst1_offset);
int dst2_index = mad24(y, dst2_step, x + dst2_offset);
double y = src[src_index];
#ifdef DEGREE
float ascale = CV_PI/180.0f;
float alpha = y * ascale;
#else
float alpha = y;
#endif
double a = cos(alpha);
double b = sin(alpha);
dst1[dst1_index] = a;
dst2[dst2_index] = b;
}
}
#endif
3b9f767242239556823d43460957858f#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void arithm_pow(__global VT * src, int src_step, int src_offset,
__global VT * dst, int dst_step, int dst_offset,
int rows, int cols, T p)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src_index = mad24(y, src_step, x + src_offset);
int dst_index = mad24(y, dst_step, x + dst_offset);
VT src_data = src[src_index];
VT tmp = src_data > (VT)0 ? (VT)exp(p * log(src_data)) : (src_data == (VT)0 ? (VT)0 : (VT)exp(p * log(fabs(src_data))));
dst[dst_index] = tmp;
}
}
a79b90c5a25fec8fe4d5af644308c7c5arithm_setidentity#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void setIdentity(__global T * src, int src_step, int src_offset,
int cols, int rows, __global const T * scalar)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src_index = mad24(y, src_step, src_offset + x);
if (x == y)
src[src_index] = *scalar;
else
src[src_index] = 0;
}
}
bc19dcbca5a8d384082c7d000b806e1barithm_sum#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#if FUNC_SUM
#define FUNC(a, b) b += a;
#elif FUNC_ABS_SUM
#define FUNC(a, b) b += a >= (dstT)(0) ? a : -a;
#elif FUNC_SQR_SUM
#define FUNC(a, b) b += a * a;
#else
#error No sum function
#endif
__kernel void arithm_op_sum(__global srcT * src, int src_step, int src_offset, int src_cols,
int total, int groupnum, __global dstT * dst)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int id = get_global_id(0);
__local dstT localmem_sum[128];
dstT sum = (dstT)(0), temp;
for (int grainSize = groupnum << 8; id < total; id += grainSize)
{
temp = convertToDstT(src[mad24(id / src_cols, src_step, id % src_cols + src_offset)]);
FUNC(temp, sum);
}
if (lid > 127)
localmem_sum[lid - 128] = sum;
barrier(CLK_LOCAL_MEM_FENCE);
if (lid < 128)
localmem_sum[lid] = sum + localmem_sum[lid];
barrier(CLK_LOCAL_MEM_FENCE);
for (int lsize = 64; lsize > 0; lsize >>= 1)
{
if (lid < lsize)
{
int lid2 = lsize + lid;
localmem_sum[lid] = localmem_sum[lid] + localmem_sum[lid2];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (lid == 0)
dst[gid] = localmem_sum[0];
}
6183a9be49fc10d3d55fa959093a607farithm_transpose#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#define TILE_DIM      32
#define BLOCK_ROWS    8
#define LDS_STEP      TILE_DIM
__kernel void transpose(__global const T* src, __global T* dst,
int src_cols, int src_rows,
int src_step, int dst_step,
int src_offset, int dst_offset)
{
int gp_x = get_group_id(0),   gp_y = get_group_id(1);
int gs_x = get_num_groups(0), gs_y = get_num_groups(1);
int groupId_x, groupId_y;
if(src_rows == src_cols)
{
groupId_y = gp_x;
groupId_x = (gp_x + gp_y) % gs_x;
}
else
{
int bid = gp_x + gs_x * gp_y;
groupId_y =  bid % gs_y;
groupId_x = ((bid / gs_y) + groupId_y) % gs_x;
}
int lx = get_local_id(0);
int ly = get_local_id(1);
int x = groupId_x * TILE_DIM + lx;
int y = groupId_y * TILE_DIM + ly;
int x_index = groupId_y * TILE_DIM + lx;
int y_index = groupId_x * TILE_DIM + ly;
__local T title[TILE_DIM * LDS_STEP];
if (x < src_cols && y < src_rows)
{
int index_src = mad24(y, src_step, x);
for(int i = 0; i < TILE_DIM; i += BLOCK_ROWS)
{
if (y + i < src_rows)
{
title[(ly + i) * LDS_STEP + lx] = src[src_offset + index_src];
index_src = mad24(BLOCK_ROWS, src_step, index_src);
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (x_index < src_rows && y_index < src_cols)
{
int index_dst = mad24(y_index, dst_step, x_index);
for(int i = 0; i < TILE_DIM; i += BLOCK_ROWS)
{
if ((y_index + i) < src_cols)
{
dst[dst_offset + index_dst] = title[lx * LDS_STEP + ly + i];
index_dst +=  dst_step * BLOCK_ROWS;
}
}
}
}
__kernel void transpose_inplace(__global T* src, __global T* dst,
int src_cols, int src_rows,
int src_step, int dst_step,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < src_rows && x < y)
{
int srcIdx = mad24(y, src_step, src_offset + x);
int dstIdx = mad24(x, dst_step, dst_offset + y);
T tmp = dst[dstIdx];
dst[dstIdx] = src[srcIdx];
src[srcIdx] = tmp;
}
}
bd4ecb4b23c96b7c71399c6faa7a71f4bgfg_mog#if defined (CN1)
#define T_FRAME uchar
#define T_MEAN_VAR float
#define CONVERT_TYPE convert_uchar_sat
#define F_ZERO (0.0f)
float cvt(uchar val)
{
return val;
}
float sqr(float val)
{
return val * val;
}
float sum(float val)
{
return val;
}
float clamp1(float var, float learningRate, float diff, float minVar)
{
return fmax(var + learningRate * (diff * diff - var), minVar);
}
#else
#define T_FRAME uchar4
#define T_MEAN_VAR float4
#define CONVERT_TYPE convert_uchar4_sat
#define F_ZERO (0.0f, 0.0f, 0.0f, 0.0f)
float4 cvt(const uchar4 val)
{
float4 result;
result.x = val.x;
result.y = val.y;
result.z = val.z;
result.w = val.w;
return result;
}
float sqr(const float4 val)
{
return val.x * val.x + val.y * val.y + val.z * val.z;
}
float sum(const float4 val)
{
return (val.x + val.y + val.z);
}
void swap4(__global float4* ptr, int x, int y, int k, int rows, int ptr_step)
{
float4 val = ptr[(k * rows + y) * ptr_step + x];
ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
ptr[((k + 1) * rows + y) * ptr_step + x] = val;
}
float4 clamp1(const float4 var, float learningRate, const float4 diff, float minVar)
{
float4 result;
result.x = fmax(var.x + learningRate * (diff.x * diff.x - var.x), minVar);
result.y = fmax(var.y + learningRate * (diff.y * diff.y - var.y), minVar);
result.z = fmax(var.z + learningRate * (diff.z * diff.z - var.z), minVar);
result.w = 0.0f;
return result;
}
#endif
typedef struct
{
float c_Tb;
float c_TB;
float c_Tg;
float c_varInit;
float c_varMin;
float c_varMax;
float c_tau;
uchar c_shadowVal;
} con_srtuct_t;
void swap(__global float* ptr, int x, int y, int k, int rows, int ptr_step)
{
float val = ptr[(k * rows + y) * ptr_step + x];
ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
ptr[((k + 1) * rows + y) * ptr_step + x] = val;
}
__kernel void mog_withoutLearning_kernel(__global T_FRAME* frame, __global uchar* fgmask,
__global float* weight, __global T_MEAN_VAR* mean, __global T_MEAN_VAR* var,
int frame_row, int frame_col, int frame_step, int fgmask_step,
int weight_step, int mean_step, int var_step,
float varThreshold, float backgroundRatio, int fgmask_offset_x,
int fgmask_offset_y, int frame_offset_x, int frame_offset_y)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < frame_col && y < frame_row)
{
T_MEAN_VAR pix = cvt(frame[(y + frame_offset_y) * frame_step + (x + frame_offset_x)]);
int kHit = -1;
int kForeground = -1;
for (int k = 0; k < (NMIXTURES); ++k)
{
if (weight[(k * frame_row + y) * weight_step + x] < 1.192092896e-07f)
break;
T_MEAN_VAR mu = mean[(k * frame_row + y) * mean_step + x];
T_MEAN_VAR _var = var[(k * frame_row + y) + var_step + x];
T_MEAN_VAR diff = pix - mu;
if (sqr(diff) < varThreshold * sum(_var))
{
kHit = k;
break;
}
}
if (kHit >= 0)
{
float wsum = 0.0f;
for (int k = 0; k < (NMIXTURES); ++k)
{
wsum += weight[(k * frame_row + y) * weight_step + x];
if (wsum > backgroundRatio)
{
kForeground = k + 1;
break;
}
}
}
if(kHit < 0 || kHit >= kForeground)
fgmask[(y + fgmask_offset_y) * fgmask_step + (x + fgmask_offset_x)] = (uchar) (-1);
else
fgmask[(y + fgmask_offset_y) * fgmask_step + (x + fgmask_offset_x)] = (uchar) (0);
}
}
__kernel void mog_withLearning_kernel(__global T_FRAME* frame, __global int* fgmask,
__global float* weight, __global float* sortKey, __global T_MEAN_VAR* mean,
__global T_MEAN_VAR* var, int frame_row, int frame_col, int frame_step, int fgmask_step,
int weight_step, int sortKey_step, int mean_step, int var_step,
float varThreshold, float backgroundRatio, float learningRate, float minVar,
int fgmask_offset_x, int fgmask_offset_y, int frame_offset_x, int frame_offset_y)
{
const float w0 = 0.05f;
const float sk0 = w0 / 30.0f;
const float var0 = 900.f;
int x = get_global_id(0);
int y = get_global_id(1);
if(x >= frame_col || y >= frame_row) return;
float wsum = 0.0f;
int kHit = -1;
int kForeground = -1;
int k = 0;
T_MEAN_VAR pix = cvt(frame[(y + frame_offset_y) * frame_step + (x + frame_offset_x)]);
for (; k < (NMIXTURES); ++k)
{
float w = weight[(k * frame_row + y) * weight_step + x];
wsum += w;
if (w < 1.192092896e-07f)
break;
T_MEAN_VAR mu = mean[(k * frame_row + y) * mean_step + x];
T_MEAN_VAR _var = var[(k * frame_row + y) * var_step + x];
float sortKey_prev, weight_prev;
T_MEAN_VAR mean_prev, var_prev;
if (sqr(pix - mu) < varThreshold * sum(_var))
{
wsum -= w;
float dw = learningRate * (1.0f - w);
_var = clamp1(_var, learningRate, pix - mu, minVar);
sortKey_prev = w / sqr(sum(_var));
sortKey[(k * frame_row + y) * sortKey_step + x] = sortKey_prev;
weight_prev = w + dw;
weight[(k * frame_row + y) * weight_step + x] = weight_prev;
mean_prev = mu + learningRate * (pix - mu);
mean[(k * frame_row + y) * mean_step + x] = mean_prev;
var_prev = _var;
var[(k * frame_row + y) * var_step + x] = var_prev;
}
int k1 = k - 1;
if (k1 >= 0 && sqr(pix - mu) < varThreshold * sum(_var))
{
float sortKey_next = sortKey[(k1 * frame_row + y) * sortKey_step + x];
float weight_next = weight[(k1 * frame_row + y) * weight_step + x];
T_MEAN_VAR mean_next = mean[(k1 * frame_row + y) * mean_step + x];
T_MEAN_VAR var_next = var[(k1 * frame_row + y) * var_step + x];
for (; sortKey_next < sortKey_prev && k1 >= 0; --k1)
{
sortKey[(k1 * frame_row + y) * sortKey_step + x] = sortKey_prev;
sortKey[((k1 + 1) * frame_row + y) * sortKey_step + x] = sortKey_next;
weight[(k1 * frame_row + y) * weight_step + x] = weight_prev;
weight[((k1 + 1) * frame_row + y) * weight_step + x] = weight_next;
mean[(k1 * frame_row + y) * mean_step + x] = mean_prev;
mean[((k1 + 1) * frame_row + y) * mean_step + x] = mean_next;
var[(k1 * frame_row + y) * var_step + x] = var_prev;
var[((k1 + 1) * frame_row + y) * var_step + x] = var_next;
sortKey_prev = sortKey_next;
sortKey_next = k1 > 0 ? sortKey[((k1 - 1) * frame_row + y) * sortKey_step + x] : 0.0f;
weight_prev = weight_next;
weight_next = k1 > 0 ? weight[((k1 - 1) * frame_row + y) * weight_step + x] : 0.0f;
mean_prev = mean_next;
mean_next = k1 > 0 ? mean[((k1 - 1) * frame_row + y) * mean_step + x] : (T_MEAN_VAR)F_ZERO;
var_prev = var_next;
var_next = k1 > 0 ? var[((k1 - 1) * frame_row + y) * var_step + x] : (T_MEAN_VAR)F_ZERO;
}
}
kHit = k1 + 1;
break;
}
if (kHit < 0)
{
kHit = k = k < ((NMIXTURES) - 1) ? k : ((NMIXTURES) - 1);
wsum += w0 - weight[(k * frame_row + y) * weight_step + x];
weight[(k * frame_row + y) * weight_step + x] = w0;
mean[(k * frame_row + y) * mean_step + x] = pix;
#if defined (CN1)
var[(k * frame_row + y) * var_step + x] = (T_MEAN_VAR)(var0);
#else
var[(k * frame_row + y) * var_step + x] = (T_MEAN_VAR)(var0, var0, var0, var0);
#endif
sortKey[(k * frame_row + y) * sortKey_step + x] = sk0;
}
else
{
for( ; k < (NMIXTURES); k++)
wsum += weight[(k * frame_row + y) * weight_step + x];
}
float wscale = 1.0f / wsum;
wsum = 0;
for (k = 0; k < (NMIXTURES); ++k)
{
float w = weight[(k * frame_row + y) * weight_step + x];
w *= wscale;
wsum += w;
weight[(k * frame_row + y) * weight_step + x] = w;
sortKey[(k * frame_row + y) * sortKey_step + x] *= wscale;
kForeground = select(kForeground, k + 1, wsum > backgroundRatio && kForeground < 0);
}
fgmask[(y + fgmask_offset_y) * fgmask_step + (x + fgmask_offset_x)] = (uchar)(-(kHit >= kForeground));
}
__kernel void getBackgroundImage_kernel(__global float* weight, __global T_MEAN_VAR* mean, __global T_FRAME* dst,
int dst_row, int dst_col, int weight_step, int mean_step, int dst_step,
float backgroundRatio)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < dst_col && y < dst_row)
{
T_MEAN_VAR meanVal = (T_MEAN_VAR)F_ZERO;
float totalWeight = 0.0f;
for (int mode = 0; mode < (NMIXTURES); ++mode)
{
float _weight = weight[(mode * dst_row + y) * weight_step + x];
T_MEAN_VAR _mean = mean[(mode * dst_row + y) * mean_step + x];
meanVal = meanVal + _weight * _mean;
totalWeight += _weight;
if(totalWeight > backgroundRatio)
break;
}
meanVal = meanVal * (1.f / totalWeight);
dst[y * dst_step + x] = CONVERT_TYPE(meanVal);
}
}
__kernel void mog2_kernel(__global T_FRAME * frame, __global int* fgmask, __global float* weight, __global T_MEAN_VAR * mean,
__global int* modesUsed, __global float* variance, int frame_row, int frame_col, int frame_step,
int fgmask_step, int weight_step, int mean_step, int modesUsed_step, int var_step, float alphaT, float alpha1, float prune,
int detectShadows_flag, int fgmask_offset_x, int fgmask_offset_y, int frame_offset_x, int frame_offset_y, __constant con_srtuct_t* constants)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < frame_col && y < frame_row)
{
T_MEAN_VAR pix = cvt(frame[(y + frame_offset_y) * frame_step + x + frame_offset_x]);
bool background = false;
bool fitsPDF = false;
int nmodes = modesUsed[y * modesUsed_step + x];
int nNewModes = nmodes;
float totalWeight = 0.0f;
for (int mode = 0; mode < nmodes; ++mode)
{
float _weight = alpha1 * weight[(mode * frame_row + y) * weight_step + x] + prune;
int swap_count = 0;
if (!fitsPDF)
{
float var = variance[(mode * frame_row + y) * var_step + x];
T_MEAN_VAR _mean = mean[(mode * frame_row + y) * mean_step + x];
T_MEAN_VAR diff = _mean - pix;
float dist2 = sqr(diff);
if (totalWeight < constants -> c_TB && dist2 < constants -> c_Tb * var)
background = true;
if (dist2 < constants -> c_Tg * var)
{
fitsPDF = true;
_weight += alphaT;
float k = alphaT / _weight;
mean[(mode * frame_row + y) * mean_step + x] = _mean - k * diff;
float varnew = var + k * (dist2 - var);
varnew = fmax(varnew, constants -> c_varMin);
varnew = fmin(varnew, constants -> c_varMax);
variance[(mode * frame_row + y) * var_step + x] = varnew;
for (int i = mode; i > 0; --i)
{
if (_weight < weight[((i - 1) * frame_row + y) * weight_step + x])
break;
swap_count++;
swap(weight, x, y, i - 1, frame_row, weight_step);
swap(variance, x, y, i - 1, frame_row, var_step);
#if defined (CN1)
swap(mean, x, y, i - 1, frame_row, mean_step);
#else
swap4(mean, x, y, i - 1, frame_row, mean_step);
#endif
}
}
}
if (_weight < -prune)
{
_weight = 0.0f;
nmodes--;
}
weight[((mode - swap_count) * frame_row + y) * weight_step + x] = _weight;
totalWeight += _weight;
}
totalWeight = 1.f / totalWeight;
for (int mode = 0; mode < nmodes; ++mode)
weight[(mode * frame_row + y) * weight_step + x] *= totalWeight;
nmodes = nNewModes;
if (!fitsPDF)
{
int mode = nmodes == (NMIXTURES) ? (NMIXTURES) - 1 : nmodes++;
if (nmodes == 1)
weight[(mode * frame_row + y) * weight_step + x] = 1.f;
else
{
weight[(mode * frame_row + y) * weight_step + x] = alphaT;
for (int i = 0; i < nmodes - 1; ++i)
weight[(i * frame_row + y) * weight_step + x] *= alpha1;
}
mean[(mode * frame_row + y) * mean_step + x] = pix;
variance[(mode * frame_row + y) * var_step + x] = constants -> c_varInit;
for (int i = nmodes - 1; i > 0; --i)
{
if (alphaT < weight[((i - 1) * frame_row + y) * weight_step + x])
break;
swap(weight, x, y, i - 1, frame_row, weight_step);
swap(variance, x, y, i - 1, frame_row, var_step);
#if defined (CN1)
swap(mean, x, y, i - 1, frame_row, mean_step);
#else
swap4(mean, x, y, i - 1, frame_row, mean_step);
#endif
}
}
modesUsed[y * modesUsed_step + x] = nmodes;
bool isShadow = false;
if (detectShadows_flag && !background)
{
float tWeight = 0.0f;
for (int mode = 0; mode < nmodes; ++mode)
{
T_MEAN_VAR _mean = mean[(mode * frame_row + y) * mean_step + x];
T_MEAN_VAR pix_mean = pix * _mean;
float numerator = sum(pix_mean);
float denominator = sqr(_mean);
if (denominator == 0)
break;
if (numerator <= denominator && numerator >= constants -> c_tau * denominator)
{
float a = numerator / denominator;
T_MEAN_VAR dD = a * _mean - pix;
if (sqr(dD) < constants -> c_Tb * variance[(mode * frame_row + y) * var_step + x] * a * a)
{
isShadow = true;
break;
}
}
tWeight += weight[(mode * frame_row + y) * weight_step + x];
if (tWeight > constants -> c_TB)
break;
}
}
fgmask[(y + fgmask_offset_y) * fgmask_step + x + fgmask_offset_x] = background ? 0 : isShadow ? constants -> c_shadowVal : 255;
}
}
__kernel void getBackgroundImage2_kernel(__global int* modesUsed, __global float* weight, __global T_MEAN_VAR* mean,
__global T_FRAME* dst, float c_TB, int modesUsed_row, int modesUsed_col, int modesUsed_step, int weight_step,
int mean_step, int dst_step, int dst_x, int dst_y)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < modesUsed_col && y < modesUsed_row)
{
int nmodes = modesUsed[y * modesUsed_step + x];
T_MEAN_VAR meanVal = (T_MEAN_VAR)F_ZERO;
float totalWeight = 0.0f;
for (int mode = 0; mode < nmodes; ++mode)
{
float _weight = weight[(mode * modesUsed_row + y) * weight_step + x];
T_MEAN_VAR _mean = mean[(mode * modesUsed_row + y) * mean_step + x];
meanVal = meanVal + _weight * _mean;
totalWeight += _weight;
if(totalWeight > c_TB)
break;
}
meanVal = meanVal * (1.f / totalWeight);
dst[(y + dst_y) * dst_step + x + dst_x] = CONVERT_TYPE(meanVal);
}
}
7fd1eedde22b5fad55ba5eb9cac67955blend_linear#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void blendLinear(__global const T * src1, int src1_offset, int src1_step,
__global const T * src2, int src2_offset, int src2_step,
__global const float * weight1, int weight1_offset, int weight1_step,
__global const float * weight2, int weight2_offset, int weight2_step,
__global T * dst, int dst_offset, int dst_step,
int rows, int cols)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int src1_index = mad24(y, src1_step, src1_offset + x);
int src2_index = mad24(y, src2_step, src2_offset + x);
int weight1_index = mad24(y, weight1_step, weight1_offset + x);
int weight2_index = mad24(y, weight2_step, weight2_offset + x);
int dst_index = mad24(y, dst_step, dst_offset + x);
FT w1 = (FT)(weight1[weight1_index]), w2 = (FT)(weight2[weight2_index]);
FT den = w1 + w2 + (FT)(1e-5f);
FT num = w1 * convertToFT(src1[src1_index]) + w2 * convertToFT(src2[src2_index]);
dst[dst_index] = convertToT(num / den);
}
}
282ca434c43357705cd20966671a57efbrute_force_match#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable
#define MAX_FLOAT 3.40282e+038f
#ifndef T
#define T float
#endif
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 16
#endif
#ifndef MAX_DESC_LEN
#define MAX_DESC_LEN 64
#endif
#ifndef DIST_TYPE
#define DIST_TYPE 0
#endif
#if   (DIST_TYPE == 0)
#   ifdef T_FLOAT
#       define DIST(x, y) fabs((x) - (y))
typedef float value_type;
typedef float result_type;
#   else
#       define DIST(x, y) abs((x) - (y))
typedef int value_type;
typedef int result_type;
#   endif
#define DIST_RES(x) (x)
#elif (DIST_TYPE == 1)
#define DIST(x, y) (((x) - (y)) * ((x) - (y)))
typedef float value_type;
typedef float result_type;
#define DIST_RES(x) sqrt(x)
#elif (DIST_TYPE == 2)
int bit1Count(int v)
{
v = v - ((v >> 1) & 0x55555555);
v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
}
#define DIST(x, y) bit1Count( (x) ^ (y) )
typedef int value_type;
typedef int result_type;
#define DIST_RES(x) (x)
#endif
result_type reduce_block(
__local value_type *s_query,
__local value_type *s_train,
int lidx,
int lidy
)
{
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(
s_query[lidy * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
}
return DIST_RES(result);
}
result_type reduce_block_match(
__local value_type *s_query,
__local value_type *s_train,
int lidx,
int lidy
)
{
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(
s_query[lidy * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
}
return (result);
}
result_type reduce_multi_block(
__local value_type *s_query,
__local value_type *s_train,
int block_index,
int lidx,
int lidy
)
{
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(
s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
}
return result;
}
__kernel void BruteForceMatch_UnrollMatch(
__global T *query,
__global T *train,
__global int *bestTrainIdx,
__global float *bestDistance,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
int queryIdx = groupidx * BLOCK_SIZE + lidy;
#pragma unroll
for (int i = 0 ;  i <  MAX_DESC_LEN / BLOCK_SIZE; i ++)
{
int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * MAX_DESC_LEN + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1)  * (step / sizeof(T)) + loadx] : 0;
}
float myBestDistance = MAX_FLOAT;
int myBestTrainIdx = -1;
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
{
result_type result = 0;
#pragma unroll
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(T)) + loadx] : 0;
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
result = DIST_RES(result);
int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance)
{
myBestDistance = result;
myBestTrainIdx = trainIdx;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float*)(sharebuffer);
__local int* s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance;
s_trainIdx[lidx] = myBestTrainIdx;
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int k = 0 ; k < BLOCK_SIZE; k++)
{
if (myBestDistance > s_distance[k])
{
myBestDistance = s_distance[k];
myBestTrainIdx = s_trainIdx[k];
}
}
if (queryIdx < query_rows && lidx == 0)
{
bestTrainIdx[queryIdx] = myBestTrainIdx;
bestDistance[queryIdx] = myBestDistance;
}
}
__kernel void BruteForceMatch_Match(
__global T *query,
__global T *train,
__global int *bestTrainIdx,
__global float *bestDistance,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
float myBestDistance = MAX_FLOAT;
int myBestTrainIdx = -1;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
for (int t = 0 ;  t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
result_type result = 0;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * BLOCK_SIZE + lidx] = 0;
s_train[lidx * BLOCK_SIZE + lidy] = 0;
if (loadx < query_cols)
{
s_query[lidy * BLOCK_SIZE + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(T)) + loadx];
s_train[lidx * BLOCK_SIZE + lidy] = train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(T)) + loadx];
}
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_block_match(s_query, s_train, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
result = DIST_RES(result);
const int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance )
{
myBestDistance = result;
myBestTrainIdx = trainIdx;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float *)sharebuffer;
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance;
s_trainIdx[lidx] = myBestTrainIdx;
barrier(CLK_LOCAL_MEM_FENCE);
for (int k = 0 ; k < BLOCK_SIZE; k++)
{
if (myBestDistance > s_distance[k])
{
myBestDistance = s_distance[k];
myBestTrainIdx = s_trainIdx[k];
}
}
if (queryIdx < query_rows && lidx == 0)
{
bestTrainIdx[queryIdx] = myBestTrainIdx;
bestDistance[queryIdx] = myBestDistance;
}
}
__kernel void BruteForceMatch_RadiusUnrollMatch(
__global T *query,
__global T *train,
float maxDistance,
__global int *bestTrainIdx,
__global float *bestDistance,
__global int *nMatches,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int bestTrainIdx_cols,
int step,
int ostep
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int groupidy = get_group_id(1);
const int queryIdx = groupidy * BLOCK_SIZE + lidy;
const int trainIdx = groupidx * BLOCK_SIZE + lidx;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
result_type result = 0;
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; ++i)
{
const int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * BLOCK_SIZE + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1)  * (step / sizeof(T)) + loadx] : 0;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < query_cols ? train[min(groupidx * BLOCK_SIZE + lidy, train_rows - 1)  * (step / sizeof(T)) + loadx] : 0;
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_block(s_query, s_train, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows &&
convert_float(result) < maxDistance)
{
int ind = atom_inc(nMatches + queryIdx);
if(ind < bestTrainIdx_cols)
{
bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
bestDistance[queryIdx * (ostep / sizeof(float)) + ind] = result;
}
}
}
__kernel void BruteForceMatch_RadiusMatch(
__global T *query,
__global T *train,
float maxDistance,
__global int *bestTrainIdx,
__global float *bestDistance,
__global int *nMatches,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int bestTrainIdx_cols,
int step,
int ostep
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int groupidy = get_group_id(1);
const int queryIdx = groupidy * BLOCK_SIZE + lidy;
const int trainIdx = groupidx * BLOCK_SIZE + lidx;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
result_type result = 0;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i)
{
const int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * BLOCK_SIZE + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1)  * (step / sizeof(T)) + loadx] : 0;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < query_cols ? train[min(groupidx * BLOCK_SIZE + lidy, train_rows - 1)  * (step / sizeof(T)) + loadx] : 0;
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_block(s_query, s_train, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows &&
convert_float(result) < maxDistance)
{
int ind = atom_inc(nMatches + queryIdx);
if(ind < bestTrainIdx_cols)
{
bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
bestDistance[queryIdx * (ostep / sizeof(float)) + ind] = result;
}
}
}
__kernel void BruteForceMatch_knnUnrollMatch(
__global T *query,
__global T *train,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
for (int i = 0 ;  i <  MAX_DESC_LEN / BLOCK_SIZE; i ++)
{
int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * MAX_DESC_LEN + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1)  * (step / sizeof(T)) + loadx] : 0;
}
float myBestDistance1 = MAX_FLOAT;
float myBestDistance2 = MAX_FLOAT;
int myBestTrainIdx1 = -1;
int myBestTrainIdx2 = -1;
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
result_type result = 0;
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(T)) + loadx] : 0;
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
result = DIST_RES(result);
const int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows)
{
if (result < myBestDistance1)
{
myBestDistance2 = myBestDistance1;
myBestTrainIdx2 = myBestTrainIdx1;
myBestDistance1 = result;
myBestTrainIdx1 = trainIdx;
}
else if (result < myBestDistance2)
{
myBestDistance2 = result;
myBestTrainIdx2 = trainIdx;
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (local float *)sharebuffer;
__local int *s_trainIdx = (local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance1;
s_trainIdx[lidx] = myBestTrainIdx1;
float bestDistance1 = MAX_FLOAT;
float bestDistance2 = MAX_FLOAT;
int bestTrainIdx1 = -1;
int bestTrainIdx2 = -1;
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
{
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
{
bestDistance2 = bestDistance1;
bestTrainIdx2 = bestTrainIdx1;
bestDistance1 = val;
bestTrainIdx1 = s_trainIdx[i];
}
else if (val < bestDistance2)
{
bestDistance2 = val;
bestTrainIdx2 = s_trainIdx[i];
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
s_distance[lidx] = myBestDistance2;
s_trainIdx[lidx] = myBestTrainIdx2;
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
{
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance2)
{
bestDistance2 = val;
bestTrainIdx2 = s_trainIdx[i];
}
}
}
myBestDistance1 = bestDistance1;
myBestDistance2 = bestDistance2;
myBestTrainIdx1 = bestTrainIdx1;
myBestTrainIdx2 = bestTrainIdx2;
if (queryIdx < query_rows && lidx == 0)
{
bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
}
}
__kernel void BruteForceMatch_knnMatch(
__global T *query,
__global T *train,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float myBestDistance1 = MAX_FLOAT;
float myBestDistance2 = MAX_FLOAT;
int myBestTrainIdx1 = -1;
int myBestTrainIdx2 = -1;
for (int  t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
result_type result = 0.0f;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * BLOCK_SIZE + lidx] = 0;
s_train[lidx * BLOCK_SIZE + lidy] = 0;
if (loadx < query_cols)
{
s_query[lidy * BLOCK_SIZE + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(T)) + loadx];
s_train[lidx * BLOCK_SIZE + lidy] = train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(T)) + loadx];
}
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_block_match(s_query, s_train, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
result = DIST_RES(result);
const int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows )
{
if (result < myBestDistance1)
{
myBestDistance2 = myBestDistance1;
myBestTrainIdx2 = myBestTrainIdx1;
myBestDistance1 = result;
myBestTrainIdx1 = trainIdx;
}
else if (result < myBestDistance2)
{
myBestDistance2 = result;
myBestTrainIdx2 = trainIdx;
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float *)sharebuffer;
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance1;
s_trainIdx[lidx] = myBestTrainIdx1;
float bestDistance1 = MAX_FLOAT;
float bestDistance2 = MAX_FLOAT;
int bestTrainIdx1 = -1;
int bestTrainIdx2 = -1;
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
{
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
{
bestDistance2 = bestDistance1;
bestTrainIdx2 = bestTrainIdx1;
bestDistance1 = val;
bestTrainIdx1 = s_trainIdx[i];
}
else if (val < bestDistance2)
{
bestDistance2 = val;
bestTrainIdx2 = s_trainIdx[i];
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
s_distance[lidx] = myBestDistance2;
s_trainIdx[lidx] = myBestTrainIdx2;
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
{
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance2)
{
bestDistance2 = val;
bestTrainIdx2 = s_trainIdx[i];
}
}
}
myBestDistance1 = bestDistance1;
myBestDistance2 = bestDistance2;
myBestTrainIdx1 = bestTrainIdx1;
myBestTrainIdx2 = bestTrainIdx2;
if (queryIdx < query_rows && lidx == 0)
{
bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
}
}
kernel void BruteForceMatch_calcDistanceUnrolled(
__global T *query,
__global T *train,
__global float *allDist,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step)
{
}
kernel void BruteForceMatch_calcDistance(
__global T *query,
__global T *train,
__global float *allDist,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step)
{
}
kernel void BruteForceMatch_findBestMatch(
__global float *allDist,
__global int *bestTrainIdx,
__global float *bestDistance,
int k
)
{
}
14abdacaf283cc25bdb9a58eb651a26bbuild_warps__kernel void buildWarpPlaneMaps(__global float * xmap, __global float * ymap,
__constant float * KRT,
int tl_u, int tl_v,
int cols, int rows,
int xmap_step, int ymap_step,
int xmap_offset, int ymap_offset,
float scale)
{
int du = get_global_id(0);
int dv = get_global_id(1);
__constant float * ck_rinv = KRT;
__constant float * ct      = KRT + 9;
if (du < cols && dv < rows)
{
int xmap_index = mad24(dv, xmap_step, xmap_offset + du);
int ymap_index = mad24(dv, ymap_step, ymap_offset + du);
float u = tl_u + du;
float v = tl_v + dv;
float x, y;
float x_ = u / scale - ct[0];
float y_ = v / scale - ct[1];
float z;
x = ck_rinv[0] * x_ + ck_rinv[1] * y_ + ck_rinv[2] * (1 - ct[2]);
y = ck_rinv[3] * x_ + ck_rinv[4] * y_ + ck_rinv[5] * (1 - ct[2]);
z = ck_rinv[6] * x_ + ck_rinv[7] * y_ + ck_rinv[8] * (1 - ct[2]);
x /= z;
y /= z;
xmap[xmap_index] = x;
ymap[ymap_index] = y;
}
}
__kernel void buildWarpCylindricalMaps(__global float * xmap, __global float * ymap,
__constant float * ck_rinv,
int tl_u, int tl_v,
int cols, int rows,
int xmap_step, int ymap_step,
int xmap_offset, int ymap_offset,
float scale)
{
int du = get_global_id(0);
int dv = get_global_id(1);
if (du < cols && dv < rows)
{
int xmap_index = mad24(dv, xmap_step, xmap_offset + du);
int ymap_index = mad24(dv, ymap_step, ymap_offset + du);
float u = tl_u + du;
float v = tl_v + dv;
float x, y;
u /= scale;
float x_ = sin(u);
float y_ = v / scale;
float z_ = cos(u);
float z;
x = ck_rinv[0] * x_ + ck_rinv[1] * y_ + ck_rinv[2] * z_;
y = ck_rinv[3] * x_ + ck_rinv[4] * y_ + ck_rinv[5] * z_;
z = ck_rinv[6] * x_ + ck_rinv[7] * y_ + ck_rinv[8] * z_;
if (z > 0) { x /= z; y /= z; }
else x = y = -1;
xmap[xmap_index] = x;
ymap[ymap_index] = y;
}
}
__kernel void buildWarpSphericalMaps(__global float * xmap, __global float * ymap,
__constant float * ck_rinv,
int tl_u, int tl_v,
int cols, int rows,
int xmap_step, int ymap_step,
int xmap_offset, int ymap_offset,
float scale)
{
int du = get_global_id(0);
int dv = get_global_id(1);
if (du < cols && dv < rows)
{
int xmap_index = mad24(dv, xmap_step, xmap_offset + du);
int ymap_index = mad24(dv, ymap_step, ymap_offset + du);
float u = tl_u + du;
float v = tl_v + dv;
float x, y;
v /= scale;
u /= scale;
float sinv = sin(v);
float x_ = sinv * sin(u);
float y_ = - cos(v);
float z_ = sinv * cos(u);
float z;
x = ck_rinv[0] * x_ + ck_rinv[1] * y_ + ck_rinv[2] * z_;
y = ck_rinv[3] * x_ + ck_rinv[4] * y_ + ck_rinv[5] * z_;
z = ck_rinv[6] * x_ + ck_rinv[7] * y_ + ck_rinv[8] * z_;
if (z > 0) { x /= z; y /= z; }
else x = y = -1;
xmap[xmap_index] = x;
ymap[ymap_index] = y;
}
}
__kernel void buildWarpAffineMaps(__global float * xmap, __global float * ymap,
__constant float * c_warpMat,
int cols, int rows,
int xmap_step, int ymap_step,
int xmap_offset, int ymap_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int xmap_index = mad24(y, xmap_step, x + xmap_offset);
int ymap_index = mad24(y, ymap_step, x + ymap_offset);
float xcoo = c_warpMat[0] * x + c_warpMat[1] * y + c_warpMat[2];
float ycoo = c_warpMat[3] * x + c_warpMat[4] * y + c_warpMat[5];
xmap[xmap_index] = xcoo;
ymap[ymap_index] = ycoo;
}
}
__kernel void buildWarpPerspectiveMaps(__global float * xmap, __global float * ymap,
__constant float * c_warpMat,
int cols, int rows,
int xmap_step, int ymap_step,
int xmap_offset, int ymap_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int xmap_index = mad24(y, xmap_step, x + xmap_offset);
int ymap_index = mad24(y, ymap_step, x + ymap_offset);
float coeff = 1.0f / (c_warpMat[6] * x + c_warpMat[7] * y + c_warpMat[8]);
float xcoo = coeff * (c_warpMat[0] * x + c_warpMat[1] * y + c_warpMat[2]);
float ycoo = coeff * (c_warpMat[3] * x + c_warpMat[4] * y + c_warpMat[5]);
xmap[xmap_index] = xcoo;
ymap[ymap_index] = ycoo;
}
}
67d1271eae72e28991ae6632b3b9bd1f#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void convertC3C4(__global const GENTYPE4 * restrict src, __global GENTYPE4 *dst,
int cols, int rows,
int dstStep_in_piexl, int pixel_end)
{
int id = get_global_id(0);
int3 pixelid = (int3)(mul24(id,3),mad24(id,3,1),mad24(id,3,2));
pixelid = clamp(pixelid,0,pixel_end);
GENTYPE4 pixel0, pixel1, pixel2, outpix0,outpix1,outpix2,outpix3;
pixel0 = src[pixelid.x];
pixel1 = src[pixelid.y];
pixel2 = src[pixelid.z];
outpix0 = (GENTYPE4)(pixel0.x,pixel0.y,pixel0.z,0);
outpix1 = (GENTYPE4)(pixel0.w,pixel1.x,pixel1.y,0);
outpix2 = (GENTYPE4)(pixel1.z,pixel1.w,pixel2.x,0);
outpix3 = (GENTYPE4)(pixel2.y,pixel2.z,pixel2.w,0);
int4 outy = (id<<2)/cols;
int4 outx = (id<<2)%cols;
outx += (int4)(0, 1, 2, 3);
outy = select(outy, outy+1, outx>=cols);
outx = select(outx, outx-cols, outx>=cols);
outy = select(outy, outy + 1, outx >= cols);
outx = select(outx, outx-cols, outx >= cols);
outy = select(outy, outy + 1, outx >= cols);
outx = select(outx, outx-cols, outx >= cols);
int4 addr = mad24(outy,(int4)dstStep_in_piexl,outx);
if(outx.w=(int4)cols);
x4 = select(x4,x4-(int4)cols,x4>=(int4)cols);
y4 = select(y4, y4 + 1,x4>=(int4)cols);
x4 = select(x4, x4 - (int4)cols,x4>=(int4)cols);
y4 = select(y4, y4 + 1,x4>=(int4)cols);
x4 = select(x4, x4-(int4)cols,x4>=(int4)cols);
y4=clamp(y4,(int4)0,(int4)(rows-1));
int4 addr = mad24(y4, (int4)srcStep_in_pixel, x4);
GENTYPE4 pixel0,pixel1,pixel2,pixel3, outpixel1, outpixel2;
pixel0 = src[addr.x];
pixel1 = src[addr.y];
pixel2 = src[addr.z];
pixel3 = src[addr.w];
pixel0.w = pixel1.x;
outpixel1.x = pixel1.y;
outpixel1.y = pixel1.z;
outpixel1.z = pixel2.x;
outpixel1.w = pixel2.y;
outpixel2.x = pixel2.z;
outpixel2.y = pixel3.x;
outpixel2.z = pixel3.y;
outpixel2.w = pixel3.z;
int4 outaddr = mul24(id>>2 , 3);
outaddr.y++;
outaddr.z+=2;
if(outaddr.z <= pixel_end)
{
dst[outaddr.x] = pixel0;
dst[outaddr.y] = outpixel1;
dst[outaddr.z] = outpixel2;
}
else if(outaddr.y <= pixel_end)
{
dst[outaddr.x] = pixel0;
dst[outaddr.y] = outpixel1;
}
else if(outaddr.x <= pixel_end)
dst[outaddr.x] = pixel0;
}
bb9cf383213d95e505f8677a285ee329cvt_color#ifndef hscale
#define hscale 0
#endif
#ifndef hrange
#define hrange 0
#endif
#ifdef DEPTH_0
#define DATA_TYPE uchar
#define VECTOR2 uchar2
#define VECTOR4 uchar4
#define VECTOR8 uchar8
#define VECTOR16 uchar16
#define COEFF_TYPE int
#define MAX_NUM  255
#define HALF_MAX 128
#define SAT_CAST(num) convert_uchar_sat_rte(num)
#define SAT_CAST2(num) convert_uchar2_sat(num)
#define SAT_CAST4(num) convert_uchar4_sat(num)
#endif
#ifdef DEPTH_2
#define DATA_TYPE ushort
#define VECTOR2 ushort2
#define VECTOR4 ushort4
#define VECTOR8 ushort8
#define VECTOR16 ushort16
#define COEFF_TYPE int
#define MAX_NUM  65535
#define HALF_MAX 32768
#define SAT_CAST(num) convert_ushort_sat_rte(num)
#define SAT_CAST2(num) convert_ushort2_sat(num)
#define SAT_CAST4(num) convert_ushort4_sat(num)
#endif
#ifdef DEPTH_5
#define DATA_TYPE float
#define VECTOR2 float2
#define VECTOR4 float4
#define VECTOR8 float8
#define VECTOR16 float16
#define COEFF_TYPE float
#define MAX_NUM  1.0f
#define HALF_MAX 0.5f
#define SAT_CAST(num) (num)
#endif
#ifndef bidx
#define bidx 0
#endif
#ifndef pixels_per_work_item
#define pixels_per_work_item 1
#endif
#define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n))
enum
{
yuv_shift  = 14,
xyz_shift  = 12,
hsv_shift  = 12,
R2Y        = 4899,
G2Y        = 9617,
B2Y        = 1868,
BLOCK_SIZE = 256
};
__constant float c_RGB2GrayCoeffs_f[3]  = { 0.114f, 0.587f, 0.299f };
__constant int   c_RGB2GrayCoeffs_i[3]  = { B2Y, G2Y, R2Y };
__kernel void RGB2Gray(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE* src, __global DATA_TYPE* dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0) * pixels_per_work_item;
int y = get_global_id(1);
if (y < rows && x < cols)
{
int src_idx = mad24(y, src_step, src_offset + (x << 2));
int dst_idx = mad24(y, dst_step, dst_offset + x);
#ifndef INTEL_DEVICE
#ifdef DEPTH_5
dst[dst_idx] = src[src_idx + bidx] * 0.114f + src[src_idx + 1] * 0.587f + src[src_idx + (bidx^2)] * 0.299f;
#else
dst[dst_idx] = (DATA_TYPE)CV_DESCALE((src[src_idx + bidx] * B2Y + src[src_idx + 1] * G2Y + src[src_idx + (bidx^2)] * R2Y), yuv_shift);
#endif
#else
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#ifdef DEPTH_5
__constant float * coeffs = c_RGB2GrayCoeffs_f;
#else
__constant int * coeffs = c_RGB2GrayCoeffs_i;
#endif
#if (1 == pixels_per_work_item)
{
#ifdef DEPTH_5
*dst_ptr = src_ptr[bidx] * coeffs[0] + src_ptr[1] * coeffs[1] + src_ptr[(bidx^2)] *coeffs[2];
#else
*dst_ptr = (DATA_TYPE)CV_DESCALE((src_ptr[bidx] * coeffs[0] + src_ptr[1] * coeffs[1] + src_ptr[(bidx^2)] * coeffs[2]), yuv_shift);
#endif
}
#elif (2 == pixels_per_work_item)
{
const VECTOR8 r0 = vload8(0, src_ptr);
#ifdef DEPTH_5
const float2 c0 = r0.s04;
const float2 c1 = r0.s15;
const float2 c2 = r0.s26;
const float2 Y = c0 * coeffs[bidx] + c1 * coeffs[1] + c2 * coeffs[bidx^2];
#else
const int2 c0 = convert_int2(r0.s04);
const int2 c1 = convert_int2(r0.s15);
const int2 c2 = convert_int2(r0.s26);
const int2 yi = CV_DESCALE(c0 * coeffs[bidx] + c1 * coeffs[1] + c2 * coeffs[bidx^2], yuv_shift);
const VECTOR2 Y = SAT_CAST2(yi);
#endif
vstore2(Y, 0, dst_ptr);
}
#elif (4 == pixels_per_work_item)
{
#ifndef DEPTH_5
const VECTOR16 r0 = vload16(0, src_ptr);
const int4 c0 = convert_int4(r0.s048c);
const int4 c1 = convert_int4(r0.s159d);
const int4 c2 = convert_int4(r0.s26ae);
const int4 Y = CV_DESCALE(c0 * coeffs[bidx] + c1 * coeffs[1] + c2 * coeffs[bidx^2], yuv_shift);
vstore4(SAT_CAST4(Y), 0, dst_ptr);
#endif
}
#endif
#endif
}
}
__kernel void Gray2RGB(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE* src, __global DATA_TYPE* dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + (x << 2));
DATA_TYPE val = src[src_idx];
dst[dst_idx] = val;
dst[dst_idx + 1] = val;
dst[dst_idx + 2] = val;
#if dcn == 4
dst[dst_idx + 3] = MAX_NUM;
#endif
}
}
__constant float c_RGB2YUVCoeffs_f[5]  = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f };
__constant int   c_RGB2YUVCoeffs_i[5]  = { B2Y, G2Y, R2Y, 8061, 14369 };
__kernel void RGB2YUV(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE* src, __global DATA_TYPE* dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0) * pixels_per_work_item;
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#ifdef DEPTH_5
__constant float * coeffs = c_RGB2YUVCoeffs_f;
#else
__constant int * coeffs = c_RGB2YUVCoeffs_i;
const int delta = HALF_MAX * (1 << yuv_shift);
#endif
#if (1 == pixels_per_work_item)
{
const DATA_TYPE rgb[] = {src_ptr[0], src_ptr[1], src_ptr[2]};
#ifdef DEPTH_5
float Y = rgb[0] * coeffs[bidx^2] + rgb[1] * coeffs[1] + rgb[2] * coeffs[bidx];
float U = (rgb[bidx^2] - Y) * coeffs[3] + HALF_MAX;
float V = (rgb[bidx] - Y) * coeffs[4] + HALF_MAX;
#else
int Y = CV_DESCALE(rgb[0] * coeffs[bidx^2] + rgb[1] * coeffs[1] + rgb[2] * coeffs[bidx], yuv_shift);
int U = CV_DESCALE((rgb[bidx^2] - Y) * coeffs[3] + delta, yuv_shift);
int V = CV_DESCALE((rgb[bidx] - Y) * coeffs[4] + delta, yuv_shift);
#endif
dst_ptr[0] = SAT_CAST( Y );
dst_ptr[1] = SAT_CAST( U );
dst_ptr[2] = SAT_CAST( V );
}
#elif (2 == pixels_per_work_item)
{
const VECTOR8 r0 = vload8(0, src_ptr);
#ifdef DEPTH_5
const float2 c0 = r0.s04;
const float2 c1 = r0.s15;
const float2 c2 = r0.s26;
const float2 Y = (bidx == 0) ? (c0 * coeffs[2] + c1 * coeffs[1] + c2 * coeffs[0]) : (c0 * coeffs[0] + c1 * coeffs[1] + c2 * coeffs[2]);
const float2 U = (bidx == 0) ? ((c2 - Y) * coeffs[3] + HALF_MAX) : ((c0 - Y) * coeffs[3] + HALF_MAX);
const float2 V = (bidx == 0) ? ((c0 - Y) * coeffs[4] + HALF_MAX) : ((c2 - Y) * coeffs[4] + HALF_MAX);
#else
const int2 c0 = convert_int2(r0.s04);
const int2 c1 = convert_int2(r0.s15);
const int2 c2 = convert_int2(r0.s26);
const int2 yi = (bidx == 0) ? CV_DESCALE(c0 * coeffs[2] + c1 * coeffs[1] + c2 * coeffs[0], yuv_shift) : CV_DESCALE(c0 * coeffs[0] + c1 * coeffs[1] + c2 * coeffs[2], yuv_shift);
const int2 ui = (bidx == 0) ? CV_DESCALE((c2 - yi) * coeffs[3] + delta, yuv_shift) : CV_DESCALE((c0 - yi) * coeffs[3] + delta, yuv_shift);
const int2 vi = (bidx == 0) ? CV_DESCALE((c0 - yi) * coeffs[4] + delta, yuv_shift) : CV_DESCALE((c2 - yi) * coeffs[4] + delta, yuv_shift);
const VECTOR2 Y = SAT_CAST2(yi);
const VECTOR2 U = SAT_CAST2(ui);
const VECTOR2 V = SAT_CAST2(vi);
#endif
vstore8((VECTOR8)(Y.s0, U.s0, V.s0, 0, Y.s1, U.s1, V.s1, 0), 0, dst_ptr);
}
#elif (4 == pixels_per_work_item)
{
#ifndef DEPTH_5
const VECTOR16 r0 = vload16(0, src_ptr);
const int4 c0 = convert_int4(r0.s048c);
const int4 c1 = convert_int4(r0.s159d);
const int4 c2 = convert_int4(r0.s26ae);
const int4 yi = (bidx == 0) ? CV_DESCALE(c0 * coeffs[2] + c1 * coeffs[1] + c2 * coeffs[0], yuv_shift) : CV_DESCALE(c0 * coeffs[0] + c1 * coeffs[1] + c2 * coeffs[2], yuv_shift);
const int4 ui = (bidx == 0) ? CV_DESCALE((c2 - yi) * coeffs[3] + delta, yuv_shift) : CV_DESCALE((c0 - yi) * coeffs[3] + delta, yuv_shift);
const int4 vi = (bidx == 0) ? CV_DESCALE((c0 - yi) * coeffs[4] + delta, yuv_shift) : CV_DESCALE((c2 - yi) * coeffs[4] + delta, yuv_shift);
const VECTOR4 Y = SAT_CAST4(yi);
const VECTOR4 U = SAT_CAST4(ui);
const VECTOR4 V = SAT_CAST4(vi);
vstore16((VECTOR16)(Y.s0, U.s0, V.s0, 0, Y.s1, U.s1, V.s1, 0, Y.s2, U.s2, V.s2, 0, Y.s3, U.s3, V.s3, 0), 0, dst_ptr);
#endif
}
#endif
}
}
__constant float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f };
__constant int   c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 };
__kernel void YUV2RGB(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE* src, __global DATA_TYPE* dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0) * pixels_per_work_item;
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#ifdef DEPTH_5
__constant float * coeffs = c_YUV2RGBCoeffs_f;
#else
__constant int * coeffs = c_YUV2RGBCoeffs_i;
#endif
#if (1 == pixels_per_work_item)
{
const DATA_TYPE yuv[] = {src_ptr[0], src_ptr[1], src_ptr[2]};
#ifdef DEPTH_5
float B = yuv[0] + (yuv[2] - HALF_MAX) * coeffs[3];
float G = yuv[0] + (yuv[2] - HALF_MAX) * coeffs[2] + (yuv[1] - HALF_MAX) * coeffs[1];
float R = yuv[0] + (yuv[1] - HALF_MAX) * coeffs[0];
#else
int B = yuv[0] + CV_DESCALE((yuv[2] - HALF_MAX) * coeffs[3], yuv_shift);
int G = yuv[0] + CV_DESCALE((yuv[2] - HALF_MAX) * coeffs[2] + (yuv[1] - HALF_MAX) * coeffs[1], yuv_shift);
int R = yuv[0] + CV_DESCALE((yuv[1] - HALF_MAX) * coeffs[0], yuv_shift);
#endif
dst_ptr[bidx]     = SAT_CAST( B );
dst_ptr[1]        = SAT_CAST( G );
dst_ptr[(bidx^2)] = SAT_CAST( R );
#if dcn == 4
dst_ptr[3]         = MAX_NUM;
#endif
}
#elif (2 == pixels_per_work_item)
{
const VECTOR8 r0 = vload8(0, src_ptr);
#ifdef DEPTH_5
const float2 Y = r0.s04;
const float2 U = r0.s15;
const float2 V = r0.s26;
const float2 c0 = (bidx == 0) ? (Y + (V - HALF_MAX) * coeffs[3]) : (Y + (U - HALF_MAX) * coeffs[0]);
const float2 c1 = Y + (V - HALF_MAX) * coeffs[2] + (U - HALF_MAX) * coeffs[1];
const float2 c2 = (bidx == 0) ? (Y + (U - HALF_MAX) * coeffs[0]) : (Y + (V - HALF_MAX) * coeffs[3]);
#else
const int2 Y = convert_int2(r0.s04);
const int2 U = convert_int2(r0.s15);
const int2 V = convert_int2(r0.s26);
const int2 c0i = (bidx == 0) ? (Y + CV_DESCALE((V - HALF_MAX) * coeffs[3], yuv_shift)) : (Y + CV_DESCALE((U - HALF_MAX) * coeffs[0], yuv_shift));
const int2 c1i = Y + CV_DESCALE((V - HALF_MAX) * coeffs[2] + (U - HALF_MAX) * coeffs[1], yuv_shift);
const int2 c2i = (bidx == 0) ? (Y + CV_DESCALE((U - HALF_MAX) * coeffs[0], yuv_shift)) : (Y + CV_DESCALE((V - HALF_MAX) * coeffs[3], yuv_shift));
const VECTOR2 c0 = SAT_CAST2(c0i);
const VECTOR2 c1 = SAT_CAST2(c1i);
const VECTOR2 c2 = SAT_CAST2(c2i);
#endif
#if dcn == 4
vstore8((VECTOR8)(c0.s0, c1.s0, c2.s0, MAX_NUM, c0.s1, c1.s1, c2.s1, MAX_NUM), 0, dst_ptr);
#else
vstore8((VECTOR8)(c0.s0, c1.s0, c2.s0, 0, c0.s1, c1.s1, c2.s1, 0), 0, dst_ptr);
#endif
}
#elif (4 == pixels_per_work_item)
{
#ifndef DEPTH_5
const VECTOR16 r0 = vload16(0, src_ptr);
const int4 Y = convert_int4(r0.s048c);
const int4 U = convert_int4(r0.s159d);
const int4 V = convert_int4(r0.s26ae);
const int4 c0i = (bidx == 0) ? (Y + CV_DESCALE((V - HALF_MAX) * coeffs[3], yuv_shift)) : (Y + CV_DESCALE((U - HALF_MAX) * coeffs[0], yuv_shift));
const int4 c1i = Y + CV_DESCALE((V - HALF_MAX) * coeffs[2] + (U - HALF_MAX) * coeffs[1], yuv_shift);
const int4 c2i = (bidx == 0) ? (Y + CV_DESCALE((U - HALF_MAX) * coeffs[0], yuv_shift)) : (Y + CV_DESCALE((V - HALF_MAX) * coeffs[3], yuv_shift));
const VECTOR4 c0 = SAT_CAST4(c0i);
const VECTOR4 c1 = SAT_CAST4(c1i);
const VECTOR4 c2 = SAT_CAST4(c2i);
#if dcn == 4
vstore16((VECTOR16)(c0.s0, c1.s0, c2.s0, MAX_NUM, c0.s1, c1.s1, c2.s1, MAX_NUM, c0.s2, c1.s2, c2.s2, MAX_NUM, c0.s3, c1.s3, c2.s3, MAX_NUM), 0, dst_ptr);
#else
vstore16((VECTOR16)(c0.s0, c1.s0, c2.s0, 0, c0.s1, c1.s1, c2.s1, 0, c0.s2, c1.s2, c2.s2, 0, c0.s3, c1.s3, c2.s3, 0), 0, dst_ptr);
#endif
#endif
}
#endif
}
}
__constant int ITUR_BT_601_CY = 1220542;
__constant int ITUR_BT_601_CUB = 2116026;
__constant int ITUR_BT_601_CUG = 409993;
__constant int ITUR_BT_601_CVG = 852492;
__constant int ITUR_BT_601_CVR = 1673527;
__constant int ITUR_BT_601_SHIFT = 20;
__kernel void YUV2RGBA_NV12(int cols, int rows, int src_step, int dst_step,
__global const uchar* src, __global uchar* dst,
int src_offset, int dst_offset)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (y < rows / 2 && x < cols / 2 )
{
__global const uchar* ysrc = src + mad24(y << 1, src_step, (x << 1) + src_offset);
__global const uchar* usrc = src + mad24(rows + y, src_step, (x << 1) + src_offset);
__global uchar*       dst1 = dst + mad24(y << 1, dst_step, (x << 3) + dst_offset);
__global uchar*       dst2 = dst + mad24((y << 1) + 1, dst_step, (x << 3) + dst_offset);
int Y1 = ysrc[0];
int Y2 = ysrc[1];
int Y3 = ysrc[src_step];
int Y4 = ysrc[src_step + 1];
int U  = usrc[0] - 128;
int V  = usrc[1] - 128;
int ruv = (1 << (ITUR_BT_601_SHIFT - 1)) + ITUR_BT_601_CVR * V;
int guv = (1 << (ITUR_BT_601_SHIFT - 1)) - ITUR_BT_601_CVG * V - ITUR_BT_601_CUG * U;
int buv = (1 << (ITUR_BT_601_SHIFT - 1)) + ITUR_BT_601_CUB * U;
Y1 = max(0, Y1 - 16) * ITUR_BT_601_CY;
dst1[2 - bidx]     = convert_uchar_sat((Y1 + ruv) >> ITUR_BT_601_SHIFT);
dst1[1]        = convert_uchar_sat((Y1 + guv) >> ITUR_BT_601_SHIFT);
dst1[bidx] = convert_uchar_sat((Y1 + buv) >> ITUR_BT_601_SHIFT);
dst1[3]        = 255;
Y2 = max(0, Y2 - 16) * ITUR_BT_601_CY;
dst1[6 - bidx] = convert_uchar_sat((Y2 + ruv) >> ITUR_BT_601_SHIFT);
dst1[5]        = convert_uchar_sat((Y2 + guv) >> ITUR_BT_601_SHIFT);
dst1[4 + bidx] = convert_uchar_sat((Y2 + buv) >> ITUR_BT_601_SHIFT);
dst1[7]        = 255;
Y3 = max(0, Y3 - 16) * ITUR_BT_601_CY;
dst2[2 - bidx]     = convert_uchar_sat((Y3 + ruv) >> ITUR_BT_601_SHIFT);
dst2[1]        = convert_uchar_sat((Y3 + guv) >> ITUR_BT_601_SHIFT);
dst2[bidx] = convert_uchar_sat((Y3 + buv) >> ITUR_BT_601_SHIFT);
dst2[3]        = 255;
Y4 = max(0, Y4 - 16) * ITUR_BT_601_CY;
dst2[6 - bidx] = convert_uchar_sat((Y4 + ruv) >> ITUR_BT_601_SHIFT);
dst2[5]        = convert_uchar_sat((Y4 + guv) >> ITUR_BT_601_SHIFT);
dst2[4 + bidx] = convert_uchar_sat((Y4 + buv) >> ITUR_BT_601_SHIFT);
dst2[7]        = 255;
}
}
__constant float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f};
__constant int   c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241};
__kernel void RGB2YCrCb(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE* src, __global DATA_TYPE* dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0) * pixels_per_work_item;
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#ifdef DEPTH_5
__constant float * coeffs = c_RGB2YCrCbCoeffs_f;
#else
__constant int * coeffs = c_RGB2YCrCbCoeffs_i;
const int delta = HALF_MAX * (1 << yuv_shift);
#endif
#if (1 == pixels_per_work_item)
{
const DATA_TYPE rgb[] = {src_ptr[0], src_ptr[1], src_ptr[2]};
#ifdef DEPTH_5
float Y  = rgb[0] * coeffs[bidx^2] + rgb[1] * coeffs[1] + rgb[2] * coeffs[bidx];
float Cr = (rgb[bidx^2] - Y) * coeffs[3] + HALF_MAX;
float Cb = (rgb[bidx] - Y) * coeffs[4] + HALF_MAX;
#else
int Y =  CV_DESCALE(rgb[0] * coeffs[bidx^2] + rgb[1] * coeffs[1] + rgb[2] * coeffs[bidx], yuv_shift);
int Cr = CV_DESCALE((rgb[bidx^2] - Y) * coeffs[3] + delta, yuv_shift);
int Cb = CV_DESCALE((rgb[bidx] - Y) * coeffs[4] + delta, yuv_shift);
#endif
dst_ptr[0] = SAT_CAST( Y );
dst_ptr[1] = SAT_CAST( Cr );
dst_ptr[2] = SAT_CAST( Cb );
}
#elif (2 == pixels_per_work_item)
{
const VECTOR8 r0 = vload8(0, src_ptr);
#ifdef DEPTH_5
const float2 c0 = r0.s04;
const float2 c1 = r0.s15;
const float2 c2 = r0.s26;
const float2 Y  = (bidx == 0) ? (c0 * coeffs[2] + c1 * coeffs[1] + c2 * coeffs[0]) : (c0 * coeffs[0] + c1 * coeffs[1] + c2 * coeffs[2]);
const float2 Cr = (bidx == 0) ? ((c2 - Y) * coeffs[3] + HALF_MAX) : ((c0 - Y) * coeffs[3] + HALF_MAX);
const float2 Cb = (bidx == 0) ? ((c0 - Y) * coeffs[4] + HALF_MAX) : ((c2 - Y) * coeffs[4] + HALF_MAX);
#else
const int2 c0 = convert_int2(r0.s04);
const int2 c1 = convert_int2(r0.s15);
const int2 c2 = convert_int2(r0.s26);
const int2 yi = (bidx == 0) ? CV_DESCALE(c0 * coeffs[2] + c1 * coeffs[1] + c2 * coeffs[0], yuv_shift) : CV_DESCALE(c0 * coeffs[0] + c1 * coeffs[1] + c2 * coeffs[2], yuv_shift);
const int2 ui = (bidx == 0) ? CV_DESCALE((c2 - yi) * coeffs[3] + delta, yuv_shift) : CV_DESCALE((c0 - yi) * coeffs[3] + delta, yuv_shift);
const int2 vi = (bidx == 0) ? CV_DESCALE((c0 - yi) * coeffs[4] + delta, yuv_shift) : CV_DESCALE((c2 - yi) * coeffs[4] + delta, yuv_shift);
const VECTOR2 Y  = SAT_CAST2(yi);
const VECTOR2 Cr = SAT_CAST2(ui);
const VECTOR2 Cb = SAT_CAST2(vi);
#endif
vstore8((VECTOR8)(Y.s0, Cr.s0, Cb.s0, 0, Y.s1, Cr.s1, Cb.s1, 0), 0, dst_ptr);
}
#elif (4 == pixels_per_work_item)
{
#ifndef DEPTH_5
const VECTOR16 r0 = vload16(0, src_ptr);
const int4 c0 = convert_int4(r0.s048c);
const int4 c1 = convert_int4(r0.s159d);
const int4 c2 = convert_int4(r0.s26ae);
const int4 yi = (bidx == 0) ? CV_DESCALE(c0 * coeffs[2] + c1 * coeffs[1] + c2 * coeffs[0], yuv_shift) : CV_DESCALE(c0 * coeffs[0] + c1 * coeffs[1] + c2 * coeffs[2], yuv_shift);
const int4 ui = (bidx == 0) ? CV_DESCALE((c2 - yi) * coeffs[3] + delta, yuv_shift) : CV_DESCALE((c0 - yi) * coeffs[3] + delta, yuv_shift);
const int4 vi = (bidx == 0) ? CV_DESCALE((c0 - yi) * coeffs[4] + delta, yuv_shift) : CV_DESCALE((c2 - yi) * coeffs[4] + delta, yuv_shift);
const VECTOR4 Y  = SAT_CAST4(yi);
const VECTOR4 Cr = SAT_CAST4(ui);
const VECTOR4 Cb = SAT_CAST4(vi);
vstore16((VECTOR16)(Y.s0, Cr.s0, Cb.s0, 0, Y.s1, Cr.s1, Cb.s1, 0, Y.s2, Cr.s2, Cb.s2, 0, Y.s3, Cr.s3, Cb.s3, 0), 0, dst_ptr);
#endif
}
#endif
}
}
__constant float c_YCrCb2RGBCoeffs_f[4] = { 1.403f, -0.714f, -0.344f, 1.773f };
__constant int   c_YCrCb2RGBCoeffs_i[4] = { 22987, -11698, -5636, 29049 };
__kernel void YCrCb2RGB(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE* src, __global DATA_TYPE* dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0) * pixels_per_work_item;
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#ifdef DEPTH_5
__constant float * coeffs = c_YCrCb2RGBCoeffs_f;
#else
__constant int * coeffs = c_YCrCb2RGBCoeffs_i;
#endif
#if (1 == pixels_per_work_item)
{
const DATA_TYPE ycrcb[] = {src_ptr[0], src_ptr[1], src_ptr[2]};
#ifdef DEPTH_5
float B = ycrcb[0] + (ycrcb[2] - HALF_MAX) * coeffs[3];
float G = ycrcb[0] + (ycrcb[2] - HALF_MAX) * coeffs[2] + (ycrcb[1] - HALF_MAX) * coeffs[1];
float R = ycrcb[0] + (ycrcb[1] - HALF_MAX) * coeffs[0];
#else
int B = ycrcb[0] + CV_DESCALE((ycrcb[2] - HALF_MAX) * coeffs[3], yuv_shift);
int G = ycrcb[0] + CV_DESCALE((ycrcb[2] - HALF_MAX) * coeffs[2] + (ycrcb[1] - HALF_MAX) * coeffs[1], yuv_shift);
int R = ycrcb[0] + CV_DESCALE((ycrcb[1] - HALF_MAX) * coeffs[0], yuv_shift);
#endif
dst_ptr[bidx]     = SAT_CAST( B );
dst_ptr[1]        = SAT_CAST( G );
dst_ptr[(bidx^2)] = SAT_CAST( R );
#if dcn == 4
dst_ptr[3]         = MAX_NUM;
#endif
}
#elif (2 == pixels_per_work_item)
{
const VECTOR8 r0 = vload8(0, src_ptr);
#ifdef DEPTH_5
const float2 Y  = r0.s04;
const float2 Cr = r0.s15;
const float2 Cb = r0.s26;
const float2 c0 = (bidx == 0) ? (Y + (Cb - HALF_MAX) * coeffs[3]) : (Y + (Cr - HALF_MAX) * coeffs[0]);
const float2 c1 = Y + (Cb - HALF_MAX) * coeffs[2] + (Cr - HALF_MAX) * coeffs[1];
const float2 c2 = (bidx == 0) ? (Y + (Cr - HALF_MAX) * coeffs[0]) : (Y + (Cb - HALF_MAX) * coeffs[3]);
#else
const int2 Y  = convert_int2(r0.s04);
const int2 Cr = convert_int2(r0.s15);
const int2 Cb = convert_int2(r0.s26);
const int2 c0i = (bidx == 0) ? (Y + CV_DESCALE((Cb - HALF_MAX) * coeffs[3], yuv_shift)) : (Y + CV_DESCALE((Cr - HALF_MAX) * coeffs[0], yuv_shift));
const int2 c1i = Y + CV_DESCALE((Cb - HALF_MAX) * coeffs[2] + (Cr - HALF_MAX) * coeffs[1], yuv_shift);
const int2 c2i = (bidx == 0) ? (Y + CV_DESCALE((Cr - HALF_MAX) * coeffs[0], yuv_shift)) : (Y + CV_DESCALE((Cb - HALF_MAX) * coeffs[3], yuv_shift));
const VECTOR2 c0 = SAT_CAST2(c0i);
const VECTOR2 c1 = SAT_CAST2(c1i);
const VECTOR2 c2 = SAT_CAST2(c2i);
#endif
#if dcn == 4
vstore8((VECTOR8)(c0.s0, c1.s0, c2.s0, MAX_NUM, c0.s1, c1.s1, c2.s1, MAX_NUM), 0, dst_ptr);
#else
vstore8((VECTOR8)(c0.s0, c1.s0, c2.s0, 0, c0.s1, c1.s1, c2.s1, 0), 0, dst_ptr);
#endif
}
#elif (4 == pixels_per_work_item)
{
#ifndef DEPTH_5
const VECTOR16 r0 = vload16(0, src_ptr);
const int4 Y  = convert_int4(r0.s048c);
const int4 Cr = convert_int4(r0.s159d);
const int4 Cb = convert_int4(r0.s26ae);
const int4 c0i = (bidx == 0) ? (Y + CV_DESCALE((Cb - HALF_MAX) * coeffs[3], yuv_shift)) : (Y + CV_DESCALE((Cr - HALF_MAX) * coeffs[0], yuv_shift));
const int4 c1i = Y + CV_DESCALE((Cb - HALF_MAX) * coeffs[2] + (Cr - HALF_MAX) * coeffs[1], yuv_shift);
const int4 c2i = (bidx == 0) ? (Y + CV_DESCALE((Cr - HALF_MAX) * coeffs[0], yuv_shift)) : (Y + CV_DESCALE((Cb - HALF_MAX) * coeffs[3], yuv_shift));
const VECTOR4 c0 = SAT_CAST4(c0i);
const VECTOR4 c1 = SAT_CAST4(c1i);
const VECTOR4 c2 = SAT_CAST4(c2i);
#if dcn == 4
vstore16((VECTOR16)(c0.s0, c1.s0, c2.s0, MAX_NUM, c0.s1, c1.s1, c2.s1, MAX_NUM, c0.s2, c1.s2, c2.s2, MAX_NUM, c0.s3, c1.s3, c2.s3, MAX_NUM), 0, dst_ptr);
#else
vstore16((VECTOR16)(c0.s0, c1.s0, c2.s0, 0, c0.s1, c1.s1, c2.s1, 0, c0.s2, c1.s2, c2.s2, 0, c0.s3, c1.s3, c2.s3, 0), 0, dst_ptr);
#endif
#endif
}
#endif
}
}
__kernel void RGB2XYZ(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE* src, __global DATA_TYPE* dst,
int src_offset, int dst_offset, __constant COEFF_TYPE * coeffs)
{
int dx = get_global_id(0) * pixels_per_work_item;
int dy = get_global_id(1);
if (dy < rows && dx < cols)
{
dx <<= 2;
int src_idx = mad24(dy, src_step, src_offset + dx);
int dst_idx = mad24(dy, dst_step, dst_offset + dx);
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#if (1 == pixels_per_work_item)
{
DATA_TYPE R = src_ptr[0], G = src_ptr[1], B = src_ptr[2];
#ifdef DEPTH_5
float X = R * coeffs[0] + G * coeffs[1] + B * coeffs[2];
float Y = R * coeffs[3] + G * coeffs[4] + B * coeffs[5];
float Z = R * coeffs[6] + G * coeffs[7] + B * coeffs[8];
#else
int X = CV_DESCALE(R * coeffs[0] + G * coeffs[1] + B * coeffs[2], xyz_shift);
int Y = CV_DESCALE(R * coeffs[3] + G * coeffs[4] + B * coeffs[5], xyz_shift);
int Z = CV_DESCALE(R * coeffs[6] + G * coeffs[7] + B * coeffs[8], xyz_shift);
#endif
dst_ptr[0] = SAT_CAST( X );
dst_ptr[1] = SAT_CAST( Y );
dst_ptr[2] = SAT_CAST( Z );
}
#elif (2 == pixels_per_work_item)
{
const VECTOR8 r0 = vload8(0, src_ptr);
#ifdef DEPTH_5
const float2 R = r0.s04;
const float2 G = r0.s15;
const float2 B = r0.s26;
const float2 X = R * coeffs[0] + G * coeffs[1] + B * coeffs[2];
const float2 Y = R * coeffs[3] + G * coeffs[4] + B * coeffs[5];
const float2 Z = R * coeffs[6] + G * coeffs[7] + B * coeffs[8];
#else
const int2 R = convert_int2(r0.s04);
const int2 G = convert_int2(r0.s15);
const int2 B = convert_int2(r0.s26);
const int2 xi = CV_DESCALE(R * coeffs[0] + G * coeffs[1] + B * coeffs[2], xyz_shift);
const int2 yi = CV_DESCALE(R * coeffs[3] + G * coeffs[4] + B * coeffs[5], xyz_shift);
const int2 zi = CV_DESCALE(R * coeffs[6] + G * coeffs[7] + B * coeffs[8], xyz_shift);
const VECTOR2 X = SAT_CAST2(xi);
const VECTOR2 Y = SAT_CAST2(yi);
const VECTOR2 Z = SAT_CAST2(zi);
#endif
vstore8((VECTOR8)(X.s0, Y.s0, Z.s0, 0, X.s1, Y.s1, Z.s1, 0), 0, dst_ptr);
}
#elif (4 == pixels_per_work_item)
{
#ifndef DEPTH_5
const VECTOR16 r0 = vload16(0, src_ptr);
const int4 R = convert_int4(r0.s048c);
const int4 G = convert_int4(r0.s159d);
const int4 B = convert_int4(r0.s26ae);
const int4 xi = CV_DESCALE(R * coeffs[0] + G * coeffs[1] + B * coeffs[2], xyz_shift);
const int4 yi = CV_DESCALE(R * coeffs[3] + G * coeffs[4] + B * coeffs[5], xyz_shift);
const int4 zi = CV_DESCALE(R * coeffs[6] + G * coeffs[7] + B * coeffs[8], xyz_shift);
const VECTOR4 X = SAT_CAST4(xi);
const VECTOR4 Y = SAT_CAST4(yi);
const VECTOR4 Z = SAT_CAST4(zi);
vstore16((VECTOR16)(X.s0, Y.s0, Z.s0, 0, X.s1, Y.s1, Z.s1, 0, X.s2, Y.s2, Z.s2, 0, X.s3, Y.s3, Z.s3, 0), 0, dst_ptr);
#endif
}
#endif
}
}
__kernel void XYZ2RGB(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE* src, __global DATA_TYPE* dst,
int src_offset, int dst_offset, __constant COEFF_TYPE * coeffs)
{
int dx = get_global_id(0) * pixels_per_work_item;
int dy = get_global_id(1);
if (dy < rows && dx < cols)
{
dx <<= 2;
int src_idx = mad24(dy, src_step, src_offset + dx);
int dst_idx = mad24(dy, dst_step, dst_offset + dx);
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#if (1 == pixels_per_work_item)
{
const DATA_TYPE X = src_ptr[0], Y = src_ptr[1], Z = src_ptr[2];
#ifdef DEPTH_5
float B = X * coeffs[0] + Y * coeffs[1] + Z * coeffs[2];
float G = X * coeffs[3] + Y * coeffs[4] + Z * coeffs[5];
float R = X * coeffs[6] + Y * coeffs[7] + Z * coeffs[8];
#else
int B = CV_DESCALE(X * coeffs[0] + Y * coeffs[1] + Z * coeffs[2], xyz_shift);
int G = CV_DESCALE(X * coeffs[3] + Y * coeffs[4] + Z * coeffs[5], xyz_shift);
int R = CV_DESCALE(X * coeffs[6] + Y * coeffs[7] + Z * coeffs[8], xyz_shift);
#endif
dst_ptr[0] = SAT_CAST( B );
dst_ptr[1] = SAT_CAST( G );
dst_ptr[2] = SAT_CAST( R );
#if dcn == 4
dst_ptr[3] = MAX_NUM;
#endif
}
#elif (2 == pixels_per_work_item)
{
const VECTOR8 r0 = vload8(0, src_ptr);
#ifdef DEPTH_5
const float2 X = r0.s04;
const float2 Y = r0.s15;
const float2 Z = r0.s26;
float2 B = X * coeffs[0] + Y * coeffs[1] + Z * coeffs[2];
float2 G = X * coeffs[3] + Y * coeffs[4] + Z * coeffs[5];
float2 R = X * coeffs[6] + Y * coeffs[7] + Z * coeffs[8];
#else
const int2 xi = convert_int2(r0.s04);
const int2 yi = convert_int2(r0.s15);
const int2 zi = convert_int2(r0.s26);
const int2 bi = CV_DESCALE(xi * coeffs[0] + yi * coeffs[1] + zi * coeffs[2], xyz_shift);
const int2 gi = CV_DESCALE(xi * coeffs[3] + yi * coeffs[4] + zi * coeffs[5], xyz_shift);
const int2 ri = CV_DESCALE(xi * coeffs[6] + yi * coeffs[7] + zi * coeffs[8], xyz_shift);
const VECTOR2 R = SAT_CAST2(ri);
const VECTOR2 G = SAT_CAST2(gi);
const VECTOR2 B = SAT_CAST2(bi);
#endif
#if dcn == 4
vstore8((VECTOR8)(B.s0, G.s0, R.s0, MAX_NUM, B.s1, G.s1, R.s1, MAX_NUM), 0, dst_ptr);
#else
vstore8((VECTOR8)(B.s0, G.s0, R.s0, 0, B.s1, G.s1, R.s1, 0), 0, dst_ptr);
#endif
}
#elif (4 == pixels_per_work_item)
{
#ifndef DEPTH_5
const VECTOR16 r0 = vload16(0, src_ptr);
const int4 xi = convert_int4(r0.s048c);
const int4 yi = convert_int4(r0.s159d);
const int4 zi = convert_int4(r0.s26ae);
const int4 bi = CV_DESCALE(xi * coeffs[0] + yi * coeffs[1] + zi * coeffs[2], xyz_shift);
const int4 gi = CV_DESCALE(xi * coeffs[3] + yi * coeffs[4] + zi * coeffs[5], xyz_shift);
const int4 ri = CV_DESCALE(xi * coeffs[6] + yi * coeffs[7] + zi * coeffs[8], xyz_shift);
const VECTOR4 R = SAT_CAST4(ri);
const VECTOR4 G = SAT_CAST4(gi);
const VECTOR4 B = SAT_CAST4(bi);
#if dcn == 4
vstore16((VECTOR16)(B.s0, G.s0, R.s0, MAX_NUM, B.s1, G.s1, R.s1, MAX_NUM, B.s2, G.s2, R.s2, MAX_NUM, B.s3, G.s3, R.s3, MAX_NUM), 0, dst_ptr);
#else
vstore16((VECTOR16)(B.s0, G.s0, R.s0, 0, B.s1, G.s1, R.s1, 0, B.s2, G.s2, R.s2, 0, B.s3, G.s3, R.s3, 0), 0, dst_ptr);
#endif
#endif
}
#endif
}
}
__kernel void RGB(int cols, int rows, int src_step, int dst_step,
__global const DATA_TYPE * src, __global DATA_TYPE * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
#ifndef INTEL_DEVICE
#ifdef REVERSE
dst[dst_idx] = src[src_idx + 2];
dst[dst_idx + 1] = src[src_idx + 1];
dst[dst_idx + 2] = src[src_idx];
#elif defined ORDER
dst[dst_idx] = src[src_idx];
dst[dst_idx + 1] = src[src_idx + 1];
dst[dst_idx + 2] = src[src_idx + 2];
#endif
#if dcn == 4
#if scn == 3
dst[dst_idx + 3] = MAX_NUM;
#else
dst[dst_idx + 3] = src[src_idx + 3];
#endif
#endif
#else
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
const VECTOR4 r0 = vload4(0, src_ptr);
#ifdef REVERSE
if (3 == dcn)
{
vstore4((VECTOR4)(r0.s210, 0), 0, dst_ptr);
}
else if (3 == scn)
{
vstore4((VECTOR4)(r0.s210, MAX_NUM), 0, dst_ptr);
}
else {
vstore4((VECTOR4)(r0.s2103), 0, dst_ptr);
}
#elif defined ORDER
if (3 == dcn)
{
vstore4((VECTOR4)(r0.s012, 0), 0, dst_ptr);
}
else if (3 == scn)
{
vstore4((VECTOR4)(r0.s012, MAX_NUM), 0, dst_ptr);
}
else {
vstore4(r0, 0, dst_ptr);
}
#endif
#endif
}
}
__kernel void RGB5x52RGB(int cols, int rows, int src_step, int dst_step,
__global const ushort * src, __global uchar * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + (x << 2));
ushort t = src[src_idx];
#if greenbits == 6
dst[dst_idx + bidx] = (uchar)(t << 3);
dst[dst_idx + 1] = (uchar)((t >> 3) & ~3);
dst[dst_idx + (bidx^2)] = (uchar)((t >> 8) & ~7);
#else
dst[dst_idx + bidx] = (uchar)(t << 3);
dst[dst_idx + 1] = (uchar)((t >> 2) & ~7);
dst[dst_idx + (bidx^2)] = (uchar)((t >> 7) & ~7);
#endif
#if dcn == 4
#if greenbits == 6
dst[dst_idx + 3] = 255;
#else
dst[dst_idx + 3] = t & 0x8000 ? 255 : 0;
#endif
#endif
}
}
__kernel void RGB2RGB5x5(int cols, int rows, int src_step, int dst_step,
__global const uchar * src, __global ushort * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
int src_idx = mad24(y, src_step, src_offset + (x << 2));
int dst_idx = mad24(y, dst_step, dst_offset + x);
#if greenbits == 6
dst[dst_idx] = (ushort)((src[src_idx + bidx] >> 3)|((src[src_idx + 1]&~3) << 3)|((src[src_idx + (bidx^2)]&~7) << 8));
#elif scn == 3
dst[dst_idx] = (ushort)((src[src_idx + bidx] >> 3)|((src[src_idx + 1]&~7) << 2)|((src[src_idx + (bidx^2)]&~7) << 7));
#else
dst[dst_idx] = (ushort)((src[src_idx + bidx] >> 3)|((src[src_idx + 1]&~7) << 2)|
((src[src_idx + (bidx^2)]&~7) << 7)|(src[src_idx + 3] ? 0x8000 : 0));
#endif
}
}
__kernel void BGR5x52Gray(int cols, int rows, int src_step, int dst_step,
__global const ushort * src, __global uchar * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
int t = src[src_idx];
#if greenbits == 6
dst[dst_idx] = (uchar)CV_DESCALE(((t << 3) & 0xf8)*B2Y +
((t >> 3) & 0xfc)*G2Y +
((t >> 8) & 0xf8)*R2Y, yuv_shift);
#else
dst[dst_idx] = (uchar)CV_DESCALE(((t << 3) & 0xf8)*B2Y +
((t >> 2) & 0xf8)*G2Y +
((t >> 7) & 0xf8)*R2Y, yuv_shift);
#endif
}
}
__kernel void Gray2BGR5x5(int cols, int rows, int src_step, int dst_step,
__global const uchar * src, __global ushort * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
int t = src[src_idx];
#if greenbits == 6
dst[dst_idx] = (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8));
#else
t >>= 3;
dst[dst_idx] = (ushort)(t|(t << 5)|(t << 10));
#endif
}
}
__constant int sector_data[][3] = { {1, 3, 0}, { 1, 0, 2 }, { 3, 0, 1 }, { 0, 2, 1 }, { 0, 1, 3 }, { 2, 1, 0 } };
#ifdef DEPTH_0
__kernel void RGB2HSV(int cols, int rows, int src_step, int dst_step,
__global const uchar * src, __global uchar * dst,
int src_offset, int dst_offset,
__constant int * sdiv_table, __constant int * hdiv_table)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
int b = src[src_idx + bidx], g = src[src_idx + 1], r = src[src_idx + (bidx^2)];
int h, s, v = b;
int vmin = b, diff;
int vr, vg;
v = max( v, g );
v = max( v, r );
vmin = min( vmin, g );
vmin = min( vmin, r );
diff = v - vmin;
vr = v == r ? -1 : 0;
vg = v == g ? -1 : 0;
s = (diff * sdiv_table[v] + (1 << (hsv_shift-1))) >> hsv_shift;
h = (vr & (g - b)) +
(~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff))));
h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift;
h += h < 0 ? hrange : 0;
dst[dst_idx] = convert_uchar_sat_rte(h);
dst[dst_idx + 1] = (uchar)s;
dst[dst_idx + 2] = (uchar)v;
}
}
__kernel void HSV2RGB(int cols, int rows, int src_step, int dst_step,
__global const uchar * src, __global uchar * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
float h = src[src_idx], s = src[src_idx + 1]*(1/255.f), v = src[src_idx + 2]*(1/255.f);
float b, g, r;
if (s != 0)
{
float tab[4];
int sector;
h *= hscale;
if( h < 0 )
do h += 6; while( h < 0 );
else if( h >= 6 )
do h -= 6; while( h >= 6 );
sector = convert_int_sat_rtn(h);
h -= sector;
if( (unsigned)sector >= 6u )
{
sector = 0;
h = 0.f;
}
tab[0] = v;
tab[1] = v*(1.f - s);
tab[2] = v*(1.f - s*h);
tab[3] = v*(1.f - s*(1.f - h));
b = tab[sector_data[sector][0]];
g = tab[sector_data[sector][1]];
r = tab[sector_data[sector][2]];
}
else
b = g = r = v;
dst[dst_idx + bidx] = convert_uchar_sat_rte(b*255.f);
dst[dst_idx + 1] = convert_uchar_sat_rte(g*255.f);
dst[dst_idx + (bidx^2)] = convert_uchar_sat_rte(r*255.f);
#if dcn == 4
dst[dst_idx + 3] = MAX_NUM;
#endif
}
}
#elif defined DEPTH_5
__kernel void RGB2HSV(int cols, int rows, int src_step, int dst_step,
__global const float * src, __global float * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
float b = src[src_idx + bidx], g = src[src_idx + 1], r = src[src_idx + (bidx^2)];
float h, s, v;
float vmin, diff;
v = vmin = r;
if( v < g ) v = g;
if( v < b ) v = b;
if( vmin > g ) vmin = g;
if( vmin > b ) vmin = b;
diff = v - vmin;
s = diff/(float)(fabs(v) + FLT_EPSILON);
diff = (float)(60./(diff + FLT_EPSILON));
if( v == r )
h = (g - b)*diff;
else if( v == g )
h = (b - r)*diff + 120.f;
else
h = (r - g)*diff + 240.f;
if( h < 0 ) h += 360.f;
dst[dst_idx] = h*hscale;
dst[dst_idx + 1] = s;
dst[dst_idx + 2] = v;
}
}
__kernel void HSV2RGB(int cols, int rows, int src_step, int dst_step,
__global const float * src, __global float * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
float h = src[src_idx], s = src[src_idx + 1], v = src[src_idx + 2];
float b, g, r;
if (s != 0)
{
float tab[4];
int sector;
h *= hscale;
if(h < 0)
do h += 6; while (h < 0);
else if (h >= 6)
do h -= 6; while (h >= 6);
sector = convert_int_sat_rtn(h);
h -= sector;
if ((unsigned)sector >= 6u)
{
sector = 0;
h = 0.f;
}
tab[0] = v;
tab[1] = v*(1.f - s);
tab[2] = v*(1.f - s*h);
tab[3] = v*(1.f - s*(1.f - h));
b = tab[sector_data[sector][0]];
g = tab[sector_data[sector][1]];
r = tab[sector_data[sector][2]];
}
else
b = g = r = v;
dst[dst_idx + bidx] = b;
dst[dst_idx + 1] = g;
dst[dst_idx + (bidx^2)] = r;
#if dcn == 4
dst[dst_idx + 3] = MAX_NUM;
#endif
}
}
#endif
#ifdef DEPTH_0
__kernel void RGB2HLS(int cols, int rows, int src_step, int dst_step,
__global const uchar * src, __global uchar * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
float b = src[src_idx + bidx]*(1/255.f), g = src[src_idx + 1]*(1/255.f), r = src[src_idx + (bidx^2)]*(1/255.f);
float h = 0.f, s = 0.f, l;
float vmin, vmax, diff;
vmax = vmin = r;
if (vmax < g) vmax = g;
if (vmax < b) vmax = b;
if (vmin > g) vmin = g;
if (vmin > b) vmin = b;
diff = vmax - vmin;
l = (vmax + vmin)*0.5f;
if (diff > FLT_EPSILON)
{
s = l < 0.5f ? diff/(vmax + vmin) : diff/(2 - vmax - vmin);
diff = 60.f/diff;
if( vmax == r )
h = (g - b)*diff;
else if( vmax == g )
h = (b - r)*diff + 120.f;
else
h = (r - g)*diff + 240.f;
if( h < 0.f ) h += 360.f;
}
dst[dst_idx] = convert_uchar_sat_rte(h*hscale);
dst[dst_idx + 1] = convert_uchar_sat_rte(l*255.f);
dst[dst_idx + 2] = convert_uchar_sat_rte(s*255.f);
}
}
__kernel void HLS2RGB(int cols, int rows, int src_step, int dst_step,
__global const uchar * src, __global uchar * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
float h = src[src_idx], l = src[src_idx + 1]*(1.f/255.f), s = src[src_idx + 2]*(1.f/255.f);
float b, g, r;
if (s != 0)
{
float tab[4];
float p2 = l <= 0.5f ? l*(1 + s) : l + s - l*s;
float p1 = 2*l - p2;
h *= hscale;
if( h < 0 )
do h += 6; while( h < 0 );
else if( h >= 6 )
do h -= 6; while( h >= 6 );
int sector = convert_int_sat_rtn(h);
h -= sector;
tab[0] = p2;
tab[1] = p1;
tab[2] = p1 + (p2 - p1)*(1-h);
tab[3] = p1 + (p2 - p1)*h;
b = tab[sector_data[sector][0]];
g = tab[sector_data[sector][1]];
r = tab[sector_data[sector][2]];
}
else
b = g = r = l;
dst[dst_idx + bidx] = convert_uchar_sat_rte(b*255.f);
dst[dst_idx + 1] = convert_uchar_sat_rte(g*255.f);
dst[dst_idx + (bidx^2)] = convert_uchar_sat_rte(r*255.f);
#if dcn == 4
dst[dst_idx + 3] = MAX_NUM;
#endif
}
}
#elif defined DEPTH_5
__kernel void RGB2HLS(int cols, int rows, int src_step, int dst_step,
__global const float * src, __global float * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
float b = src[src_idx + bidx], g = src[src_idx + 1], r = src[src_idx + (bidx^2)];
float h = 0.f, s = 0.f, l;
float vmin, vmax, diff;
vmax = vmin = r;
if (vmax < g) vmax = g;
if (vmax < b) vmax = b;
if (vmin > g) vmin = g;
if (vmin > b) vmin = b;
diff = vmax - vmin;
l = (vmax + vmin)*0.5f;
if (diff > FLT_EPSILON)
{
s = l < 0.5f ? diff/(vmax + vmin) : diff/(2 - vmax - vmin);
diff = 60.f/diff;
if( vmax == r )
h = (g - b)*diff;
else if( vmax == g )
h = (b - r)*diff + 120.f;
else
h = (r - g)*diff + 240.f;
if( h < 0.f ) h += 360.f;
}
dst[dst_idx] = h*hscale;
dst[dst_idx + 1] = l;
dst[dst_idx + 2] = s;
}
}
__kernel void HLS2RGB(int cols, int rows, int src_step, int dst_step,
__global const float * src, __global float * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
float h = src[src_idx], l = src[src_idx + 1], s = src[src_idx + 2];
float b, g, r;
if (s != 0)
{
float tab[4];
int sector;
float p2 = l <= 0.5f ? l*(1 + s) : l + s - l*s;
float p1 = 2*l - p2;
h *= hscale;
if( h < 0 )
do h += 6; while( h < 0 );
else if( h >= 6 )
do h -= 6; while( h >= 6 );
sector = convert_int_sat_rtn(h);
h -= sector;
tab[0] = p2;
tab[1] = p1;
tab[2] = p1 + (p2 - p1)*(1-h);
tab[3] = p1 + (p2 - p1)*h;
b = tab[sector_data[sector][0]];
g = tab[sector_data[sector][1]];
r = tab[sector_data[sector][2]];
}
else
b = g = r = l;
dst[dst_idx + bidx] = b;
dst[dst_idx + 1] = g;
dst[dst_idx + (bidx^2)] = r;
#if dcn == 4
dst[dst_idx + 3] = MAX_NUM;
#endif
}
}
#endif
#ifdef DEPTH_0
__kernel void RGBA2mRGBA(int cols, int rows, int src_step, int dst_step,
__global const uchar * src, __global uchar * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0) * pixels_per_work_item;
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#if (1 == pixels_per_work_item)
{
const uchar4 r0 = vload4(0, src_ptr);
dst_ptr[0] = (r0.s0 * r0.s3 + HALF_MAX) / MAX_NUM;
dst_ptr[1] = (r0.s1 * r0.s3 + HALF_MAX) / MAX_NUM;
dst_ptr[2] = (r0.s2 * r0.s3 + HALF_MAX) / MAX_NUM;
dst_ptr[3] = r0.s3;
}
#elif (2 == pixels_per_work_item)
{
const uchar8 r0 = vload8(0, src_ptr);
const int2 v0 = convert_int2(r0.s04);
const int2 v1 = convert_int2(r0.s15);
const int2 v2 = convert_int2(r0.s26);
const int2 v3 = convert_int2(r0.s37);
const int2 ri = (v0 * v3 + HALF_MAX) / MAX_NUM;
const int2 gi = (v1 * v3 + HALF_MAX) / MAX_NUM;
const int2 bi = (v2 * v3 + HALF_MAX) / MAX_NUM;
const uchar2 r = convert_uchar2(ri);
const uchar2 g = convert_uchar2(gi);
const uchar2 b = convert_uchar2(bi);
vstore8((uchar8)(r.s0, g.s0, b.s0, v3.s0, r.s1, g.s1, b.s1, v3.s1), 0, dst_ptr);
}
#elif (4 == pixels_per_work_item)
{
const uchar16 r0 = vload16(0, src_ptr);
const int4 v0 = convert_int4(r0.s048c);
const int4 v1 = convert_int4(r0.s159d);
const int4 v2 = convert_int4(r0.s26ae);
const int4 v3 = convert_int4(r0.s37bf);
const int4 ri = (v0 * v3 + HALF_MAX) / MAX_NUM;
const int4 gi = (v1 * v3 + HALF_MAX) / MAX_NUM;
const int4 bi = (v2 * v3 + HALF_MAX) / MAX_NUM;
const uchar4 r = convert_uchar4(ri);
const uchar4 g = convert_uchar4(gi);
const uchar4 b = convert_uchar4(bi);
vstore16((uchar16)(r.s0, g.s0, b.s0, v3.s0, r.s1, g.s1, b.s1, v3.s1, r.s2, g.s2, b.s2, v3.s2, r.s3, g.s3, b.s3, v3.s3), 0, dst_ptr);
}
#endif
}
}
__kernel void mRGBA2RGBA(int cols, int rows, int src_step, int dst_step,
__global const uchar * src, __global uchar * dst,
int src_offset, int dst_offset)
{
int x = get_global_id(0) * pixels_per_work_item;
int y = get_global_id(1);
if (y < rows && x < cols)
{
x <<= 2;
int src_idx = mad24(y, src_step, src_offset + x);
int dst_idx = mad24(y, dst_step, dst_offset + x);
global DATA_TYPE *src_ptr = (global DATA_TYPE *)(src + src_idx);
global DATA_TYPE *dst_ptr = (global DATA_TYPE *)(dst + dst_idx);
#if (1 == pixels_per_work_item)
{
const uchar4 r0 = vload4(0, src_ptr);
const uchar v3_half = r0.s3 / 2;
const uchar r = (r0.s3 == 0) ? 0 : (r0.s0 * MAX_NUM + v3_half) / r0.s3;
const uchar g = (r0.s3 == 0) ? 0 : (r0.s1 * MAX_NUM + v3_half) / r0.s3;
const uchar b = (r0.s3 == 0) ? 0 : (r0.s2 * MAX_NUM + v3_half) / r0.s3;
vstore4((uchar4)(r, g, b, r0.s3), 0, dst_ptr);
}
#elif (2 == pixels_per_work_item)
{
const uchar8 r0 = vload8(0, src_ptr);
const int2 v0 = convert_int2(r0.s04);
const int2 v1 = convert_int2(r0.s15);
const int2 v2 = convert_int2(r0.s26);
const int2 v3 = convert_int2(r0.s37);
const int2 v3_half = v3 / 2;
const int2 ri = (v3 == 0) ? 0 : (v0 * MAX_NUM + v3_half) / v3;
const int2 gi = (v3 == 0) ? 0 : (v1 * MAX_NUM + v3_half) / v3;
const int2 bi = (v3 == 0) ? 0 : (v2 * MAX_NUM + v3_half) / v3;
const uchar2 r = convert_uchar2(ri);
const uchar2 g = convert_uchar2(gi);
const uchar2 b = convert_uchar2(bi);
vstore8((uchar8)(r.s0, g.s0, b.s0, v3.s0, r.s1, g.s1, b.s1, v3.s1), 0, dst_ptr);
}
#elif (4 == pixels_per_work_item)
{
const uchar16 r0 = vload16(0, src_ptr);
const int4 v0 = convert_int4(r0.s048c);
const int4 v1 = convert_int4(r0.s159d);
const int4 v2 = convert_int4(r0.s26ae);
const int4 v3 = convert_int4(r0.s37bf);
const int4 v3_half = v3 / 2;
const int4 ri = (v3 == 0) ? 0 : (v0 * MAX_NUM + v3_half) / v3;
const int4 gi = (v3 == 0) ? 0 : (v1 * MAX_NUM + v3_half) / v3;
const int4 bi = (v3 == 0) ? 0 : (v2 * MAX_NUM + v3_half) / v3;
const uchar4 r = convert_uchar4(ri);
const uchar4 g = convert_uchar4(gi);
const uchar4 b = convert_uchar4(bi);
vstore16((uchar16)(r.s0, g.s0, b.s0, v3.s0, r.s1, g.s1, b.s1, v3.s1, r.s2, g.s2, b.s2, v3.s2, r.s3, g.s3, b.s3, v3.s3), 0, dst_ptr);
}
#endif
}
}
#endif
d8989aadab81f92a35b32db9740d9b62filter_sep_col#define READ_TIMES_COL ((2*(RADIUSY+LSIZE1)-1)/LSIZE1)
#define RADIUS 1
#if CN ==1
#define ALIGN (((RADIUS)+3)>>2<<2)
#elif CN==2
#define ALIGN (((RADIUS)+1)>>1<<1)
#elif CN==3
#define ALIGN (((RADIUS)+3)>>2<<2)
#elif CN==4
#define ALIGN (RADIUS)
#define READ_TIMES_ROW ((2*(RADIUS+LSIZE0)-1)/LSIZE0)
#endif
__kernel __attribute__((reqd_work_group_size(LSIZE0,LSIZE1,1))) void col_filter
(__global const GENTYPE_SRC * restrict src,
__global GENTYPE_DST * dst,
const int dst_cols,
const int dst_rows,
const int src_whole_cols,
const int src_whole_rows,
const int src_step_in_pixel,
const int dst_step_in_pixel,
const int dst_offset_in_pixel,
__constant float * mat_kernel __attribute__((max_constant_size(4*(2*RADIUSY+1)))))
{
int x = get_global_id(0);
int y = get_global_id(1);
int l_x = get_local_id(0);
int l_y = get_local_id(1);
int start_addr = mad24(y, src_step_in_pixel, x);
int end_addr = mad24(src_whole_rows - 1, src_step_in_pixel, src_whole_cols);
int i;
GENTYPE_SRC sum, temp[READ_TIMES_COL];
__local GENTYPE_SRC LDS_DAT[LSIZE1 * READ_TIMES_COL][LSIZE0 + 1];
for(i = 0;i>2<<2)
#elif CN==2
#define ALIGN (((RADIUS)+1)>>1<<1)
#elif CN==3
#define ALIGN (((RADIUS)+3)>>2<<2)
#elif CN==4
#define ALIGN (RADIUS)
#endif
#ifdef BORDER_CONSTANT
#define ELEM(i,l_edge,r_edge,elem1,elem2) (i)<(l_edge) | (i) >= (r_edge) ? (elem1) : (elem2)
#elif defined BORDER_REPLICATE
#define EXTRAPOLATE(x, maxV) \
{ \
x = max(min(x, maxV - 1), 0); \
}
#elif defined BORDER_WRAP
#define EXTRAPOLATE(x, maxV) \
{ \
if (x < 0) \
x -= ((x - maxV + 1) / maxV) * maxV; \
if (x >= maxV) \
x %= maxV; \
}
#elif defined(BORDER_REFLECT) || defined(BORDER_REFLECT_101)
#define EXTRAPOLATE_(x, maxV, delta) \
{ \
if (maxV == 1) \
x = 0; \
else \
do \
{ \
if ( x < 0 ) \
x = -x - 1 + delta; \
else \
x = maxV - 1 - (x - maxV) - delta; \
} \
while (x >= maxV || x < 0); \
}
#ifdef BORDER_REFLECT
#define EXTRAPOLATE(x, maxV) EXTRAPOLATE_(x, maxV, 0)
#else
#define EXTRAPOLATE(x, maxV) EXTRAPOLATE_(x, maxV, 1)
#endif
#else
#error No extrapolation method
#endif
__kernel __attribute__((reqd_work_group_size(LSIZE0,LSIZE1,1))) void row_filter_C1_D0
(__global uchar * restrict src,
__global float * dst,
int dst_cols, int dst_rows,
int src_whole_cols, int src_whole_rows,
int src_step_in_pixel,
int src_offset_x, int src_offset_y,
int dst_step_in_pixel, int radiusy,
__constant float * mat_kernel __attribute__((max_constant_size(4*(2*RADIUSX+1)))))
{
int x = get_global_id(0)<<2;
int y = get_global_id(1);
int l_x = get_local_id(0);
int l_y = get_local_id(1);
int start_x = x+src_offset_x - RADIUSX & 0xfffffffc;
int offset = src_offset_x - RADIUSX & 3;
int start_y = y + src_offset_y - radiusy;
int start_addr = mad24(start_y, src_step_in_pixel, start_x);
int i;
float4 sum;
uchar4 temp[READ_TIMES_ROW];
__local uchar4 LDS_DAT[LSIZE1][READ_TIMES_ROW*LSIZE0+1];
#ifdef BORDER_CONSTANT
int end_addr = mad24(src_whole_rows - 1, src_step_in_pixel, src_whole_cols);
for (i = 0; i < READ_TIMES_ROW; i++)
{
int current_addr = start_addr+i*LSIZE0*4;
current_addr = ((current_addr < end_addr) && (current_addr > 0)) ? current_addr : 0;
temp[i] = *(__global uchar4*)&src[current_addr];
}
for (i = 0; isrc_whole_cols)| (start_y<0) | (start_y >= src_whole_rows);
int4 index[READ_TIMES_ROW];
int4 addr;
int s_y;
if (not_all_in_range)
{
for (i = 0; i < READ_TIMES_ROW; i++)
{
index[i] = (int4)(start_x+i*LSIZE0*4) + (int4)(0, 1, 2, 3);
EXTRAPOLATE(index[i].x, src_whole_cols);
EXTRAPOLATE(index[i].y, src_whole_cols);
EXTRAPOLATE(index[i].z, src_whole_cols);
EXTRAPOLATE(index[i].w, src_whole_cols);
}
s_y = start_y;
EXTRAPOLATE(s_y, src_whole_rows);
for (i = 0; i 0)) ? current_addr : 0;
temp[i] = src[current_addr];
}
for (i = 0; i 0)) ? current_addr : 0;
temp[i] = src[current_addr];
}
for (i = 0; i 0)) ? current_addr : 0;
temp[i] = src[current_addr];
}
for (i = 0; i= (r_edge) ? (elem1) : (elem2)
#elif defined BORDER_REPLICATE
#define EXTRAPOLATE(x, maxV) \
{ \
x = max(min(x, maxV - 1), 0); \
}
#elif defined BORDER_WRAP
#define EXTRAPOLATE(x, maxV) \
{ \
if (x < 0) \
x -= ((x - maxV + 1) / maxV) * maxV; \
if (x >= maxV) \
x %= maxV; \
}
#elif defined(BORDER_REFLECT) || defined(BORDER_REFLECT_101)
#define EXTRAPOLATE_(x, maxV, delta) \
{ \
if (maxV == 1) \
x = 0; \
else \
do \
{ \
if ( x < 0 ) \
x = -x - 1 + delta; \
else \
x = maxV - 1 - (x - maxV) - delta; \
} \
while (x >= maxV || x < 0); \
}
#ifdef BORDER_REFLECT
#define EXTRAPOLATE(x, maxV) EXTRAPOLATE_(x, maxV, 0)
#else
#define EXTRAPOLATE(x, maxV) EXTRAPOLATE_(x, maxV, 1)
#endif
#else
#error No extrapolation method
#endif
__kernel void
adaptiveBilateralFilter_C4_D0(
__global const uchar4 * restrict src,
__global uchar4 *dst,
float alpha,
int src_offset,
int src_whole_rows,
int src_whole_cols,
int src_step,
int dst_offset,
int dst_rows,
int dst_cols,
int dst_step,
__global const float* lut,
int lut_step)
{
int col = get_local_id(0);
const int gX = get_group_id(0);
const int gY = get_group_id(1);
int src_x_off = (src_offset % src_step) >> 2;
int src_y_off = src_offset / src_step;
int dst_x_off = (dst_offset % dst_step) >> 2;
int dst_y_off = dst_offset / dst_step;
int startX = gX * (THREADS-ksX+1) - anX + src_x_off;
int startY = (gY * (1+EXTRA)) - anY + src_y_off;
int dst_startX = gX * (THREADS-ksX+1) + dst_x_off;
int dst_startY = (gY * (1+EXTRA)) + dst_y_off;
int posX = dst_startX - dst_x_off + col;
int posY = (gY * (1+EXTRA))  ;
__local uchar4 data[ksY+EXTRA][THREADS];
float4 tmp_sum[1+EXTRA];
for(int tmpint = 0; tmpint < 1+EXTRA; tmpint++)
tmp_sum[tmpint] = (float4)(0,0,0,0);
#ifdef BORDER_CONSTANT
bool con;
uchar4 ss;
for(int j = 0;  j < ksY+EXTRA; j++)
{
con = (startX+col >= 0 && startX+col < src_whole_cols && startY+j >= 0 && startY+j < src_whole_rows);
int cur_col = clamp(startX + col, 0, src_whole_cols);
if (con)
ss = src[(startY+j)*(src_step>>2) + cur_col];
data[j][col] = con ? ss : (uchar4)0;
}
#else
for(int j= 0; j < ksY+EXTRA; j++)
{
int selected_row = startY+j, selected_col = startX+col;
EXTRAPOLATE(selected_row, src_whole_rows)
EXTRAPOLATE(selected_col, src_whole_cols)
data[j][col] = src[selected_row * (src_step>>2) + selected_col];
}
#endif
barrier(CLK_LOCAL_MEM_FENCE);
float4 var[1+EXTRA];
#if VAR_PER_CHANNEL
float4 weight;
float4 totalWeight = (float4)(0,0,0,0);
#else
float weight;
float totalWeight = 0;
#endif
int4 currValCenter;
int4 currWRTCenter;
int4 sumVal = 0;
int4 sumValSqr = 0;
if(col < (THREADS-(ksX-1)))
{
int4 currVal;
int howManyAll = (2*anX+1)*(ksY);
int startLMj;
int endLMj ;
for(int extraCnt = 0; extraCnt <=EXTRA; extraCnt++)
{
#if CALCVAR
startLMj = extraCnt;
endLMj =  ksY+extraCnt-1;
sumVal = (int4)0;
sumValSqr= (int4)0;
for(int j = startLMj; j < endLMj; j++)
for(int i=-anX; i<=anX; i++)
{
currVal = convert_int4(data[j][col+anX+i]);
sumVal += currVal;
sumValSqr += mul24(currVal, currVal);
}
var[extraCnt] = clamp( convert_float4( ( (sumValSqr * howManyAll)- mul24(sumVal , sumVal) ) ) /  ( (float)(howManyAll*howManyAll) ), (float4)(0.1f, 0.1f, 0.1f, 0.1f), (float4)(MAX_VAR_VAL, MAX_VAR_VAL, MAX_VAR_VAL, MAX_VAR_VAL)) ;
#else
var[extraCnt] = (float4)(MAX_VAR_VAL, MAX_VAR_VAL, MAX_VAR_VAL, MAX_VAR_VAL);
#endif
}
for(int extraCnt = 0; extraCnt <= EXTRA; extraCnt++)
{
startLMj = extraCnt;
endLMj =  extraCnt + ksY;
currValCenter = convert_int4( data[ (startLMj + endLMj)/2][col+anX] );
for(int j = startLMj, lut_j = 0; j < endLMj; j++, lut_j++)
{
for(int i=-anX; i<=anX; i++)
{
#if FIXED_WEIGHT
#if VAR_PER_CHANNEL
weight.x = 1.0f;
weight.y = 1.0f;
weight.z = 1.0f;
weight.w = 1.0f;
#else
weight = 1.0f;
#endif
#else
currVal = convert_int4(data[j][col+anX+i]);
currWRTCenter = currVal-currValCenter;
#if ABF_GAUSSIAN
#if VAR_PER_CHANNEL
weight = exp( (float4)(-0.5f, -0.5f, -0.5f, -0.5f) * convert_float4(currWRTCenter * currWRTCenter) / var[extraCnt] )*
(float4)(lut[lut_j*lut_step+anX+i]);
#else
weight = exp( -0.5f * (mul24(currWRTCenter.x, currWRTCenter.x) + mul24(currWRTCenter.y, currWRTCenter.y) +
mul24(currWRTCenter.z, currWRTCenter.z) ) / (var[extraCnt].x+var[extraCnt].y+var[extraCnt].z) ) * lut[lut_j*lut_step+anX+i];
#endif
#else
#if VAR_PER_CHANNEL
weight = var[extraCnt] / (var[extraCnt] + convert_float4(currWRTCenter * currWRTCenter)) *
(float4)(lut[lut_j*lut_step+anX+i]);
#else
weight = ((float)lut[lut_j*lut_step+anX+i]) /(1.0f+( mul24(currWRTCenter.x, currWRTCenter.x) + mul24(currWRTCenter.y, currWRTCenter.y) +
mul24(currWRTCenter.z, currWRTCenter.z))/(var[extraCnt].x+var[extraCnt].y+var[extraCnt].z));
#endif
#endif
#endif
tmp_sum[extraCnt] += convert_float4(data[j][col+anX+i]) * weight;
totalWeight += weight;
}
}
if(posX >= 0 && posX < dst_cols && (posY+extraCnt) >= 0 && (posY+extraCnt) < dst_rows)
dst[(dst_startY+extraCnt) * (dst_step>>2)+ dst_startX + col] = convert_uchar4_rtz( (tmp_sum[extraCnt] / (float4)totalWeight) + (float4)0.5f);
#if VAR_PER_CHANNEL
totalWeight = (float4)(0,0,0,0);
#else
totalWeight = 0.0f;
#endif
}
}
}
__kernel void
adaptiveBilateralFilter_C1_D0(
__global const uchar * restrict src,
__global uchar *dst,
float alpha,
int src_offset,
int src_whole_rows,
int src_whole_cols,
int src_step,
int dst_offset,
int dst_rows,
int dst_cols,
int dst_step,
__global const float * lut,
int lut_step)
{
int col = get_local_id(0);
const int gX = get_group_id(0);
const int gY = get_group_id(1);
int src_x_off = (src_offset % src_step);
int src_y_off = src_offset / src_step;
int dst_x_off = (dst_offset % dst_step);
int dst_y_off = dst_offset / dst_step;
int startX = gX * (THREADS-ksX+1) - anX + src_x_off;
int startY = (gY * (1+EXTRA)) - anY + src_y_off;
int dst_startX = gX * (THREADS-ksX+1) + dst_x_off;
int dst_startY = (gY * (1+EXTRA)) + dst_y_off;
int posX = dst_startX - dst_x_off + col;
int posY = (gY * (1+EXTRA))  ;
__local uchar data[ksY+EXTRA][THREADS];
float tmp_sum[1+EXTRA];
for(int tmpint = 0; tmpint < 1+EXTRA; tmpint++)
{
tmp_sum[tmpint] = (float)(0);
}
#ifdef BORDER_CONSTANT
bool con;
uchar ss;
for(int j = 0;  j < ksY+EXTRA; j++)
{
con = (startX+col >= 0 && startX+col < src_whole_cols && startY+j >= 0 && startY+j < src_whole_rows);
int cur_col = clamp(startX + col, 0, src_whole_cols);
if(con)
{
ss = src[(startY+j)*(src_step) + cur_col];
}
data[j][col] = con ? ss : 0;
}
#else
for(int j= 0; j < ksY+EXTRA; j++)
{
int selected_row = startY+j, selected_col = startX+col;
EXTRAPOLATE(selected_row, src_whole_rows)
EXTRAPOLATE(selected_col, src_whole_cols)
data[j][col] = src[selected_row * (src_step) + selected_col];
}
#endif
barrier(CLK_LOCAL_MEM_FENCE);
float var[1+EXTRA];
float weight;
float totalWeight = 0;
int currValCenter;
int currWRTCenter;
int sumVal = 0;
int sumValSqr = 0;
if(col < (THREADS-(ksX-1)))
{
int currVal;
int howManyAll = (2*anX+1)*(ksY);
int startLMj;
int endLMj;
for(int extraCnt=0; extraCnt<=EXTRA; extraCnt++)
{
#if CALCVAR
startLMj = extraCnt;
endLMj =  ksY+extraCnt-1;
sumVal = 0;
sumValSqr =0;
for(int j = startLMj; j < endLMj; j++)
{
for(int i=-anX; i<=anX; i++)
{
currVal  = (uint)(data[j][col+anX+i])  ;
sumVal += currVal;
sumValSqr += mul24(currVal, currVal);
}
}
var[extraCnt] =  clamp((float)( ( (sumValSqr * howManyAll)- mul24(sumVal , sumVal) ) ) /  ( (float)(howManyAll*howManyAll) ) , 0.1f, (float)(MAX_VAR_VAL) );
#else
var[extraCnt] = (float)(MAX_VAR_VAL);
#endif
}
for(int extraCnt = 0; extraCnt <= EXTRA; extraCnt++)
{
startLMj = extraCnt;
endLMj =  extraCnt + ksY;
currValCenter = (int)( data[ (startLMj + endLMj)/2][col+anX] );
for(int j = startLMj, lut_j = 0; j < endLMj; j++, lut_j++)
{
for(int i=-anX; i<=anX; i++)
{
#if FIXED_WEIGHT
weight = 1.0f;
#else
currVal  = (int)(data[j][col+anX+i])  ;
currWRTCenter = currVal-currValCenter;
#if ABF_GAUSSIAN
weight = exp( -0.5f * (float)mul24(currWRTCenter,currWRTCenter)/var[extraCnt]) * lut[lut_j*lut_step+anX+i] ;
#else
weight = var[extraCnt] / (var[extraCnt] + (float)mul24(currWRTCenter,currWRTCenter)) * lut[lut_j*lut_step+anX+i] ;
#endif
#endif
tmp_sum[extraCnt] += (float)(data[j][col+anX+i] * weight);
totalWeight += weight;
}
}
if(posX >= 0 && posX < dst_cols && (posY+extraCnt) >= 0 && (posY+extraCnt) < dst_rows)
{
dst[(dst_startY+extraCnt) * (dst_step)+ dst_startX + col] = convert_uchar_rtz(tmp_sum[extraCnt]/totalWeight+0.5f);
}
totalWeight = 0;
}
}
}
9bf8fa5a50a63ee945172dc0f70760f2filtering_boxFilter#ifdef BORDER_REPLICATE
#define ADDR_L(i, l_edge, r_edge)  ((i) <  (l_edge) ? (l_edge)   : (i))
#define ADDR_R(i, r_edge, addr)    ((i) >= (r_edge) ? (r_edge)-1 : (addr))
#define ADDR_H(i, t_edge, b_edge)  ((i) <  (t_edge) ? (t_edge)   :(i))
#define ADDR_B(i, b_edge, addr)    ((i) >= (b_edge) ? (b_edge)-1 :(addr))
#endif
#ifdef BORDER_REFLECT
#define ADDR_L(i, l_edge, r_edge)  ((i) <  (l_edge) ? -(i)-1               : (i))
#define ADDR_R(i, r_edge, addr)    ((i) >= (r_edge) ? -(i)-1+((r_edge)<<1) : (addr))
#define ADDR_H(i, t_edge, b_edge)  ((i) <  (t_edge) ? -(i)-1 : (i))
#define ADDR_B(i, b_edge, addr)    ((i) >= (b_edge) ? -(i)-1+((b_edge)<<1) : (addr))
#endif
#ifdef BORDER_REFLECT_101
#define ADDR_L(i, l_edge, r_edge)  ((i) <  (l_edge) ? -(i)                 : (i))
#define ADDR_R(i, r_edge, addr)    ((i) >= (r_edge) ? -(i)-2+((r_edge)<<1) : (addr))
#define ADDR_H(i, t_edge, b_edge)  ((i) <  (t_edge) ? -(i)                 : (i))
#define ADDR_B(i, b_edge, addr)    ((i) >= (b_edge) ? -(i)-2+((b_edge)<<1) : (addr))
#endif
#ifdef BORDER_WRAP
#define ADDR_L(i, l_edge, r_edge)  ((i) <  (l_edge) ? (i)+(r_edge) : (i))
#define ADDR_R(i, r_edge, addr)    ((i) >= (r_edge) ? (i)-(r_edge) : (addr))
#define ADDR_H(i, t_edge, b_edge)  ((i) <  (t_edge) ? (i)+(b_edge) : (i))
#define ADDR_B(i, b_edge, addr)    ((i) >= (b_edge) ? (i)-(b_edge) : (addr))
#endif
#ifdef EXTRA_EXTRAPOLATION
#ifdef BORDER_CONSTANT
#elif defined BORDER_REPLICATE
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) \
{ \
x = max(min(x, maxX - 1), minX); \
y = max(min(y, maxY - 1), minY); \
}
#elif defined BORDER_WRAP
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) \
{ \
if (x < minX) \
x -= ((x - maxX + 1) / maxX) * maxX; \
if (x >= maxX) \
x %= maxX; \
if (y < minY) \
y -= ((y - maxY + 1) / maxY) * maxY; \
if (y >= maxY) \
y %= maxY; \
}
#elif defined(BORDER_REFLECT) || defined(BORDER_REFLECT_101)
#define EXTRAPOLATE_(x, y, minX, minY, maxX, maxY, delta) \
{ \
if (maxX - minX == 1) \
x = minX; \
else \
do \
{ \
if (x < minX) \
x = -(x - minX) - 1 + delta; \
else \
x = maxX - 1 - (x - maxX) - delta; \
} \
while (x >= maxX || x < minX); \
\
if (maxY - minY == 1) \
y = minY; \
else \
do \
{ \
if (y < minY) \
y = -(y - minY) - 1 + delta; \
else \
y = maxY - 1 - (y - maxY) - delta; \
} \
while (y >= maxY || y < minY); \
}
#ifdef BORDER_REFLECT
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) EXTRAPOLATE_(x, y, minX, minY, maxX, maxY, 0)
#elif defined(BORDER_REFLECT_101)
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) EXTRAPOLATE_(x, y, minX, minY, maxX, maxY, 1)
#endif
#else
#error No extrapolation method
#endif
#else
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) \
{ \
int _row = y - minY, _col = x - minX; \
_row = ADDR_H(_row, 0, maxY - minY); \
_row = ADDR_B(_row, maxY - minY, _row); \
y = _row + minY; \
\
_col = ADDR_L(_col, 0, maxX - minX); \
_col = ADDR_R(_col, maxX - minX, _col); \
x = _col + minX; \
}
#endif
#if USE_DOUBLE
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define FPTYPE double
#define CONVERT_TO_FPTYPE CAT(convert_double, VEC_SIZE)
#else
#define FPTYPE float
#define CONVERT_TO_FPTYPE CAT(convert_float, VEC_SIZE)
#endif
#if DATA_DEPTH == 0
#define BASE_TYPE uchar
#elif DATA_DEPTH == 1
#define BASE_TYPE char
#elif DATA_DEPTH == 2
#define BASE_TYPE ushort
#elif DATA_DEPTH == 3
#define BASE_TYPE short
#elif DATA_DEPTH == 4
#define BASE_TYPE int
#elif DATA_DEPTH == 5
#define BASE_TYPE float
#elif DATA_DEPTH == 6
#define BASE_TYPE double
#else
#error data_depth
#endif
#define __CAT(x, y) x##y
#define CAT(x, y) __CAT(x, y)
#define uchar1 uchar
#define char1 char
#define ushort1 ushort
#define short1 short
#define int1 int
#define float1 float
#define double1 double
#define convert_uchar1_sat_rte convert_uchar_sat_rte
#define convert_char1_sat_rte convert_char_sat_rte
#define convert_ushort1_sat_rte convert_ushort_sat_rte
#define convert_short1_sat_rte convert_short_sat_rte
#define convert_int1_sat_rte convert_int_sat_rte
#define convert_float1
#define convert_double1
#if DATA_DEPTH == 5 || DATA_DEPTH == 6
#define CONVERT_TO_TYPE CAT(CAT(convert_, BASE_TYPE), VEC_SIZE)
#else
#define CONVERT_TO_TYPE CAT(CAT(CAT(convert_, BASE_TYPE), VEC_SIZE), _sat_rte)
#endif
#define VEC_SIZE DATA_CHAN
#define VEC_TYPE CAT(BASE_TYPE, VEC_SIZE)
#define TYPE VEC_TYPE
#define SCALAR_TYPE CAT(FPTYPE, VEC_SIZE)
#define INTERMEDIATE_TYPE CAT(FPTYPE, VEC_SIZE)
struct RectCoords
{
int x1, y1, x2, y2;
};
#ifdef DEBUG
#define DEBUG_ONLY(x) x
#define ASSERT(condition) do { if (!(condition)) { printf("BUG in boxFilter kernel (global=%d,%d): " #condition "\n", get_global_id(0), get_global_id(1)); } } while (0)
#else
#define DEBUG_ONLY(x)
#define ASSERT(condition)
#endif
INTERMEDIATE_TYPE readSrcPixel(int2 pos, __global TYPE *src, const unsigned int srcStepBytes, const struct RectCoords srcCoords
#ifdef BORDER_CONSTANT
, SCALAR_TYPE borderValue
#endif
)
{
#ifdef BORDER_ISOLATED
if(pos.x >= srcCoords.x1 && pos.y >= srcCoords.y1 && pos.x < srcCoords.x2 && pos.y < srcCoords.y2)
#else
if(pos.x >= 0 && pos.y >= 0 && pos.x < srcCoords.x2 && pos.y < srcCoords.y2)
#endif
{
__global TYPE* ptr = (__global TYPE*)((__global char*)src + pos.x * sizeof(TYPE) + pos.y * srcStepBytes);
return CONVERT_TO_FPTYPE(*ptr);
}
else
{
#ifdef BORDER_CONSTANT
return borderValue;
#else
int selected_col = pos.x;
int selected_row = pos.y;
EXTRAPOLATE(selected_col, selected_row,
#ifdef BORDER_ISOLATED
srcCoords.x1, srcCoords.y1,
#else
0, 0,
#endif
srcCoords.x2, srcCoords.y2
);
pos = (int2)(selected_col, selected_row);
if(pos.x >= 0 && pos.y >= 0 && pos.x < srcCoords.x2 && pos.y < srcCoords.y2)
{
__global TYPE* ptr = (__global TYPE*)((__global char*)src + pos.x * sizeof(TYPE) + pos.y * srcStepBytes);
return CONVERT_TO_FPTYPE(*ptr);
}
else
{
DEBUG_ONLY(printf("BUG in boxFilter kernel\n"));
return (FPTYPE)(0.0f);
}
#endif
}
}
__kernel
__attribute__((reqd_work_group_size(LOCAL_SIZE, 1, 1)))
void boxFilter(__global TYPE *src, const unsigned int srcStepBytes, const int4 srcRC,
__global TYPE *dst, const unsigned int dstStepBytes, const int4 dstRC,
#ifdef BORDER_CONSTANT
SCALAR_TYPE borderValue,
#endif
FPTYPE alpha
)
{
const struct RectCoords srcCoords = {srcRC.s0, srcRC.s1, srcRC.s2, srcRC.s3};
const struct RectCoords dstCoords = {dstRC.s0, dstRC.s1, dstRC.s2, dstRC.s3};
const int x = get_local_id(0) + (LOCAL_SIZE - (KERNEL_SIZE_X - 1)) * get_group_id(0) - ANCHOR_X;
const int y = get_global_id(1) * BLOCK_SIZE_Y;
const int local_id = get_local_id(0);
INTERMEDIATE_TYPE data[KERNEL_SIZE_Y];
__local INTERMEDIATE_TYPE sumOfCols[LOCAL_SIZE];
int2 srcPos = (int2)(srcCoords.x1 + x, srcCoords.y1 + y - ANCHOR_Y);
for(int sy = 0; sy < KERNEL_SIZE_Y; sy++, srcPos.y++)
{
data[sy] = readSrcPixel(srcPos, src, srcStepBytes, srcCoords
#ifdef BORDER_CONSTANT
, borderValue
#endif
);
}
INTERMEDIATE_TYPE tmp_sum = 0;
for(int sy = 0; sy < KERNEL_SIZE_Y; sy++)
{
tmp_sum += (data[sy]);
}
sumOfCols[local_id] = tmp_sum;
barrier(CLK_LOCAL_MEM_FENCE);
int2 pos = (int2)(dstCoords.x1 + x, dstCoords.y1 + y);
__global TYPE* dstPtr = (__global TYPE*)((__global char*)dst + pos.x * sizeof(TYPE) + pos.y * dstStepBytes);
int sy_index = 0;
int stepsY = min(dstCoords.y2 - pos.y, BLOCK_SIZE_Y);
ASSERT(stepsY > 0);
for (; ;)
{
ASSERT(pos.y < dstCoords.y2);
if(local_id >= ANCHOR_X && local_id < LOCAL_SIZE - (KERNEL_SIZE_X - 1 - ANCHOR_X) &&
pos.x >= dstCoords.x1 && pos.x < dstCoords.x2)
{
ASSERT(pos.y >= dstCoords.y1 && pos.y < dstCoords.y2);
INTERMEDIATE_TYPE total_sum = 0;
#pragma unroll
for (int sx = 0; sx < KERNEL_SIZE_X; sx++)
{
total_sum += sumOfCols[local_id + sx - ANCHOR_X];
}
*dstPtr = CONVERT_TO_TYPE(((INTERMEDIATE_TYPE)alpha) * total_sum);
}
#if BLOCK_SIZE_Y == 1
break;
#else
if (--stepsY == 0)
break;
barrier(CLK_LOCAL_MEM_FENCE);
tmp_sum = sumOfCols[local_id];
tmp_sum -= data[sy_index];
data[sy_index] = readSrcPixel(srcPos, src, srcStepBytes, srcCoords
#ifdef BORDER_CONSTANT
, borderValue
#endif
);
srcPos.y++;
tmp_sum += data[sy_index];
sumOfCols[local_id] = tmp_sum;
sy_index = (sy_index + 1 < KERNEL_SIZE_Y) ? sy_index + 1 : 0;
barrier(CLK_LOCAL_MEM_FENCE);
DEBUG_ONLY(pos.y++);
dstPtr = (__global TYPE*)((__global char*)dstPtr + dstStepBytes);
#endif
}
}
4886fb0eca8474fbd9c13bc03089baf2filtering_filter2D#ifdef BORDER_REPLICATE
#define ADDR_L(i, l_edge, r_edge)  ((i) <  (l_edge) ? (l_edge)   : (i))
#define ADDR_R(i, r_edge, addr)    ((i) >= (r_edge) ? (r_edge)-1 : (addr))
#define ADDR_H(i, t_edge, b_edge)  ((i) <  (t_edge) ? (t_edge)   :(i))
#define ADDR_B(i, b_edge, addr)    ((i) >= (b_edge) ? (b_edge)-1 :(addr))
#endif
#ifdef BORDER_REFLECT
#define ADDR_L(i, l_edge, r_edge)  ((i) <  (l_edge) ? -(i)-1               : (i))
#define ADDR_R(i, r_edge, addr)    ((i) >= (r_edge) ? -(i)-1+((r_edge)<<1) : (addr))
#define ADDR_H(i, t_edge, b_edge)  ((i) <  (t_edge) ? -(i)-1 : (i))
#define ADDR_B(i, b_edge, addr)    ((i) >= (b_edge) ? -(i)-1+((b_edge)<<1) : (addr))
#endif
#ifdef BORDER_REFLECT_101
#define ADDR_L(i, l_edge, r_edge)  ((i) <  (l_edge) ? -(i)                 : (i))
#define ADDR_R(i, r_edge, addr)    ((i) >= (r_edge) ? -(i)-2+((r_edge)<<1) : (addr))
#define ADDR_H(i, t_edge, b_edge)  ((i) <  (t_edge) ? -(i)                 : (i))
#define ADDR_B(i, b_edge, addr)    ((i) >= (b_edge) ? -(i)-2+((b_edge)<<1) : (addr))
#endif
#ifdef BORDER_WRAP
#define ADDR_L(i, l_edge, r_edge)  ((i) <  (l_edge) ? (i)+(r_edge) : (i))
#define ADDR_R(i, r_edge, addr)    ((i) >= (r_edge) ? (i)-(r_edge) : (addr))
#define ADDR_H(i, t_edge, b_edge)  ((i) <  (t_edge) ? (i)+(b_edge) : (i))
#define ADDR_B(i, b_edge, addr)    ((i) >= (b_edge) ? (i)-(b_edge) : (addr))
#endif
#ifdef EXTRA_EXTRAPOLATION
#ifdef BORDER_CONSTANT
#elif defined BORDER_REPLICATE
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) \
{ \
x = max(min(x, maxX - 1), minX); \
y = max(min(y, maxY - 1), minY); \
}
#elif defined BORDER_WRAP
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) \
{ \
if (x < minX) \
x -= ((x - maxX + 1) / maxX) * maxX; \
if (x >= maxX) \
x %= maxX; \
if (y < minY) \
y -= ((y - maxY + 1) / maxY) * maxY; \
if (y >= maxY) \
y %= maxY; \
}
#elif defined(BORDER_REFLECT) || defined(BORDER_REFLECT_101)
#define EXTRAPOLATE_(x, y, minX, minY, maxX, maxY, delta) \
{ \
if (maxX - minX == 1) \
x = minX; \
else \
do \
{ \
if (x < minX) \
x = -(x - minX) - 1 + delta; \
else \
x = maxX - 1 - (x - maxX) - delta; \
} \
while (x >= maxX || x < minX); \
\
if (maxY - minY == 1) \
y = minY; \
else \
do \
{ \
if (y < minY) \
y = -(y - minY) - 1 + delta; \
else \
y = maxY - 1 - (y - maxY) - delta; \
} \
while (y >= maxY || y < minY); \
}
#ifdef BORDER_REFLECT
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) EXTRAPOLATE_(x, y, minX, minY, maxX, maxY, 0)
#elif defined(BORDER_REFLECT_101)
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) EXTRAPOLATE_(x, y, minX, minY, maxX, maxY, 1)
#endif
#else
#error No extrapolation method
#endif
#else
#define EXTRAPOLATE(x, y, minX, minY, maxX, maxY) \
{ \
int _row = y - minY, _col = x - minX; \
_row = ADDR_H(_row, 0, maxY - minY); \
_row = ADDR_B(_row, maxY - minY, _row); \
y = _row + minY; \
\
_col = ADDR_L(_col, 0, maxX - minX); \
_col = ADDR_R(_col, maxX - minX, _col); \
x = _col + minX; \
}
#endif
#if USE_DOUBLE
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define FPTYPE double
#define CONVERT_TO_FPTYPE CAT(convert_double, VEC_SIZE)
#else
#define FPTYPE float
#define CONVERT_TO_FPTYPE CAT(convert_float, VEC_SIZE)
#endif
#if DATA_DEPTH == 0
#define BASE_TYPE uchar
#elif DATA_DEPTH == 1
#define BASE_TYPE char
#elif DATA_DEPTH == 2
#define BASE_TYPE ushort
#elif DATA_DEPTH == 3
#define BASE_TYPE short
#elif DATA_DEPTH == 4
#define BASE_TYPE int
#elif DATA_DEPTH == 5
#define BASE_TYPE float
#elif DATA_DEPTH == 6
#define BASE_TYPE double
#else
#error data_depth
#endif
#define __CAT(x, y) x##y
#define CAT(x, y) __CAT(x, y)
#define uchar1 uchar
#define char1 char
#define ushort1 ushort
#define short1 short
#define int1 int
#define float1 float
#define double1 double
#define convert_uchar1_sat_rte convert_uchar_sat_rte
#define convert_char1_sat_rte convert_char_sat_rte
#define convert_ushort1_sat_rte convert_ushort_sat_rte
#define convert_short1_sat_rte convert_short_sat_rte
#define convert_int1_sat_rte convert_int_sat_rte
#define convert_float1
#define convert_double1
#if DATA_DEPTH == 5 || DATA_DEPTH == 6
#define CONVERT_TO_TYPE CAT(CAT(convert_, BASE_TYPE), VEC_SIZE)
#else
#define CONVERT_TO_TYPE CAT(CAT(CAT(convert_, BASE_TYPE), VEC_SIZE), _sat_rte)
#endif
#define VEC_SIZE DATA_CHAN
#define VEC_TYPE CAT(BASE_TYPE, VEC_SIZE)
#define TYPE VEC_TYPE
#define SCALAR_TYPE CAT(FPTYPE, VEC_SIZE)
#define INTERMEDIATE_TYPE CAT(FPTYPE, VEC_SIZE)
struct RectCoords
{
int x1, y1, x2, y2;
};
#ifdef DEBUG
#define DEBUG_ONLY(x) x
#define ASSERT(condition) do { if (!(condition)) { printf("BUG in boxFilter kernel (global=%d,%d): " #condition "\n", get_global_id(0), get_global_id(1)); } } while (0)
#else
#define DEBUG_ONLY(x) (void)0
#define ASSERT(condition) (void)0
#endif
INTERMEDIATE_TYPE readSrcPixel(int2 pos, __global TYPE *src, const unsigned int srcStepBytes, const struct RectCoords srcCoords
#ifdef BORDER_CONSTANT
, SCALAR_TYPE borderValue
#endif
)
{
#ifdef BORDER_ISOLATED
if(pos.x >= srcCoords.x1 && pos.y >= srcCoords.y1 && pos.x < srcCoords.x2 && pos.y < srcCoords.y2)
#else
if(pos.x >= 0 && pos.y >= 0 && pos.x < srcCoords.x2 && pos.y < srcCoords.y2)
#endif
{
__global TYPE* ptr = (__global TYPE*)((__global char*)src + pos.x * sizeof(TYPE) + pos.y * srcStepBytes);
return CONVERT_TO_FPTYPE(*ptr);
}
else
{
#ifdef BORDER_CONSTANT
return borderValue;
#else
int selected_col = pos.x;
int selected_row = pos.y;
EXTRAPOLATE(selected_col, selected_row,
#ifdef BORDER_ISOLATED
srcCoords.x1, srcCoords.y1,
#else
0, 0,
#endif
srcCoords.x2, srcCoords.y2
);
pos = (int2)(selected_col, selected_row);
if(pos.x >= 0 && pos.y >= 0 && pos.x < srcCoords.x2 && pos.y < srcCoords.y2)
{
__global TYPE* ptr = (__global TYPE*)((__global char*)src + pos.x * sizeof(TYPE) + pos.y * srcStepBytes);
return CONVERT_TO_FPTYPE(*ptr);
}
else
{
DEBUG_ONLY(printf("BUG in boxFilter kernel\n"));
return (FPTYPE)(0.0f);
}
#endif
}
}
__kernel
__attribute__((reqd_work_group_size(LOCAL_SIZE, 1, 1)))
void filter2D(__global TYPE *src, const unsigned int srcStepBytes, const int4 srcRC,
__global TYPE *dst, const unsigned int dstStepBytes, const int4 dstRC,
#ifdef BORDER_CONSTANT
SCALAR_TYPE borderValue,
#endif
__constant FPTYPE* kernelData
)
{
const struct RectCoords srcCoords = {srcRC.s0, srcRC.s1, srcRC.s2, srcRC.s3};
struct RectCoords dstCoords = {dstRC.s0, dstRC.s1, dstRC.s2, dstRC.s3};
const int local_id = get_local_id(0);
const int x = local_id + (LOCAL_SIZE - (KERNEL_SIZE_X - 1)) * get_group_id(0) - ANCHOR_X;
const int y = get_global_id(1) * BLOCK_SIZE_Y;
INTERMEDIATE_TYPE data[KERNEL_SIZE_Y];
__local INTERMEDIATE_TYPE sumOfCols[LOCAL_SIZE];
int2 srcPos = (int2)(srcCoords.x1 + x, srcCoords.y1 + y - ANCHOR_Y);
int2 pos = (int2)(dstCoords.x1 + x, dstCoords.y1 + y);
__global TYPE* dstPtr = (__global TYPE*)((__global char*)dst + pos.x * sizeof(TYPE) + pos.y * dstStepBytes);
bool writeResult = (local_id >= ANCHOR_X && local_id < LOCAL_SIZE - (KERNEL_SIZE_X - 1 - ANCHOR_X) &&
pos.x >= dstCoords.x1 && pos.x < dstCoords.x2);
#if BLOCK_SIZE_Y > 1
bool readAllpixels = true;
int sy_index = 0;
dstCoords.y2 = min(dstCoords.y2, pos.y + BLOCK_SIZE_Y);
for (;
pos.y < dstCoords.y2;
pos.y++,
dstPtr = (__global TYPE*)((__global char*)dstPtr + dstStepBytes))
#endif
{
ASSERT(pos.y < dstCoords.y2);
for (
#if BLOCK_SIZE_Y > 1
int sy = readAllpixels ? 0 : -1; sy < (readAllpixels ? KERNEL_SIZE_Y : 0);
#else
int sy = 0, sy_index = 0; sy < KERNEL_SIZE_Y;
#endif
sy++, srcPos.y++)
{
data[sy + sy_index] = readSrcPixel(srcPos, src, srcStepBytes, srcCoords
#ifdef BORDER_CONSTANT
, borderValue
#endif
);
}
INTERMEDIATE_TYPE total_sum = 0;
for (int sx = 0; sx < KERNEL_SIZE_X; sx++)
{
{
__constant FPTYPE* k = &kernelData[KERNEL_SIZE_Y2_ALIGNED * sx
#if BLOCK_SIZE_Y > 1
+ KERNEL_SIZE_Y - sy_index
#endif
];
INTERMEDIATE_TYPE tmp_sum = 0;
for (int sy = 0; sy < KERNEL_SIZE_Y; sy++)
{
tmp_sum += data[sy] * k[sy];
}
sumOfCols[local_id] = tmp_sum;
barrier(CLK_LOCAL_MEM_FENCE);
}
int id = local_id + sx - ANCHOR_X;
if (id >= 0 && id < LOCAL_SIZE)
total_sum += sumOfCols[id];
barrier(CLK_LOCAL_MEM_FENCE);
}
if (writeResult)
{
ASSERT(pos.y >= dstCoords.y1 && pos.y < dstCoords.y2);
*dstPtr = CONVERT_TO_TYPE(total_sum);
}
#if BLOCK_SIZE_Y > 1
readAllpixels = false;
#if BLOCK_SIZE_Y > KERNEL_SIZE_Y
sy_index = (sy_index + 1 <= KERNEL_SIZE_Y) ? sy_index + 1 : 1;
#else
sy_index++;
#endif
#endif
}
}
ca32031fbf7bad598b8468e60df95be6filtering_morph#ifdef ERODE
#define MORPH_OP(A,B) min((A),(B))
#endif
#ifdef DILATE
#define MORPH_OP(A,B) max((A),(B))
#endif
#define ELEM(i,l_edge,r_edge,elem1,elem2) (i)<(l_edge) | (i) >= (r_edge) ? (elem1) : (elem2)
#ifndef GENTYPE
__kernel void morph_C1_D0(__global const uchar * restrict src,
__global uchar *dst,
int src_offset_x, int src_offset_y,
int cols, int rows,
int src_step_in_pixel, int dst_step_in_pixel,
__constant uchar * mat_kernel,
int src_whole_cols, int src_whole_rows,
int dst_offset_in_pixel)
{
int l_x = get_local_id(0);
int l_y = get_local_id(1);
int x = get_group_id(0)*4*LSIZE0;
int y = get_group_id(1)*LSIZE1;
int start_x = x+src_offset_x-RADIUSX & 0xfffffffc;
int end_x = x + src_offset_x+LSIZE0*4+RADIUSX & 0xfffffffc;
int width = (end_x -start_x+4)>>2;
int offset = src_offset_x-RADIUSX & 3;
int start_y = y+src_offset_y-RADIUSY;
int point1 = mad24(l_y,LSIZE0,l_x);
int point2 = point1 + LSIZE0*LSIZE1;
int tl_x = (point1 % width)<<2;
int tl_y = point1 / width;
int tl_x2 = (point2 % width)<<2;
int tl_y2 = point2 / width;
int cur_x = start_x + tl_x;
int cur_y = start_y + tl_y;
int cur_x2 = start_x + tl_x2;
int cur_y2 = start_y + tl_y2;
int start_addr = mad24(cur_y,src_step_in_pixel,cur_x);
int start_addr2 = mad24(cur_y2,src_step_in_pixel,cur_x2);
uchar4 temp0,temp1;
__local uchar4 LDS_DAT[2*LSIZE1*LSIZE0];
int end_addr = mad24(src_whole_rows - 1,src_step_in_pixel,src_whole_cols);
start_addr = ((start_addr < end_addr) && (start_addr > 0)) ? start_addr : 0;
start_addr2 = ((start_addr2 < end_addr) && (start_addr2 > 0)) ? start_addr2 : 0;
temp0 = *(__global uchar4*)&src[start_addr];
temp1 = *(__global uchar4*)&src[start_addr2];
temp0.x= ELEM(cur_x,0,src_whole_cols,VAL,temp0.x);
temp0.y= ELEM(cur_x+1,0,src_whole_cols,VAL,temp0.y);
temp0.z= ELEM(cur_x+2,0,src_whole_cols,VAL,temp0.z);
temp0.w= ELEM(cur_x+3,0,src_whole_cols,VAL,temp0.w);
temp0= ELEM(cur_y,0,src_whole_rows,(uchar4)VAL,temp0);
temp1.x= ELEM(cur_x2,0,src_whole_cols,VAL,temp1.x);
temp1.y= ELEM(cur_x2+1,0,src_whole_cols,VAL,temp1.y);
temp1.z= ELEM(cur_x2+2,0,src_whole_cols,VAL,temp1.z);
temp1.w= ELEM(cur_x2+3,0,src_whole_cols,VAL,temp1.w);
temp1= ELEM(cur_y2,0,src_whole_rows,(uchar4)VAL,temp1);
LDS_DAT[point1] = temp0;
LDS_DAT[point2] = temp1;
barrier(CLK_LOCAL_MEM_FENCE);
uchar4 res = (uchar4)VAL;
for(int i=0; i<2*RADIUSY+1; i++)
for(int j=0; j<2*RADIUSX+1; j++)
{
res =
#ifndef RECTKERNEL
mat_kernel[i*(2*RADIUSX+1)+j] ?
#endif
MORPH_OP(res,vload4(0,(__local uchar*)&LDS_DAT[mad24((l_y+i),width,l_x)]+offset+j))
#ifndef RECTKERNEL
:res
#endif
;
}
int gidx = get_global_id(0)<<2;
int gidy = get_global_id(1);
int out_addr = mad24(gidy,dst_step_in_pixel,gidx+dst_offset_in_pixel);
if(gidx+3 0)) ? start_addr : 0;
start_addr2 = ((start_addr2 < end_addr) && (start_addr2 > 0)) ? start_addr2 : 0;
temp0 = src[start_addr];
temp1 = src[start_addr2];
temp0= ELEM(cur_x,0,src_whole_cols,(GENTYPE)VAL,temp0);
temp0= ELEM(cur_y,0,src_whole_rows,(GENTYPE)VAL,temp0);
temp1= ELEM(cur_x2,0,src_whole_cols,(GENTYPE)VAL,temp1);
temp1= ELEM(cur_y2,0,src_whole_rows,(GENTYPE)VAL,temp1);
LDS_DAT[point1] = temp0;
LDS_DAT[point2] = temp1;
barrier(CLK_LOCAL_MEM_FENCE);
GENTYPE res = (GENTYPE)VAL;
for(int i=0; i<2*RADIUSY+1; i++)
for(int j=0; j<2*RADIUSX+1; j++)
{
res =
#ifndef RECTKERNEL
mat_kernel[i*(2*RADIUSX+1)+j] ?
#endif
MORPH_OP(res,LDS_DAT[mad24(l_y+i,width,l_x+j)])
#ifndef RECTKERNEL
:res
#endif
;
}
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int out_addr = mad24(gidy,dst_step_in_pixel,gidx+dst_offset_in_pixel);
if(gidx= (r_edge) | (_y)<0 | (_y) >= (t_edge) ? (const_v) : SRC((_x),(_y))
#else
#define ELEM(_x,_y,r_edge,t_edge,const_v) SRC((_x),(_y))
#endif
#define DST(_x,_y) (((global DSTTYPE*)(Dst+DstOffset+(_y)*DstPitch))[_x])
#define DIG(a) a,
__constant uint mat_kernelX[] = {KERNEL_MATRIX_X};
__constant uint mat_kernelY[] = {KERNEL_MATRIX_Y};
__kernel __attribute__((reqd_work_group_size(BLK_X,BLK_Y,1))) void sep_filter_singlepass
(
__global uchar* Src,
const uint      SrcPitch,
const int       srcOffsetX,
const int       srcOffsetY,
__global uchar* Dst,
const int       DstOffset,
const uint      DstPitch,
int             width,
int             height,
int             dstWidth,
int             dstHeight
)
{
__local WORKTYPE lsmem[BLK_Y+2*RADIUSY][BLK_X+2*RADIUSX];
__local WORKTYPE lsmemDy[BLK_Y][BLK_X+2*RADIUSX];
int lix = get_local_id(0);
int liy = get_local_id(1);
int x = (int)get_global_id(0);
int y = (int)get_global_id(1);
int srcX = x + srcOffsetX - RADIUSX;
int srcY = y + srcOffsetY - RADIUSY;
int xb = srcX;
int yb = srcY;
int clocY = liy;
int cSrcY = srcY;
do
{
int yb = cSrcY;
EXTRAPOLATE(yb, (height));
int clocX = lix;
int cSrcX = srcX;
do
{
int xb = cSrcX;
EXTRAPOLATE(xb,(width));
lsmem[clocY][clocX] = ELEM(xb, yb, (width), (height), 0 );
clocX += BLK_X;
cSrcX += BLK_X;
}
while(clocX < BLK_X+(RADIUSX*2));
clocY += BLK_Y;
cSrcY += BLK_Y;
}
while(clocY < BLK_Y+(RADIUSY*2));
barrier(CLK_LOCAL_MEM_FENCE);
int i;
WORKTYPE sum = 0.0f;
int clocX = lix;
do
{
sum = 0.0f;
for(i=0; i<=2*RADIUSY; i++)
sum = mad(lsmem[liy+i][clocX], as_float(mat_kernelY[i]), sum);
lsmemDy[liy][clocX] = sum;
clocX += BLK_X;
}
while(clocX < BLK_X+(RADIUSX*2));
barrier(CLK_LOCAL_MEM_FENCE);
if( x >= dstWidth || y >=dstHeight )  return;
sum = 0.0f;
for(i=0; i<=2*RADIUSX; i++)
sum = mad(lsmemDy[liy][lix+i], as_float(mat_kernelX[i]), sum);
DST(x,y) = CONVERT_DSTTYPE(sum);
}
f48a37bd2a76b2d14666ef7fc1f6d88ehaarobjectdetect#define CV_HAAR_FEATURE_MAX           3
#define calc_sum(rect,offset)        (sum[(rect).p0+offset] - sum[(rect).p1+offset] - sum[(rect).p2+offset] + sum[(rect).p3+offset])
#define calc_sum1(rect,offset,i)     (sum[(rect).p0[i]+offset] - sum[(rect).p1[i]+offset] - sum[(rect).p2[i]+offset] + sum[(rect).p3[i]+offset])
typedef int   sumtype;
typedef float sqsumtype;
#ifndef STUMP_BASED
#define STUMP_BASED 1
#endif
typedef struct __attribute__((aligned (128) )) GpuHidHaarTreeNode
{
int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned (64)));
float weight[CV_HAAR_FEATURE_MAX];
float threshold;
float alpha[3] __attribute__((aligned (16)));
int left __attribute__((aligned (4)));
int right __attribute__((aligned (4)));
}
GpuHidHaarTreeNode;
typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier
{
int count __attribute__((aligned (4)));
GpuHidHaarTreeNode* node __attribute__((aligned (8)));
float* alpha __attribute__((aligned (8)));
}
GpuHidHaarClassifier;
typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier
{
int  count __attribute__((aligned (4)));
float threshold __attribute__((aligned (4)));
int two_rects __attribute__((aligned (4)));
int reserved0 __attribute__((aligned (8)));
int reserved1 __attribute__((aligned (8)));
int reserved2 __attribute__((aligned (8)));
int reserved3 __attribute__((aligned (8)));
}
GpuHidHaarStageClassifier;
typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
{
int  count __attribute__((aligned (4)));
int  is_stump_based __attribute__((aligned (4)));
int  has_tilted_features __attribute__((aligned (4)));
int  is_tree __attribute__((aligned (4)));
int pq0 __attribute__((aligned (4)));
int pq1 __attribute__((aligned (4)));
int pq2 __attribute__((aligned (4)));
int pq3 __attribute__((aligned (4)));
int p0 __attribute__((aligned (4)));
int p1 __attribute__((aligned (4)));
int p2 __attribute__((aligned (4)));
int p3 __attribute__((aligned (4)));
float inv_window_area __attribute__((aligned (4)));
} GpuHidHaarClassifierCascade;
#ifdef PACKED_CLASSIFIER
__kernel void gpuRunHaarClassifierCascadePacked(
global const GpuHidHaarStageClassifier * stagecascadeptr,
global const int4 * info,
global const GpuHidHaarTreeNode * nodeptr,
global const int * restrict sum,
global const float * restrict sqsum,
volatile global int4 * candidate,
const int pixelstep,
const int loopcount,
const int start_stage,
const int split_stage,
const int end_stage,
const int startnode,
const int splitnode,
const int4 p,
const int4 pq,
const float correction,
global const int* pNodesPK,
global const int4* pWGInfo
)
{
int     gid = (int)get_group_id(0);
int     lid_x = (int)get_local_id(0);
int     lid_y = (int)get_local_id(1);
int     lid = lid_y*LSx+lid_x;
int4    WGInfo = pWGInfo[WGSTART+gid];
int     GroupX = (WGInfo.y >> 16)&0xFFFF;
int     GroupY = (WGInfo.y >> 0 )& 0xFFFF;
int     Width  = (WGInfo.x >> 16)&0xFFFF;
int     Height = (WGInfo.x >> 0 )& 0xFFFF;
int     ImgOffset = WGInfo.z;
float   ScaleFactor = as_float(WGInfo.w);
#define DATA_SIZE_X (PIXEL_STEP*LSx+WND_SIZE_X)
#define DATA_SIZE_Y (PIXEL_STEP*LSy+WND_SIZE_Y)
#define DATA_SIZE (DATA_SIZE_X*DATA_SIZE_Y)
local int SumL[DATA_SIZE];
for(int i = 0; i=0.f) ? sqrt(variance_norm_factor) : 1.f;
}
int result = (1.0f>0.0f);
for(int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++ )
{
float   stage_sum = 0.0f;
int2    stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float   stagethreshold = as_float(stageinfo.y);
int     lcl_off = (lid_y*DATA_SIZE_X)+(lid_x);
for(int nodeloop = 0; nodeloop < stageinfo.x; nodecounter++,nodeloop++ )
{
#define M0(_t) ((_t)&0xFFFF)
#define M1(_t) (((_t)>>16)&0xFFFF)
global const int4* pN = (__global int4*)(pNodesPK+nodecounter*NODE_SIZE);
int4    n0 = pN[0];
int4    n1 = pN[1];
int4    n2 = pN[2];
float   nodethreshold  = as_float(n2.y) * variance_norm_factor;
float classsum =
(SumL[M0(n0.x)+lcl_off] - SumL[M1(n0.x)+lcl_off] - SumL[M0(n0.y)+lcl_off] + SumL[M1(n0.y)+lcl_off]) * as_float(n1.z) +
(SumL[M0(n0.z)+lcl_off] - SumL[M1(n0.z)+lcl_off] - SumL[M0(n0.w)+lcl_off] + SumL[M1(n0.w)+lcl_off]) * as_float(n1.w) +
(SumL[M0(n1.x)+lcl_off] - SumL[M1(n1.x)+lcl_off] - SumL[M0(n1.y)+lcl_off] + SumL[M1(n1.y)+lcl_off]) * as_float(n2.x);
stage_sum += (classsum >= nodethreshold) ? as_float(n2.w) : as_float(n2.z);
}
result = (stage_sum >= stagethreshold);
}
if(result)
{
int index = 1+atomic_inc((volatile global int*)candidate);
if(index>2)<<2;
int readheight = grpszy-1+WINDOWSIZE;
int read_horiz_cnt = readwidth >> 2;
int total_read = mul24(read_horiz_cnt,readheight);
int read_loop = (total_read + lcl_sz - 1) >> 6;
candidate[outputoff+(lcl_id<<2)] = (int4)0;
candidate[outputoff+(lcl_id<<2)+1] = (int4)0;
candidate[outputoff+(lcl_id<<2)+2] = (int4)0;
candidate[outputoff+(lcl_id<<2)+3] = (int4)0;
for(int scalei = 0; scalei > 16;
int totalgrp = scaleinfo1.y & 0xffff;
int imgoff = scaleinfo1.z;
float factor = as_float(scaleinfo1.w);
__global const int * sum = sum1 + imgoff;
__global const float * sqsum = sqsum1 + imgoff;
for(int grploop=grpidx; grploop=0.f ? sqrt(variance_norm_factor) : 1.f;
for(int stageloop = start_stage; (stageloop < split_stage)  && result; stageloop++ )
{
float stage_sum = 0.f;
int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
for(int nodeloop = 0; nodeloop < stageinfo.x; )
{
__global GpuHidHaarTreeNode* currentnodeptr = (nodeptr + nodecounter);
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4*)(&(currentnodeptr->weight[0]));
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));
float nodethreshold  = w.w * variance_norm_factor;
info1.x +=lcl_off;
info1.z +=lcl_off;
info2.x +=lcl_off;
info2.z +=lcl_off;
float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] -
lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x;
classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] -
lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y;
info3.x +=lcl_off;
info3.z +=lcl_off;
classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] -
lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z;
bool passThres = classsum >= nodethreshold;
#if STUMP_BASED
stage_sum += passThres ? alpha3.y : alpha3.x;
nodecounter++;
nodeloop++;
#else
bool isRootNode = (nodecounter & 1) == 0;
if(isRootNode)
{
if( (passThres && currentnodeptr->right) ||
(!passThres && currentnodeptr->left))
{
nodecounter ++;
}
else
{
stage_sum += alpha3.x;
nodecounter += 2;
nodeloop ++;
}
}
else
{
stage_sum += passThres ? alpha3.z : alpha3.y;
nodecounter ++;
nodeloop ++;
}
#endif
}
result = (stage_sum >= stagethreshold);
}
if(factor < 2)
{
if(result && lclidx %2 ==0 && lclidy %2 ==0 )
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex<<1] = (lclidy << 16) | lclidx;
lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor);
}
}
else
{
if(result)
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex<<1] = (lclidy << 16) | lclidx;
lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount  = lclcount[0];
barrier(CLK_LOCAL_MEM_FENCE);
nodecounter = splitnode;
for(int stageloop = split_stage; stageloop< end_stage && queuecount>0; stageloop++)
{
lclcount[0]=0;
barrier(CLK_LOCAL_MEM_FENCE);
int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
int perfscale = queuecount > 4 ? 3 : 2;
int queuecount_loop = (queuecount + (1<> perfscale;
int lcl_compute_win = lcl_sz >> perfscale;
int lcl_compute_win_id = (lcl_id >>(6-perfscale));
int lcl_loops = (stageinfo.x + lcl_compute_win -1) >> (6-perfscale);
int lcl_compute_id = lcl_id - (lcl_compute_win_id << (6-perfscale));
for(int queueloop=0; queueloop>16),readwidth,temp_coord & 0xffff);
if(lcl_compute_win_id < queuecount)
{
int tempnodecounter = lcl_compute_id;
float part_sum = 0.f;
const int stump_factor = STUMP_BASED ? 1 : 2;
int root_offset = 0;
for(int lcl_loop=0; lcl_loopp[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4*)(&(currentnodeptr->weight[0]));
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));
float nodethreshold  = w.w * variance_norm_factor;
info1.x +=queue_pixel;
info1.z +=queue_pixel;
info2.x +=queue_pixel;
info2.z +=queue_pixel;
float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] -
lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x;
classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] -
lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y;
info3.x +=queue_pixel;
info3.z +=queue_pixel;
classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] -
lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z;
bool passThres = classsum >= nodethreshold;
#if STUMP_BASED
part_sum += passThres ? alpha3.y : alpha3.x;
tempnodecounter += lcl_compute_win;
lcl_loop++;
#else
if(root_offset == 0)
{
if( (passThres && currentnodeptr->right) ||
(!passThres && currentnodeptr->left))
{
root_offset = 1;
}
else
{
part_sum += alpha3.x;
tempnodecounter += lcl_compute_win;
lcl_loop++;
}
}
else
{
part_sum += passThres ? alpha3.z : alpha3.y;
tempnodecounter += lcl_compute_win;
lcl_loop++;
root_offset = 0;
}
#endif
}
partialsum[lcl_id]=part_sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lcl_compute_win_id < queuecount)
{
for(int i=0; i= stagethreshold && (lcl_compute_id==0))
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex<<1] = temp_coord;
lcloutindex[(queueindex<<1)+1] = as_int(variance_norm_factor);
}
lcl_compute_win_id +=(1<> 16));
temp = glboutindex[0];
int4 candidate_result;
candidate_result.zw = (int2)convert_int_rte(factor*20.f);
candidate_result.x = convert_int_rte(x*factor);
candidate_result.y = convert_int_rte(y*factor);
atomic_inc(glboutindex);
int i = outputoff+temp+lcl_id;
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
}
else
{
for(i=i+1;;i++)
{
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
break;
}
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
}
#endif
590337901df52f2d5c88385fa212f8b5haarobjectdetect_scaled2#define CV_HAAR_FEATURE_MAX           3
typedef int   sumtype;
typedef float sqsumtype;
typedef struct __attribute__((aligned(128))) GpuHidHaarTreeNode
{
int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned(64)));
float weight[CV_HAAR_FEATURE_MAX] ;
float threshold ;
float alpha[3] __attribute__((aligned(16)));
int left __attribute__((aligned(4)));
int right __attribute__((aligned(4)));
}
GpuHidHaarTreeNode;
typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier
{
int count __attribute__((aligned(4)));
GpuHidHaarTreeNode *node __attribute__((aligned(8)));
float *alpha __attribute__((aligned(8)));
}
GpuHidHaarClassifier;
typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier
{
int  count __attribute__((aligned(4)));
float threshold __attribute__((aligned(4)));
int two_rects __attribute__((aligned(4)));
int reserved0 __attribute__((aligned(8)));
int reserved1 __attribute__((aligned(8)));
int reserved2 __attribute__((aligned(8)));
int reserved3 __attribute__((aligned(8)));
}
GpuHidHaarStageClassifier;
typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade
{
int  count __attribute__((aligned(4)));
int  is_stump_based __attribute__((aligned(4)));
int  has_tilted_features __attribute__((aligned(4)));
int  is_tree __attribute__((aligned(4)));
int pq0 __attribute__((aligned(4)));
int pq1 __attribute__((aligned(4)));
int pq2 __attribute__((aligned(4)));
int pq3 __attribute__((aligned(4)));
int p0 __attribute__((aligned(4)));
int p1 __attribute__((aligned(4)));
int p2 __attribute__((aligned(4)));
int p3 __attribute__((aligned(4)));
float inv_window_area __attribute__((aligned(4)));
} GpuHidHaarClassifierCascade;
__kernel void gpuRunHaarClassifierCascade_scaled2(
global GpuHidHaarStageClassifier *stagecascadeptr,
global int4 *info,
global GpuHidHaarTreeNode *nodeptr,
global const int *restrict sum,
global const float   *restrict sqsum,
global int4 *candidate,
const int rows,
const int cols,
const int step,
const int loopcount,
const int start_stage,
const int split_stage,
const int end_stage,
const int startnode,
global int4 *p,
global float *correction,
const int nodecount)
{
int grpszx = get_local_size(0);
int grpszy = get_local_size(1);
int grpnumx = get_num_groups(0);
int grpidx = get_group_id(0);
int lclidx = get_local_id(0);
int lclidy = get_local_id(1);
int lcl_id = mad24(lclidy, grpszx, lclidx);
__local int glboutindex[1];
__local int lclcount[1];
__local int lcloutindex[64];
glboutindex[0] = 0;
int outputoff = mul24(grpidx, 256);
candidate[outputoff + (lcl_id << 2)] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 1] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 2] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 3] = (int4)0;
int max_idx = rows * cols - 1;
for (int scalei = 0; scalei < loopcount; scalei++)
{
int4 scaleinfo1;
scaleinfo1 = info[scalei];
int grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16;
int totalgrp = scaleinfo1.y & 0xffff;
float factor = as_float(scaleinfo1.w);
float correction_t = correction[scalei];
float ystep = max(2.0f, factor);
for (int grploop = get_group_id(0); grploop < totalgrp; grploop += grpnumx)
{
int4 cascadeinfo = p[scalei];
int grpidy = grploop / grpnumperline;
int grpidx = grploop - mul24(grpidy, grpnumperline);
int ix = mad24(grpidx, grpszx, lclidx);
int iy = mad24(grpidy, grpszy, lclidy);
int x = round(ix * ystep);
int y = round(iy * ystep);
lcloutindex[lcl_id] = 0;
lclcount[0] = 0;
int nodecounter;
float mean, variance_norm_factor;
{
const int p_offset = mad24(y, step, x);
cascadeinfo.x += p_offset;
cascadeinfo.z += p_offset;
mean = (sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)]
- sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] -
sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)]
+ sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)])
* correction_t;
variance_norm_factor = sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)]
- sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] -
sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)]
+ sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)];
variance_norm_factor = variance_norm_factor * correction_t - mean * mean;
variance_norm_factor = variance_norm_factor >= 0.f ? sqrt(variance_norm_factor) : 1.f;
bool result = true;
nodecounter = startnode + nodecount * scalei;
for (int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++)
{
float stage_sum = 0.f;
int   stagecount = stagecascadeptr[stageloop].count;
for (int nodeloop = 0; nodeloop < stagecount;)
{
__global GpuHidHaarTreeNode *currentnodeptr = (nodeptr + nodecounter);
int4 info1 = *(__global int4 *)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4 *)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4 *)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4 *)(&(currentnodeptr->weight[0]));
float3 alpha3 = *(__global float3 *)(&(currentnodeptr->alpha[0]));
float nodethreshold  = w.w * variance_norm_factor;
info1.x += p_offset;
info1.z += p_offset;
info2.x += p_offset;
info2.z += p_offset;
info3.x += p_offset;
info3.z += p_offset;
float classsum = (sum[clamp(mad24(info1.y, step, info1.x), 0, max_idx)]
- sum[clamp(mad24(info1.y, step, info1.z), 0, max_idx)] -
sum[clamp(mad24(info1.w, step, info1.x), 0, max_idx)]
+ sum[clamp(mad24(info1.w, step, info1.z), 0, max_idx)]) * w.x;
classsum += (sum[clamp(mad24(info2.y, step, info2.x), 0, max_idx)]
- sum[clamp(mad24(info2.y, step, info2.z), 0, max_idx)] -
sum[clamp(mad24(info2.w, step, info2.x), 0, max_idx)]
+ sum[clamp(mad24(info2.w, step, info2.z), 0, max_idx)]) * w.y;
classsum += (sum[clamp(mad24(info3.y, step, info3.x), 0, max_idx)]
- sum[clamp(mad24(info3.y, step, info3.z), 0, max_idx)] -
sum[clamp(mad24(info3.w, step, info3.x), 0, max_idx)]
+ sum[clamp(mad24(info3.w, step, info3.z), 0, max_idx)]) * w.z;
bool passThres = classsum >= nodethreshold;
#if STUMP_BASED
stage_sum += passThres ? alpha3.y : alpha3.x;
nodecounter++;
nodeloop++;
#else
bool isRootNode = (nodecounter & 1) == 0;
if(isRootNode)
{
if( (passThres && currentnodeptr->right) ||
(!passThres && currentnodeptr->left))
{
nodecounter ++;
}
else
{
stage_sum += alpha3.x;
nodecounter += 2;
nodeloop ++;
}
}
else
{
stage_sum += (passThres ? alpha3.z : alpha3.y);
nodecounter ++;
nodeloop ++;
}
#endif
}
result = (int)(stage_sum >= stagecascadeptr[stageloop].threshold);
}
barrier(CLK_LOCAL_MEM_FENCE);
if (result)
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex] = (y << 16) | x;
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount = lclcount[0];
if (lcl_id < queuecount)
{
int temp = lcloutindex[lcl_id];
int x = temp & 0xffff;
int y = (temp & (int)0xffff0000) >> 16;
temp = atomic_inc(glboutindex);
int4 candidate_result;
candidate_result.zw = (int2)convert_int_rte(factor * 20.f);
candidate_result.x = x;
candidate_result.y = y;
int i = outputoff+temp+lcl_id;
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
}
else
{
for(i=i+1;;i++)
{
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
break;
}
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
}
}
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, int nodenum)
{
int counter = get_global_id(0);
int tr_x[3], tr_y[3], tr_h[3], tr_w[3], i = 0;
GpuHidHaarTreeNode t1 = *(orinode + counter);
#pragma unroll
for (i = 0; i < 3; i++)
{
tr_x[i] = (int)(t1.p[i][0] * scale + 0.5f);
tr_y[i] = (int)(t1.p[i][1] * scale + 0.5f);
tr_w[i] = (int)(t1.p[i][2] * scale + 0.5f);
tr_h[i] = (int)(t1.p[i][3] * scale + 0.5f);
}
t1.weight[0] = -(t1.weight[1] * tr_h[1] * tr_w[1] + t1.weight[2] * tr_h[2] * tr_w[2]) / (tr_h[0] * tr_w[0]);
counter += nodenum;
#pragma unroll
for (i = 0; i < 3; i++)
{
newnode[counter].p[i][0] = tr_x[i];
newnode[counter].p[i][1] = tr_y[i];
newnode[counter].p[i][2] = tr_x[i] + tr_w[i];
newnode[counter].p[i][3] = tr_y[i] + tr_h[i];
newnode[counter].weight[i] = t1.weight[i] * weight_scale;
}
newnode[counter].left = t1.left;
newnode[counter].right = t1.right;
newnode[counter].threshold = t1.threshold;
newnode[counter].alpha[0] = t1.alpha[0];
newnode[counter].alpha[1] = t1.alpha[1];
newnode[counter].alpha[2] = t1.alpha[2];
}
37a0dce47f004dc26ec48a141c200e2dimgproc_bilateral__kernel void bilateral_C1_D0(__global uchar *dst,
__global const uchar *src,
const int dst_rows,
const int dst_cols,
const int maxk,
const int radius,
const int dst_step,
const int dst_offset,
const int src_step,
const int src_rows,
const int src_cols,
__constant float *color_weight,
__constant float *space_weight,
__constant int *space_ofs)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < dst_rows && x < dst_cols)
{
int src_index = mad24(y + radius, src_step, x + radius);
int dst_index = mad24(y, dst_step, x + dst_offset);
float sum = 0.f, wsum = 0.f;
int val0 = (int)src[src_index];
for(int k = 0; k < maxk; k++ )
{
int val = (int)src[src_index + space_ofs[k]];
float w = space_weight[k] * color_weight[abs(val - val0)];
sum += (float)(val) * w;
wsum += w;
}
dst[dst_index] = convert_uchar_rtz(sum / wsum + 0.5f);
}
}
__kernel void bilateral2_C1_D0(__global uchar *dst,
__global const uchar *src,
const int dst_rows,
const int dst_cols,
const int maxk,
const int radius,
const int dst_step,
const int dst_offset,
const int src_step,
const int src_rows,
const int src_cols,
__constant float *color_weight,
__constant float *space_weight,
__constant int *space_ofs)
{
int x = get_global_id(0) << 2;
int y = get_global_id(1);
if (y < dst_rows && x < dst_cols)
{
int src_index = mad24(y + radius, src_step, x + radius);
int dst_index = mad24(y, dst_step, x + dst_offset);
float4 sum = (float4)(0.f), wsum = (float4)(0.f);
int4 val0 = convert_int4(vload4(0,src + src_index));
for(int k = 0; k < maxk; k++ )
{
int4 val = convert_int4(vload4(0,src+src_index + space_ofs[k]));
float4 w = (float4)(space_weight[k]) * (float4)(color_weight[abs(val.x - val0.x)], color_weight[abs(val.y - val0.y)],
color_weight[abs(val.z - val0.z)], color_weight[abs(val.w - val0.w)]);
sum += convert_float4(val) * w;
wsum += w;
}
*(__global uchar4*)(dst+dst_index) = convert_uchar4_rtz(sum/wsum+0.5f);
}
}
__kernel void bilateral_C4_D0(__global uchar4 *dst,
__global const uchar4 *src,
const int dst_rows,
const int dst_cols,
const int maxk,
const int radius,
const int dst_step,
const int dst_offset,
const int src_step,
const int src_rows,
const int src_cols,
__constant float *color_weight,
__constant float *space_weight,
__constant int *space_ofs)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < dst_rows && x < dst_cols)
{
int src_index = mad24(y + radius, src_step, x + radius);
int dst_index = mad24(y, dst_step, x + dst_offset);
float4 sum = (float4)0.f;
float wsum = 0.f;
int4 val0 = convert_int4(src[src_index]);
for(int k = 0; k < maxk; k++ )
{
int4 val = convert_int4(src[src_index + space_ofs[k]]);
float w = space_weight[k] * color_weight[abs(val.x - val0.x) + abs(val.y - val0.y) + abs(val.z - val0.z)];
sum += convert_float4(val) * (float4)w;
wsum += w;
}
wsum = 1.f / wsum;
dst[dst_index] = convert_uchar4_rtz(sum * (float4)wsum + (float4)0.5f);
}
}
9b8b7d38ce4703393eef8505f1b2c7d3imgproc_calcHarris#if defined (DOUBLE_SUPPORT) && defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#define FPTYPE double
#else
#define FPTYPE float
#endif
#ifdef BORDER_CONSTANT
#elif defined BORDER_REPLICATE
#define EXTRAPOLATE(x, maxV) \
{ \
x = max(min(x, maxV - 1), 0); \
}
#elif defined BORDER_WRAP
#define EXTRAPOLATE(x, maxV) \
{ \
if (x < 0) \
x -= ((x - maxV + 1) / maxV) * maxV; \
if (x >= maxV) \
x %= maxV; \
}
#elif defined(BORDER_REFLECT) || defined(BORDER_REFLECT101)
#define EXTRAPOLATE_(x, maxV, delta) \
{ \
if (maxV == 1) \
x = 0; \
else \
do \
{ \
if ( x < 0 ) \
x = -x - 1 + delta; \
else \
x = maxV - 1 - (x - maxV) - delta; \
} \
while (x >= maxV || x < 0); \
}
#ifdef BORDER_REFLECT
#define EXTRAPOLATE(x, maxV) EXTRAPOLATE_(x, maxV, 0)
#else
#define EXTRAPOLATE(x, maxV) EXTRAPOLATE_(x, maxV, 1)
#endif
#else
#error No extrapolation method
#endif
#define THREADS 256
__kernel void calcHarris(__global const float *Dx, __global const float *Dy, __global float *dst,
int dx_offset, int dx_whole_rows, int dx_whole_cols, int dx_step,
int dy_offset, int dy_whole_rows, int dy_whole_cols, int dy_step,
int dst_offset, int dst_rows, int dst_cols, int dst_step, float k)
{
int col = get_local_id(0);
int gX = get_group_id(0);
int gY = get_group_id(1);
int gly = get_global_id(1);
int dx_x_off = (dx_offset % dx_step) >> 2;
int dx_y_off = dx_offset / dx_step;
int dy_x_off = (dy_offset % dy_step) >> 2;
int dy_y_off = dy_offset / dy_step;
int dst_x_off = (dst_offset % dst_step) >> 2;
int dst_y_off = dst_offset / dst_step;
int dx_startX = gX * (THREADS-ksX+1) - anX + dx_x_off;
int dx_startY = (gY << 1) - anY + dx_y_off;
int dy_startX = gX * (THREADS-ksX+1) - anX + dy_x_off;
int dy_startY = (gY << 1) - anY + dy_y_off;
int dst_startX = gX * (THREADS-ksX+1) + dst_x_off;
int dst_startY = (gY << 1) + dst_y_off;
float dx_data[ksY+1],dy_data[ksY+1], data[3][ksY+1];
__local FPTYPE temp[6][THREADS];
#ifdef BORDER_CONSTANT
for (int i=0; i < ksY+1; i++)
{
bool dx_con = dx_startX+col >= 0 && dx_startX+col < dx_whole_cols && dx_startY+i >= 0 && dx_startY+i < dx_whole_rows;
int indexDx = (dx_startY+i)*(dx_step>>2)+(dx_startX+col);
float dx_s = dx_con ? Dx[indexDx] : 0.0f;
dx_data[i] = dx_s;
bool dy_con = dy_startX+col >= 0 && dy_startX+col < dy_whole_cols && dy_startY+i >= 0 && dy_startY+i < dy_whole_rows;
int indexDy = (dy_startY+i)*(dy_step>>2)+(dy_startX+col);
float dy_s = dy_con ? Dy[indexDy] : 0.0f;
dy_data[i] = dy_s;
data[0][i] = dx_data[i] * dx_data[i];
data[1][i] = dx_data[i] * dy_data[i];
data[2][i] = dy_data[i] * dy_data[i];
}
#else
int clamped_col = min(2*dst_cols, col);
for (int i=0; i < ksY+1; i++)
{
int dx_selected_row = dx_startY+i, dx_selected_col = dx_startX+clamped_col;
EXTRAPOLATE(dx_selected_row, dx_whole_rows)
EXTRAPOLATE(dx_selected_col, dx_whole_cols)
dx_data[i] = Dx[dx_selected_row * (dx_step>>2) + dx_selected_col];
int dy_selected_row = dy_startY+i, dy_selected_col = dy_startX+clamped_col;
EXTRAPOLATE(dy_selected_row, dy_whole_rows)
EXTRAPOLATE(dy_selected_col, dy_whole_cols)
dy_data[i] = Dy[dy_selected_row * (dy_step>>2) + dy_selected_col];
data[0][i] = dx_data[i] * dx_data[i];
data[1][i] = dx_data[i] * dy_data[i];
data[2][i] = dy_data[i] * dy_data[i];
}
#endif
FPTYPE sum0 = 0.0f, sum1 = 0.0f, sum2 = 0.0f;
for (int i=1; i < ksY; i++)
{
sum0 += data[0][i];
sum1 += data[1][i];
sum2 += data[2][i];
}
FPTYPE sum01 = sum0 + data[0][0];
FPTYPE sum02 = sum0 + data[0][ksY];
temp[0][col] = sum01;
temp[1][col] = sum02;
FPTYPE sum11 = sum1 + data[1][0];
FPTYPE sum12 = sum1 + data[1][ksY];
temp[2][col] = sum11;
temp[3][col] = sum12;
FPTYPE sum21 = sum2 + data[2][0];
FPTYPE sum22 = sum2 + data[2][ksY];
temp[4][col] = sum21;
temp[5][col] = sum22;
barrier(CLK_LOCAL_MEM_FENCE);
if (col < (THREADS- (ksX - 1)))
{
col += anX;
int posX = dst_startX - dst_x_off + col - anX;
int posY = (gly << 1);
int till = (ksX + 1)%2;
float tmp_sum[6] = { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f };
for (int k=0; k<6; k++)
{
FPTYPE temp_sum = 0;
for (int i=-anX; i<=anX - till; i++)
{
temp_sum += temp[k][col+i];
}
tmp_sum[k] = temp_sum;
}
if (posX < dst_cols && (posY) < dst_rows)
{
dst[(dst_startY+0) * (dst_step>>2)+ dst_startX + col - anX] =
tmp_sum[0] * tmp_sum[4] - tmp_sum[2] * tmp_sum[2] - k * (tmp_sum[0] + tmp_sum[4]) * (tmp_sum[0] + tmp_sum[4]);
}
if (posX < dst_cols && (posY + 1) < dst_rows)
{
dst[(dst_startY+1) * (dst_step>>2)+ dst_startX + col - anX] =
tmp_sum[1] * tmp_sum[5] - tmp_sum[3] * tmp_sum[3] - k * (tmp_sum[1] + tmp_sum[5]) * (tmp_sum[1] + tmp_sum[5]);
}
}
}
f2c0f92edc8f64eb31dc2f2034bc799fimgproc_calcMinEigenVal#ifdef BORDER_CONSTANT
#elif defined BORDER_REPLICATE
#define EXTRAPOLATE(x, maxV) \
{ \
x = max(min(x, maxV - 1), 0); \
}
#elif defined BORDER_WRAP
#define EXTRAPOLATE(x, maxV) \
{ \
if (x < 0) \
x -= ((x - maxV + 1) / maxV) * maxV; \
if (x >= maxV) \
x %= maxV; \
}
#elif defined(BORDER_REFLECT) || defined(BORDER_REFLECT101)
#define EXTRAPOLATE_(x, maxV, delta) \
{ \
if (maxV == 1) \
x = 0; \
else \
do \
{ \
if ( x < 0 ) \
x = -x - 1 + delta; \
else \
x = maxV - 1 - (x - maxV) - delta; \
} \
while (x >= maxV || x < 0); \
}
#ifdef BORDER_REFLECT
#define EXTRAPOLATE(x, maxV) EXTRAPOLATE_(x, maxV, 0)
#else
#define EXTRAPOLATE(x, maxV) EXTRAPOLATE_(x, maxV, 1)
#endif
#else
#error No extrapolation method
#endif
#define THREADS 256
__kernel void calcMinEigenVal(__global const float *Dx,__global const float *Dy, __global float *dst,
int dx_offset, int dx_whole_rows, int dx_whole_cols, int dx_step,
int dy_offset, int dy_whole_rows, int dy_whole_cols, int dy_step,
int dst_offset, int dst_rows, int dst_cols, int dst_step, float k)
{
int col = get_local_id(0);
int gX = get_group_id(0);
int gY = get_group_id(1);
int gly = get_global_id(1);
int dx_x_off = (dx_offset % dx_step) >> 2;
int dx_y_off = dx_offset / dx_step;
int dy_x_off = (dy_offset % dy_step) >> 2;
int dy_y_off = dy_offset / dy_step;
int dst_x_off = (dst_offset % dst_step) >> 2;
int dst_y_off = dst_offset / dst_step;
int dx_startX = gX * (THREADS-ksX+1) - anX + dx_x_off;
int dx_startY = (gY << 1) - anY + dx_y_off;
int dy_startX = gX * (THREADS-ksX+1) - anX + dy_x_off;
int dy_startY = (gY << 1) - anY + dy_y_off;
int dst_startX = gX * (THREADS-ksX+1) + dst_x_off;
int dst_startY = (gY << 1) + dst_y_off;
float dx_data[ksY+1], dy_data[ksY+1], data[3][ksY+1];
__local float temp[6][THREADS];
#ifdef BORDER_CONSTANT
for (int i=0; i < ksY+1; i++)
{
bool dx_con = dx_startX+col >= 0 && dx_startX+col < dx_whole_cols && dx_startY+i >= 0 && dx_startY+i < dx_whole_rows;
int indexDx = (dx_startY+i)*(dx_step>>2)+(dx_startX+col);
float dx_s = dx_con ? Dx[indexDx] : 0.0f;
dx_data[i] = dx_s;
bool dy_con = dy_startX+col >= 0 && dy_startX+col < dy_whole_cols && dy_startY+i >= 0 && dy_startY+i < dy_whole_rows;
int indexDy = (dy_startY+i)*(dy_step>>2)+(dy_startX+col);
float dy_s = dy_con ? Dy[indexDy] : 0.0f;
dy_data[i] = dy_s;
data[0][i] = dx_data[i] * dx_data[i];
data[1][i] = dx_data[i] * dy_data[i];
data[2][i] = dy_data[i] * dy_data[i];
}
#else
int clamped_col = min(dst_cols, col);
for (int i=0; i < ksY+1; i++)
{
int dx_selected_row = dx_startY+i, dx_selected_col = dx_startX+clamped_col;
EXTRAPOLATE(dx_selected_row, dx_whole_rows)
EXTRAPOLATE(dx_selected_col, dx_whole_cols)
dx_data[i] = Dx[dx_selected_row * (dx_step>>2) + dx_selected_col];
int dy_selected_row = dy_startY+i, dy_selected_col = dy_startX+clamped_col;
EXTRAPOLATE(dy_selected_row, dy_whole_rows)
EXTRAPOLATE(dy_selected_col, dy_whole_cols)
dy_data[i] = Dy[dy_selected_row * (dy_step>>2) + dy_selected_col];
data[0][i] = dx_data[i] * dx_data[i];
data[1][i] = dx_data[i] * dy_data[i];
data[2][i] = dy_data[i] * dy_data[i];
}
#endif
float sum0 = 0.0f, sum1 = 0.0f, sum2 = 0.0f;
for (int i=1; i < ksY; i++)
{
sum0 += (data[0][i]);
sum1 += (data[1][i]);
sum2 += (data[2][i]);
}
float sum01 = sum0 + (data[0][0]);
float sum02 = sum0 + (data[0][ksY]);
temp[0][col] = sum01;
temp[1][col] = sum02;
float sum11 = sum1 + (data[1][0]);
float sum12 = sum1 + (data[1][ksY]);
temp[2][col] = sum11;
temp[3][col] = sum12;
float sum21 = sum2 + (data[2][0]);
float sum22 = sum2 + (data[2][ksY]);
temp[4][col] = sum21;
temp[5][col] = sum22;
barrier(CLK_LOCAL_MEM_FENCE);
if(col < (THREADS-(ksX-1)))
{
col += anX;
int posX = dst_startX - dst_x_off + col - anX;
int posY = (gly << 1);
int till = (ksX + 1)%2;
float tmp_sum[6] = { 0.0f, 0.0f , 0.0f, 0.0f, 0.0f, 0.0f };
for (int k=0; k<6; k++)
for (int i=-anX; i<=anX - till; i++)
tmp_sum[k] += temp[k][col+i];
if(posX < dst_cols && (posY) < dst_rows)
{
float a = tmp_sum[0] * 0.5f;
float b = tmp_sum[2];
float c = tmp_sum[4] * 0.5f;
dst[(dst_startY+0) * (dst_step>>2)+ dst_startX + col - anX] = (float)((a+c) - sqrt((a-c)*(a-c) + b*b));
}
if (posX < dst_cols && (posY + 1) < dst_rows)
{
float a = tmp_sum[1] * 0.5f;
float b = tmp_sum[3];
float c = tmp_sum[5] * 0.5f;
dst[(dst_startY+1) * (dst_step>>2)+ dst_startX + col - anX] = (float)((a+c) - sqrt((a-c)*(a-c) + b*b));
}
}
}
af755a2a32c7b43016576fecac43fa29imgproc_canny#ifdef L2GRAD
float calc(int x, int y)
{
return sqrt((float)(x * x + y * y));
}
#else
float calc(int x, int y)
{
return (float)abs(x) + abs(y);
}
#endif
__kernel
void
__attribute__((reqd_work_group_size(16,16,1)))
calcSobelRowPass
(
__global const uchar * src,
__global int * dx_buf,
__global int * dy_buf,
int rows,
int cols,
int src_step,
int src_offset,
int dx_buf_step,
int dx_buf_offset,
int dy_buf_step,
int dy_buf_offset
)
{
dx_buf_step   /= sizeof(*dx_buf);
dx_buf_offset /= sizeof(*dx_buf);
dy_buf_step   /= sizeof(*dy_buf);
dy_buf_offset /= sizeof(*dy_buf);
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int lidx = get_local_id(0);
int lidy = get_local_id(1);
__local int smem[16][18];
smem[lidy][lidx + 1] =
src[gidx + min(gidy, rows - 1) * src_step + src_offset];
if(lidx == 0)
{
smem[lidy][0]  =
src[max(gidx - 1,  0)        + min(gidy, rows - 1) * src_step + src_offset];
smem[lidy][17] =
src[min(gidx + 16, cols - 1) + min(gidy, rows - 1) * src_step + src_offset];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(gidy < rows && gidx < cols)
{
dx_buf[gidx + gidy * dx_buf_step + dx_buf_offset] =
-smem[lidy][lidx] + smem[lidy][lidx + 2];
dy_buf[gidx + gidy * dy_buf_step + dy_buf_offset] =
smem[lidy][lidx] + 2 * smem[lidy][lidx + 1] + smem[lidy][lidx + 2];
}
}
__kernel
void
__attribute__((reqd_work_group_size(16,16,1)))
calcMagnitude_buf
(
__global const int * dx_buf,
__global const int * dy_buf,
__global int * dx,
__global int * dy,
__global float * mag,
int rows,
int cols,
int dx_buf_step,
int dx_buf_offset,
int dy_buf_step,
int dy_buf_offset,
int dx_step,
int dx_offset,
int dy_step,
int dy_offset,
int mag_step,
int mag_offset
)
{
dx_buf_step    /= sizeof(*dx_buf);
dx_buf_offset  /= sizeof(*dx_buf);
dy_buf_step    /= sizeof(*dy_buf);
dy_buf_offset  /= sizeof(*dy_buf);
dx_step    /= sizeof(*dx);
dx_offset  /= sizeof(*dx);
dy_step    /= sizeof(*dy);
dy_offset  /= sizeof(*dy);
mag_step   /= sizeof(*mag);
mag_offset /= sizeof(*mag);
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int lidx = get_local_id(0);
int lidy = get_local_id(1);
__local int sdx[18][16];
__local int sdy[18][16];
sdx[lidy + 1][lidx] =
dx_buf[gidx + min(gidy, rows - 1) * dx_buf_step + dx_buf_offset];
sdy[lidy + 1][lidx] =
dy_buf[gidx + min(gidy, rows - 1) * dy_buf_step + dy_buf_offset];
if(lidy == 0)
{
sdx[0][lidx]  =
dx_buf[gidx + min(max(gidy-1,0),rows-1) * dx_buf_step + dx_buf_offset];
sdx[17][lidx] =
dx_buf[gidx + min(gidy + 16, rows - 1)  * dx_buf_step + dx_buf_offset];
sdy[0][lidx]  =
dy_buf[gidx + min(max(gidy-1,0),rows-1) * dy_buf_step + dy_buf_offset];
sdy[17][lidx] =
dy_buf[gidx + min(gidy + 16, rows - 1)  * dy_buf_step + dy_buf_offset];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(gidx < cols && gidy < rows)
{
int x =  sdx[lidy][lidx] + 2 * sdx[lidy + 1][lidx] + sdx[lidy + 2][lidx];
int y = -sdy[lidy][lidx] + sdy[lidy + 2][lidx];
dx[gidx + gidy * dx_step + dx_offset] = x;
dy[gidx + gidy * dy_step + dy_offset] = y;
mag[(gidx + 1) + (gidy + 1) * mag_step + mag_offset] = calc(x, y);
}
}
__kernel
void calcMagnitude
(
__global const int * dx,
__global const int * dy,
__global float * mag,
int rows,
int cols,
int dx_step,
int dx_offset,
int dy_step,
int dy_offset,
int mag_step,
int mag_offset
)
{
dx_step    /= sizeof(*dx);
dx_offset  /= sizeof(*dx);
dy_step    /= sizeof(*dy);
dy_offset  /= sizeof(*dy);
mag_step   /= sizeof(*mag);
mag_offset /= sizeof(*mag);
int gidx = get_global_id(0);
int gidy = get_global_id(1);
if(gidy < rows && gidx < cols)
{
mag[(gidx + 1) + (gidy + 1) * mag_step + mag_offset] =
calc(
dx[gidx + gidy * dx_step + dx_offset],
dy[gidx + gidy * dy_step + dy_offset]
);
}
}
#define CANNY_SHIFT 15
#ifdef DOUBLE_SUPPORT
#define TG22        (int)(0.4142135623730950488016887242097*(1< low_thresh)
{
const int tg22x = x * TG22;
const int tg67x = tg22x + (x << (1 + CANNY_SHIFT));
y <<= CANNY_SHIFT;
if(y < tg22x)
{
if(m > smem[lidy + 1][lidx] && m >= smem[lidy + 1][lidx + 2])
{
edge_type = 1 + (int)(m > high_thresh);
}
}
else if (y > tg67x)
{
if(m > smem[lidy][lidx + 1]&& m >= smem[lidy + 2][lidx + 1])
{
edge_type = 1 + (int)(m > high_thresh);
}
}
else
{
if(m > smem[lidy][lidx + 1 - s]&& m > smem[lidy + 2][lidx + 1 + s])
{
edge_type = 1 + (int)(m > high_thresh);
}
}
}
map[gidx + 1 + (gidy + 1) * map_step] = edge_type;
}
}
#undef CANNY_SHIFT
#undef TG22
struct PtrStepSz {
__global int *ptr;
int step;
int rows, cols;
};
int get(struct PtrStepSz data, int y, int x) { return *((__global int *)((__global char*)data.ptr + data.step * (y + 1) + sizeof(int) * (x + 1))); }
void set(struct PtrStepSz data, int y, int x, int value) { *((__global int *)((__global char*)data.ptr + data.step * (y + 1) + sizeof(int) * (x + 1))) = value; }
__kernel
void
__attribute__((reqd_work_group_size(16,16,1)))
edgesHysteresisLocal
(
__global int * map_ptr,
__global ushort2 * st,
__global unsigned int * counter,
int rows,
int cols,
int map_step,
int map_offset
)
{
#if 0
map_step   /= sizeof(*map);
map_offset /= sizeof(*map);
const __global int* map = map_ptr + map_offset;
__local int smem[18][18];
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int lidx = get_local_id(0);
int lidy = get_local_id(1);
int grp_idx = get_global_id(0) & 0xFFFFF0;
int grp_idy = get_global_id(1) & 0xFFFFF0;
int tid = lidx + lidy * 16;
int lx = tid % 18;
int ly = tid / 18;
if(ly < 14)
{
smem[ly][lx] =
map[grp_idx + lx + min(grp_idy + ly, rows - 1) * map_step];
}
if(ly < 4 && grp_idy + ly + 14 <= rows && grp_idx + lx <= cols)
{
smem[ly + 14][lx] =
map[grp_idx + lx + min(grp_idy + ly + 14, rows - 1) * map_step];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(gidy < rows && gidx < cols)
{
int n;
#pragma unroll
for (int k = 0; k < 16; ++k)
{
n = 0;
if (smem[lidy + 1][lidx + 1] == 1)
{
n += smem[lidy    ][lidx    ] == 2;
n += smem[lidy    ][lidx + 1] == 2;
n += smem[lidy    ][lidx + 2] == 2;
n += smem[lidy + 1][lidx    ] == 2;
n += smem[lidy + 1][lidx + 2] == 2;
n += smem[lidy + 2][lidx    ] == 2;
n += smem[lidy + 2][lidx + 1] == 2;
n += smem[lidy + 2][lidx + 2] == 2;
}
if (n > 0)
smem[lidy + 1][lidx + 1] = 2;
}
const int e = smem[lidy + 1][lidx + 1];
map[gidx + 1 + (gidy + 1) * map_step] = e;
n = 0;
if(e == 2)
{
n += smem[lidy    ][lidx    ] == 1;
n += smem[lidy    ][lidx + 1] == 1;
n += smem[lidy    ][lidx + 2] == 1;
n += smem[lidy + 1][lidx    ] == 1;
n += smem[lidy + 1][lidx + 2] == 1;
n += smem[lidy + 2][lidx    ] == 1;
n += smem[lidy + 2][lidx + 1] == 1;
n += smem[lidy + 2][lidx + 2] == 1;
}
if(n > 0)
{
unsigned int ind = atomic_inc(counter);
st[ind] = (ushort2)(gidx + 1, gidy + 1);
}
}
#else
struct PtrStepSz map = {((__global int *)((__global char*)map_ptr + map_offset)), map_step, rows + 1, cols + 1};
__local int smem[18][18];
int2 blockIdx = (int2)(get_group_id(0), get_group_id(1));
int2 blockDim = (int2)(get_local_size(0), get_local_size(1));
int2 threadIdx = (int2)(get_local_id(0), get_local_id(1));
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
smem[threadIdx.y + 1][threadIdx.x + 1] = x < map.cols && y < map.rows ? get(map, y, x) : 0;
if (threadIdx.y == 0)
smem[0][threadIdx.x + 1] = x < map.cols ? get(map, y - 1, x) : 0;
if (threadIdx.y == blockDim.y - 1)
smem[blockDim.y + 1][threadIdx.x + 1] = y + 1 < map.rows ? get(map, y + 1, x) : 0;
if (threadIdx.x == 0)
smem[threadIdx.y + 1][0] = y < map.rows ? get(map, y, x - 1) : 0;
if (threadIdx.x == blockDim.x - 1)
smem[threadIdx.y + 1][blockDim.x + 1] = x + 1 < map.cols && y < map.rows ? get(map, y, x + 1) : 0;
if (threadIdx.x == 0 && threadIdx.y == 0)
smem[0][0] = y > 0 && x > 0 ? get(map, y - 1, x - 1) : 0;
if (threadIdx.x == blockDim.x - 1 && threadIdx.y == 0)
smem[0][blockDim.x + 1] = y > 0 && x + 1 < map.cols ? get(map, y - 1, x + 1) : 0;
if (threadIdx.x == 0 && threadIdx.y == blockDim.y - 1)
smem[blockDim.y + 1][0] = y + 1 < map.rows && x > 0 ? get(map, y + 1, x - 1) : 0;
if (threadIdx.x == blockDim.x - 1 && threadIdx.y == blockDim.y - 1)
smem[blockDim.y + 1][blockDim.x + 1] = y + 1 < map.rows && x + 1 < map.cols ? get(map, y + 1, x + 1) : 0;
barrier(CLK_LOCAL_MEM_FENCE);
if (x >= cols || y >= rows)
return;
int n;
#pragma unroll
for (int k = 0; k < 16; ++k)
{
n = 0;
if (smem[threadIdx.y + 1][threadIdx.x + 1] == 1)
{
n += smem[threadIdx.y    ][threadIdx.x    ] == 2;
n += smem[threadIdx.y    ][threadIdx.x + 1] == 2;
n += smem[threadIdx.y    ][threadIdx.x + 2] == 2;
n += smem[threadIdx.y + 1][threadIdx.x    ] == 2;
n += smem[threadIdx.y + 1][threadIdx.x + 2] == 2;
n += smem[threadIdx.y + 2][threadIdx.x    ] == 2;
n += smem[threadIdx.y + 2][threadIdx.x + 1] == 2;
n += smem[threadIdx.y + 2][threadIdx.x + 2] == 2;
}
if (n > 0)
smem[threadIdx.y + 1][threadIdx.x + 1] = 2;
}
const int e = smem[threadIdx.y + 1][threadIdx.x + 1];
set(map, y, x, e);
n = 0;
if (e == 2)
{
n += smem[threadIdx.y    ][threadIdx.x    ] == 1;
n += smem[threadIdx.y    ][threadIdx.x + 1] == 1;
n += smem[threadIdx.y    ][threadIdx.x + 2] == 1;
n += smem[threadIdx.y + 1][threadIdx.x    ] == 1;
n += smem[threadIdx.y + 1][threadIdx.x + 2] == 1;
n += smem[threadIdx.y + 2][threadIdx.x    ] == 1;
n += smem[threadIdx.y + 2][threadIdx.x + 1] == 1;
n += smem[threadIdx.y + 2][threadIdx.x + 2] == 1;
}
if (n > 0)
{
const int ind = atomic_inc(counter);
st[ind] = (ushort2)(x + 1, y + 1);
}
#endif
}
__constant int c_dx[8] = {-1,  0,  1, -1, 1, -1, 0, 1};
__constant int c_dy[8] = {-1, -1, -1,  0, 0,  1, 1, 1};
#define stack_size 512
__kernel
void
__attribute__((reqd_work_group_size(128,1,1)))
edgesHysteresisGlobal
(
__global int * map,
__global ushort2 * st1,
__global ushort2 * st2,
__global int * counter,
int rows,
int cols,
int count,
int map_step,
int map_offset
)
{
map_step   /= sizeof(*map);
map_offset /= sizeof(*map);
map += map_offset;
int lidx = get_local_id(0);
int grp_idx = get_group_id(0);
int grp_idy = get_group_id(1);
__local unsigned int s_counter;
__local unsigned int s_ind;
__local ushort2 s_st[stack_size];
if(lidx == 0)
{
s_counter = 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
int ind = mad24(grp_idy, (int)get_local_size(0), grp_idx);
if(ind < count)
{
ushort2 pos = st1[ind];
if (lidx < 8)
{
pos.x += c_dx[lidx];
pos.y += c_dy[lidx];
if (pos.x > 0 && pos.x <= cols && pos.y > 0 && pos.y <= rows && map[pos.x + pos.y * map_step] == 1)
{
map[pos.x + pos.y * map_step] = 2;
ind = atomic_inc(&s_counter);
s_st[ind] = pos;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
while (s_counter > 0 && s_counter <= stack_size - get_local_size(0))
{
const int subTaskIdx = lidx >> 3;
const int portion = min(s_counter, (uint)(get_local_size(0)>> 3));
if (subTaskIdx < portion)
pos = s_st[s_counter - 1 - subTaskIdx];
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
s_counter -= portion;
barrier(CLK_LOCAL_MEM_FENCE);
if (subTaskIdx < portion)
{
pos.x += c_dx[lidx & 7];
pos.y += c_dy[lidx & 7];
if (pos.x > 0 && pos.x <= cols && pos.y > 0 && pos.y <= rows && map[pos.x + pos.y * map_step] == 1)
{
map[pos.x + pos.y * map_step] = 2;
ind = atomic_inc(&s_counter);
s_st[ind] = pos;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (s_counter > 0)
{
if (lidx == 0)
{
ind = atomic_add(counter, s_counter);
s_ind = ind - s_counter;
}
barrier(CLK_LOCAL_MEM_FENCE);
ind = s_ind;
for (int i = lidx; i < (int)s_counter; i += get_local_size(0))
{
st2[ind + i] = s_st[i];
}
}
}
}
#undef stack_size
__kernel
void getEdges
(
__global const int * map,
__global uchar * dst,
int rows,
int cols,
int map_step,
int map_offset,
int dst_step,
int dst_offset
)
{
map_step   /= sizeof(*map);
map_offset /= sizeof(*map);
int gidx = get_global_id(0);
int gidy = get_global_id(1);
if(gidy < rows && gidx < cols)
{
dst[gidx + gidy * dst_step] = (uchar)(-(map[gidx + 1 + (gidy + 1) * map_step + map_offset] >> 1));
}
}
917a8bcd294871fe5c17dc71f6d660e8imgproc_clahe#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
int calc_lut(__local int* smem, int val, int tid)
{
smem[tid] = val;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid == 0)
for (int i = 1; i < 256; ++i)
smem[i] += smem[i - 1];
barrier(CLK_LOCAL_MEM_FENCE);
return smem[tid];
}
#ifdef CPU
void reduce(volatile __local int* smem, int val, int tid)
{
smem[tid] = val;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 128)
smem[tid] = val += smem[tid + 128];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 64)
smem[tid] = val += smem[tid + 64];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 32)
smem[tid] += smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
smem[tid] += smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
smem[tid] += smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 4)
smem[tid] += smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 2)
smem[tid] += smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 1)
smem[256] = smem[tid] + smem[tid + 1];
barrier(CLK_LOCAL_MEM_FENCE);
}
#else
void reduce(__local volatile int* smem, int val, int tid)
{
smem[tid] = val;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 128)
smem[tid] = val += smem[tid + 128];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 64)
smem[tid] = val += smem[tid + 64];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 32)
{
smem[tid] += smem[tid + 32];
#if WAVE_SIZE < 32
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
#endif
smem[tid] += smem[tid + 16];
#if WAVE_SIZE < 16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
smem[tid] += smem[tid + 8];
smem[tid] += smem[tid + 4];
smem[tid] += smem[tid + 2];
smem[tid] += smem[tid + 1];
}
}
#endif
__kernel void calcLut(__global __const uchar * src, __global uchar * lut,
const int srcStep, const int dstStep,
const int2 tileSize, const int tilesX,
const int clipLimit, const float lutScale,
const int src_offset, const int dst_offset)
{
__local int smem[512];
int tx = get_group_id(0);
int ty = get_group_id(1);
int tid = get_local_id(1) * get_local_size(0)
+ get_local_id(0);
smem[tid] = 0;
barrier(CLK_LOCAL_MEM_FENCE);
for (int i = get_local_id(1); i < tileSize.y; i += get_local_size(1))
{
__global const uchar* srcPtr = src + mad24(ty * tileSize.y + i, srcStep, tx * tileSize.x + src_offset);
for (int j = get_local_id(0); j < tileSize.x; j += get_local_size(0))
{
const int data = srcPtr[j];
atomic_inc(&smem[data]);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
int tHistVal = smem[tid];
barrier(CLK_LOCAL_MEM_FENCE);
if (clipLimit > 0)
{
int clipped = 0;
if (tHistVal > clipLimit)
{
clipped = tHistVal - clipLimit;
tHistVal = clipLimit;
}
reduce(smem, clipped, tid);
barrier(CLK_LOCAL_MEM_FENCE);
#ifdef CPU
clipped = smem[256];
#else
clipped = smem[0];
#endif
__local int totalClipped;
if (tid == 0)
totalClipped = clipped;
barrier(CLK_LOCAL_MEM_FENCE);
int redistBatch = totalClipped / 256;
tHistVal += redistBatch;
int residual = totalClipped - redistBatch * 256;
if (tid < residual)
++tHistVal;
}
const int lutVal = calc_lut(smem, tHistVal, tid);
uint ires = (uint)convert_int_rte(lutScale * lutVal);
lut[(ty * tilesX + tx) * dstStep + tid + dst_offset] =
convert_uchar(clamp(ires, (uint)0, (uint)255));
}
__kernel void transform(__global __const uchar * src,
__global uchar * dst,
__global uchar * lut,
const int srcStep, const int dstStep, const int lutStep,
const int cols, const int rows,
const int2 tileSize,
const int tilesX, const int tilesY,
const int src_offset, const int dst_offset, int lut_offset)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (x >= cols || y >= rows)
return;
const float tyf = (convert_float(y) / tileSize.y) - 0.5f;
int ty1 = convert_int_rtn(tyf);
int ty2 = ty1 + 1;
const float ya = tyf - ty1;
ty1 = max(ty1, 0);
ty2 = min(ty2, tilesY - 1);
const float txf = (convert_float(x) / tileSize.x) - 0.5f;
int tx1 = convert_int_rtn(txf);
int tx2 = tx1 + 1;
const float xa = txf - tx1;
tx1 = max(tx1, 0);
tx2 = min(tx2, tilesX - 1);
const int srcVal = src[mad24(y, srcStep, x + src_offset)];
float res = 0;
res += lut[mad24(ty1 * tilesX + tx1, lutStep, srcVal + lut_offset)] * ((1.0f - xa) * (1.0f - ya));
res += lut[mad24(ty1 * tilesX + tx2, lutStep, srcVal + lut_offset)] * ((xa) * (1.0f - ya));
res += lut[mad24(ty2 * tilesX + tx1, lutStep, srcVal + lut_offset)] * ((1.0f - xa) * (ya));
res += lut[mad24(ty2 * tilesX + tx2, lutStep, srcVal + lut_offset)] * ((xa) * (ya));
uint ires = (uint)convert_int_rte(res);
dst[mad24(y, dstStep, x + dst_offset)] = convert_uchar(clamp(ires, (uint)0, (uint)255));
}
8b121e325b7df292f7ca7b394daddbbeimgproc_columnsum__kernel void columnSum_C1_D5(__global float * src, __global float * dst,
int cols, int rows, int src_step, int dst_step, int src_offset, int dst_offset)
{
const int x = get_global_id(0);
if (x < cols)
{
int srcIdx = x + src_offset;
int dstIdx = x + dst_offset;
float sum = 0;
for (int y = 0; y < rows; ++y)
{
sum += src[srcIdx];
dst[dstIdx] = sum;
srcIdx += src_step;
dstIdx += dst_step;
}
}
}
b6c673aadf09dcda2c42262f9d0d8f40imgproc_convolve#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void convolve_D5(__global float *src, __global float *temp1, __global float *dst,
int rows, int cols, int src_step, int dst_step,int k_step, int kWidth, int kHeight,
int src_offset, int dst_offset, int koffset)
{
__local float smem[16 + 2 * 8][16 + 2 * 8];
int x = get_local_id(0);
int y = get_local_id(1);
int gx = get_global_id(0);
int gy = get_global_id(1);
smem[y][x] = src[min(max(gy - 8, 0), rows - 1) * src_step + min(max(gx - 8, 0), cols - 1) + src_offset];
smem[y][x + 16] = src[min(max(gy - 8, 0), rows - 1) * src_step + min(gx + 8, cols - 1) + src_offset];
smem[y + 16][x] = src[min(gy + 8, rows - 1) * src_step + min(max(gx - 8, 0), cols - 1) + src_offset];
smem[y + 16][x + 16] = src[min(gy + 8, rows - 1) * src_step + min(gx + 8, cols - 1) + src_offset];
barrier(CLK_LOCAL_MEM_FENCE);
if (gx < cols && gy < rows)
{
float res = 0;
for (int i = 0; i < kHeight; ++i)
for (int j = 0; j < kWidth; ++j)
res += smem[y + 8 - kHeight / 2 + i][x + 8 - kWidth / 2 + j] * temp1[i * k_step + j + koffset];
dst[gy * dst_step + gx + dst_offset] = res;
}
}
f7190f8357538986dbf934323c735bd8imgproc_copymakeboder#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#ifdef BORDER_CONSTANT
#define EXTRAPOLATE(x, y, v) v = scalar;
#elif defined BORDER_REPLICATE
#define EXTRAPOLATE(x, y, v) \
{ \
x = max(min(x, src_cols - 1), 0); \
y = max(min(y, src_rows - 1), 0); \
v = src[mad24(y, src_step, x + src_offset)]; \
}
#elif defined BORDER_WRAP
#define EXTRAPOLATE(x, y, v) \
{ \
if (x < 0) \
x -= ((x - src_cols + 1) / src_cols) * src_cols; \
if (x >= src_cols) \
x %= src_cols; \
\
if (y < 0) \
y -= ((y - src_rows + 1) / src_rows) * src_rows; \
if( y >= src_rows ) \
y %= src_rows; \
v = src[mad24(y, src_step, x + src_offset)]; \
}
#elif defined(BORDER_REFLECT) || defined(BORDER_REFLECT_101)
#ifdef BORDER_REFLECT
#define DELTA int delta = 0
#else
#define DELTA int delta = 1
#endif
#define EXTRAPOLATE(x, y, v) \
{ \
DELTA; \
if (src_cols == 1) \
x = 0; \
else \
do \
{ \
if( x < 0 ) \
x = -x - 1 + delta; \
else \
x = src_cols - 1 - (x - src_cols) - delta; \
} \
while (x >= src_cols || x < 0); \
\
if (src_rows == 1) \
y = 0; \
else \
do \
{ \
if( y < 0 ) \
y = -y - 1 + delta; \
else \
y = src_rows - 1 - (y - src_rows) - delta; \
} \
while (y >= src_rows || y < 0); \
v = src[mad24(y, src_step, x + src_offset)]; \
}
#else
#error No extrapolation method
#endif
#define NEED_EXTRAPOLATION(gx, gy) (gx >= src_cols || gy >= src_rows || gx < 0 || gy < 0)
__kernel void copymakeborder
(__global const GENTYPE *src,
__global GENTYPE *dst,
int dst_cols, int dst_rows,
int src_cols, int src_rows,
int src_step, int src_offset,
int dst_step, int dst_offset,
int top, int left, GENTYPE scalar)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
int src_x = x - left;
int src_y = y - top;
int dst_index = mad24(y, dst_step, x + dst_offset);
if (NEED_EXTRAPOLATION(src_x, src_y))
EXTRAPOLATE(src_x, src_y, dst[dst_index])
else
{
int src_index = mad24(src_y, src_step, src_x + src_offset);
dst[dst_index] = src[src_index];
}
}
}
d7165bbf10e602973a40db33d5cfc4adimgproc_gftt#ifndef WITH_MASK
#define WITH_MASK 0
#endif
#define GET_SRC_32F(_x, _y) ((__global const float*)(eig + (_y)*eig_pitch))[_x]
__kernel
void findCorners
(
__global const char*    eig,
const int               eig_pitch,
__global const char*    mask,
__global float2*        corners,
const int               mask_strip,
__global const float*   pMinMax,
const float             qualityLevel,
const int               rows,
const int               cols,
const int               max_count,
__global int*           g_counter
)
{
float threshold = qualityLevel*pMinMax[1];
const int j = get_global_id(0);
const int i = get_global_id(1);
if (i > 0 && i < rows - 1 && j > 0 && j < cols - 1
#if WITH_MASK
&& mask[i * mask_strip + j] != 0
#endif
)
{
const float val = GET_SRC_32F(j, i);
if (val > threshold)
{
float maxVal = val;
maxVal = fmax(GET_SRC_32F(j - 1, i - 1), maxVal);
maxVal = fmax(GET_SRC_32F(j    , i - 1), maxVal);
maxVal = fmax(GET_SRC_32F(j + 1, i - 1), maxVal);
maxVal = fmax(GET_SRC_32F(j - 1, i), maxVal);
maxVal = fmax(GET_SRC_32F(j + 1, i), maxVal);
maxVal = fmax(GET_SRC_32F(j - 1, i + 1), maxVal);
maxVal = fmax(GET_SRC_32F(j    , i + 1), maxVal);
maxVal = fmax(GET_SRC_32F(j + 1, i + 1), maxVal);
if (val == maxVal)
{
const int ind = atomic_inc(g_counter);
if (ind < max_count)
{
corners[ind].x = val;
corners[ind].y = as_float(j|(i<<16));
}
}
}
}
}
#undef GET_SRC_32F
__kernel
void sortCorners_bitonicSort
(
__global float2 * corners,
const int count,
const int stage,
const int passOfStage
)
{
const int threadId = get_global_id(0);
if(threadId >= count / 2)
{
return;
}
const int sortOrder = (((threadId/(1 << stage)) % 2)) == 1 ? 1 : 0;
const int pairDistance = 1 << (stage - passOfStage);
const int blockWidth   = 2 * pairDistance;
const int leftId = min( (threadId % pairDistance)
+ (threadId / pairDistance) * blockWidth, count );
const int rightId = min( leftId + pairDistance, count );
const float2 leftPt  = corners[leftId];
const float2 rightPt = corners[rightId];
const float leftVal  = leftPt.x;
const float rightVal = rightPt.x;
const bool compareResult = leftVal > rightVal;
float2 greater = compareResult ? leftPt:rightPt;
float2 lesser  = compareResult ? rightPt:leftPt;
corners[leftId]  = sortOrder ? lesser : greater;
corners[rightId] = sortOrder ? greater : lesser;
}
__kernel void arithm_op_minMax_final(__global float * data, int groupnum,__global int * g_counter)
{
g_counter[0] = 0;
float minVal = data[0];
float maxVal = data[groupnum];
for(int i=1;i> 2); i++, idx += lsize)
{
subhist[idx] = 0;
subhist[idx+=lsize] = 0;
subhist[idx+=lsize] = 0;
subhist[idx+=lsize] = 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
int y = gid/cols;
int x = gid - mul24(y, cols);
for(int idx=gid; idx>= shift;
temp2 = ((data & mask) << NBANKS_BIT) + offset;
data >>= shift;
temp3 = ((data & mask) << NBANKS_BIT) + offset;
data >>= shift;
temp4 = ((data & mask) << NBANKS_BIT) + offset;
atomic_inc(subhist + temp1.x);
atomic_inc(subhist + temp1.y);
atomic_inc(subhist + temp1.z);
atomic_inc(subhist + temp1.w);
atomic_inc(subhist + temp2.x);
atomic_inc(subhist + temp2.y);
atomic_inc(subhist + temp2.z);
atomic_inc(subhist + temp2.w);
atomic_inc(subhist + temp3.x);
atomic_inc(subhist + temp3.y);
atomic_inc(subhist + temp3.z);
atomic_inc(subhist + temp3.w);
atomic_inc(subhist + temp4.x);
atomic_inc(subhist + temp4.y);
atomic_inc(subhist + temp4.z);
atomic_inc(subhist + temp4.w);
x += inc_x;
int off = ((x>=cols) ? -1 : 0);
x = mad24(off, cols, x);
y += inc_y - off;
}
barrier(CLK_LOCAL_MEM_FENCE);
int bin1=0, bin2=0, bin3=0, bin4=0;
for(int i=0; i=left_col) ? (gidx+cols) : gidx);
if(gidy 0; stride >>= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
if(lx < stride)
data[lx] += data[lx + stride];
}
if(lx == 0)
hist[gx] = data[0];
}
__kernel __attribute__((reqd_work_group_size(256,1,1)))
void calLUT(__global uchar * dst, __constant int * hist, int total)
{
int lid = get_local_id(0);
__local int sumhist[HISTOGRAM256_BIN_COUNT];
__local float scale;
sumhist[lid] = hist[lid];
barrier(CLK_LOCAL_MEM_FENCE);
if (lid == 0)
{
int sum = 0, i = 0;
while (!sumhist[i])
++i;
if (total == sumhist[i])
{
scale = 1;
for (int j = 0; j < HISTOGRAM256_BIN_COUNT; ++j)
sumhist[i] = i;
}
else
{
scale = 255.f/(total - sumhist[i]);
for (sumhist[i++] = 0; i < HISTOGRAM256_BIN_COUNT; i++)
{
sum += sumhist[i];
sumhist[i] = sum;
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
dst[lid]= convert_uchar_sat_rte(convert_float(sumhist[lid])*scale);
}
c7700f9857976933b98297cf20dfc711imgproc_integral#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#define LSIZE 256
#define LSIZE_1 255
#define LSIZE_2 254
#define HF_LSIZE 128
#define LOG_LSIZE 8
#define LOG_NUM_BANKS 5
#define NUM_BANKS 32
#define GET_CONFLICT_OFFSET(lid) ((lid) >> LOG_NUM_BANKS)
kernel void integral_cols_D4(__global uchar4 *src,__global int *sum ,__global float *sqsum,
int src_offset,int pre_invalid,int rows,int cols,int src_step,int dst_step)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int4 src_t[2], sum_t[2];
float4 sqsum_t[2];
__local int4 lm_sum[2][LSIZE + LOG_LSIZE];
__local float4 lm_sqsum[2][LSIZE + LOG_LSIZE];
__local int* sum_p;
__local float* sqsum_p;
src_step = src_step >> 2;
gid = gid << 1;
for(int i = 0; i < rows; i =i + LSIZE_1)
{
src_t[0] = (i + lid < rows ? convert_int4(src[src_offset + (lid+i) * src_step + min(gid, cols - 1)]) : 0);
src_t[1] = (i + lid < rows ? convert_int4(src[src_offset + (lid+i) * src_step + min(gid + 1, cols - 1)]) : 0);
sum_t[0] = (i == 0 ? 0 : lm_sum[0][LSIZE_2 + LOG_LSIZE]);
sqsum_t[0] = (i == 0 ? (float4)0 : lm_sqsum[0][LSIZE_2 + LOG_LSIZE]);
sum_t[1] =  (i == 0 ? 0 : lm_sum[1][LSIZE_2 + LOG_LSIZE]);
sqsum_t[1] =  (i == 0 ? (float4)0 : lm_sqsum[1][LSIZE_2 + LOG_LSIZE]);
barrier(CLK_LOCAL_MEM_FENCE);
int bf_loc = lid + GET_CONFLICT_OFFSET(lid);
lm_sum[0][bf_loc] = src_t[0];
lm_sqsum[0][bf_loc] = convert_float4(src_t[0] * src_t[0]);
lm_sum[1][bf_loc] = src_t[1];
lm_sqsum[1][bf_loc] = convert_float4(src_t[1] * src_t[1]);
int offset = 1;
for(int d = LSIZE >> 1 ;  d > 0; d>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi]  +=  lm_sum[lid >> 7][ai];
lm_sqsum[lid >> 7][bi]  +=  lm_sqsum[lid >> 7][ai];
}
offset <<= 1;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 2)
{
lm_sum[lid][LSIZE_2 + LOG_LSIZE] = 0;
lm_sqsum[lid][LSIZE_2 + LOG_LSIZE] = 0;
}
for(int d = 1;  d < LSIZE; d <<= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
offset >>= 1;
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi] += lm_sum[lid >> 7][ai];
lm_sum[lid >> 7][ai] = lm_sum[lid >> 7][bi] - lm_sum[lid >> 7][ai];
lm_sqsum[lid >> 7][bi] += lm_sqsum[lid >> 7][ai];
lm_sqsum[lid >> 7][ai] = lm_sqsum[lid >> 7][bi] - lm_sqsum[lid >> 7][ai];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
int loc_s0 = gid * dst_step + i + lid - 1 - pre_invalid * dst_step / 4, loc_s1 = loc_s0 + dst_step ;
if(lid > 0 && (i+lid) <= rows)
{
lm_sum[0][bf_loc] += sum_t[0];
lm_sum[1][bf_loc] += sum_t[1];
lm_sqsum[0][bf_loc] += sqsum_t[0];
lm_sqsum[1][bf_loc] += sqsum_t[1];
sum_p = (__local int*)(&(lm_sum[0][bf_loc]));
sqsum_p = (__local float*)(&(lm_sqsum[0][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 4 + k >= cols + pre_invalid || gid * 4 + k < pre_invalid) continue;
sum[loc_s0 + k * dst_step / 4] = sum_p[k];
sqsum[loc_s0 + k * dst_step / 4] = sqsum_p[k];
}
sum_p = (__local int*)(&(lm_sum[1][bf_loc]));
sqsum_p = (__local float*)(&(lm_sqsum[1][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 4 + k + 4 >= cols + pre_invalid) break;
sum[loc_s1 + k * dst_step / 4] = sum_p[k];
sqsum[loc_s1 + k * dst_step / 4] = sqsum_p[k];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
kernel void integral_rows_D4(__global int4 *srcsum,__global float4 * srcsqsum,__global int *sum ,
__global float *sqsum,int rows,int cols,int src_step,int sum_step,
int sqsum_step,int sum_offset,int sqsum_offset)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int4 src_t[2], sum_t[2];
float4 sqsrc_t[2],sqsum_t[2];
__local int4 lm_sum[2][LSIZE + LOG_LSIZE];
__local float4 lm_sqsum[2][LSIZE + LOG_LSIZE];
__local int *sum_p;
__local float *sqsum_p;
src_step = src_step >> 4;
for(int i = 0; i < rows; i =i + LSIZE_1)
{
src_t[0] = i + lid < rows ? srcsum[(lid+i) * src_step + gid * 2] : (int4)0;
sqsrc_t[0] = i + lid < rows ? srcsqsum[(lid+i) * src_step + gid * 2] : (float4)0;
src_t[1] = i + lid < rows ? srcsum[(lid+i) * src_step + gid * 2 + 1] : (int4)0;
sqsrc_t[1] = i + lid < rows ? srcsqsum[(lid+i) * src_step + gid * 2 + 1] : (float4)0;
sum_t[0] =  (i == 0 ? 0 : lm_sum[0][LSIZE_2 + LOG_LSIZE]);
sqsum_t[0] =  (i == 0 ? (float4)0 : lm_sqsum[0][LSIZE_2 + LOG_LSIZE]);
sum_t[1] =  (i == 0 ? 0 : lm_sum[1][LSIZE_2 + LOG_LSIZE]);
sqsum_t[1] =  (i == 0 ? (float4)0 : lm_sqsum[1][LSIZE_2 + LOG_LSIZE]);
barrier(CLK_LOCAL_MEM_FENCE);
int bf_loc = lid + GET_CONFLICT_OFFSET(lid);
lm_sum[0][bf_loc] = src_t[0];
lm_sqsum[0][bf_loc] = sqsrc_t[0];
lm_sum[1][bf_loc] = src_t[1];
lm_sqsum[1][bf_loc] = sqsrc_t[1];
int offset = 1;
for(int d = LSIZE >> 1 ;  d > 0; d>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi]  +=  lm_sum[lid >> 7][ai];
lm_sqsum[lid >> 7][bi]  +=  lm_sqsum[lid >> 7][ai];
}
offset <<= 1;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 2)
{
lm_sum[lid][LSIZE_2 + LOG_LSIZE] = 0;
lm_sqsum[lid][LSIZE_2 + LOG_LSIZE] = 0;
}
for(int d = 1;  d < LSIZE; d <<= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
offset >>= 1;
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi] += lm_sum[lid >> 7][ai];
lm_sum[lid >> 7][ai] = lm_sum[lid >> 7][bi] - lm_sum[lid >> 7][ai];
lm_sqsum[lid >> 7][bi] += lm_sqsum[lid >> 7][ai];
lm_sqsum[lid >> 7][ai] = lm_sqsum[lid >> 7][bi] - lm_sqsum[lid >> 7][ai];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(gid == 0 && (i + lid) <= rows)
{
sum[sum_offset + i + lid] = 0;
sqsum[sqsum_offset + i + lid] = 0;
}
if(i + lid == 0)
{
int loc0 = gid * 2 * sum_step;
int loc1 = gid * 2 * sqsum_step;
for(int k = 1; k <= 8; k++)
{
if(gid * 8 + k > cols) break;
sum[sum_offset + loc0 + k * sum_step / 4] = 0;
sqsum[sqsum_offset + loc1 + k * sqsum_step / 4] = 0;
}
}
int loc_s0 = sum_offset + gid * 2 * sum_step + sum_step / 4 + i + lid, loc_s1 = loc_s0 + sum_step ;
int loc_sq0 = sqsum_offset + gid * 2 * sqsum_step + sqsum_step / 4 + i + lid, loc_sq1 = loc_sq0 + sqsum_step ;
if(lid > 0 && (i+lid) <= rows)
{
lm_sum[0][bf_loc] += sum_t[0];
lm_sum[1][bf_loc] += sum_t[1];
lm_sqsum[0][bf_loc] += sqsum_t[0];
lm_sqsum[1][bf_loc] += sqsum_t[1];
sum_p = (__local int*)(&(lm_sum[0][bf_loc]));
sqsum_p = (__local float*)(&(lm_sqsum[0][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 8 + k >= cols) break;
sum[loc_s0 + k * sum_step / 4] = sum_p[k];
sqsum[loc_sq0 + k * sqsum_step / 4] = sqsum_p[k];
}
sum_p = (__local int*)(&(lm_sum[1][bf_loc]));
sqsum_p = (__local float*)(&(lm_sqsum[1][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 8 + 4 + k >= cols) break;
sum[loc_s1 + k * sum_step / 4] = sum_p[k];
sqsum[loc_sq1 + k * sqsum_step / 4] = sqsum_p[k];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
kernel void integral_cols_D5(__global uchar4 *src,__global float *sum ,__global float *sqsum,
int src_offset,int pre_invalid,int rows,int cols,int src_step,int dst_step)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
float4 src_t[2], sum_t[2];
float4 sqsum_t[2];
__local float4 lm_sum[2][LSIZE + LOG_LSIZE];
__local float4 lm_sqsum[2][LSIZE + LOG_LSIZE];
__local float* sum_p;
__local float* sqsum_p;
src_step = src_step >> 2;
gid = gid << 1;
for(int i = 0; i < rows; i =i + LSIZE_1)
{
src_t[0] = (i + lid < rows ? convert_float4(src[src_offset + (lid+i) * src_step + min(gid, cols - 1)]) : (float4)0);
src_t[1] = (i + lid < rows ? convert_float4(src[src_offset + (lid+i) * src_step + min(gid + 1, cols - 1)]) : (float4)0);
sum_t[0] = (i == 0 ? (float4)0 : lm_sum[0][LSIZE_2 + LOG_LSIZE]);
sqsum_t[0] = (i == 0 ? (float4)0 : lm_sqsum[0][LSIZE_2 + LOG_LSIZE]);
sum_t[1] =  (i == 0 ? (float4)0 : lm_sum[1][LSIZE_2 + LOG_LSIZE]);
sqsum_t[1] =  (i == 0 ? (float4)0 : lm_sqsum[1][LSIZE_2 + LOG_LSIZE]);
barrier(CLK_LOCAL_MEM_FENCE);
int bf_loc = lid + GET_CONFLICT_OFFSET(lid);
lm_sum[0][bf_loc] = src_t[0];
lm_sqsum[0][bf_loc] = convert_float4(src_t[0] * src_t[0]);
lm_sum[1][bf_loc] = src_t[1];
lm_sqsum[1][bf_loc] = convert_float4(src_t[1] * src_t[1]);
int offset = 1;
for(int d = LSIZE >> 1 ;  d > 0; d>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi]  +=  lm_sum[lid >> 7][ai];
lm_sqsum[lid >> 7][bi]  +=  lm_sqsum[lid >> 7][ai];
}
offset <<= 1;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 2)
{
lm_sum[lid][LSIZE_2 + LOG_LSIZE] = 0;
lm_sqsum[lid][LSIZE_2 + LOG_LSIZE] = 0;
}
for(int d = 1;  d < LSIZE; d <<= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
offset >>= 1;
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi] += lm_sum[lid >> 7][ai];
lm_sum[lid >> 7][ai] = lm_sum[lid >> 7][bi] - lm_sum[lid >> 7][ai];
lm_sqsum[lid >> 7][bi] += lm_sqsum[lid >> 7][ai];
lm_sqsum[lid >> 7][ai] = lm_sqsum[lid >> 7][bi] - lm_sqsum[lid >> 7][ai];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
int loc_s0 = gid * dst_step + i + lid - 1 - pre_invalid * dst_step / 4, loc_s1 = loc_s0 + dst_step ;
if(lid > 0 && (i+lid) <= rows)
{
lm_sum[0][bf_loc] += sum_t[0];
lm_sum[1][bf_loc] += sum_t[1];
lm_sqsum[0][bf_loc] += sqsum_t[0];
lm_sqsum[1][bf_loc] += sqsum_t[1];
sum_p = (__local float*)(&(lm_sum[0][bf_loc]));
sqsum_p = (__local float*)(&(lm_sqsum[0][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 4 + k >= cols + pre_invalid || gid * 4 + k < pre_invalid) continue;
sum[loc_s0 + k * dst_step / 4] = sum_p[k];
sqsum[loc_s0 + k * dst_step / 4] = sqsum_p[k];
}
sum_p = (__local float*)(&(lm_sum[1][bf_loc]));
sqsum_p = (__local float*)(&(lm_sqsum[1][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 4 + k + 4 >= cols + pre_invalid) break;
sum[loc_s1 + k * dst_step / 4] = sum_p[k];
sqsum[loc_s1 + k * dst_step / 4] = sqsum_p[k];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
kernel void integral_rows_D5(__global float4 *srcsum,__global float4 * srcsqsum,__global float *sum ,
__global float *sqsum,int rows,int cols,int src_step,int sum_step,
int sqsum_step,int sum_offset,int sqsum_offset)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
float4 src_t[2], sum_t[2];
float4 sqsrc_t[2],sqsum_t[2];
__local float4 lm_sum[2][LSIZE + LOG_LSIZE];
__local float4 lm_sqsum[2][LSIZE + LOG_LSIZE];
__local float *sum_p;
__local float *sqsum_p;
src_step = src_step >> 4;
for(int i = 0; i < rows; i =i + LSIZE_1)
{
src_t[0] = i + lid < rows ? srcsum[(lid+i) * src_step + gid * 2] : (float4)0;
sqsrc_t[0] = i + lid < rows ? srcsqsum[(lid+i) * src_step + gid * 2] : (float4)0;
src_t[1] = i + lid < rows ? srcsum[(lid+i) * src_step + gid * 2 + 1] : (float4)0;
sqsrc_t[1] = i + lid < rows ? srcsqsum[(lid+i) * src_step + gid * 2 + 1] : (float4)0;
sum_t[0] =  (i == 0 ? (float4)0 : lm_sum[0][LSIZE_2 + LOG_LSIZE]);
sqsum_t[0] =  (i == 0 ? (float4)0 : lm_sqsum[0][LSIZE_2 + LOG_LSIZE]);
sum_t[1] =  (i == 0 ? (float4)0 : lm_sum[1][LSIZE_2 + LOG_LSIZE]);
sqsum_t[1] =  (i == 0 ? (float4)0 : lm_sqsum[1][LSIZE_2 + LOG_LSIZE]);
barrier(CLK_LOCAL_MEM_FENCE);
int bf_loc = lid + GET_CONFLICT_OFFSET(lid);
lm_sum[0][bf_loc] = src_t[0];
lm_sqsum[0][bf_loc] = sqsrc_t[0];
lm_sum[1][bf_loc] = src_t[1];
lm_sqsum[1][bf_loc] = sqsrc_t[1];
int offset = 1;
for(int d = LSIZE >> 1 ;  d > 0; d>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi]  +=  lm_sum[lid >> 7][ai];
lm_sqsum[lid >> 7][bi]  +=  lm_sqsum[lid >> 7][ai];
}
offset <<= 1;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 2)
{
lm_sum[lid][LSIZE_2 + LOG_LSIZE] = 0;
lm_sqsum[lid][LSIZE_2 + LOG_LSIZE] = 0;
}
for(int d = 1;  d < LSIZE; d <<= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
offset >>= 1;
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi] += lm_sum[lid >> 7][ai];
lm_sum[lid >> 7][ai] = lm_sum[lid >> 7][bi] - lm_sum[lid >> 7][ai];
lm_sqsum[lid >> 7][bi] += lm_sqsum[lid >> 7][ai];
lm_sqsum[lid >> 7][ai] = lm_sqsum[lid >> 7][bi] - lm_sqsum[lid >> 7][ai];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(gid == 0 && (i + lid) <= rows)
{
sum[sum_offset + i + lid] = 0;
sqsum[sqsum_offset + i + lid] = 0;
}
if(i + lid == 0)
{
int loc0 = gid * 2 * sum_step;
int loc1 = gid * 2 * sqsum_step;
for(int k = 1; k <= 8; k++)
{
if(gid * 8 + k > cols) break;
sum[sum_offset + loc0 + k * sum_step / 4] = 0;
sqsum[sqsum_offset + loc1 + k * sqsum_step / 4] = 0;
}
}
int loc_s0 = sum_offset + gid * 2 * sum_step + sum_step / 4 + i + lid, loc_s1 = loc_s0 + sum_step ;
int loc_sq0 = sqsum_offset + gid * 2 * sqsum_step + sqsum_step / 4 + i + lid, loc_sq1 = loc_sq0 + sqsum_step ;
if(lid > 0 && (i+lid) <= rows)
{
lm_sum[0][bf_loc] += sum_t[0];
lm_sum[1][bf_loc] += sum_t[1];
lm_sqsum[0][bf_loc] += sqsum_t[0];
lm_sqsum[1][bf_loc] += sqsum_t[1];
sum_p = (__local float*)(&(lm_sum[0][bf_loc]));
sqsum_p = (__local float*)(&(lm_sqsum[0][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 8 + k >= cols) break;
sum[loc_s0 + k * sum_step / 4] = sum_p[k];
sqsum[loc_sq0 + k * sqsum_step / 4] = sqsum_p[k];
}
sum_p = (__local float*)(&(lm_sum[1][bf_loc]));
sqsum_p = (__local float*)(&(lm_sqsum[1][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 8 + 4 + k >= cols) break;
sum[loc_s1 + k * sum_step / 4] = sum_p[k];
sqsum[loc_sq1 + k * sqsum_step / 4] = sqsum_p[k];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
5e6a87796fabb22a98ead9c7596269ebimgproc_integral_sum#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#define LSIZE 256
#define LSIZE_1 255
#define LSIZE_2 254
#define HF_LSIZE 128
#define LOG_LSIZE 8
#define LOG_NUM_BANKS 5
#define NUM_BANKS 32
#define GET_CONFLICT_OFFSET(lid) ((lid) >> LOG_NUM_BANKS)
kernel void integral_sum_cols_D4(__global uchar4 *src,__global int *sum ,
int src_offset,int pre_invalid,int rows,int cols,int src_step,int dst_step)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int4 src_t[2], sum_t[2];
__local int4 lm_sum[2][LSIZE + LOG_LSIZE];
__local int* sum_p;
src_step = src_step >> 2;
gid = gid << 1;
for(int i = 0; i < rows; i =i + LSIZE_1)
{
src_t[0] = (i + lid < rows ? convert_int4(src[src_offset + (lid+i) * src_step + gid]) : 0);
src_t[1] = (i + lid < rows ? convert_int4(src[src_offset + (lid+i) * src_step + gid + 1]) : 0);
sum_t[0] =  (i == 0 ? 0 : lm_sum[0][LSIZE_2 + LOG_LSIZE]);
sum_t[1] =  (i == 0 ? 0 : lm_sum[1][LSIZE_2 + LOG_LSIZE]);
barrier(CLK_LOCAL_MEM_FENCE);
int bf_loc = lid + GET_CONFLICT_OFFSET(lid);
lm_sum[0][bf_loc] = src_t[0];
lm_sum[1][bf_loc] = src_t[1];
int offset = 1;
for(int d = LSIZE >> 1 ;  d > 0; d>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi]  +=  lm_sum[lid >> 7][ai];
}
offset <<= 1;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 2)
{
lm_sum[lid][LSIZE_2 + LOG_LSIZE] = 0;
}
for(int d = 1;  d < LSIZE; d <<= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
offset >>= 1;
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi] += lm_sum[lid >> 7][ai];
lm_sum[lid >> 7][ai] = lm_sum[lid >> 7][bi] - lm_sum[lid >> 7][ai];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid > 0 && (i+lid) <= rows)
{
int loc_s0 = gid * dst_step + i + lid - 1 - pre_invalid * dst_step / 4, loc_s1 = loc_s0 + dst_step ;
lm_sum[0][bf_loc] += sum_t[0];
lm_sum[1][bf_loc] += sum_t[1];
sum_p = (__local int*)(&(lm_sum[0][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 4 + k >= cols + pre_invalid || gid * 4 + k < pre_invalid) continue;
sum[loc_s0 + k * dst_step / 4] = sum_p[k];
}
sum_p = (__local int*)(&(lm_sum[1][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 4 + k + 4 >= cols + pre_invalid) break;
sum[loc_s1 + k * dst_step / 4] = sum_p[k];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
kernel void integral_sum_rows_D4(__global int4 *srcsum,__global int *sum ,
int rows,int cols,int src_step,int sum_step,
int sum_offset)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
int4 src_t[2], sum_t[2];
__local int4 lm_sum[2][LSIZE + LOG_LSIZE];
__local int *sum_p;
src_step = src_step >> 4;
for(int i = 0; i < rows; i =i + LSIZE_1)
{
src_t[0] = i + lid < rows ? srcsum[(lid+i) * src_step + gid * 2] : 0;
src_t[1] = i + lid < rows ? srcsum[(lid+i) * src_step + gid * 2 + 1] : 0;
sum_t[0] =  (i == 0 ? 0 : lm_sum[0][LSIZE_2 + LOG_LSIZE]);
sum_t[1] =  (i == 0 ? 0 : lm_sum[1][LSIZE_2 + LOG_LSIZE]);
barrier(CLK_LOCAL_MEM_FENCE);
int bf_loc = lid + GET_CONFLICT_OFFSET(lid);
lm_sum[0][bf_loc] = src_t[0];
lm_sum[1][bf_loc] = src_t[1];
int offset = 1;
for(int d = LSIZE >> 1 ;  d > 0; d>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi]  +=  lm_sum[lid >> 7][ai];
}
offset <<= 1;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 2)
{
lm_sum[lid][LSIZE_2 + LOG_LSIZE] = 0;
}
for(int d = 1;  d < LSIZE; d <<= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
offset >>= 1;
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi] += lm_sum[lid >> 7][ai];
lm_sum[lid >> 7][ai] = lm_sum[lid >> 7][bi] - lm_sum[lid >> 7][ai];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(gid == 0 && (i + lid) <= rows)
{
sum[sum_offset + i + lid] = 0;
}
if(i + lid == 0)
{
int loc0 = gid * 2 * sum_step;
for(int k = 1; k <= 8; k++)
{
if(gid * 8 + k > cols) break;
sum[sum_offset + loc0 + k * sum_step / 4] = 0;
}
}
if(lid > 0 && (i+lid) <= rows)
{
int loc_s0 = sum_offset + gid * 2 * sum_step + sum_step / 4 + i + lid, loc_s1 = loc_s0 + sum_step ;
lm_sum[0][bf_loc] += sum_t[0];
lm_sum[1][bf_loc] += sum_t[1];
sum_p = (__local int*)(&(lm_sum[0][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 8 + k >= cols) break;
sum[loc_s0 + k * sum_step / 4] = sum_p[k];
}
sum_p = (__local int*)(&(lm_sum[1][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 8 + 4 + k >= cols) break;
sum[loc_s1 + k * sum_step / 4] = sum_p[k];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
kernel void integral_sum_cols_D5(__global uchar4 *src,__global float *sum ,
int src_offset,int pre_invalid,int rows,int cols,int src_step,int dst_step)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
float4 src_t[2], sum_t[2];
__local float4 lm_sum[2][LSIZE + LOG_LSIZE];
__local float* sum_p;
src_step = src_step >> 2;
gid = gid << 1;
for(int i = 0; i < rows; i =i + LSIZE_1)
{
src_t[0] = (i + lid < rows ? convert_float4(src[src_offset + (lid+i) * src_step + gid]) : (float4)0);
src_t[1] = (i + lid < rows ? convert_float4(src[src_offset + (lid+i) * src_step + gid + 1]) : (float4)0);
sum_t[0] =  (i == 0 ? (float4)0 : lm_sum[0][LSIZE_2 + LOG_LSIZE]);
sum_t[1] =  (i == 0 ? (float4)0 : lm_sum[1][LSIZE_2 + LOG_LSIZE]);
barrier(CLK_LOCAL_MEM_FENCE);
int bf_loc = lid + GET_CONFLICT_OFFSET(lid);
lm_sum[0][bf_loc] = src_t[0];
lm_sum[1][bf_loc] = src_t[1];
int offset = 1;
for(int d = LSIZE >> 1 ;  d > 0; d>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi]  +=  lm_sum[lid >> 7][ai];
}
offset <<= 1;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 2)
{
lm_sum[lid][LSIZE_2 + LOG_LSIZE] = 0;
}
for(int d = 1;  d < LSIZE; d <<= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
offset >>= 1;
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi] += lm_sum[lid >> 7][ai];
lm_sum[lid >> 7][ai] = lm_sum[lid >> 7][bi] - lm_sum[lid >> 7][ai];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid > 0 && (i+lid) <= rows)
{
int loc_s0 = gid * dst_step + i + lid - 1 - pre_invalid * dst_step / 4, loc_s1 = loc_s0 + dst_step ;
lm_sum[0][bf_loc] += sum_t[0];
lm_sum[1][bf_loc] += sum_t[1];
sum_p = (__local float*)(&(lm_sum[0][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 4 + k >= cols + pre_invalid || gid * 4 + k < pre_invalid) continue;
sum[loc_s0 + k * dst_step / 4] = sum_p[k];
}
sum_p = (__local float*)(&(lm_sum[1][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 4 + k + 4 >= cols + pre_invalid) break;
sum[loc_s1 + k * dst_step / 4] = sum_p[k];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
kernel void integral_sum_rows_D5(__global float4 *srcsum,__global float *sum ,
int rows,int cols,int src_step,int sum_step,
int sum_offset)
{
int lid = get_local_id(0);
int gid = get_group_id(0);
float4 src_t[2], sum_t[2];
__local float4 lm_sum[2][LSIZE + LOG_LSIZE];
__local float *sum_p;
src_step = src_step >> 4;
for(int i = 0; i < rows; i =i + LSIZE_1)
{
src_t[0] = i + lid < rows ? srcsum[(lid+i) * src_step + gid * 2] : (float4)0;
src_t[1] = i + lid < rows ? srcsum[(lid+i) * src_step + gid * 2 + 1] : (float4)0;
sum_t[0] =  (i == 0 ? (float4)0 : lm_sum[0][LSIZE_2 + LOG_LSIZE]);
sum_t[1] =  (i == 0 ? (float4)0 : lm_sum[1][LSIZE_2 + LOG_LSIZE]);
barrier(CLK_LOCAL_MEM_FENCE);
int bf_loc = lid + GET_CONFLICT_OFFSET(lid);
lm_sum[0][bf_loc] = src_t[0];
lm_sum[1][bf_loc] = src_t[1];
int offset = 1;
for(int d = LSIZE >> 1 ;  d > 0; d>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi]  +=  lm_sum[lid >> 7][ai];
}
offset <<= 1;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < 2)
{
lm_sum[lid][LSIZE_2 + LOG_LSIZE] = 0;
}
for(int d = 1;  d < LSIZE; d <<= 1)
{
barrier(CLK_LOCAL_MEM_FENCE);
offset >>= 1;
int ai = offset * (((lid & 127)<<1) +1) - 1,bi = ai + offset;
ai += GET_CONFLICT_OFFSET(ai);
bi += GET_CONFLICT_OFFSET(bi);
if((lid & 127) < d)
{
lm_sum[lid >> 7][bi] += lm_sum[lid >> 7][ai];
lm_sum[lid >> 7][ai] = lm_sum[lid >> 7][bi] - lm_sum[lid >> 7][ai];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(gid == 0 && (i + lid) <= rows)
{
sum[sum_offset + i + lid] = 0;
}
if(i + lid == 0)
{
int loc0 = gid * 2 * sum_step;
for(int k = 1; k <= 8; k++)
{
if(gid * 8 + k > cols) break;
sum[sum_offset + loc0 + k * sum_step / 4] = 0;
}
}
if(lid > 0 && (i+lid) <= rows)
{
int loc_s0 = sum_offset + gid * 2 * sum_step + sum_step / 4 + i + lid, loc_s1 = loc_s0 + sum_step ;
lm_sum[0][bf_loc] += sum_t[0];
lm_sum[1][bf_loc] += sum_t[1];
sum_p = (__local float*)(&(lm_sum[0][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 8 + k >= cols) break;
sum[loc_s0 + k * sum_step / 4] = sum_p[k];
}
sum_p = (__local float*)(&(lm_sum[1][bf_loc]));
for(int k = 0; k < 4; k++)
{
if(gid * 8 + 4 + k >= cols) break;
sum[loc_s1 + k * sum_step / 4] = sum_p[k];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
e59da438d1e7b62e64504698acb985b2imgproc_median#define op(a,b) {mid=a; a=min(a,b); b=max(mid,b);}
__kernel void medianFilter3_C4_D0(__global uchar4 * src, __global uchar4 * dst,  int srcOffset, int dstOffset, int cols,
int rows, int srcStep, int dstStep)
{
__local uchar4 data[18][18];
__global uchar4* source=src + srcOffset;
int dx = get_global_id(0) - get_local_id(0) -1;
int dy = get_global_id(1) - get_local_id(1) -1;
const int id = min((int)(get_local_id(0)*16+get_local_id(1)), 9*18-1);
int dr=id/18;
int dc=id%18;
int r=clamp(dy+dr, 0, rows-1);
int c=clamp(dx+dc, 0, cols-1);
data[dr][dc] = source[r*srcStep + c];
r=clamp(dy+dr+9, 0, rows-1);
data[dr+9][dc] = source[r*srcStep + c];
barrier(CLK_LOCAL_MEM_FENCE);
int x =get_local_id(0);
int y =get_local_id(1);
uchar4 p0=data[y][x], p1=data[y][x+1], p2=data[y][x+2];
uchar4 p3=data[y+1][x], p4=data[y+1][x+1], p5=data[y+1][x+2];
uchar4 p6=data[y+2][x], p7=data[y+2][x+1], p8=data[y+2][x+2];
uchar4 mid;
op(p1, p2); op(p4, p5); op(p7, p8); op(p0, p1);
op(p3, p4); op(p6, p7); op(p1, p2); op(p4, p5);
op(p7, p8); op(p0, p3); op(p5, p8); op(p4, p7);
op(p3, p6); op(p1, p4); op(p2, p5); op(p4, p7);
op(p4, p2); op(p6, p4); op(p4, p2);
if((int)get_global_id(1)= src_cols) \
v2.x %= src_cols; \
\
if (v2.y < 0) \
v2.y -= ((v2.y - src_rows + 1) / src_rows) * src_rows; \
if( v2.y >= src_rows ) \
v2.y %= src_rows; \
v = convertToWT(src[mad24(v2.y, src_step, v2.x + src_offset)]); \
}
#elif defined(BORDER_REFLECT) || defined(BORDER_REFLECT_101)
#ifdef BORDER_REFLECT
#define DELTA int delta = 0
#else
#define DELTA int delta = 1
#endif
#define EXTRAPOLATE(v2, v) \
{ \
DELTA; \
if (src_cols == 1) \
v2.x = 0; \
else \
do \
{ \
if( v2.x < 0 ) \
v2.x = -v2.x - 1 + delta; \
else \
v2.x = src_cols - 1 - (v2.x - src_cols) - delta; \
} \
while (v2.x >= src_cols || v2.x < 0); \
\
if (src_rows == 1) \
v2.y = 0; \
else \
do \
{ \
if( v2.y < 0 ) \
v2.y = -v2.y - 1 + delta; \
else \
v2.y = src_rows - 1 - (v2.y - src_rows) - delta; \
} \
while (v2.y >= src_rows || v2.y < 0); \
v = convertToWT(src[mad24(v2.y, src_step, v2.x + src_offset)]); \
}
#else
#error No extrapolation method
#endif
#define NEED_EXTRAPOLATION(gx, gy) (gx >= src_cols || gy >= src_rows || gx < 0 || gy < 0)
#ifdef INTER_NEAREST
__kernel void remap_2_32FC1(__global const T * restrict src, __global T * dst,
__global float * map1, __global float * map2,
int src_offset, int dst_offset, int map1_offset, int map2_offset,
int src_step, int dst_step, int map1_step, int map2_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, T scalar)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
int dstIdx = mad24(y, dst_step, x + dst_offset);
int map1Idx = mad24(y, map1_step, x + map1_offset);
int map2Idx = mad24(y, map2_step, x + map2_offset);
int gx = convert_int_sat_rte(map1[map1Idx]);
int gy = convert_int_sat_rte(map2[map2Idx]);
if (NEED_EXTRAPOLATION(gx, gy))
{
#ifndef BORDER_CONSTANT
int2 gxy = (int2)(gx, gy);
#endif
EXTRAPOLATE(gxy, dst[dstIdx]);
}
else
{
int srcIdx = mad24(gy, src_step, gx + src_offset);
dst[dstIdx] = src[srcIdx];
}
}
}
__kernel void remap_32FC2(__global const T * restrict src, __global T * dst, __global float2 * map1,
int src_offset, int dst_offset, int map1_offset,
int src_step, int dst_step, int map1_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, T scalar)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
int dstIdx = mad24(y, dst_step, x + dst_offset);
int map1Idx = mad24(y, map1_step, x + map1_offset);
int2 gxy = convert_int2_sat_rte(map1[map1Idx]);
int gx = gxy.x, gy = gxy.y;
if (NEED_EXTRAPOLATION(gx, gy))
EXTRAPOLATE(gxy, dst[dstIdx])
else
{
int srcIdx = mad24(gy, src_step, gx + src_offset);
dst[dstIdx] = src[srcIdx];
}
}
}
__kernel void remap_16SC2(__global const T * restrict src, __global T * dst, __global short2 * map1,
int src_offset, int dst_offset, int map1_offset,
int src_step, int dst_step, int map1_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, T scalar)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
int dstIdx = mad24(y, dst_step, x + dst_offset);
int map1Idx = mad24(y, map1_step, x + map1_offset);
int2 gxy = convert_int2(map1[map1Idx]);
int gx = gxy.x, gy = gxy.y;
if (NEED_EXTRAPOLATION(gx, gy))
EXTRAPOLATE(gxy, dst[dstIdx])
else
{
int srcIdx = mad24(gy, src_step, gx + src_offset);
dst[dstIdx] = src[srcIdx];
}
}
}
__kernel void remap_16SC2_16UC1(__global const T * restrict src, __global T * dst, __global short2 * map1, __global ushort * map2,
int src_offset, int dst_offset, int map1_offset, int map2_offset,
int src_step, int dst_step, int map1_step, int map2_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, T scalar)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
int dstIdx = mad24(y, dst_step, x + dst_offset);
int map1Idx = mad24(y, map1_step, x + map1_offset);
int map2Idx = mad24(y, map2_step, x + map2_offset);
int map2Value = convert_int(map2[map2Idx]) & (INTER_TAB_SIZE2 - 1);
int dx = (map2Value & (INTER_TAB_SIZE - 1)) < (INTER_TAB_SIZE >> 1) ? 1 : 0;
int dy = (map2Value >> INTER_BITS) < (INTER_TAB_SIZE >> 1) ? 1 : 0;
int2 gxy = convert_int2(map1[map1Idx]) + (int2)(dx, dy);
int gx = gxy.x, gy = gxy.y;
if (NEED_EXTRAPOLATION(gx, gy))
EXTRAPOLATE(gxy, dst[dstIdx])
else
{
int srcIdx = mad24(gy, src_step, gx + src_offset);
dst[dstIdx] = src[srcIdx];
}
}
}
#elif INTER_LINEAR
__kernel void remap_16SC2_16UC1(__global T const * restrict src, __global T * dst,
__global short2 * restrict map1, __global ushort * restrict map2,
int src_offset, int dst_offset, int map1_offset, int map2_offset,
int src_step, int dst_step, int map1_step, int map2_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, T nVal)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
int dstIdx = mad24(y, dst_step, x + dst_offset);
int map1Idx = mad24(y, map1_step, x + map1_offset);
int map2Idx = mad24(y, map2_step, x + map2_offset);
int2 map_dataA = convert_int2(map1[map1Idx]);
int2 map_dataB = (int2)(map_dataA.x + 1, map_dataA.y);
int2 map_dataC = (int2)(map_dataA.x, map_dataA.y + 1);
int2 map_dataD = (int2)(map_dataA.x + 1, map_dataA.y + 1);
ushort map2Value = (ushort)(map2[map2Idx] & (INTER_TAB_SIZE2 - 1));
WT2 u = (WT2)(map2Value & (INTER_TAB_SIZE - 1), map2Value >> INTER_BITS) / (WT2)(INTER_TAB_SIZE);
WT scalar = convertToWT(nVal);
WT a = scalar, b = scalar, c = scalar, d = scalar;
if (!NEED_EXTRAPOLATION(map_dataA.x, map_dataA.y))
a = convertToWT(src[mad24(map_dataA.y, src_step, map_dataA.x + src_offset)]);
else
EXTRAPOLATE(map_dataA, a);
if (!NEED_EXTRAPOLATION(map_dataB.x, map_dataB.y))
b = convertToWT(src[mad24(map_dataB.y, src_step, map_dataB.x + src_offset)]);
else
EXTRAPOLATE(map_dataB, b);
if (!NEED_EXTRAPOLATION(map_dataC.x, map_dataC.y))
c = convertToWT(src[mad24(map_dataC.y, src_step, map_dataC.x + src_offset)]);
else
EXTRAPOLATE(map_dataC, c);
if (!NEED_EXTRAPOLATION(map_dataD.x, map_dataD.y))
d = convertToWT(src[mad24(map_dataD.y, src_step, map_dataD.x + src_offset)]);
else
EXTRAPOLATE(map_dataD, d);
WT dst_data = a * (1 - u.x) * (1 - u.y) +
b * (u.x)     * (1 - u.y) +
c * (1 - u.x) * (u.y) +
d * (u.x)     * (u.y);
dst[dstIdx] = convertToT(dst_data);
}
}
__kernel void remap_2_32FC1(__global T const * restrict  src, __global T * dst,
__global float * map1, __global float * map2,
int src_offset, int dst_offset, int map1_offset, int map2_offset,
int src_step, int dst_step, int map1_step, int map2_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, T nVal)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
int dstIdx = mad24(y, dst_step, x + dst_offset);
int map1Idx = mad24(y, map1_step, x + map1_offset);
int map2Idx = mad24(y, map2_step, x + map2_offset);
float2 map_data = (float2)(map1[map1Idx], map2[map2Idx]);
int2 map_dataA = convert_int2_sat_rtn(map_data);
int2 map_dataB = (int2)(map_dataA.x + 1, map_dataA.y);
int2 map_dataC = (int2)(map_dataA.x, map_dataA.y + 1);
int2 map_dataD = (int2)(map_dataA.x + 1, map_dataA.y + 1);
float2 _u = map_data - convert_float2(map_dataA);
WT2 u = convertToWT2(convert_int2_rte(convertToWT2(_u) * (WT2)INTER_TAB_SIZE)) / (WT2)INTER_TAB_SIZE;
WT scalar = convertToWT(nVal);
WT a = scalar, b = scalar, c = scalar, d = scalar;
if (!NEED_EXTRAPOLATION(map_dataA.x, map_dataA.y))
a = convertToWT(src[mad24(map_dataA.y, src_step, map_dataA.x + src_offset)]);
else
EXTRAPOLATE(map_dataA, a);
if (!NEED_EXTRAPOLATION(map_dataB.x, map_dataB.y))
b = convertToWT(src[mad24(map_dataB.y, src_step, map_dataB.x + src_offset)]);
else
EXTRAPOLATE(map_dataB, b);
if (!NEED_EXTRAPOLATION(map_dataC.x, map_dataC.y))
c = convertToWT(src[mad24(map_dataC.y, src_step, map_dataC.x + src_offset)]);
else
EXTRAPOLATE(map_dataC, c);
if (!NEED_EXTRAPOLATION(map_dataD.x, map_dataD.y))
d = convertToWT(src[mad24(map_dataD.y, src_step, map_dataD.x + src_offset)]);
else
EXTRAPOLATE(map_dataD, d);
WT dst_data = a * (1 - u.x) * (1 - u.y) +
b * (u.x)     * (1 - u.y) +
c * (1 - u.x) * (u.y) +
d * (u.x)     * (u.y);
dst[dstIdx] = convertToT(dst_data);
}
}
__kernel void remap_32FC2(__global T const * restrict  src, __global T * dst,
__global float2 * map1,
int src_offset, int dst_offset, int map1_offset,
int src_step, int dst_step, int map1_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, T nVal)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
int dstIdx = mad24(y, dst_step, x + dst_offset);
int map1Idx = mad24(y, map1_step, x + map1_offset);
float2 map_data = map1[map1Idx];
int2 map_dataA = convert_int2_sat_rtn(map_data);
int2 map_dataB = (int2)(map_dataA.x + 1, map_dataA.y);
int2 map_dataC = (int2)(map_dataA.x, map_dataA.y + 1);
int2 map_dataD = (int2)(map_dataA.x + 1, map_dataA.y + 1);
float2 _u = map_data - convert_float2(map_dataA);
WT2 u = convertToWT2(convert_int2_rte(convertToWT2(_u) * (WT2)INTER_TAB_SIZE)) / (WT2)INTER_TAB_SIZE;
WT scalar = convertToWT(nVal);
WT a = scalar, b = scalar, c = scalar, d = scalar;
if (!NEED_EXTRAPOLATION(map_dataA.x, map_dataA.y))
a = convertToWT(src[mad24(map_dataA.y, src_step, map_dataA.x + src_offset)]);
else
EXTRAPOLATE(map_dataA, a);
if (!NEED_EXTRAPOLATION(map_dataB.x, map_dataB.y))
b = convertToWT(src[mad24(map_dataB.y, src_step, map_dataB.x + src_offset)]);
else
EXTRAPOLATE(map_dataB, b);
if (!NEED_EXTRAPOLATION(map_dataC.x, map_dataC.y))
c = convertToWT(src[mad24(map_dataC.y, src_step, map_dataC.x + src_offset)]);
else
EXTRAPOLATE(map_dataC, c);
if (!NEED_EXTRAPOLATION(map_dataD.x, map_dataD.y))
d = convertToWT(src[mad24(map_dataD.y, src_step, map_dataD.x + src_offset)]);
else
EXTRAPOLATE(map_dataD, d);
WT dst_data = a * (1 - u.x) * (1 - u.y) +
b * (u.x)     * (1 - u.y) +
c * (1 - u.x) * (u.y) +
d * (u.x)     * (u.y);
dst[dstIdx] = convertToT(dst_data);
}
}
#endif
97286bf2357a81af2a1da7e008a02729imgproc_resize#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#define INTER_RESIZE_COEF_BITS 11
#define INTER_RESIZE_COEF_SCALE (1 << INTER_RESIZE_COEF_BITS)
#define CAST_BITS (INTER_RESIZE_COEF_BITS << 1)
#define CAST_SCALE (1.0f/(1<= (l) ? (x):((x)+1))
#ifdef LN
__kernel void resizeLN_C1_D0(__global uchar * dst, __global uchar const * restrict src,
int dst_offset, int src_offset,int dst_step, int src_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, float ifx, float ify )
{
int gx = get_global_id(0);
int dy = get_global_id(1);
float4  sx, u, xf;
int4 x, DX;
gx = (gx<<2) - (dst_offset&3);
DX = (int4)(gx, gx+1, gx+2, gx+3);
sx = (convert_float4(DX) + 0.5f) * ifx - 0.5f;
xf = floor(sx);
x = convert_int4(xf);
u = sx - xf;
float sy = ((dy+0.5f) * ify - 0.5f);
int y = floor(sy);
float v = sy - y;
u = x < (int4)0 ? (float4)0 : u;
u = (x >= (int4)src_cols) ? (float4)0 : u;
x = x < (int4)0 ? (int4)0 : x;
x = (x >= (int4)src_cols) ? (int4)(src_cols-1) : x;
y<0 ? y=0,v=0 : y;
y>=src_rows ? y=src_rows-1,v=0 : y;
int4 U, U1;
int V, V1;
float4 utmp1, utmp2;
float vtmp;
float4 scale_vec = INTER_RESIZE_COEF_SCALE;
utmp1 = u * scale_vec;
utmp2 = scale_vec - utmp1;
U = convert_int4(rint(utmp1));
U1 = convert_int4(rint(utmp2));
vtmp = v * INTER_RESIZE_COEF_SCALE;
V = rint(vtmp);
V1= rint(INTER_RESIZE_COEF_SCALE - vtmp);
int y_ = INC(y,src_rows);
int4 x_;
x_ =  ((x+1 >= src_cols) != 0) ? x : x+1;
int4 val1, val2, val;
int4 sdata1, sdata2, sdata3, sdata4;
int4 pos1 = mad24((int4)y, (int4)src_step, x+(int4)src_offset);
int4 pos2 = mad24((int4)y, (int4)src_step, x_+(int4)src_offset);
int4 pos3 = mad24((int4)y_, (int4)src_step, x+(int4)src_offset);
int4 pos4 = mad24((int4)y_, (int4)src_step, x_+(int4)src_offset);
sdata1.s0 = src[pos1.s0];
sdata1.s1 = src[pos1.s1];
sdata1.s2 = src[pos1.s2];
sdata1.s3 = src[pos1.s3];
sdata2.s0 = src[pos2.s0];
sdata2.s1 = src[pos2.s1];
sdata2.s2 = src[pos2.s2];
sdata2.s3 = src[pos2.s3];
sdata3.s0 = src[pos3.s0];
sdata3.s1 = src[pos3.s1];
sdata3.s2 = src[pos3.s2];
sdata3.s3 = src[pos3.s3];
sdata4.s0 = src[pos4.s0];
sdata4.s1 = src[pos4.s1];
sdata4.s2 = src[pos4.s2];
sdata4.s3 = src[pos4.s3];
val1 = mul24(U1 , sdata1) + mul24(U , sdata2);
val2 = mul24(U1 , sdata3) + mul24(U , sdata4);
val = mul24((int4)V1 , val1) + mul24((int4)V , val2);
val = ((val + (1<<(CAST_BITS-1))) >> CAST_BITS);
pos4 = mad24(dy, dst_step, gx+dst_offset);
pos4.y++;
pos4.z+=2;
pos4.w+=3;
uchar4 uval = convert_uchar4_sat(val);
int con = (gx >= 0 && gx+3 < dst_cols && dy >= 0 && dy < dst_rows && (dst_offset&3)==0);
if(con)
{
*(__global uchar4*)(dst + pos4.x)=uval;
}
else
{
if(gx >= 0 && gx < dst_cols && dy >= 0 && dy < dst_rows)
{
dst[pos4.x]=uval.x;
}
if(gx+1 >= 0 && gx+1 < dst_cols && dy >= 0 && dy < dst_rows)
{
dst[pos4.y]=uval.y;
}
if(gx+2 >= 0 && gx+2 < dst_cols && dy >= 0 && dy < dst_rows)
{
dst[pos4.z]=uval.z;
}
if(gx+3 >= 0 && gx+3 < dst_cols && dy >= 0 && dy < dst_rows)
{
dst[pos4.w]=uval.w;
}
}
}
__kernel void resizeLN_C4_D0(__global uchar4 * dst, __global uchar4 * src,
int dst_offset, int src_offset,int dst_step, int src_step,
int src_cols, int src_rows, int dst_cols, int dst_rows, float ifx, float ify )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
float sx = ((dx+0.5f) * ifx - 0.5f), sy = ((dy+0.5f) * ify - 0.5f);
int x = floor(sx), y = floor(sy);
float u = sx - x, v = sy - y;
if ( x<0 ) x=0,u=0;
if ( x>=src_cols ) x=src_cols-1,u=0;
if ( y<0 ) y=0,v=0;
if (y>=src_rows ) y=src_rows-1,v=0;
u = u * INTER_RESIZE_COEF_SCALE;
v = v * INTER_RESIZE_COEF_SCALE;
int U = rint(u);
int V = rint(v);
int U1= rint(INTER_RESIZE_COEF_SCALE - u);
int V1= rint(INTER_RESIZE_COEF_SCALE - v);
int y_ = INC(y,src_rows);
int x_ = INC(x,src_cols);
int4 srcpos;
srcpos.x = mad24(y, src_step, x+src_offset);
srcpos.y = mad24(y, src_step, x_+src_offset);
srcpos.z = mad24(y_, src_step, x+src_offset);
srcpos.w = mad24(y_, src_step, x_+src_offset);
int4 data0 = convert_int4(src[srcpos.x]);
int4 data1 = convert_int4(src[srcpos.y]);
int4 data2 = convert_int4(src[srcpos.z]);
int4 data3 = convert_int4(src[srcpos.w]);
int4 val = mul24((int4)mul24(U1, V1) ,  data0) + mul24((int4)mul24(U, V1) ,  data1)
+mul24((int4)mul24(U1, V) ,  data2)+mul24((int4)mul24(U, V) ,  data3);
int dstpos = mad24(dy, dst_step, dx+dst_offset);
uchar4 uval =   convert_uchar4((val + (1<<(CAST_BITS-1)))>>CAST_BITS);
if(dx>=0 && dx=0 && dy=src_cols ) x=src_cols-1,u=0;
if ( y<0 ) y=0,v=0;
if (y>=src_rows ) y=src_rows-1,v=0;
int y_ = INC(y,src_rows);
int x_ = INC(x,src_cols);
float u1 = 1.f-u;
float v1 = 1.f-v;
int4 srcpos;
srcpos.x = mad24(y, src_step, x+src_offset);
srcpos.y = mad24(y, src_step, x_+src_offset);
srcpos.z = mad24(y_, src_step, x+src_offset);
srcpos.w = mad24(y_, src_step, x_+src_offset);
float data0 = src[srcpos.x];
float data1 = src[srcpos.y];
float data2 = src[srcpos.z];
float data3 = src[srcpos.w];
float val1 = u1 *  data0 +
u  *  data1 ;
float val2 = u1 *  data2 +
u *  data3;
float val = v1 * val1 + v * val2;
int dstpos = mad24(dy, dst_step, dx+dst_offset);
if(dx>=0 && dx=0 && dy=src_cols ) x=src_cols-1,u=0;
if ( y<0 ) y=0,v=0;
if (y>=src_rows ) y=src_rows-1,v=0;
int y_ = INC(y,src_rows);
int x_ = INC(x,src_cols);
float u1 = 1.f-u;
float v1 = 1.f-v;
int4 srcpos;
srcpos.x = mad24(y, src_step, x+src_offset);
srcpos.y = mad24(y, src_step, x_+src_offset);
srcpos.z = mad24(y_, src_step, x+src_offset);
srcpos.w = mad24(y_, src_step, x_+src_offset);
float4 s_data1, s_data2, s_data3, s_data4;
s_data1 = src[srcpos.x];
s_data2 = src[srcpos.y];
s_data3 = src[srcpos.z];
s_data4 = src[srcpos.w];
float4 val = u1 * v1 * s_data1 + u * v1 * s_data2
+u1 * v *s_data3 + u * v *s_data4;
int dstpos = mad24(dy, dst_step, dx+dst_offset);
if(dx>=0 && dx=0 && dy= (r_edge) | (_y)<0 | (_y) >= (t_edge) ? (const_v) : SRC((_x),(_y))
#else
#define ELEM(_x,_y,r_edge,t_edge,const_v) SRC((_x),(_y))
#endif
#define DSTX(_x,_y) (((global float*)(DstX+DstXOffset+(_y)*DstXPitch))[_x])
#define DSTY(_x,_y) (((global float*)(DstY+DstYOffset+(_y)*DstYPitch))[_x])
#define INIT_AND_READ_LOCAL_SOURCE(width, height, fill_const, kernel_border) \
int srcX = x + srcOffsetX - (kernel_border); \
int srcY = y + srcOffsetY - (kernel_border); \
int xb = srcX; \
int yb = srcY; \
\
EXTRAPOLATE(xb, (width)); \
EXTRAPOLATE(yb, (height)); \
lsmem[liy][lix] = ELEM(xb, yb, (width), (height), (fill_const) ); \
\
if(lix < ((kernel_border)*2)) \
{ \
int xb = srcX+BLK_X; \
EXTRAPOLATE(xb,(width)); \
lsmem[liy][lix+BLK_X] = ELEM(xb, yb, (width), (height), (fill_const) ); \
} \
if(liy< ((kernel_border)*2)) \
{ \
int yb = srcY+BLK_Y; \
EXTRAPOLATE(yb, (height)); \
lsmem[liy+BLK_Y][lix] = ELEM(xb, yb, (width), (height), (fill_const) ); \
} \
if(lix<((kernel_border)*2) && liy<((kernel_border)*2)) \
{ \
int xb = srcX+BLK_X; \
int yb = srcY+BLK_Y; \
EXTRAPOLATE(xb,(width)); \
EXTRAPOLATE(yb,(height)); \
lsmem[liy+BLK_Y][lix+BLK_X] = ELEM(xb, yb, (width), (height), (fill_const) ); \
}
__kernel void sobel3(
__global uchar* Src,
const uint      SrcPitch,
const int       srcOffsetX,
const int       srcOffsetY,
__global uchar* DstX,
const int       DstXOffset,
const uint      DstXPitch,
__global uchar* DstY,
const int       DstYOffset,
const uint      DstYPitch,
int             width,
int             height,
int             dstWidth,
int             dstHeight,
float           scale
)
{
__local float lsmem[BLK_Y+2][BLK_X+2];
int lix = get_local_id(0);
int liy = get_local_id(1);
int x = (int)get_global_id(0);
int y = (int)get_global_id(1);
INIT_AND_READ_LOCAL_SOURCE(width, height, 0, 1)
barrier(CLK_LOCAL_MEM_FENCE);
if( x >= dstWidth || y >=dstHeight )  return;
float u1 = lsmem[liy][lix];
float u2 = lsmem[liy][lix+1];
float u3 = lsmem[liy][lix+2];
float m1 = lsmem[liy+1][lix];
float m3 = lsmem[liy+1][lix+2];
float b1 = lsmem[liy+2][lix];
float b2 = lsmem[liy+2][lix+1];
float b3 = lsmem[liy+2][lix+2];
#ifdef SCHARR
DSTX(x,y) = mad(10.0f, m3 - m1, 3.0f * (u3 - u1 + b3 - b1)) * scale;
DSTY(x,y) = mad(10.0f, b2 - u2, 3.0f * (b1 - u1 + b3 - u3)) * scale;
#else
DSTX(x,y) = mad(2.0f, m3 - m1, u3 - u1 + b3 - b1) * scale;
DSTY(x,y) = mad(2.0f, b2 - u2, b1 - u1 + b3 - u3) * scale;
#endif
}
__kernel void sobel5(
__global uchar* Src,
const uint      SrcPitch,
const int       srcOffsetX,
const int       srcOffsetY,
__global uchar* DstX,
const int       DstXOffset,
const uint      DstXPitch,
__global uchar* DstY,
const int       DstYOffset,
const uint      DstYPitch,
int             width,
int             height,
int             dstWidth,
int             dstHeight,
float           scale
)
{
__local float lsmem[BLK_Y+4][BLK_X+4];
int lix = get_local_id(0);
int liy = get_local_id(1);
int x = (int)get_global_id(0);
int y = (int)get_global_id(1);
INIT_AND_READ_LOCAL_SOURCE(width, height, 0, 2)
barrier(CLK_LOCAL_MEM_FENCE);
if( x >= dstWidth || y >=dstHeight )  return;
float t1 = lsmem[liy][lix];
float t2 = lsmem[liy][lix+1];
float t3 = lsmem[liy][lix+2];
float t4 = lsmem[liy][lix+3];
float t5 = lsmem[liy][lix+4];
float u1 = lsmem[liy+1][lix];
float u2 = lsmem[liy+1][lix+1];
float u3 = lsmem[liy+1][lix+2];
float u4 = lsmem[liy+1][lix+3];
float u5 = lsmem[liy+1][lix+4];
float m1 = lsmem[liy+2][lix];
float m2 = lsmem[liy+2][lix+1];
float m4 = lsmem[liy+2][lix+3];
float m5 = lsmem[liy+2][lix+4];
float l1 = lsmem[liy+3][lix];
float l2 = lsmem[liy+3][lix+1];
float l3 = lsmem[liy+3][lix+2];
float l4 = lsmem[liy+3][lix+3];
float l5 = lsmem[liy+3][lix+4];
float b1 = lsmem[liy+4][lix];
float b2 = lsmem[liy+4][lix+1];
float b3 = lsmem[liy+4][lix+2];
float b4 = lsmem[liy+4][lix+3];
float b5 = lsmem[liy+4][lix+4];
DSTX(x,y) = scale *
mad(12.0f, m4 - m2,
mad(6.0f, m5 - m1,
mad(8.0f, u4 - u2 + l4 - l2,
mad(4.0f, u5 - u1 + l5 - l1,
mad(2.0f, t4 - t2 + b4 - b2, t5 - t1 + b5 - b1 )
)
)
)
);
DSTY(x,y) = scale *
mad(12.0f, l3 - u3,
mad(6.0f, b3 - t3,
mad(8.0f, l2 - u2 + l4 - u4,
mad(4.0f, b2 - t2 + b4 - t4,
mad(2.0f, l1 - u1 + l5 - u5, b1 - t1 + b5 - t5 )
)
)
)
);
}
__kernel void sobel7(
__global uchar* Src,
const uint      SrcPitch,
const int       srcOffsetX,
const int       srcOffsetY,
__global uchar* DstX,
const int       DstXOffset,
const uint      DstXPitch,
__global uchar* DstY,
const int       DstYOffset,
const uint      DstYPitch,
int             width,
int             height,
int             dstWidth,
int             dstHeight,
float           scale
)
{
__local float lsmem[BLK_Y+6][BLK_X+6];
int lix = get_local_id(0);
int liy = get_local_id(1);
int x = (int)get_global_id(0);
int y = (int)get_global_id(1);
INIT_AND_READ_LOCAL_SOURCE(width, height, 0, 3)
barrier(CLK_LOCAL_MEM_FENCE);
if( x >= dstWidth || y >=dstHeight )  return;
float tt1 = lsmem[liy][lix];
float tt2 = lsmem[liy][lix+1];
float tt3 = lsmem[liy][lix+2];
float tt4 = lsmem[liy][lix+3];
float tt5 = lsmem[liy][lix+4];
float tt6 = lsmem[liy][lix+5];
float tt7 = lsmem[liy][lix+6];
float t1 = lsmem[liy+1][lix];
float t2 = lsmem[liy+1][lix+1];
float t3 = lsmem[liy+1][lix+2];
float t4 = lsmem[liy+1][lix+3];
float t5 = lsmem[liy+1][lix+4];
float t6 = lsmem[liy+1][lix+5];
float t7 = lsmem[liy+1][lix+6];
float u1 = lsmem[liy+2][lix];
float u2 = lsmem[liy+2][lix+1];
float u3 = lsmem[liy+2][lix+2];
float u4 = lsmem[liy+2][lix+3];
float u5 = lsmem[liy+2][lix+4];
float u6 = lsmem[liy+2][lix+5];
float u7 = lsmem[liy+2][lix+6];
float m1 = lsmem[liy+3][lix];
float m2 = lsmem[liy+3][lix+1];
float m3 = lsmem[liy+3][lix+2];
float m5 = lsmem[liy+3][lix+4];
float m6 = lsmem[liy+3][lix+5];
float m7 = lsmem[liy+3][lix+6];
float l1 = lsmem[liy+4][lix];
float l2 = lsmem[liy+4][lix+1];
float l3 = lsmem[liy+4][lix+2];
float l4 = lsmem[liy+4][lix+3];
float l5 = lsmem[liy+4][lix+4];
float l6 = lsmem[liy+4][lix+5];
float l7 = lsmem[liy+4][lix+6];
float b1 = lsmem[liy+5][lix];
float b2 = lsmem[liy+5][lix+1];
float b3 = lsmem[liy+5][lix+2];
float b4 = lsmem[liy+5][lix+3];
float b5 = lsmem[liy+5][lix+4];
float b6 = lsmem[liy+5][lix+5];
float b7 = lsmem[liy+5][lix+6];
float bb1 = lsmem[liy+6][lix];
float bb2 = lsmem[liy+6][lix+1];
float bb3 = lsmem[liy+6][lix+2];
float bb4 = lsmem[liy+6][lix+3];
float bb5 = lsmem[liy+6][lix+4];
float bb6 = lsmem[liy+6][lix+5];
float bb7 = lsmem[liy+6][lix+6];
DSTX(x,y) = scale *
mad(100.0f, m5 - m3,
mad(80.0f, m6 - m2,
mad(20.0f, m7 - m1,
mad(75.0f, u5 - u3 + l5 - l3,
mad(60.0f, u6 - u2 + l6 - l2,
mad(15.0f, u7 - u1 + l7 - l1,
mad(30.0f, t5 - t3 + b5 - b3,
mad(24.0f, t6 - t2 + b6 - b2,
mad(6.0f, t7 - t1 + b7 - b1,
mad(5.0f, tt5 - tt3 + bb5 - bb3,
mad(4.0f, tt6 - tt2 + bb6 - bb2, tt7 - tt1 + bb7 - bb1 )
)
)
)
)
)
)
)
)
)
);
DSTY(x,y) = scale *
mad(100.0f, l4 - u4,
mad(80.0f, b4 - t4,
mad(20.0f, bb4 - tt4,
mad(75.0f, l5 - u5 + l3 - u3,
mad(60.0f, b5 - t5 + b3 - t3,
mad(15.0f, bb5 - tt5 + bb3 - tt3,
mad(30.0f, l6 - u6 + l2 - u2,
mad(24.0f, b6 - t6 + b2 - t2,
mad(6.0f, bb6 - tt6 + bb2 - tt2,
mad(5.0f, l7 - u7 + l1 - u1,
mad(4.0f, b7 - t7 + b1 - t1, bb7 - tt7 + bb1 - tt1 )
)
)
)
)
)
)
)
)
)
);
}
d91d5e7d7c63952c08769030655a5c90imgproc_threshold#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#ifdef VECTORIZED
__kernel void threshold(__global const T * restrict src, int src_offset, int src_step,
__global T * dst, int dst_offset, int dst_step,
T thresh, T max_val, int max_index, int rows, int cols)
{
int gx = get_global_id(0);
int gy = get_global_id(1);
if (gx < cols && gy < rows)
{
gx *= VECSIZE;
int src_index = mad24(gy, src_step, src_offset + gx);
int dst_index = mad24(gy, dst_step, dst_offset + gx);
#ifdef SRC_ALIGNED
VT sdata = *((__global VT *)(src + src_index));
#else
VT sdata = VLOADN(0, src + src_index);
#endif
VT vthresh = (VT)(thresh);
#ifdef THRESH_BINARY
VT vecValue = sdata > vthresh ? (VT)max_val : (VT)(0);
#elif defined THRESH_BINARY_INV
VT vecValue = sdata > vthresh ? (VT)(0) : (VT)max_val;
#elif defined THRESH_TRUNC
VT vecValue = sdata > vthresh ? (VT)thresh : sdata;
#elif defined THRESH_TOZERO
VT vecValue = sdata > vthresh ? sdata : (VT)(0);
#elif defined THRESH_TOZERO_INV
VT vecValue = sdata > vthresh ? (VT)(0) : sdata;
#endif
if (gx + VECSIZE <= max_index)
#ifdef DST_ALIGNED
*(__global VT*)(dst + dst_index) = vecValue;
#else
VSTOREN(vecValue, 0, dst + dst_index);
#endif
else
{
__attribute__(( aligned(sizeof(VT)) )) T array[VECSIZE];
*((VT*)array) = vecValue;
#pragma unroll
for (int i = 0; i < VECSIZE; ++i)
if (gx + i < max_index)
dst[dst_index + i] = array[i];
}
}
}
#else
__kernel void threshold(__global const T * restrict src, int src_offset, int src_step,
__global T * dst, int dst_offset, int dst_step,
T thresh, T max_val, int rows, int cols)
{
int gx = get_global_id(0);
int gy = get_global_id(1);
if (gx < cols && gy < rows)
{
int src_index = mad24(gy, src_step, src_offset + gx);
int dst_index = mad24(gy, dst_step, dst_offset + gx);
T sdata = src[src_index];
#ifdef THRESH_BINARY
dst[dst_index] = sdata > thresh ? max_val : (T)(0);
#elif defined THRESH_BINARY_INV
dst[dst_index] = sdata > thresh ? (T)(0) : max_val;
#elif defined THRESH_TRUNC
dst[dst_index] = sdata > thresh ? thresh : sdata;
#elif defined THRESH_TOZERO
dst[dst_index] = sdata > thresh ? sdata : (T)(0);
#elif defined THRESH_TOZERO_INV
dst[dst_index] = sdata > thresh ? (T)(0) : sdata;
#endif
}
}
#endif
3d9631e061a5b037ea5cfb86b156d984imgproc_warpAffine#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
typedef double F;
typedef double4 F4;
#define convert_F4 convert_double4
#else
typedef float F;
typedef float4 F4;
#define convert_F4 convert_float4
#endif
#define INTER_BITS 5
#define INTER_TAB_SIZE (1 << INTER_BITS)
#define INTER_SCALE 1.f/INTER_TAB_SIZE
#define AB_BITS max(10, (int)INTER_BITS)
#define AB_SCALE (1 << AB_BITS)
#define INTER_REMAP_COEF_BITS 15
#define INTER_REMAP_COEF_SCALE (1 << INTER_REMAP_COEF_BITS)
void interpolateCubic( float x, float* coeffs )
{
const float A = -0.75f;
coeffs[0] = ((A*(x + 1.f) - 5.0f*A)*(x + 1.f) + 8.0f*A)*(x + 1.f) - 4.0f*A;
coeffs[1] = ((A + 2.f)*x - (A + 3.f))*x*x + 1.f;
coeffs[2] = ((A + 2.f)*(1.f - x) - (A + 3.f))*(1.f - x)*(1.f - x) + 1.f;
coeffs[3] = 1.f - coeffs[0] - coeffs[1] - coeffs[2];
}
__kernel void warpAffineNN_C1_D0(__global uchar const * restrict src, __global uchar * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
dx = (dx<<2) - (dst_offset&3);
int round_delta = (AB_SCALE>>1);
int4 X, Y;
int4 sx, sy;
int4 DX = (int4)(dx, dx+1, dx+2, dx+3);
DX = (DX << AB_BITS);
F4 M0DX, M3DX;
M0DX = M[0] * convert_F4(DX);
M3DX = M[3] * convert_F4(DX);
X = convert_int4(rint(M0DX));
Y = convert_int4(rint(M3DX));
int tmp1, tmp2;
tmp1 = rint((M[1]*dy + M[2]) * AB_SCALE);
tmp2 = rint((M[4]*dy + M[5]) * AB_SCALE);
X += tmp1 + round_delta;
Y += tmp2 + round_delta;
sx = convert_int4(convert_short4(X >> AB_BITS));
sy = convert_int4(convert_short4(Y >> AB_BITS));
__global uchar4 * d = (__global uchar4 *)(dst+dst_offset+dy*dstStep+dx);
uchar4 dval = *d;
DX = (int4)(dx, dx+1, dx+2, dx+3);
int4 dcon = DX >= 0 && DX < dst_cols && dy >= 0 && dy < dst_rows;
int4 scon = sx >= 0 && sx < src_cols && sy >= 0 && sy < src_rows;
int4 spos = src_offset + sy * srcStep + sx;
uchar4 sval;
sval.s0 = scon.s0 ? src[spos.s0] : 0;
sval.s1 = scon.s1 ? src[spos.s1] : 0;
sval.s2 = scon.s2 ? src[spos.s2] : 0;
sval.s3 = scon.s3 ? src[spos.s3] : 0;
dval = convert_uchar4(dcon) != (uchar4)(0,0,0,0) ? sval : dval;
*d = dval;
}
}
__kernel void warpAffineLinear_C1_D0(__global const uchar * restrict src, __global uchar * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
dx = (dx<<2) - (dst_offset&3);
int round_delta = ((AB_SCALE >> INTER_BITS) >> 1);
int4 X, Y;
short4  ax, ay;
int4 sx, sy;
int4 DX = (int4)(dx, dx+1, dx+2, dx+3);
DX = (DX << AB_BITS);
F4 M0DX, M3DX;
M0DX = M[0] * convert_F4(DX);
M3DX = M[3] * convert_F4(DX);
X = convert_int4(rint(M0DX));
Y = convert_int4(rint(M3DX));
int tmp1, tmp2;
tmp1 = rint((M[1]*dy + M[2]) * AB_SCALE);
tmp2 = rint((M[4]*dy + M[5]) * AB_SCALE);
X += tmp1 + round_delta;
Y += tmp2 + round_delta;
X = X >> (AB_BITS - INTER_BITS);
Y = Y >> (AB_BITS - INTER_BITS);
sx = convert_int4(convert_short4(X >> INTER_BITS));
sy = convert_int4(convert_short4(Y >> INTER_BITS));
ax = convert_short4(X & (INTER_TAB_SIZE-1));
ay = convert_short4(Y & (INTER_TAB_SIZE-1));
uchar4 v0, v1, v2,v3;
int4 scon0, scon1, scon2, scon3;
int4 spos0, spos1, spos2, spos3;
scon0 = (sx >= 0 && sx < src_cols && sy >= 0 && sy < src_rows);
scon1 = (sx+1 >= 0 && sx+1 < src_cols && sy >= 0 && sy < src_rows);
scon2 = (sx >= 0 && sx < src_cols && sy+1 >= 0 && sy+1 < src_rows);
scon3 = (sx+1 >= 0 && sx+1 < src_cols && sy+1 >= 0 && sy+1 < src_rows);
spos0 = src_offset + sy * srcStep + sx;
spos1 = src_offset + sy * srcStep + sx + 1;
spos2 = src_offset + (sy+1) * srcStep + sx;
spos3 = src_offset + (sy+1) * srcStep + sx + 1;
v0.s0 = scon0.s0 ? src[spos0.s0] : 0;
v1.s0 = scon1.s0 ? src[spos1.s0] : 0;
v2.s0 = scon2.s0 ? src[spos2.s0] : 0;
v3.s0 = scon3.s0 ? src[spos3.s0] : 0;
v0.s1 = scon0.s1 ? src[spos0.s1] : 0;
v1.s1 = scon1.s1 ? src[spos1.s1] : 0;
v2.s1 = scon2.s1 ? src[spos2.s1] : 0;
v3.s1 = scon3.s1 ? src[spos3.s1] : 0;
v0.s2 = scon0.s2 ? src[spos0.s2] : 0;
v1.s2 = scon1.s2 ? src[spos1.s2] : 0;
v2.s2 = scon2.s2 ? src[spos2.s2] : 0;
v3.s2 = scon3.s2 ? src[spos3.s2] : 0;
v0.s3 = scon0.s3 ? src[spos0.s3] : 0;
v1.s3 = scon1.s3 ? src[spos1.s3] : 0;
v2.s3 = scon2.s3 ? src[spos2.s3] : 0;
v3.s3 = scon3.s3 ? src[spos3.s3] : 0;
short4 itab0, itab1, itab2, itab3;
float4 taby, tabx;
taby = INTER_SCALE * convert_float4(ay);
tabx = INTER_SCALE * convert_float4(ax);
itab0 = convert_short4_sat(( (1.0f-taby)*(1.0f-tabx) * (float4)INTER_REMAP_COEF_SCALE ));
itab1 = convert_short4_sat(( (1.0f-taby)*tabx * (float4)INTER_REMAP_COEF_SCALE ));
itab2 = convert_short4_sat(( taby*(1.0f-tabx) * (float4)INTER_REMAP_COEF_SCALE ));
itab3 = convert_short4_sat(( taby*tabx * (float4)INTER_REMAP_COEF_SCALE ));
int4 val;
uchar4 tval;
val = convert_int4(v0) * convert_int4(itab0) + convert_int4(v1) * convert_int4(itab1)
+ convert_int4(v2) * convert_int4(itab2) + convert_int4(v3) * convert_int4(itab3);
tval = convert_uchar4_sat ( (val + (1 << (INTER_REMAP_COEF_BITS-1))) >> INTER_REMAP_COEF_BITS ) ;
__global uchar4 * d =(__global uchar4 *)(dst+dst_offset+dy*dstStep+dx);
uchar4 dval = *d;
DX = (int4)(dx, dx+1, dx+2, dx+3);
int4 dcon = DX >= 0 && DX < dst_cols && dy >= 0 && dy < dst_rows;
dval = convert_uchar4(dcon != 0) ? tval : dval;
*d = dval;
}
}
__kernel void warpAffineCubic_C1_D0(__global uchar * src, __global uchar * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = ((AB_SCALE>>INTER_BITS)>>1);
int X0 = rint(M[0] * dx * AB_SCALE);
int Y0 = rint(M[3] * dx * AB_SCALE);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
int X = X0 >> (AB_BITS - INTER_BITS);
int Y = Y0 >> (AB_BITS - INTER_BITS);
short sx = (short)(X >> INTER_BITS) - 1;
short sy = (short)(Y >> INTER_BITS) - 1;
short ay = (short)(Y & (INTER_TAB_SIZE-1));
short ax = (short)(X & (INTER_TAB_SIZE-1));
uchar v[16];
int i, j;
#pragma unroll 4
for(i=0; i<4;  i++)
for(j=0; j<4;  j++)
{
v[i*4+j] = (sx+j >= 0 && sx+j < src_cols && sy+i >= 0 && sy+i < src_rows) ? src[src_offset+(sy+i) * srcStep + (sx+j)] : 0;
}
short itab[16];
float tab1y[4], tab1x[4];
float axx, ayy;
ayy = 1.f/INTER_TAB_SIZE * ay;
axx = 1.f/INTER_TAB_SIZE * ax;
interpolateCubic(ayy, tab1y);
interpolateCubic(axx, tab1x);
int isum = 0;
#pragma unroll 16
for( i=0; i<16; i++ )
{
F v = tab1y[(i>>2)] * tab1x[(i&3)];
isum += itab[i] = convert_short_sat( rint( v * INTER_REMAP_COEF_SCALE ) );
}
if( isum != INTER_REMAP_COEF_SCALE )
{
int k1, k2;
int diff = isum - INTER_REMAP_COEF_SCALE;
int Mk1=2, Mk2=2, mk1=2, mk2=2;
for( k1 = 2; k1 < 4; k1++ )
for( k2 = 2; k2 < 4; k2++ )
{
if( itab[(k1<<2)+k2] < itab[(mk1<<2)+mk2] )
mk1 = k1, mk2 = k2;
else if( itab[(k1<<2)+k2] > itab[(Mk1<<2)+Mk2] )
Mk1 = k1, Mk2 = k2;
}
diff<0 ? (itab[(Mk1<<2)+Mk2]=(short)(itab[(Mk1<<2)+Mk2]-diff)) : (itab[(mk1<<2)+mk2]=(short)(itab[(mk1<<2)+mk2]-diff));
}
if( dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
int sum=0;
for ( i =0; i<16; i++ )
{
sum += v[i] * itab[i] ;
}
dst[dst_offset+dy*dstStep+dx] = convert_uchar_sat( (sum + (1 << (INTER_REMAP_COEF_BITS-1))) >> INTER_REMAP_COEF_BITS ) ;
}
}
}
__kernel void warpAffineNN_C4_D0(__global uchar4 const * restrict src, __global uchar4 * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = (AB_SCALE >> 1);
int X0 = rint(M[0] * dx * AB_SCALE);
int Y0 = rint(M[3] * dx * AB_SCALE);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
int sx0 = (short)(X0 >> AB_BITS);
int sy0 = (short)(Y0 >> AB_BITS);
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>2)+dy*(dstStep>>2)+dx]= (sx0>=0 && sx0=0 && sy0>2)+sy0*(srcStep>>2)+sx0] : (uchar4)0;
}
}
__kernel void warpAffineLinear_C4_D0(__global uchar4 const * restrict src, __global uchar4 * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = AB_SCALE/INTER_TAB_SIZE/2;
src_offset = (src_offset>>2);
srcStep = (srcStep>>2);
int tmp = (dx << AB_BITS);
int X0 = rint(M[0] * tmp);
int Y0 = rint(M[3] * tmp);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
X0 = X0 >> (AB_BITS - INTER_BITS);
Y0 = Y0 >> (AB_BITS - INTER_BITS);
short sx0 = (short)(X0 >> INTER_BITS);
short sy0 = (short)(Y0 >> INTER_BITS);
short ax0 = (short)(X0 & (INTER_TAB_SIZE-1));
short ay0 = (short)(Y0 & (INTER_TAB_SIZE-1));
int4 v0, v1, v2, v3;
v0 = (sx0 >= 0 && sx0 < src_cols && sy0 >= 0 && sy0 < src_rows) ? convert_int4(src[src_offset+sy0 * srcStep + sx0]) : 0;
v1 = (sx0+1 >= 0 && sx0+1 < src_cols && sy0 >= 0 && sy0 < src_rows) ? convert_int4(src[src_offset+sy0 * srcStep + sx0+1]) : 0;
v2 = (sx0 >= 0 && sx0 < src_cols && sy0+1 >= 0 && sy0+1 < src_rows) ? convert_int4(src[src_offset+(sy0+1) * srcStep + sx0]) : 0;
v3 = (sx0+1 >= 0 && sx0+1 < src_cols && sy0+1 >= 0 && sy0+1 < src_rows) ? convert_int4(src[src_offset+(sy0+1) * srcStep + sx0+1]) : 0;
int itab0, itab1, itab2, itab3;
float taby, tabx;
taby = 1.f/INTER_TAB_SIZE*ay0;
tabx = 1.f/INTER_TAB_SIZE*ax0;
itab0 = convert_short_sat(rint( (1.0f-taby)*(1.0f-tabx) * INTER_REMAP_COEF_SCALE ));
itab1 = convert_short_sat(rint( (1.0f-taby)*tabx * INTER_REMAP_COEF_SCALE ));
itab2 = convert_short_sat(rint( taby*(1.0f-tabx) * INTER_REMAP_COEF_SCALE ));
itab3 = convert_short_sat(rint( taby*tabx * INTER_REMAP_COEF_SCALE ));
int4 val;
val = v0 * itab0 +  v1 * itab1 + v2 * itab2 + v3 * itab3;
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>2)+dy*(dstStep>>2)+dx] =  convert_uchar4_sat ( (val + (1 << (INTER_REMAP_COEF_BITS-1))) >> INTER_REMAP_COEF_BITS ) ;
}
}
__kernel void warpAffineCubic_C4_D0(__global uchar4 const * restrict src, __global uchar4 * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = ((AB_SCALE>>INTER_BITS)>>1);
src_offset = (src_offset>>2);
srcStep = (srcStep>>2);
dst_offset = (dst_offset>>2);
dstStep = (dstStep>>2);
int tmp = (dx << AB_BITS);
int X0 = rint(M[0] * tmp);
int Y0 = rint(M[3] * tmp);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
X0 = X0 >> (AB_BITS - INTER_BITS);
Y0 = Y0 >> (AB_BITS - INTER_BITS);
int sx = (short)(X0 >> INTER_BITS) - 1;
int sy = (short)(Y0 >> INTER_BITS) - 1;
int ay = (short)(Y0 & (INTER_TAB_SIZE-1));
int ax = (short)(X0 & (INTER_TAB_SIZE-1));
uchar4 v[16];
int i,j;
#pragma unroll 4
for(i=0; i<4; i++)
for(j=0; j<4; j++)
{
v[i*4+j] = (sx+j >= 0 && sx+j < src_cols && sy+i >= 0 && sy+i < src_rows) ? (src[src_offset+(sy+i) * srcStep + (sx+j)])  : (uchar4)0;
}
int itab[16];
float tab1y[4], tab1x[4];
float axx, ayy;
ayy = INTER_SCALE * ay;
axx = INTER_SCALE * ax;
interpolateCubic(ayy, tab1y);
interpolateCubic(axx, tab1x);
int isum = 0;
#pragma unroll 16
for( i=0; i<16; i++ )
{
float tmp;
tmp = tab1y[(i>>2)] * tab1x[(i&3)] * INTER_REMAP_COEF_SCALE;
itab[i] = rint(tmp);
isum += itab[i];
}
if( isum != INTER_REMAP_COEF_SCALE )
{
int k1, k2;
int diff = isum - INTER_REMAP_COEF_SCALE;
int Mk1=2, Mk2=2, mk1=2, mk2=2;
for( k1 = 2; k1 < 4; k1++ )
for( k2 = 2; k2 < 4; k2++ )
{
if( itab[(k1<<2)+k2] < itab[(mk1<<2)+mk2] )
mk1 = k1, mk2 = k2;
else if( itab[(k1<<2)+k2] > itab[(Mk1<<2)+Mk2] )
Mk1 = k1, Mk2 = k2;
}
diff<0 ? (itab[(Mk1<<2)+Mk2]=(short)(itab[(Mk1<<2)+Mk2]-diff)) : (itab[(mk1<<2)+mk2]=(short)(itab[(mk1<<2)+mk2]-diff));
}
if( dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
int4 sum=0;
for ( i =0; i<16; i++ )
{
sum += convert_int4(v[i]) * itab[i];
}
dst[dst_offset+dy*dstStep+dx] = convert_uchar4_sat( (sum + (1 << (INTER_REMAP_COEF_BITS-1))) >> INTER_REMAP_COEF_BITS ) ;
}
}
}
__kernel void warpAffineNN_C1_D5(__global float * src, __global float * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = AB_SCALE/2;
int X0 = rint(M[0] * dx * AB_SCALE);
int Y0 = rint(M[3] * dx * AB_SCALE);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
short sx0 = (short)(X0 >> AB_BITS);
short sy0 = (short)(Y0 >> AB_BITS);
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>2)+dy*dstStep+dx]= (sx0>=0 && sx0=0 && sy0>2)+sy0*srcStep+sx0] : 0;
}
}
__kernel void warpAffineLinear_C1_D5(__global float * src, __global float * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = AB_SCALE/INTER_TAB_SIZE/2;
src_offset = (src_offset>>2);
int X0 = rint(M[0] * dx * AB_SCALE);
int Y0 = rint(M[3] * dx * AB_SCALE);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
X0 = X0 >> (AB_BITS - INTER_BITS);
Y0 = Y0 >> (AB_BITS - INTER_BITS);
short sx0 = (short)(X0 >> INTER_BITS);
short sy0 = (short)(Y0 >> INTER_BITS);
short ax0 = (short)(X0 & (INTER_TAB_SIZE-1));
short ay0 = (short)(Y0 & (INTER_TAB_SIZE-1));
float v0, v1, v2, v3;
v0 = (sx0 >= 0 && sx0 < src_cols && sy0 >= 0 && sy0 < src_rows) ? src[src_offset+sy0 * srcStep + sx0] : 0;
v1 = (sx0+1 >= 0 && sx0+1 < src_cols && sy0 >= 0 && sy0 < src_rows) ? src[src_offset+sy0 * srcStep + sx0+1] : 0;
v2 = (sx0 >= 0 && sx0 < src_cols && sy0+1 >= 0 && sy0+1 < src_rows) ? src[src_offset+(sy0+1) * srcStep + sx0] : 0;
v3 = (sx0+1 >= 0 && sx0+1 < src_cols && sy0+1 >= 0 && sy0+1 < src_rows) ? src[src_offset+(sy0+1) * srcStep + sx0+1] : 0;
float tab[4];
float taby[2], tabx[2];
taby[0] = 1.0f - 1.f/INTER_TAB_SIZE*ay0;
taby[1] = 1.f/INTER_TAB_SIZE*ay0;
tabx[0] = 1.0f - 1.f/INTER_TAB_SIZE*ax0;
tabx[1] = 1.f/INTER_TAB_SIZE*ax0;
tab[0] = taby[0] * tabx[0];
tab[1] = taby[0] * tabx[1];
tab[2] = taby[1] * tabx[0];
tab[3] = taby[1] * tabx[1];
float sum = 0;
sum += v0 * tab[0] +  v1 * tab[1] +  v2 * tab[2] +  v3 * tab[3];
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>2)+dy*dstStep+dx] = sum;
}
}
__kernel void warpAffineCubic_C1_D5(__global float * src, __global float * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = AB_SCALE/INTER_TAB_SIZE/2;
src_offset = (src_offset>>2);
dst_offset = (dst_offset>>2);
int X0 = rint(M[0] * dx * AB_SCALE);
int Y0 = rint(M[3] * dx * AB_SCALE);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
X0 = X0 >> (AB_BITS - INTER_BITS);
Y0 = Y0 >> (AB_BITS - INTER_BITS);
short sx = (short)(X0 >> INTER_BITS) - 1;
short sy = (short)(Y0 >> INTER_BITS) - 1;
short ay = (short)(Y0 & (INTER_TAB_SIZE-1));
short ax = (short)(X0 & (INTER_TAB_SIZE-1));
float v[16];
int i;
for(i=0; i<16;  i++)
v[i] = (sx+(i&3) >= 0 && sx+(i&3) < src_cols && sy+(i>>2) >= 0 && sy+(i>>2) < src_rows) ? src[src_offset+(sy+(i>>2)) * srcStep + (sx+(i&3))] : 0;
float tab[16];
float tab1y[4], tab1x[4];
float axx, ayy;
ayy = 1.f/INTER_TAB_SIZE * ay;
axx = 1.f/INTER_TAB_SIZE * ax;
interpolateCubic(ayy, tab1y);
interpolateCubic(axx, tab1x);
#pragma unroll 4
for( i=0; i<16; i++ )
{
tab[i] = tab1y[(i>>2)] * tab1x[(i&3)];
}
if( dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
float sum = 0;
#pragma unroll 4
for ( i =0; i<16; i++ )
{
sum += v[i] * tab[i];
}
dst[dst_offset+dy*dstStep+dx] = sum;
}
}
}
__kernel void warpAffineNN_C4_D5(__global float4 * src, __global float4 * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = AB_SCALE/2;
int X0 = rint(M[0] * dx * AB_SCALE);
int Y0 = rint(M[3] * dx * AB_SCALE);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
short sx0 = (short)(X0 >> AB_BITS);
short sy0 = (short)(Y0 >> AB_BITS);
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>4)+dy*(dstStep>>2)+dx]= (sx0>=0 && sx0=0 && sy0>4)+sy0*(srcStep>>2)+sx0] : (float4)0;
}
}
__kernel void warpAffineLinear_C4_D5(__global float4 * src, __global float4 * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = AB_SCALE/INTER_TAB_SIZE/2;
src_offset = (src_offset>>4);
dst_offset = (dst_offset>>4);
srcStep = (srcStep>>2);
dstStep = (dstStep>>2);
int X0 = rint(M[0] * dx * AB_SCALE);
int Y0 = rint(M[3] * dx * AB_SCALE);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
X0 = X0 >> (AB_BITS - INTER_BITS);
Y0 = Y0 >> (AB_BITS - INTER_BITS);
short sx0 = (short)(X0 >> INTER_BITS);
short sy0 = (short)(Y0 >> INTER_BITS);
short ax0 = (short)(X0 & (INTER_TAB_SIZE-1));
short ay0 = (short)(Y0 & (INTER_TAB_SIZE-1));
float4 v0, v1, v2, v3;
v0 = (sx0 >= 0 && sx0 < src_cols && sy0 >= 0 && sy0 < src_rows) ? src[src_offset+sy0 * srcStep + sx0] : (float4)0;
v1 = (sx0+1 >= 0 && sx0+1 < src_cols && sy0 >= 0 && sy0 < src_rows) ? src[src_offset+sy0 * srcStep + sx0+1] : (float4)0;
v2 = (sx0 >= 0 && sx0 < src_cols && sy0+1 >= 0 && sy0+1 < src_rows) ? src[src_offset+(sy0+1) * srcStep + sx0] : (float4)0;
v3 = (sx0+1 >= 0 && sx0+1 < src_cols && sy0+1 >= 0 && sy0+1 < src_rows) ? src[src_offset+(sy0+1) * srcStep + sx0+1] : (float4)0;
float tab[4];
float taby[2], tabx[2];
taby[0] = 1.0f - 1.f/INTER_TAB_SIZE*ay0;
taby[1] = 1.f/INTER_TAB_SIZE*ay0;
tabx[0] = 1.0f - 1.f/INTER_TAB_SIZE*ax0;
tabx[1] = 1.f/INTER_TAB_SIZE*ax0;
tab[0] = taby[0] * tabx[0];
tab[1] = taby[0] * tabx[1];
tab[2] = taby[1] * tabx[0];
tab[3] = taby[1] * tabx[1];
float4 sum = 0;
sum += v0 * tab[0] +  v1 * tab[1] +  v2 * tab[2] +  v3 * tab[3];
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[dst_offset+dy*dstStep+dx] = sum;
}
}
__kernel void warpAffineCubic_C4_D5(__global float4 * src, __global float4 * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
int round_delta = AB_SCALE/INTER_TAB_SIZE/2;
src_offset = (src_offset>>4);
dst_offset = (dst_offset>>4);
srcStep = (srcStep>>2);
dstStep = (dstStep>>2);
int X0 = rint(M[0] * dx * AB_SCALE);
int Y0 = rint(M[3] * dx * AB_SCALE);
X0 += rint((M[1]*dy + M[2]) * AB_SCALE) + round_delta;
Y0 += rint((M[4]*dy + M[5]) * AB_SCALE) + round_delta;
X0 = X0 >> (AB_BITS - INTER_BITS);
Y0 = Y0 >> (AB_BITS - INTER_BITS);
short sx = (short)(X0 >> INTER_BITS) - 1;
short sy = (short)(Y0 >> INTER_BITS) - 1;
short ay = (short)(Y0 & (INTER_TAB_SIZE-1));
short ax = (short)(X0 & (INTER_TAB_SIZE-1));
float4 v[16];
int i;
for(i=0; i<16;  i++)
v[i] = (sx+(i&3) >= 0 && sx+(i&3) < src_cols && sy+(i>>2) >= 0 && sy+(i>>2) < src_rows) ? src[src_offset+(sy+(i>>2)) * srcStep + (sx+(i&3))] : (float4)0;
float tab[16];
float tab1y[4], tab1x[4];
float axx, ayy;
ayy = 1.f/INTER_TAB_SIZE * ay;
axx = 1.f/INTER_TAB_SIZE * ax;
interpolateCubic(ayy, tab1y);
interpolateCubic(axx, tab1x);
#pragma unroll 4
for( i=0; i<16; i++ )
{
tab[i] = tab1y[(i>>2)] * tab1x[(i&3)];
}
if( dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
float4 sum = 0;
#pragma unroll 4
for ( i =0; i<16; i++ )
{
sum += v[i] * tab[i];
}
dst[dst_offset+dy*dstStep+dx] = sum;
}
}
}
0a1ddf381453d856788b2d2f92df08a8imgproc_warpPerspective#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
typedef double F;
typedef double4 F4;
#define convert_F4 convert_double4
#else
typedef float F;
typedef float4 F4;
#define convert_F4 convert_float4
#endif
#define INTER_BITS 5
#define INTER_TAB_SIZE (1 << INTER_BITS)
#define INTER_SCALE 1.f/INTER_TAB_SIZE
#define AB_BITS max(10, (int)INTER_BITS)
#define AB_SCALE (1 << AB_BITS)
#define INTER_REMAP_COEF_BITS 15
#define INTER_REMAP_COEF_SCALE (1 << INTER_REMAP_COEF_BITS)
void interpolateCubic( float x, float* coeffs )
{
const float A = -0.75f;
coeffs[0] = ((A*(x + 1.f) - 5.0f*A)*(x + 1.f) + 8.0f*A)*(x + 1.f) - 4.0f*A;
coeffs[1] = ((A + 2.f)*x - (A + 3.f))*x*x + 1.f;
coeffs[2] = ((A + 2.f)*(1.f - x) - (A + 3.f))*(1.f - x)*(1.f - x) + 1.f;
coeffs[3] = 1.f - coeffs[0] - coeffs[1] - coeffs[2];
}
__kernel void warpPerspectiveNN_C1_D0(__global uchar const * restrict src, __global uchar * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
dx = (dx<<2) - (dst_offset&3);
F4 DX = (F4)(dx, dx+1, dx+2, dx+3);
F4 X0 = M[0]*DX + M[1]*dy + M[2];
F4 Y0 = M[3]*DX + M[4]*dy + M[5];
F4 W = M[6]*DX + M[7]*dy + M[8],one=1,zero=0;
W = (W!=zero) ? one/W : zero;
short4 X = convert_short4_sat_rte(X0*W);
short4 Y = convert_short4_sat_rte(Y0*W);
int4 sx = convert_int4(X);
int4 sy = convert_int4(Y);
int4 DXD = (int4)(dx, dx+1, dx+2, dx+3);
__global uchar4 * d = (__global uchar4 *)(dst+dst_offset+dy*dstStep+dx);
uchar4 dval = *d;
int4 dcon = DXD >= 0 && DXD < dst_cols && dy >= 0 && dy < dst_rows;
int4 scon = sx >= 0 && sx < src_cols && sy >= 0 && sy < src_rows;
int4 spos = src_offset + sy * srcStep + sx;
uchar4 sval;
sval.s0 = scon.s0 ? src[spos.s0] : 0;
sval.s1 = scon.s1 ? src[spos.s1] : 0;
sval.s2 = scon.s2 ? src[spos.s2] : 0;
sval.s3 = scon.s3 ? src[spos.s3] : 0;
dval = convert_uchar4(dcon) != (uchar4)(0,0,0,0) ? sval : dval;
*d = dval;
}
}
__kernel void warpPerspectiveLinear_C1_D0(__global const uchar * restrict src, __global uchar * dst,
int src_cols, int src_rows, int dst_cols, int dst_rows, int srcStep,
int dstStep, int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? INTER_TAB_SIZE/W : 0.0f;
int X = rint(X0*W);
int Y = rint(Y0*W);
int sx = convert_short_sat(X >> INTER_BITS);
int sy = convert_short_sat(Y >> INTER_BITS);
int ay = (short)(Y & (INTER_TAB_SIZE-1));
int ax = (short)(X & (INTER_TAB_SIZE-1));
uchar v[4];
int i;
#pragma unroll 4
for(i=0; i<4;  i++)
v[i] = (sx+(i&1) >= 0 && sx+(i&1) < src_cols && sy+(i>>1) >= 0 && sy+(i>>1) < src_rows) ? src[src_offset + (sy+(i>>1)) * srcStep + (sx+(i&1))] : (uchar)0;
short itab[4];
float tab1y[2], tab1x[2];
tab1y[0] = 1.0f - 1.f/INTER_TAB_SIZE*ay;
tab1y[1] = 1.f/INTER_TAB_SIZE*ay;
tab1x[0] = 1.0f - 1.f/INTER_TAB_SIZE*ax;
tab1x[1] = 1.f/INTER_TAB_SIZE*ax;
#pragma unroll 4
for(i=0; i<4;  i++)
{
float v = tab1y[(i>>1)] * tab1x[(i&1)];
itab[i] = convert_short_sat_rte( v * INTER_REMAP_COEF_SCALE );
}
if(dx >=0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
int sum = 0;
for ( i =0; i<4; i++ )
{
sum += v[i] * itab[i] ;
}
dst[dst_offset+dy*dstStep+dx] = convert_uchar_sat ( (sum + (1 << (INTER_REMAP_COEF_BITS-1))) >> INTER_REMAP_COEF_BITS ) ;
}
}
}
__kernel void warpPerspectiveCubic_C1_D0(__global uchar * src, __global uchar * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? INTER_TAB_SIZE/W : 0.0f;
int X = rint(X0*W);
int Y = rint(Y0*W);
short sx = convert_short_sat(X >> INTER_BITS) - 1;
short sy = convert_short_sat(Y >> INTER_BITS) - 1;
short ay = (short)(Y & (INTER_TAB_SIZE-1));
short ax = (short)(X & (INTER_TAB_SIZE-1));
uchar v[16];
int i, j;
#pragma unroll 4
for(i=0; i<4;  i++)
for(j=0; j<4;  j++)
{
v[i*4+j] = (sx+j >= 0 && sx+j < src_cols && sy+i >= 0 && sy+i < src_rows) ? src[src_offset+(sy+i) * srcStep + (sx+j)] : (uchar)0;
}
short itab[16];
float tab1y[4], tab1x[4];
float axx, ayy;
ayy = 1.f/INTER_TAB_SIZE * ay;
axx = 1.f/INTER_TAB_SIZE * ax;
interpolateCubic(ayy, tab1y);
interpolateCubic(axx, tab1x);
int isum = 0;
#pragma unroll 16
for( i=0; i<16; i++ )
{
F v = tab1y[(i>>2)] * tab1x[(i&3)];
isum += itab[i] = convert_short_sat( rint( v * INTER_REMAP_COEF_SCALE ) );
}
if( isum != INTER_REMAP_COEF_SCALE )
{
int k1, k2;
int diff = isum - INTER_REMAP_COEF_SCALE;
int Mk1=2, Mk2=2, mk1=2, mk2=2;
for( k1 = 2; k1 < 4; k1++ )
for( k2 = 2; k2 < 4; k2++ )
{
if( itab[(k1<<2)+k2] < itab[(mk1<<2)+mk2] )
mk1 = k1, mk2 = k2;
else if( itab[(k1<<2)+k2] > itab[(Mk1<<2)+Mk2] )
Mk1 = k1, Mk2 = k2;
}
diff<0 ? (itab[(Mk1<<2)+Mk2]=(short)(itab[(Mk1<<2)+Mk2]-diff)) : (itab[(mk1<<2)+mk2]=(short)(itab[(mk1<<2)+mk2]-diff));
}
if( dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
int sum=0;
for ( i =0; i<16; i++ )
{
sum += v[i] * itab[i] ;
}
dst[dst_offset+dy*dstStep+dx] = convert_uchar_sat( (sum + (1 << (INTER_REMAP_COEF_BITS-1))) >> INTER_REMAP_COEF_BITS ) ;
}
}
}
__kernel void warpPerspectiveNN_C4_D0(__global uchar4 const * restrict src, __global uchar4 * dst,
int src_cols, int src_rows, int dst_cols, int dst_rows, int srcStep,
int dstStep, int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? 1.f/W : 0.0f;
short sx = convert_short_sat_rte(X0*W);
short sy = convert_short_sat_rte(Y0*W);
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>2)+dy*(dstStep>>2)+dx]= (sx>=0 && sx=0 && sy>2)+sy*(srcStep>>2)+sx] : (uchar4)0;
}
}
__kernel void warpPerspectiveLinear_C4_D0(__global uchar4 const * restrict src, __global uchar4 * dst,
int src_cols, int src_rows, int dst_cols, int dst_rows, int srcStep,
int dstStep, int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
src_offset = (src_offset>>2);
srcStep = (srcStep>>2);
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? INTER_TAB_SIZE/W : 0.0f;
int X = rint(X0*W);
int Y = rint(Y0*W);
short sx = convert_short_sat(X >> INTER_BITS);
short sy = convert_short_sat(Y >> INTER_BITS);
short ay = (short)(Y & (INTER_TAB_SIZE-1));
short ax = (short)(X & (INTER_TAB_SIZE-1));
int4 v0, v1, v2, v3;
v0 = (sx >= 0 && sx < src_cols && sy >= 0 && sy < src_rows) ? convert_int4(src[src_offset+sy * srcStep + sx]) : (int4)0;
v1 = (sx+1 >= 0 && sx+1 < src_cols && sy >= 0 && sy < src_rows) ? convert_int4(src[src_offset+sy * srcStep + sx+1]) : (int4)0;
v2 = (sx >= 0 && sx < src_cols && sy+1 >= 0 && sy+1 < src_rows) ? convert_int4(src[src_offset+(sy+1) * srcStep + sx]) : (int4)0;
v3 = (sx+1 >= 0 && sx+1 < src_cols && sy+1 >= 0 && sy+1 < src_rows) ? convert_int4(src[src_offset+(sy+1) * srcStep + sx+1]) : (int4)0;
int itab0, itab1, itab2, itab3;
float taby, tabx;
taby = 1.f/INTER_TAB_SIZE*ay;
tabx = 1.f/INTER_TAB_SIZE*ax;
itab0 = convert_short_sat(rint( (1.0f-taby)*(1.0f-tabx) * INTER_REMAP_COEF_SCALE ));
itab1 = convert_short_sat(rint( (1.0f-taby)*tabx * INTER_REMAP_COEF_SCALE ));
itab2 = convert_short_sat(rint( taby*(1.0f-tabx) * INTER_REMAP_COEF_SCALE ));
itab3 = convert_short_sat(rint( taby*tabx * INTER_REMAP_COEF_SCALE ));
int4 val;
val = v0 * itab0 +  v1 * itab1 + v2 * itab2 + v3 * itab3;
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>2)+dy*(dstStep>>2)+dx] =  convert_uchar4_sat ( (val + (1 << (INTER_REMAP_COEF_BITS-1))) >> INTER_REMAP_COEF_BITS ) ;
}
}
__kernel void warpPerspectiveCubic_C4_D0(__global uchar4 const * restrict src, __global uchar4 * dst,
int src_cols, int src_rows, int dst_cols, int dst_rows, int srcStep,
int dstStep, int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
src_offset = (src_offset>>2);
srcStep = (srcStep>>2);
dst_offset = (dst_offset>>2);
dstStep = (dstStep>>2);
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? INTER_TAB_SIZE/W : 0.0f;
int X = rint(X0*W);
int Y = rint(Y0*W);
short sx = convert_short_sat(X >> INTER_BITS) - 1;
short sy = convert_short_sat(Y >> INTER_BITS) - 1;
short ay = (short)(Y & (INTER_TAB_SIZE-1));
short ax = (short)(X & (INTER_TAB_SIZE-1));
uchar4 v[16];
int i,j;
#pragma unroll 4
for(i=0; i<4; i++)
for(j=0; j<4; j++)
{
v[i*4+j] = (sx+j >= 0 && sx+j < src_cols && sy+i >= 0 && sy+i < src_rows) ? (src[src_offset+(sy+i) * srcStep + (sx+j)])  : (uchar4)0;
}
int itab[16];
float tab1y[4], tab1x[4];
float axx, ayy;
ayy = INTER_SCALE * ay;
axx = INTER_SCALE * ax;
interpolateCubic(ayy, tab1y);
interpolateCubic(axx, tab1x);
int isum = 0;
#pragma unroll 16
for( i=0; i<16; i++ )
{
float tmp;
tmp = tab1y[(i>>2)] * tab1x[(i&3)] * INTER_REMAP_COEF_SCALE;
itab[i] = rint(tmp);
isum += itab[i];
}
if( isum != INTER_REMAP_COEF_SCALE )
{
int k1, k2;
int diff = isum - INTER_REMAP_COEF_SCALE;
int Mk1=2, Mk2=2, mk1=2, mk2=2;
for( k1 = 2; k1 < 4; k1++ )
for( k2 = 2; k2 < 4; k2++ )
{
if( itab[(k1<<2)+k2] < itab[(mk1<<2)+mk2] )
mk1 = k1, mk2 = k2;
else if( itab[(k1<<2)+k2] > itab[(Mk1<<2)+Mk2] )
Mk1 = k1, Mk2 = k2;
}
diff<0 ? (itab[(Mk1<<2)+Mk2]=(short)(itab[(Mk1<<2)+Mk2]-diff)) : (itab[(mk1<<2)+mk2]=(short)(itab[(mk1<<2)+mk2]-diff));
}
if( dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
int4 sum=0;
for ( i =0; i<16; i++ )
{
sum += convert_int4(v[i]) * itab[i];
}
dst[dst_offset+dy*dstStep+dx] = convert_uchar4_sat( (sum + (1 << (INTER_REMAP_COEF_BITS-1))) >> INTER_REMAP_COEF_BITS ) ;
}
}
}
__kernel void warpPerspectiveNN_C1_D5(__global float * src, __global float * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? 1.f/W : 0.0f;
short sx = convert_short_sat_rte(X0*W);
short sy = convert_short_sat_rte(Y0*W);
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>2)+dy*dstStep+dx]= (sx>=0 && sx=0 && sy>2)+sy*srcStep+sx] : 0;
}
}
__kernel void warpPerspectiveLinear_C1_D5(__global float * src, __global float * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
src_offset = (src_offset>>2);
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? INTER_TAB_SIZE/W : 0.0f;
int X = rint(X0*W);
int Y = rint(Y0*W);
short sx = convert_short_sat(X >> INTER_BITS);
short sy = convert_short_sat(Y >> INTER_BITS);
short ay = (short)(Y & (INTER_TAB_SIZE-1));
short ax = (short)(X & (INTER_TAB_SIZE-1));
float v0, v1, v2, v3;
v0 = (sx >= 0 && sx < src_cols && sy >= 0 && sy < src_rows) ? src[src_offset+sy * srcStep + sx] : (float)0;
v1 = (sx+1 >= 0 && sx+1 < src_cols && sy >= 0 && sy < src_rows) ? src[src_offset+sy * srcStep + sx+1] : (float)0;
v2 = (sx >= 0 && sx < src_cols && sy+1 >= 0 && sy+1 < src_rows) ? src[src_offset+(sy+1) * srcStep + sx] : (float)0;
v3 = (sx+1 >= 0 && sx+1 < src_cols && sy+1 >= 0 && sy+1 < src_rows) ? src[src_offset+(sy+1) * srcStep + sx+1] : (float)0;
float tab[4];
float taby[2], tabx[2];
taby[0] = 1.0f - 1.f/INTER_TAB_SIZE*ay;
taby[1] = 1.f/INTER_TAB_SIZE*ay;
tabx[0] = 1.0f - 1.f/INTER_TAB_SIZE*ax;
tabx[1] = 1.f/INTER_TAB_SIZE*ax;
tab[0] = taby[0] * tabx[0];
tab[1] = taby[0] * tabx[1];
tab[2] = taby[1] * tabx[0];
tab[3] = taby[1] * tabx[1];
float sum = 0;
sum += v0 * tab[0] +  v1 * tab[1] +  v2 * tab[2] +  v3 * tab[3];
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>2)+dy*dstStep+dx] = sum;
}
}
__kernel void warpPerspectiveCubic_C1_D5(__global float * src, __global float * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
src_offset = (src_offset>>2);
dst_offset = (dst_offset>>2);
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? INTER_TAB_SIZE/W : 0.0f;
int X = rint(X0*W);
int Y = rint(Y0*W);
short sx = convert_short_sat(X >> INTER_BITS) - 1;
short sy = convert_short_sat(Y >> INTER_BITS) - 1;
short ay = (short)(Y & (INTER_TAB_SIZE-1));
short ax = (short)(X & (INTER_TAB_SIZE-1));
float v[16];
int i;
for(i=0; i<16;  i++)
v[i] = (sx+(i&3) >= 0 && sx+(i&3) < src_cols && sy+(i>>2) >= 0 && sy+(i>>2) < src_rows) ? src[src_offset+(sy+(i>>2)) * srcStep + (sx+(i&3))] : (float)0;
float tab[16];
float tab1y[4], tab1x[4];
float axx, ayy;
ayy = 1.f/INTER_TAB_SIZE * ay;
axx = 1.f/INTER_TAB_SIZE * ax;
interpolateCubic(ayy, tab1y);
interpolateCubic(axx, tab1x);
#pragma unroll 4
for( i=0; i<16; i++ )
{
tab[i] = tab1y[(i>>2)] * tab1x[(i&3)];
}
if( dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
float sum = 0;
#pragma unroll 4
for ( i =0; i<16; i++ )
{
sum += v[i] * tab[i];
}
dst[dst_offset+dy*dstStep+dx] = sum;
}
}
}
__kernel void warpPerspectiveNN_C4_D5(__global float4 * src, __global float4 * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W =(W != 0.0f)? 1.f/W : 0.0f;
short sx = convert_short_sat_rte(X0*W);
short sy = convert_short_sat_rte(Y0*W);
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[(dst_offset>>4)+dy*(dstStep>>2)+dx]= (sx>=0 && sx=0 && sy>4)+sy*(srcStep>>2)+sx] : (float)0;
}
}
__kernel void warpPerspectiveLinear_C4_D5(__global float4 * src, __global float4 * dst, int src_cols, int src_rows,
int dst_cols, int dst_rows, int srcStep, int dstStep,
int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows)
{
src_offset = (src_offset>>4);
dst_offset = (dst_offset>>4);
srcStep = (srcStep>>2);
dstStep = (dstStep>>2);
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? INTER_TAB_SIZE/W : 0.0f;
int X = rint(X0*W);
int Y = rint(Y0*W);
short sx0 = convert_short_sat(X >> INTER_BITS);
short sy0 = convert_short_sat(Y >> INTER_BITS);
short ay0 = (short)(Y & (INTER_TAB_SIZE-1));
short ax0 = (short)(X & (INTER_TAB_SIZE-1));
float4 v0, v1, v2, v3;
v0 = (sx0 >= 0 && sx0 < src_cols && sy0 >= 0 && sy0 < src_rows) ? src[src_offset+sy0 * srcStep + sx0] : (float4)0;
v1 = (sx0+1 >= 0 && sx0+1 < src_cols && sy0 >= 0 && sy0 < src_rows) ? src[src_offset+sy0 * srcStep + sx0+1] : (float4)0;
v2 = (sx0 >= 0 && sx0 < src_cols && sy0+1 >= 0 && sy0+1 < src_rows) ? src[src_offset+(sy0+1) * srcStep + sx0] : (float4)0;
v3 = (sx0+1 >= 0 && sx0+1 < src_cols && sy0+1 >= 0 && sy0+1 < src_rows) ? src[src_offset+(sy0+1) * srcStep + sx0+1] : (float4)0;
float tab[4];
float taby[2], tabx[2];
taby[0] = 1.0f - 1.f/INTER_TAB_SIZE*ay0;
taby[1] = 1.f/INTER_TAB_SIZE*ay0;
tabx[0] = 1.0f - 1.f/INTER_TAB_SIZE*ax0;
tabx[1] = 1.f/INTER_TAB_SIZE*ax0;
tab[0] = taby[0] * tabx[0];
tab[1] = taby[0] * tabx[1];
tab[2] = taby[1] * tabx[0];
tab[3] = taby[1] * tabx[1];
float4 sum = 0;
sum += v0 * tab[0] +  v1 * tab[1] +  v2 * tab[2] +  v3 * tab[3];
if(dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
dst[dst_offset+dy*dstStep+dx] = sum;
}
}
__kernel void warpPerspectiveCubic_C4_D5(__global float4 * src, __global float4 * dst,
int src_cols, int src_rows, int dst_cols, int dst_rows, int srcStep,
int dstStep, int src_offset, int dst_offset,  __constant F * M, int threadCols )
{
int dx = get_global_id(0);
int dy = get_global_id(1);
if( dx < threadCols && dy < dst_rows )
{
src_offset = (src_offset>>4);
dst_offset = (dst_offset>>4);
srcStep = (srcStep>>2);
dstStep = (dstStep>>2);
F X0 = M[0]*dx + M[1]*dy + M[2];
F Y0 = M[3]*dx + M[4]*dy + M[5];
F W = M[6]*dx + M[7]*dy + M[8];
W = (W != 0.0f) ? INTER_TAB_SIZE/W : 0.0f;
int X = rint(X0*W);
int Y = rint(Y0*W);
short sx = convert_short_sat(X >> INTER_BITS)-1;
short sy = convert_short_sat(Y >> INTER_BITS)-1;
short ay = (short)(Y & (INTER_TAB_SIZE-1));
short ax = (short)(X & (INTER_TAB_SIZE-1));
float4 v[16];
int i;
for(i=0; i<16;  i++)
v[i] = (sx+(i&3) >= 0 && sx+(i&3) < src_cols && sy+(i>>2) >= 0 && sy+(i>>2) < src_rows) ? src[src_offset+(sy+(i>>2)) * srcStep + (sx+(i&3))] : (float4)0;
float tab[16];
float tab1y[4], tab1x[4];
float axx, ayy;
ayy = 1.f/INTER_TAB_SIZE * ay;
axx = 1.f/INTER_TAB_SIZE * ax;
interpolateCubic(ayy, tab1y);
interpolateCubic(axx, tab1x);
#pragma unroll 4
for( i=0; i<16; i++ )
{
tab[i] = tab1y[(i>>2)] * tab1x[(i&3)];
}
if( dx >= 0 && dx < dst_cols && dy >= 0 && dy < dst_rows)
{
float4 sum = 0;
#pragma unroll 4
for ( i =0; i<16; i++ )
{
sum += v[i] * tab[i];
}
dst[dst_offset+dy*dstStep+dx] = sum;
}
}
}
13ff30fed80ece6c6828a26c11a07de7interpolate_frames#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics : enable
#pragma OPENCL EXTENSION cl_khr_local_int32_base_atomics : enable
__constant sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_LINEAR;
void atomic_addf(volatile __global float *source, const float operand) {
union {
unsigned int intVal;
float floatVal;
} newVal;
union {
unsigned int intVal;
float floatVal;
} prevVal;
do {
prevVal.floatVal = *source;
newVal.floatVal = prevVal.floatVal + operand;
} while (atomic_cmpxchg((volatile __global unsigned int *)source, prevVal.intVal, newVal.intVal) != prevVal.intVal);
}
__kernel void memsetKernel(
float val,
__global float * image,
int width,
int height,
int step,
int offset
)
{
if(get_global_id(0) >= width || get_global_id(1) >= height)
{
return;
}
image += offset;
image[get_global_id(0) + get_global_id(1) * step] = val;
}
__kernel void normalizeKernel(
__global float * buffer,
int width,
int height,
int step,
int f_offset,
int d_offset
)
{
__global float * factors = buffer + f_offset;
__global float * dst     = buffer + d_offset;
int j = get_global_id(0);
int i = get_global_id(1);
if(j >= width || i >= height)
{
return;
}
float scale = factors[step * i + j];
float invScale = (scale == 0.0f) ? 1.0f : (1.0f / scale);
dst[step * i + j] *= invScale;
}
__kernel void forwardWarpKernel(
__global const float * src,
__global float * buffer,
__global const float * u,
__global const float * v,
const int w,
const int h,
const int flow_stride,
const int image_stride,
const int factor_offset,
const int dst_offset,
const float time_scale
)
{
int j = get_global_id(0);
int i = get_global_id(1);
if (i >= h || j >= w) return;
volatile __global float * normalization_factor = (volatile __global float *) buffer + factor_offset;
volatile __global float * dst = (volatile __global float *)buffer + dst_offset;
int flow_row_offset  = i * flow_stride;
int image_row_offset = i * image_stride;
float cx = u[flow_row_offset + j] * time_scale + (float)j + 1.0f;
float cy = v[flow_row_offset + j] * time_scale + (float)i + 1.0f;
float px;
float py;
float dx = modf(cx, &px);
float dy = modf(cy, &py);
int tx;
int ty;
tx = (int) px;
ty = (int) py;
float value = src[image_row_offset + j];
float weight;
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
weight = dx * dy;
atomic_addf(dst + ty * image_stride + tx, value * weight);
atomic_addf(normalization_factor + ty * image_stride + tx, weight);
}
tx -= 1;
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
weight = (1.0f - dx) * dy;
atomic_addf(dst + ty * image_stride + tx, value * weight);
atomic_addf(normalization_factor + ty * image_stride + tx, weight);
}
ty -= 1;
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
weight = (1.0f - dx) * (1.0f - dy);
atomic_addf(dst + ty * image_stride + tx, value * weight);
atomic_addf(normalization_factor + ty * image_stride + tx, weight);
}
tx += 1;
if (!((tx >= w) || (tx < 0) || (ty >= h) || (ty < 0)))
{
weight = dx * (1.0f - dy);
atomic_addf(dst + ty * image_stride + tx, value * weight);
atomic_addf(normalization_factor + ty * image_stride + tx, weight);
}
}
enum
{
O0_OS = 0,
O1_OS,
U_OS,
V_OS,
UR_OS,
VR_OS
};
__kernel void blendFramesKernel(
image2d_t tex_src0,
image2d_t tex_src1,
__global float * buffer,
__global float * out,
int w,
int h,
int step,
float theta
)
{
__global float * u  = buffer + h * step * U_OS;
__global float * v  = buffer + h * step * V_OS;
__global float * ur = buffer + h * step * UR_OS;
__global float * vr = buffer + h * step * VR_OS;
__global float * o0 = buffer + h * step * O0_OS;
__global float * o1 = buffer + h * step * O1_OS;
int ix = get_global_id(0);
int iy = get_global_id(1);
if(ix >= w || iy >= h) return;
int pos = ix + step * iy;
float _u  = u[pos];
float _v  = v[pos];
float _ur = ur[pos];
float _vr = vr[pos];
float x = (float)ix + 0.5f;
float y = (float)iy + 0.5f;
bool b0 = o0[pos] > 1e-4f;
bool b1 = o1[pos] > 1e-4f;
float2 coord0 = (float2)(x - _u * theta, y - _v * theta);
float2 coord1 = (float2)(x + _u * (1.0f - theta), y + _v * (1.0f - theta));
if (b0 && b1)
{
out[pos] = read_imagef(tex_src0, sampler, coord0).x * (1.0f - theta) +
read_imagef(tex_src1, sampler, coord1).x * theta;
}
else if (b0)
{
out[pos] = read_imagef(tex_src0, sampler, coord0).x;
}
else
{
out[pos] = read_imagef(tex_src1, sampler, coord1).x;
}
}
984886a43f456d32a2f199a31ba3d12akernel_radix_sort_by_key#pragma OPENCL EXTENSION cl_khr_byte_addressable_store : enable
#ifndef N
#define N 4
#endif
#ifndef K_T
#define K_T float
#endif
#ifndef V_T
#define V_T float
#endif
#ifndef IS_GT
#define IS_GT 0
#endif
uint convertKey(uint converted_key)
{
#ifdef K_FLT
unsigned int mask = (converted_key & 0x80000000) ? 0xffffffff : 0x80000000;
converted_key ^= mask;
#elif defined(K_INT)
const uint SIGN_MASK = 1u << ((sizeof(int) * 8) - 1);
converted_key ^= SIGN_MASK;
#else
#endif
return converted_key;
}
kernel
void naiveScanAddition(
__global int * input,
__global int * output,
int size
)
{
if(get_global_id(0) == 0)
{
output[0] = 0;
for(int i = 1; i < size; i ++)
{
output[i] = output[i - 1] + input[i - 1];
}
}
}
kernel
void histogramRadixN (
__global K_T* unsortedKeys,
__global int * buckets,
uint shiftCount
)
{
const int RADIX_T     = N;
const int RADICES_T   = (1 << RADIX_T);
const int NUM_OF_ELEMENTS_PER_WORK_ITEM_T = RADICES_T;
const int MASK_T      = (1 << RADIX_T) - 1;
int localBuckets[16] = {0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0};
int globalId    = get_global_id(0);
int numOfGroups = get_num_groups(0);
for(int i = 0; i < NUM_OF_ELEMENTS_PER_WORK_ITEM_T; ++i)
{
uint value = convertKey(as_uint(unsortedKeys[mad24(globalId, NUM_OF_ELEMENTS_PER_WORK_ITEM_T, i)]));
value = (value >> shiftCount) & MASK_T;
#if IS_GT
localBuckets[RADICES_T - value - 1]++;
#else
localBuckets[value]++;
#endif
}
for(int i = 0; i < NUM_OF_ELEMENTS_PER_WORK_ITEM_T; ++i)
{
buckets[mad24(i, RADICES_T * numOfGroups, globalId) ] = localBuckets[i];
}
}
kernel
void permuteRadixN (
__global K_T*  unsortedKeys,
__global V_T*  unsortedVals,
__global int* scanedBuckets,
uint shiftCount,
__global K_T*  sortedKeys,
__global V_T*  sortedVals
)
{
const int RADIX_T     = N;
const int RADICES_T   = (1 << RADIX_T);
const int MASK_T = (1<> shiftCount) & MASK_T;
uint index = localIndex[maskedValue];
sortedKeys[index] = ovalue;
sortedVals[index] = unsortedVals[old_idx];
localIndex[maskedValue] = index + 1;
}
}
0e6fe5592aca6ddfade79641efe19a59kernel_sort_by_key#ifndef K_T
#define K_T float
#endif
#ifndef V_T
#define V_T float
#endif
#ifndef IS_GT
#define IS_GT false
#endif
#if IS_GT
#define my_comp(x,y) ((x) > (y))
#else
#define my_comp(x,y) ((x) < (y))
#endif
__kernel
void bitonicSort
(
__global K_T * keys,
__global V_T * vals,
int count,
int stage,
int passOfStage
)
{
const int threadId = get_global_id(0);
if(threadId >= count / 2)
{
return;
}
const int pairDistance = 1 << (stage - passOfStage);
const int blockWidth   = 2 * pairDistance;
int leftId = min( (threadId % pairDistance)
+ (threadId / pairDistance) * blockWidth, count );
int rightId = min( leftId + pairDistance, count );
int temp;
const V_T lval = vals[leftId];
const V_T rval = vals[rightId];
const K_T lkey = keys[leftId];
const K_T rkey = keys[rightId];
int sameDirectionBlockWidth = 1 << stage;
if((threadId/sameDirectionBlockWidth) % 2 == 1)
{
temp = rightId;
rightId = leftId;
leftId = temp;
}
const bool compareResult = my_comp(lkey, rkey);
if(compareResult)
{
keys[rightId] = rkey;
keys[leftId]  = lkey;
vals[rightId] = rval;
vals[leftId]  = lval;
}
else
{
keys[rightId] = lkey;
keys[leftId]  = rkey;
vals[rightId] = lval;
vals[leftId]  = rval;
}
}
__kernel
void selectionSortLocal
(
__global K_T * keys,
__global V_T * vals,
const int count,
__local  K_T * scratch
)
{
int          i  = get_local_id(0);
int numOfGroups = get_num_groups(0);
int groupID     = get_group_id(0);
int         wg  = get_local_size(0);
int n;
int offset   = groupID * wg;
int same     = 0;
vals      += offset;
keys      += offset;
n = (groupID == (numOfGroups-1))? (count - wg*(numOfGroups-1)) : wg;
int clamped_i= min(i, n - 1);
K_T key1 = keys[clamped_i], key2;
V_T val1 = vals[clamped_i];
scratch[i] = key1;
barrier(CLK_LOCAL_MEM_FENCE);
if(i >= n)
{
return;
}
int pos = 0;
for (int j=0;j= count)
return;
V_T val1 = vals[offset + i];
K_T key1 = keys[offset + i];
K_T key2;
for(int j=0; j (y))
#else
#define my_comp(x,y) ((x) < (y))
#endif
uint lowerBoundBinary( global K_T* data, uint left, uint right, K_T searchVal)
{
uint firstIndex = left;
uint lastIndex = right;
while( firstIndex < lastIndex )
{
uint midIndex = ( firstIndex + lastIndex ) / 2;
K_T midValue = data[ midIndex ];
if( my_comp( midValue, searchVal ) )
{
firstIndex = midIndex+1;
}
else
{
lastIndex = midIndex;
}
}
return firstIndex;
}
uint upperBoundBinary( global K_T* data, uint left, uint right, K_T searchVal)
{
uint upperBound = lowerBoundBinary( data, left, right, searchVal );
if( upperBound != right )
{
K_T upperValue = data[ upperBound ];
while( !my_comp( upperValue, searchVal ) && !my_comp( searchVal, upperValue) && (upperBound != right) )
{
upperBound++;
upperValue = data[ upperBound ];
}
}
return upperBound;
}
kernel void merge(
global K_T*   iKey_ptr,
global V_T*   iValue_ptr,
global K_T*   oKey_ptr,
global V_T*   oValue_ptr,
const uint    srcVecSize,
const uint    srcLogicalBlockSize,
local K_T*    key_lds,
local V_T*    val_lds
)
{
size_t globalID     = get_global_id( 0 );
if( globalID >= srcVecSize )
return;
uint srcBlockNum   = globalID / srcLogicalBlockSize;
uint srcBlockIndex = globalID % srcLogicalBlockSize;
uint dstLogicalBlockSize = srcLogicalBlockSize<<1;
uint leftBlockIndex = globalID & ~((dstLogicalBlockSize) - 1 );
leftBlockIndex += (srcBlockNum & 0x1) ? 0 : srcLogicalBlockSize;
leftBlockIndex = min( leftBlockIndex, srcVecSize );
uint rightBlockIndex = min( leftBlockIndex + srcLogicalBlockSize, srcVecSize );
uint insertionIndex = 0;
if( (srcBlockNum & 0x1) == 0 )
{
insertionIndex = lowerBoundBinary( iKey_ptr, leftBlockIndex, rightBlockIndex, iKey_ptr[ globalID ] ) - leftBlockIndex;
}
else
{
insertionIndex = upperBoundBinary( iKey_ptr, leftBlockIndex, rightBlockIndex, iKey_ptr[ globalID ] ) - leftBlockIndex;
}
uint dstBlockIndex = srcBlockIndex + insertionIndex;
uint dstBlockNum = srcBlockNum/2;
oKey_ptr[ (dstBlockNum*dstLogicalBlockSize)+dstBlockIndex ] = iKey_ptr[ globalID ];
oValue_ptr[ (dstBlockNum*dstLogicalBlockSize)+dstBlockIndex ] = iValue_ptr[ globalID ];
}
kernel void blockInsertionSort(
global K_T*   key_ptr,
global V_T*   value_ptr,
const uint    vecSize,
local K_T*    key_lds,
local V_T*    val_lds
)
{
int gloId    = get_global_id( 0 );
int groId    = get_group_id( 0 );
int locId    = get_local_id( 0 );
int wgSize   = get_local_size( 0 );
bool in_range = gloId < (int)vecSize;
K_T key;
V_T val;
if (in_range)
{
key = key_ptr[ gloId ];
val = value_ptr[ gloId ];
key_lds[ locId ] = key;
val_lds[ locId ] = val;
}
barrier( CLK_LOCAL_MEM_FENCE );
if( locId == 0 && in_range )
{
int endIndex = vecSize - ( groId * wgSize );
endIndex = min( endIndex, wgSize );
for( int currIndex = 1; currIndex < endIndex; ++currIndex )
{
key = key_lds[ currIndex ];
val = val_lds[ currIndex ];
int scanIndex = currIndex;
K_T ldsKey = key_lds[scanIndex - 1];
while( scanIndex > 0 && my_comp( key, ldsKey ) )
{
V_T ldsVal = val_lds[scanIndex - 1];
key_lds[ scanIndex ] = ldsKey;
val_lds[ scanIndex ] = ldsVal;
scanIndex = scanIndex - 1;
ldsKey = key_lds[ max( 0, scanIndex - 1 ) ];
}
key_lds[ scanIndex ] = key;
val_lds[ scanIndex ] = val;
}
}
barrier( CLK_LOCAL_MEM_FENCE );
if(in_range)
{
key = key_lds[ locId ];
key_ptr[ gloId ] = key;
val = val_lds[ locId ];
value_ptr[ gloId ] = val;
}
}
51ab9beffb4c03717063811491652aa2kmeans_kernelfloat distance_(__global const float * center, __global const float * src, int feature_length)
{
float res = 0;
float4 v0, v1, v2;
int i = 0;
#ifdef L1_DIST
float4 sum = (float4)(0.0f);
#endif
for ( ; i <= feature_length - 4; i += 4)
{
v0 = vload4(0, center + i);
v1 = vload4(0, src + i);
v2 = v1 - v0;
#ifdef L1_DIST
v0 = fabs(v2);
sum += v0;
#else
res += dot(v2, v2);
#endif
}
#ifdef L1_DIST
res = sum.x + sum.y + sum.z + sum.w;
#endif
for ( ; i < feature_length; ++i)
{
float t0 = src[i];
float t1 = center[i];
#ifdef L1_DIST
res += fabs(t0 - t1);
#else
float t2 = t0 - t1;
res += t2 * t2;
#endif
}
return res;
}
__kernel void distanceToCenters(__global const float * src, __global const float * centers,
__global float * dists, int feature_length,
int src_step, int centers_step,
int features_count, int centers_count,
int src_offset, int centers_offset)
{
int gid = get_global_id(0);
if (gid < (features_count * centers_count))
{
int feature_index = gid / centers_count;
int center_index = gid % centers_count;
int center_idx = mad24(center_index, centers_step, centers_offset);
int src_idx = mad24(feature_index, src_step, src_offset);
dists[gid] = distance_(centers + center_idx, src + src_idx, feature_length);
}
}
f860c405407c40547fd9e1af945944adknearest#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define TYPE double
#else
#define TYPE float
#endif
#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t))
__kernel void knn_find_nearest(__global float* sample, int sample_row, int sample_col, int sample_step,
int k, __global float* samples_ocl, int sample_ocl_row, int sample_ocl_step,
__global float* _results, int _results_step, int _regression, int K1,
int sample_ocl_col, int nThreads, __local float* nr)
{
int k1 = 0;
int k2 = 0;
bool regression = false;
if(_regression)
regression = true;
TYPE inv_scale;
#ifdef DOUBLE_SUPPORT
inv_scale = 1.0/K1;
#else
inv_scale = 1.0f/K1;
#endif
int y = get_global_id(1);
int j, j1;
int threadY = (y % nThreads);
__local float* dd = nr + nThreads * k;
if(y >= sample_row)
{
return;
}
for(j = 0; j < sample_ocl_row; j++)
{
TYPE sum;
#ifdef DOUBLE_SUPPORT
sum = 0.0;
#else
sum = 0.0f;
#endif
float si;
int t, ii, ii1;
for(t = 0; t < sample_col - 16; t += 16)
{
float16 t0 = vload16(0, sample + y * sample_step + t) - vload16(0, samples_ocl + j * sample_ocl_step + t);
t0 *= t0;
sum += t0.s0 + t0.s1 + t0.s2 + t0.s3 + t0.s4 + t0.s5 + t0.s6 + t0.s7 +
t0.s8 + t0.s9 + t0.sa + t0.sb + t0.sc + t0.sd + t0.se + t0.sf;
}
for(; t < sample_col; t++)
{
#ifdef DOUBLE_SUPPORT
double t0 = sample[y * sample_step + t] - samples_ocl[j * sample_ocl_step + t];
#else
float t0 = sample[y * sample_step + t] - samples_ocl[j * sample_ocl_step + t];
#endif
sum = sum + t0 * t0;
}
si = (float)sum;
for(ii = k1 - 1; ii >= 0; ii--)
{
if(as_int(si) > as_int(dd[ii * nThreads + threadY]))
break;
}
if(ii < k - 1)
{
for(ii1 = k2 - 1; ii1 > ii; ii1--)
{
dd[(ii1 + 1) * nThreads + threadY] = dd[ii1 * nThreads + threadY];
nr[(ii1 + 1) * nThreads + threadY] = nr[ii1 * nThreads + threadY];
}
dd[(ii + 1) * nThreads + threadY] = si;
nr[(ii + 1) * nThreads + threadY] = samples_ocl[sample_col + j * sample_ocl_step];
}
k1 = (k1 + 1) < k ? (k1 + 1) : k;
k2 = k1 < (k - 1) ? k1 : (k - 1);
}
if (regression)
{
TYPE s;
#ifdef DOUBLE_SUPPORT
s = 0.0;
#else
s = 0.0f;
#endif
for(j = 0; j < K1; j++)
s += nr[j * nThreads + threadY];
_results[y * _results_step] = (float)(s * inv_scale);
}
else
{
int prev_start = 0, best_count = 0, cur_count;
float best_val;
for(j = K1 - 1; j > 0; j--)
{
bool swap_f1 = false;
for(j1 = 0; j1 < j; j1++)
{
if(nr[j1 * nThreads + threadY] > nr[(j1 + 1) * nThreads + threadY])
{
int t;
CV_SWAP(nr[j1 * nThreads + threadY], nr[(j1 + 1) * nThreads + threadY], t);
swap_f1 = true;
}
}
if(!swap_f1)
break;
}
best_val = 0;
for(j = 1; j <= K1; j++)
if(j == K1 || nr[j * nThreads + threadY] != nr[(j - 1) * nThreads + threadY])
{
cur_count = j - prev_start;
if(best_count < cur_count)
{
best_count = cur_count;
best_val = nr[(j - 1) * nThreads + threadY];
}
prev_start = j;
}
_results[y * _results_step] = best_val;
}
}
21d1d481592261bb0d7daeef8c69e121match_template#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define TYPE_IMAGE_SQSUM double
#else
#define TYPE_IMAGE_SQSUM float
#endif
#ifndef CN4
#define CN4 1
#else
#define CN4 4
#endif
#define SQSUMS_PTR(ox, oy) mad24(gidy + oy, img_sqsums_step, (gidx + img_sqsums_offset + ox) * CN4)
#define SUMS_PTR(ox, oy) mad24(gidy + oy, img_sums_step, gidx + img_sums_offset + ox)
float normAcc(float num, float denum)
{
if(fabs(num) < denum)
{
return num / denum;
}
if(fabs(num) < denum * 1.125f)
{
return num > 0 ? 1 : -1;
}
return 0;
}
float normAcc_SQDIFF(float num, float denum)
{
if(fabs(num) < denum)
{
return num / denum;
}
if(fabs(num) < denum * 1.125f)
{
return num > 0 ? 1 : -1;
}
return 1;
}
__kernel
void normalizeKernel_C1_D0
(
__global const float * img_sqsums,
__global float * res,
ulong tpl_sqsum,
int res_rows,
int res_cols,
int tpl_rows,
int tpl_cols,
int img_sqsums_offset,
int img_sqsums_step,
int res_offset,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
img_sqsums_step /= sizeof(*img_sqsums);
img_sqsums_offset /= sizeof(*img_sqsums);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
float image_sqsum_ = (float)(
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
res[res_idx] = normAcc(res[res_idx], sqrt(image_sqsum_ * tpl_sqsum));
}
}
__kernel
void matchTemplate_Prepared_SQDIFF_C1_D0
(
__global const TYPE_IMAGE_SQSUM * img_sqsums,
__global float * res,
ulong tpl_sqsum,
int res_rows,
int res_cols,
int tpl_rows,
int tpl_cols,
int img_sqsums_offset,
int img_sqsums_step,
int res_offset,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
img_sqsums_step /= sizeof(*img_sqsums);
img_sqsums_offset /= sizeof(*img_sqsums);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
float image_sqsum_ = (float)(
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
res[res_idx] = image_sqsum_ - 2.f * res[res_idx] + tpl_sqsum;
}
}
__kernel
void matchTemplate_Prepared_SQDIFF_NORMED_C1_D0
(
__global const float * img_sqsums,
__global float * res,
ulong tpl_sqsum,
int res_rows,
int res_cols,
int tpl_rows,
int tpl_cols,
int img_sqsums_offset,
int img_sqsums_step,
int res_offset,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
img_sqsums_step /= sizeof(*img_sqsums);
img_sqsums_offset /= sizeof(*img_sqsums);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
float image_sqsum_ = (float)(
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
res[res_idx] = normAcc_SQDIFF(image_sqsum_ - 2.f * res[res_idx] + tpl_sqsum,
sqrt(image_sqsum_ * tpl_sqsum));
}
}
__kernel
void matchTemplate_Naive_SQDIFF_C1_D0
(
__global const uchar * img,
__global const uchar * tpl,
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int img_offset,
int tpl_offset,
int res_offset,
int img_step,
int tpl_step,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int i,j;
int delta;
int sum = 0;
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
for(i = 0; i < tpl_rows; i ++)
{
__global const uchar * img_ptr = img + mad24(gidy + i, img_step, gidx + img_offset);
__global const uchar * tpl_ptr = tpl + mad24(i, tpl_step, tpl_offset);
for(j = 0; j < tpl_cols; j ++)
{
delta = img_ptr[j] - tpl_ptr[j];
sum   = mad24(delta, delta, sum);
}
}
res[res_idx] = sum;
}
}
__kernel
void matchTemplate_Naive_SQDIFF_C1_D5
(
__global const float * img,
__global const float * tpl,
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int img_offset,
int tpl_offset,
int res_offset,
int img_step,
int tpl_step,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int i,j;
float delta;
float sum = 0;
img_step   /= sizeof(*img);
img_offset /= sizeof(*img);
tpl_step   /= sizeof(*tpl);
tpl_offset /= sizeof(*tpl);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
for(i = 0; i < tpl_rows; i ++)
{
__global const float * img_ptr = img + mad24(gidy + i, img_step, gidx + img_offset);
__global const float * tpl_ptr = tpl + mad24(i, tpl_step, tpl_offset);
for(j = 0; j < tpl_cols; j ++)
{
delta = img_ptr[j] - tpl_ptr[j];
sum   = mad(delta, delta, sum);
}
}
res[res_idx] = sum;
}
}
__kernel
void matchTemplate_Naive_SQDIFF_C4_D0
(
__global const uchar4 * img,
__global const uchar4 * tpl,
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int img_offset,
int tpl_offset,
int res_offset,
int img_step,
int tpl_step,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int i,j;
int4 delta;
int4 sum = (int4)(0, 0, 0, 0);
img_step   /= sizeof(*img);
img_offset /= sizeof(*img);
tpl_step   /= sizeof(*tpl);
tpl_offset /= sizeof(*tpl);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
for(i = 0; i < tpl_rows; i ++)
{
__global const uchar4 * img_ptr = img + mad24(gidy + i, img_step, gidx + img_offset);
__global const uchar4 * tpl_ptr = tpl + mad24(i, tpl_step, tpl_offset);
for(j = 0; j < tpl_cols; j ++)
{
delta.x = img_ptr[j].x - tpl_ptr[j].x;
delta.y = img_ptr[j].y - tpl_ptr[j].y;
delta.z = img_ptr[j].z - tpl_ptr[j].z;
delta.w = img_ptr[j].w - tpl_ptr[j].w;
sum   = mad24(delta, delta, sum);
}
}
res[res_idx] = sum.x + sum.y + sum.z + sum.w;
}
}
__kernel
void matchTemplate_Naive_SQDIFF_C4_D5
(
__global const float4 * img,
__global const float4 * tpl,
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int img_offset,
int tpl_offset,
int res_offset,
int img_step,
int tpl_step,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int i,j;
float4 delta;
float4 sum = (float4)(0, 0, 0, 0);
img_step   /= sizeof(*img);
img_offset /= sizeof(*img);
tpl_step   /= sizeof(*tpl);
tpl_offset /= sizeof(*tpl);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
for(i = 0; i < tpl_rows; i ++)
{
__global const float4 * img_ptr = img + mad24(gidy + i, img_step, gidx + img_offset);
__global const float4 * tpl_ptr = tpl + mad24(i, tpl_step, tpl_offset);
for(j = 0; j < tpl_cols; j ++)
{
delta.x = img_ptr[j].x - tpl_ptr[j].x;
delta.y = img_ptr[j].y - tpl_ptr[j].y;
delta.z = img_ptr[j].z - tpl_ptr[j].z;
delta.w = img_ptr[j].w - tpl_ptr[j].w;
sum   = mad(delta, delta, sum);
}
}
res[res_idx] = sum.x + sum.y + sum.z + sum.w;
}
}
__kernel
void matchTemplate_Naive_CCORR_C1_D0
(
__global const uchar * img,
__global const uchar * tpl,
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int img_offset,
int tpl_offset,
int res_offset,
int img_step,
int tpl_step,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int i,j;
int sum = 0;
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
for(i = 0; i < tpl_rows; i ++)
{
__global const uchar * img_ptr = img + mad24(gidy + i, img_step, gidx + img_offset);
__global const uchar * tpl_ptr = tpl + mad24(i, tpl_step, tpl_offset);
for(j = 0; j < tpl_cols; j ++)
{
sum = mad24(convert_int(img_ptr[j]), convert_int(tpl_ptr[j]), sum);
}
}
res[res_idx] = (float)sum;
}
}
__kernel
void matchTemplate_Naive_CCORR_C1_D5
(
__global const float * img,
__global const float * tpl,
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int img_offset,
int tpl_offset,
int res_offset,
int img_step,
int tpl_step,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int i,j;
float sum = 0;
img_step   /= sizeof(*img);
img_offset /= sizeof(*img);
tpl_step   /= sizeof(*tpl);
tpl_offset /= sizeof(*tpl);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
for(i = 0; i < tpl_rows; i ++)
{
__global const float * img_ptr = img + mad24(gidy + i, img_step, gidx + img_offset);
__global const float * tpl_ptr = tpl + mad24(i, tpl_step, tpl_offset);
for(j = 0; j < tpl_cols; j ++)
{
sum = mad(img_ptr[j], tpl_ptr[j], sum);
}
}
res[res_idx] = sum;
}
}
__kernel
void matchTemplate_Naive_CCORR_C4_D0
(
__global const uchar4 * img,
__global const uchar4 * tpl,
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int img_offset,
int tpl_offset,
int res_offset,
int img_step,
int tpl_step,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int i,j;
int4 sum = (int4)(0, 0, 0, 0);
img_step   /= sizeof(*img);
img_offset /= sizeof(*img);
tpl_step   /= sizeof(*tpl);
tpl_offset /= sizeof(*tpl);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
for(i = 0; i < tpl_rows; i ++)
{
__global const uchar4 * img_ptr = img + mad24(gidy + i, img_step, gidx + img_offset);
__global const uchar4 * tpl_ptr = tpl + mad24(i, tpl_step, tpl_offset);
for(j = 0; j < tpl_cols; j ++)
{
sum   = mad24(convert_int4(img_ptr[j]), convert_int4(tpl_ptr[j]), sum);
}
}
res[res_idx] = (float)(sum.x + sum.y + sum.z + sum.w);
}
}
__kernel
void matchTemplate_Naive_CCORR_C4_D5
(
__global const float4 * img,
__global const float4 * tpl,
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int img_offset,
int tpl_offset,
int res_offset,
int img_step,
int tpl_step,
int res_step
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
int i,j;
float4 sum = (float4)(0, 0, 0, 0);
img_step   /= sizeof(*img);
img_offset /= sizeof(*img);
tpl_step   /= sizeof(*tpl);
tpl_offset /= sizeof(*tpl);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
for(i = 0; i < tpl_rows; i ++)
{
__global const float4 * img_ptr = img + mad24(gidy + i, img_step, gidx + img_offset);
__global const float4 * tpl_ptr = tpl + mad24(i, tpl_step, tpl_offset);
for(j = 0; j < tpl_cols; j ++)
{
sum = mad(convert_float4(img_ptr[j]), convert_float4(tpl_ptr[j]), sum);
}
}
res[res_idx] = sum.x + sum.y + sum.z + sum.w;
}
}
__kernel
void matchTemplate_Prepared_CCOFF_C1_D0
(
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int res_offset,
int res_step,
__global const uint * img_sums,
int img_sums_offset,
int img_sums_step,
float tpl_sum
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
img_sums_offset   /= sizeof(*img_sums);
img_sums_step     /= sizeof(*img_sums);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
float sum = (float)((img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
-(img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
res[res_idx] -= sum * tpl_sum;
}
}
__kernel
void matchTemplate_Prepared_CCOFF_C4_D0
(
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int res_offset,
int res_step,
__global const uint * img_sums_c0,
__global const uint * img_sums_c1,
__global const uint * img_sums_c2,
__global const uint * img_sums_c3,
int img_sums_offset,
int img_sums_step,
float tpl_sum_c0,
float tpl_sum_c1,
float tpl_sum_c2,
float tpl_sum_c3
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
img_sums_offset   /= sizeof(*img_sums_c0);
img_sums_step     /= sizeof(*img_sums_c0);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
float ccorr = res[res_idx];
ccorr -= tpl_sum_c0*(float)(
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
ccorr -= tpl_sum_c1*(float)(
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
ccorr -= tpl_sum_c2*(float)(
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
ccorr -= tpl_sum_c3*(float)(
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
res[res_idx] = ccorr;
}
}
__kernel
void matchTemplate_Prepared_CCOFF_NORMED_C1_D0
(
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int res_offset,
int res_step,
float weight,
__global const uint * img_sums,
int img_sums_offset,
int img_sums_step,
__global const float * img_sqsums,
int img_sqsums_offset,
int img_sqsums_step,
float tpl_sum,
float tpl_sqsum
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
img_sqsums_step   /= sizeof(*img_sqsums);
img_sqsums_offset /= sizeof(*img_sqsums);
img_sums_offset   /= sizeof(*img_sums);
img_sums_step     /= sizeof(*img_sums);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
float image_sum_ =  (float)(
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
float image_sqsum_ = (float)(
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
res[res_idx] = normAcc(res[res_idx] - image_sum_ * tpl_sum,
sqrt(tpl_sqsum * (image_sqsum_ - weight * image_sum_ * image_sum_)));
}
}
__kernel
void matchTemplate_Prepared_CCOFF_NORMED_C4_D0
(
__global float * res,
int img_rows,
int img_cols,
int tpl_rows,
int tpl_cols,
int res_rows,
int res_cols,
int res_offset,
int res_step,
float weight,
__global const uint * img_sums_c0,
__global const uint * img_sums_c1,
__global const uint * img_sums_c2,
__global const uint * img_sums_c3,
int img_sums_offset,
int img_sums_step,
__global const float * img_sqsums_c0,
__global const float * img_sqsums_c1,
__global const float * img_sqsums_c2,
__global const float * img_sqsums_c3,
int img_sqsums_offset,
int img_sqsums_step,
float tpl_sum_c0,
float tpl_sum_c1,
float tpl_sum_c2,
float tpl_sum_c3,
float tpl_sqsum
)
{
int gidx = get_global_id(0);
int gidy = get_global_id(1);
img_sqsums_step   /= sizeof(*img_sqsums_c0);
img_sqsums_offset /= sizeof(*img_sqsums_c0);
img_sums_offset   /= sizeof(*img_sums_c0);
img_sums_step     /= sizeof(*img_sums_c0);
res_step   /= sizeof(*res);
res_offset /= sizeof(*res);
int res_idx = mad24(gidy, res_step, res_offset + gidx);
if(gidx < res_cols && gidy < res_rows)
{
float image_sum_c0 =  (float)(
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
float image_sum_c1 =  (float)(
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
float image_sum_c2 =  (float)(
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
float image_sum_c3 =  (float)(
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
float image_sqsum_c0 = (float)(
(img_sqsums_c0[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c0[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(0, 0)]));
float image_sqsum_c1 = (float)(
(img_sqsums_c1[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c1[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(0, 0)]));
float image_sqsum_c2 = (float)(
(img_sqsums_c2[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c2[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(0, 0)]));
float image_sqsum_c3 = (float)(
(img_sqsums_c3[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(tpl_cols, 0)]) -
(img_sqsums_c3[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(0, 0)]));
float num = res[res_idx] -
image_sum_c0 * tpl_sum_c0 -
image_sum_c1 * tpl_sum_c1 -
image_sum_c2 * tpl_sum_c2 -
image_sum_c3 * tpl_sum_c3;
float denum = sqrt( tpl_sqsum * (
image_sqsum_c0 - weight * image_sum_c0 * image_sum_c0 +
image_sqsum_c1 - weight * image_sum_c1 * image_sum_c1 +
image_sqsum_c2 - weight * image_sum_c2 * image_sum_c2 +
image_sqsum_c3 - weight * image_sum_c0 * image_sum_c3)
);
res[res_idx] = normAcc(num, denum);
}
}
__kernel
void extractFirstChannel
(
const __global float4* img,
__global float* res,
int rows,
int cols,
int img_offset,
int res_offset,
int img_step,
int res_step
)
{
img_step   /= sizeof(float4);
res_step   /= sizeof(float);
img_offset /= sizeof(float4);
res_offset /= sizeof(float);
img += img_offset;
res += res_offset;
int gidx = get_global_id(0);
int gidy = get_global_id(1);
if(gidx < cols && gidy < rows)
{
res[gidx + gidy * res_step] = img[gidx + gidy * img_step].x;
}
}
2f026d4f003285067e2fc4c6fc5866bameanShiftshort2 do_mean_shift(int x0, int y0, __global uchar4* out,int out_step,
__global uchar4* in, int in_step, int dst_off, int src_off,
int cols, int rows, int sp, int sr, int maxIter, float eps)
{
int isr2 = sr*sr;
in_step = in_step >> 2;
out_step = out_step >> 2;
src_off = src_off >> 2;
dst_off = dst_off >> 2;
int idx = src_off + y0 * in_step + x0;
uchar4 c = in[idx];
int base = dst_off + get_global_id(1)*out_step + get_global_id(0) ;
for( int iter = 0; iter < maxIter; iter++ )
{
int count = 0;
int4 s = (int4)0;
int sx = 0, sy = 0;
int minx = (x0-sp)>0 ? x0-sp : 0;
int miny = (y0-sp)>0 ? y0-sp : 0;
int maxx = (x0+sp)>= 2;
outspstep >>= 2;
int basesp = outsp_off + y0 * outspstep + x0;
outsp[basesp] = do_mean_shift(x0, y0, outr, outrstep, in, instep, outr_off, in_off, cols, rows, sp, sr, maxIter, eps);
}
}
14dbf4c2ad30df918cadb74aa130aa77merge_mat#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void merge_vector_C2_D0(__global uchar *mat_dst,  int dst_step,  int dst_offset,
__global uchar *mat_src0, int src0_step, int src0_offset,
__global uchar *mat_src1, int src1_step, int src1_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
x = x << 1;
#define dst_align  ((dst_offset & 3) >> 1)
int src0_index = mad24(y, src0_step, src0_offset + x - dst_align);
int src1_index = mad24(y, src1_step, src1_offset + x - dst_align);
int dst_start  = mad24(y, dst_step, dst_offset);
int dst_end    = mad24(y, dst_step, dst_offset + dst_step1);
int dst_index  = mad24(y, dst_step, dst_offset + (x << 1) & (int)0xfffffffc);
__global uchar4 * dst  = (__global uchar4 *)(mat_dst + dst_index);
__global uchar  * src0 = mat_src0 + src0_index;
__global uchar  * src1 = src0     + 1;
__global uchar  * src2 = mat_src1 + src1_index;
__global uchar  * src3 = src2     + 1;
uchar4 dst_data = *dst;
uchar  data_0   = *(src0);
uchar  data_1   = *(src1);
uchar  data_2   = *(src2);
uchar  data_3   = *(src3);
uchar4 tmp_data = (uchar4)(data_0, data_2, data_1, data_3);
tmp_data.xy = dst_index + 0 >= dst_start ? tmp_data.xy : dst_data.xy;
tmp_data.zw = dst_index + 2 <  dst_end   ? tmp_data.zw : dst_data.zw;
*dst = tmp_data;
}
}
__kernel void merge_vector_C2_D1(__global char *mat_dst,  int dst_step,  int dst_offset,
__global char *mat_src0, int src0_step, int src0_offset,
__global char *mat_src1, int src1_step, int src1_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
x = x << 1;
#define dst_align  ((dst_offset & 3) >> 1)
int src0_index = mad24(y, src0_step, src0_offset + x - dst_align);
int src1_index = mad24(y, src1_step, src1_offset + x - dst_align);
int dst_start  = mad24(y, dst_step, dst_offset);
int dst_end    = mad24(y, dst_step, dst_offset + dst_step1);
int dst_index  = mad24(y, dst_step, dst_offset + (x << 1) & (int)0xfffffffc);
__global char4 * dst  = (__global char4 *)(mat_dst + dst_index);
__global char  * src0 = mat_src0 + src0_index;
__global char  * src1 = src0     + 1;
__global char  * src2 = mat_src1 + src1_index;
__global char  * src3 = src2     + 1;
char4 dst_data = *dst;
char  data_0   = *(src0);
char  data_1   = *(src1);
char  data_2   = *(src2);
char  data_3   = *(src3);
char4 tmp_data = (char4)(data_0, data_2, data_1, data_3);
tmp_data.xy = dst_index + 0 >= dst_start ? tmp_data.xy : dst_data.xy;
tmp_data.zw = dst_index + 2 <  dst_end   ? tmp_data.zw : dst_data.zw;
*dst = tmp_data;
}
}
__kernel void merge_vector_C2_D2(__global ushort *mat_dst,  int dst_step,  int dst_offset,
__global ushort *mat_src0, int src0_step, int src0_offset,
__global ushort *mat_src1, int src1_step, int src1_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
__global ushort*  src0 = (__global ushort * )((__global uchar *)mat_src0 + src0_index + (x << 1));
__global ushort*  src1 = (__global ushort * )((__global uchar *)mat_src1 + src1_index + (x << 1));
__global ushort2* dist = (__global ushort2 *)((__global uchar *)mat_dst  + dst_index  + (x << 2));
ushort  src0_data = *src0;
ushort  src1_data = *src1;
*dist = (ushort2)(src0_data, src1_data);
}
}
__kernel void merge_vector_C2_D3(__global short *mat_dst,  int dst_step,  int dst_offset,
__global short *mat_src0, int src0_step, int src0_offset,
__global short *mat_src1, int src1_step, int src1_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
__global short*  src0 = (__global short * )((__global uchar *)mat_src0 + src0_index + (x << 1));
__global short*  src1 = (__global short * )((__global uchar *)mat_src1 + src1_index + (x << 1));
__global short2* dist = (__global short2 *)((__global uchar *)mat_dst  + dst_index   + (x << 2));
short  src0_data = *src0;
short  src1_data = *src1;
*dist = (short2)(src0_data, src1_data);
}
}
__kernel void merge_vector_C2_D4(__global int *mat_dst,  int dst_step,  int dst_offset,
__global int *mat_src0, int src0_step, int src0_offset,
__global int *mat_src1, int src1_step, int src1_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
int src0 = *((__global int *)((__global uchar *)mat_src0 + src0_index + (x << 2)));
int src1 = *((__global int *)((__global uchar *)mat_src1 + src1_index + (x << 2)));
*((__global int2 *)((__global uchar *)mat_dst  + dst_index + (x << 3))) = (int2)(src0, src1);
}
}
__kernel void merge_vector_C2_D5(__global float *mat_dst,  int dst_step,  int dst_offset,
__global float *mat_src0, int src0_step, int src0_offset,
__global float *mat_src1, int src1_step, int src1_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
float src0 = *((__global float *)((__global uchar *)mat_src0 + src0_index + (x << 2)));
float src1 = *((__global float *)((__global uchar *)mat_src1 + src1_index + (x << 2)));
*((__global float2 *)((__global uchar *)mat_dst  + dst_index + (x << 3))) = (float2)(src0, src1);
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void merge_vector_C2_D6(__global double *mat_dst,  int dst_step,  int dst_offset,
__global double *mat_src0, int src0_step, int src0_offset,
__global double *mat_src1, int src1_step, int src1_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
double src0 = *((__global double *)((__global uchar *)mat_src0 + src0_index + (x << 3)));
double src1 = *((__global double *)((__global uchar *)mat_src1 + src1_index + (x << 3)));
*((__global double2 *)((__global uchar *)mat_dst  + dst_index + (x << 4))) = (double2)(src0, src1);
}
}
#endif
__kernel void merge_vector_C3_D0(__global uchar *mat_dst,  int dst_step,  int dst_offset,
__global uchar *mat_src0, int src0_step, int src0_offset,
__global uchar *mat_src1, int src1_step, int src1_offset,
__global uchar *mat_src2, int src2_step, int src2_offset, int offset_cols,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
x = x << 2;
int src0_index = mad24(y, src0_step, x + src0_offset - offset_cols);
int src1_index = mad24(y, src1_step, x + src1_offset - offset_cols);
int src2_index = mad24(y, src2_step, x + src2_offset - offset_cols);
int dst_start = mad24(y, dst_step, dst_offset);
int dst_end   = mad24(y, dst_step, dst_offset + dst_step1);
int dst_index = mad24(y, dst_step, dst_offset + 3 * x - offset_cols * 3);
uchar data0_0 = *(mat_src0 + src0_index + 0);
uchar data0_1 = *(mat_src0 + src0_index + 1);
uchar data0_2 = *(mat_src0 + src0_index + 2);
uchar data0_3 = *(mat_src0 + src0_index + 3);
uchar data1_0 = *(mat_src1 + src1_index + 0);
uchar data1_1 = *(mat_src1 + src1_index + 1);
uchar data1_2 = *(mat_src1 + src1_index + 2);
uchar data1_3 = *(mat_src1 + src1_index + 3);
uchar data2_0 = *(mat_src2 + src2_index + 0);
uchar data2_1 = *(mat_src2 + src2_index + 1);
uchar data2_2 = *(mat_src2 + src2_index + 2);
uchar data2_3 = *(mat_src2 + src2_index + 3);
uchar4 tmp_data0 = (uchar4)(data0_0, data1_0, data2_0, data0_1);
uchar4 tmp_data1 = (uchar4)(data1_1, data2_1, data0_2, data1_2);
uchar4 tmp_data2 = (uchar4)(data2_2, data0_3, data1_3, data2_3);
uchar4 dst_data0 = *((__global uchar4*)(mat_dst + dst_index + 0));
uchar4 dst_data1 = *((__global uchar4*)(mat_dst + dst_index + 4));
uchar4 dst_data2 = *((__global uchar4*)(mat_dst + dst_index + 8));
tmp_data0.x = ((dst_index + 0  >= dst_start) && (dst_index + 0  < dst_end)) ? tmp_data0.x : dst_data0.x;
tmp_data0.y = ((dst_index + 1  >= dst_start) && (dst_index + 1  < dst_end)) ? tmp_data0.y : dst_data0.y;
tmp_data0.z = ((dst_index + 2  >= dst_start) && (dst_index + 2  < dst_end)) ? tmp_data0.z : dst_data0.z;
tmp_data0.w = ((dst_index + 3  >= dst_start) && (dst_index + 3  < dst_end)) ? tmp_data0.w : dst_data0.w;
tmp_data1.x = ((dst_index + 4  >= dst_start) && (dst_index + 4  < dst_end)) ? tmp_data1.x : dst_data1.x;
tmp_data1.y = ((dst_index + 5  >= dst_start) && (dst_index + 5  < dst_end)) ? tmp_data1.y : dst_data1.y;
tmp_data1.z = ((dst_index + 6  >= dst_start) && (dst_index + 6  < dst_end)) ? tmp_data1.z : dst_data1.z;
tmp_data1.w = ((dst_index + 7  >= dst_start) && (dst_index + 7  < dst_end)) ? tmp_data1.w : dst_data1.w;
tmp_data2.x = ((dst_index + 8  >= dst_start) && (dst_index + 8  < dst_end)) ? tmp_data2.x : dst_data2.x;
tmp_data2.y = ((dst_index + 9  >= dst_start) && (dst_index + 9  < dst_end)) ? tmp_data2.y : dst_data2.y;
tmp_data2.z = ((dst_index + 10 >= dst_start) && (dst_index + 10 < dst_end)) ? tmp_data2.z : dst_data2.z;
tmp_data2.w = ((dst_index + 11 >= dst_start) && (dst_index + 11 < dst_end)) ? tmp_data2.w : dst_data2.w;
*((__global uchar4*)(mat_dst + dst_index + 0)) = tmp_data0;
*((__global uchar4*)(mat_dst + dst_index + 4)) = tmp_data1;
*((__global uchar4*)(mat_dst + dst_index + 8)) = tmp_data2;
}
}
__kernel void merge_vector_C3_D1(__global char *mat_dst,  int dst_step,  int dst_offset,
__global char *mat_src0, int src0_step, int src0_offset,
__global char *mat_src1, int src1_step, int src1_offset,
__global char *mat_src2, int src2_step, int src2_offset, int offset_cols,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
x = x << 2;
int src0_index = mad24(y, src0_step, x + src0_offset - offset_cols);
int src1_index = mad24(y, src1_step, x + src1_offset - offset_cols);
int src2_index = mad24(y, src2_step, x + src2_offset - offset_cols);
int dst_start = mad24(y, dst_step, dst_offset);
int dst_end   = mad24(y, dst_step, dst_offset + dst_step1);
int dst_index = mad24(y, dst_step, dst_offset + 3 * x - offset_cols * 3);
char data0_0 = *(mat_src0 + src0_index + 0);
char data0_1 = *(mat_src0 + src0_index + 1);
char data0_2 = *(mat_src0 + src0_index + 2);
char data0_3 = *(mat_src0 + src0_index + 3);
char data1_0 = *(mat_src1 + src1_index + 0);
char data1_1 = *(mat_src1 + src1_index + 1);
char data1_2 = *(mat_src1 + src1_index + 2);
char data1_3 = *(mat_src1 + src1_index + 3);
char data2_0 = *(mat_src2 + src2_index + 0);
char data2_1 = *(mat_src2 + src2_index + 1);
char data2_2 = *(mat_src2 + src2_index + 2);
char data2_3 = *(mat_src2 + src2_index + 3);
char4 tmp_data0 = (char4)(data0_0, data1_0, data2_0, data0_1);
char4 tmp_data1 = (char4)(data1_1, data2_1, data0_2, data1_2);
char4 tmp_data2 = (char4)(data2_2, data0_3, data1_3, data2_3);
char4 dst_data0 = *((__global char4*)(mat_dst + dst_index + 0));
char4 dst_data1 = *((__global char4*)(mat_dst + dst_index + 4));
char4 dst_data2 = *((__global char4*)(mat_dst + dst_index + 8));
tmp_data0.x = ((dst_index + 0  >= dst_start) && (dst_index + 0  < dst_end)) ? tmp_data0.x : dst_data0.x;
tmp_data0.y = ((dst_index + 1  >= dst_start) && (dst_index + 1  < dst_end)) ? tmp_data0.y : dst_data0.y;
tmp_data0.z = ((dst_index + 2  >= dst_start) && (dst_index + 2  < dst_end)) ? tmp_data0.z : dst_data0.z;
tmp_data0.w = ((dst_index + 3  >= dst_start) && (dst_index + 3  < dst_end)) ? tmp_data0.w : dst_data0.w;
tmp_data1.x = ((dst_index + 4  >= dst_start) && (dst_index + 4  < dst_end)) ? tmp_data1.x : dst_data1.x;
tmp_data1.y = ((dst_index + 5  >= dst_start) && (dst_index + 5  < dst_end)) ? tmp_data1.y : dst_data1.y;
tmp_data1.z = ((dst_index + 6  >= dst_start) && (dst_index + 6  < dst_end)) ? tmp_data1.z : dst_data1.z;
tmp_data1.w = ((dst_index + 7  >= dst_start) && (dst_index + 7  < dst_end)) ? tmp_data1.w : dst_data1.w;
tmp_data2.x = ((dst_index + 8  >= dst_start) && (dst_index + 8  < dst_end)) ? tmp_data2.x : dst_data2.x;
tmp_data2.y = ((dst_index + 9  >= dst_start) && (dst_index + 9  < dst_end)) ? tmp_data2.y : dst_data2.y;
tmp_data2.z = ((dst_index + 10 >= dst_start) && (dst_index + 10 < dst_end)) ? tmp_data2.z : dst_data2.z;
tmp_data2.w = ((dst_index + 11 >= dst_start) && (dst_index + 11 < dst_end)) ? tmp_data2.w : dst_data2.w;
*((__global char4*)(mat_dst + dst_index + 0)) = tmp_data0;
*((__global char4*)(mat_dst + dst_index + 4)) = tmp_data1;
*((__global char4*)(mat_dst + dst_index + 8)) = tmp_data2;
}
}
__kernel void merge_vector_C3_D2(__global ushort *mat_dst,  int dst_step,  int dst_offset,
__global ushort *mat_src0, int src0_step, int src0_offset,
__global ushort *mat_src1, int src1_step, int src1_offset,
__global ushort *mat_src2, int src2_step, int src2_offset, int offset_cols,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
x = x << 1;
int src0_index = mad24(y, src0_step, (x << 1) + src0_offset - offset_cols);
int src1_index = mad24(y, src1_step, (x << 1) + src1_offset - offset_cols);
int src2_index = mad24(y, src2_step, (x << 1) + src2_offset - offset_cols);
int dst_start = mad24(y, dst_step, dst_offset);
int dst_end   = mad24(y, dst_step, dst_offset + dst_step1);
int dst_index = mad24(y, dst_step, dst_offset + 6 * x - offset_cols * 6);
ushort data0_0 = *((__global ushort *)((__global char *)mat_src0 + src0_index + 0));
ushort data0_1 = *((__global ushort *)((__global char *)mat_src0 + src0_index + 2));
ushort data1_0 = *((__global ushort *)((__global char *)mat_src1 + src1_index + 0));
ushort data1_1 = *((__global ushort *)((__global char *)mat_src1 + src1_index + 2));
ushort data2_0 = *((__global ushort *)((__global char *)mat_src2 + src2_index + 0));
ushort data2_1 = *((__global ushort *)((__global char *)mat_src2 + src2_index + 2));
ushort2 tmp_data0 = (ushort2)(data0_0, data1_0);
ushort2 tmp_data1 = (ushort2)(data2_0, data0_1);
ushort2 tmp_data2 = (ushort2)(data1_1, data2_1);
ushort2 dst_data0 = *((__global ushort2*)((__global char *)mat_dst + dst_index + 0));
ushort2 dst_data1 = *((__global ushort2*)((__global char *)mat_dst + dst_index + 4));
ushort2 dst_data2 = *((__global ushort2*)((__global char *)mat_dst + dst_index + 8));
tmp_data0.x = ((dst_index + 0  >= dst_start) && (dst_index + 0  < dst_end)) ? tmp_data0.x : dst_data0.x;
tmp_data0.y = ((dst_index + 2  >= dst_start) && (dst_index + 2  < dst_end)) ? tmp_data0.y : dst_data0.y;
tmp_data1.x = ((dst_index + 4  >= dst_start) && (dst_index + 4  < dst_end)) ? tmp_data1.x : dst_data1.x;
tmp_data1.y = ((dst_index + 6  >= dst_start) && (dst_index + 6  < dst_end)) ? tmp_data1.y : dst_data1.y;
tmp_data2.x = ((dst_index + 8  >= dst_start) && (dst_index + 8  < dst_end)) ? tmp_data2.x : dst_data2.x;
tmp_data2.y = ((dst_index + 10 >= dst_start) && (dst_index + 10 < dst_end)) ? tmp_data2.y : dst_data2.y;
*((__global ushort2*)((__global char *)mat_dst + dst_index + 0)) = tmp_data0;
*((__global ushort2*)((__global char *)mat_dst + dst_index + 4)) = tmp_data1;
*((__global ushort2*)((__global char *)mat_dst + dst_index + 8)) = tmp_data2;
}
}
__kernel void merge_vector_C3_D3(__global short *mat_dst,  int dst_step,  int dst_offset,
__global short *mat_src0, int src0_step, int src0_offset,
__global short *mat_src1, int src1_step, int src1_offset,
__global short *mat_src2, int src2_step, int src2_offset, int offset_cols,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
x = x << 1;
int src0_index = mad24(y, src0_step, (x << 1) + src0_offset - offset_cols);
int src1_index = mad24(y, src1_step, (x << 1) + src1_offset - offset_cols);
int src2_index = mad24(y, src2_step, (x << 1) + src2_offset - offset_cols);
int dst_start = mad24(y, dst_step, dst_offset);
int dst_end   = mad24(y, dst_step, dst_offset + dst_step1);
int dst_index = mad24(y, dst_step, dst_offset + 6 * x - offset_cols * 6);
short data0_0 = *((__global short *)((__global char *)mat_src0 + src0_index + 0));
short data0_1 = *((__global short *)((__global char *)mat_src0 + src0_index + 2));
short data1_0 = *((__global short *)((__global char *)mat_src1 + src1_index + 0));
short data1_1 = *((__global short *)((__global char *)mat_src1 + src1_index + 2));
short data2_0 = *((__global short *)((__global char *)mat_src2 + src2_index + 0));
short data2_1 = *((__global short *)((__global char *)mat_src2 + src2_index + 2));
short2 tmp_data0 = (short2)(data0_0, data1_0);
short2 tmp_data1 = (short2)(data2_0, data0_1);
short2 tmp_data2 = (short2)(data1_1, data2_1);
short2 dst_data0 = *((__global short2*)((__global char *)mat_dst + dst_index + 0));
short2 dst_data1 = *((__global short2*)((__global char *)mat_dst + dst_index + 4));
short2 dst_data2 = *((__global short2*)((__global char *)mat_dst + dst_index + 8));
tmp_data0.x = ((dst_index + 0  >= dst_start) && (dst_index + 0  < dst_end)) ? tmp_data0.x : dst_data0.x;
tmp_data0.y = ((dst_index + 2  >= dst_start) && (dst_index + 2  < dst_end)) ? tmp_data0.y : dst_data0.y;
tmp_data1.x = ((dst_index + 4  >= dst_start) && (dst_index + 4  < dst_end)) ? tmp_data1.x : dst_data1.x;
tmp_data1.y = ((dst_index + 6  >= dst_start) && (dst_index + 6  < dst_end)) ? tmp_data1.y : dst_data1.y;
tmp_data2.x = ((dst_index + 8  >= dst_start) && (dst_index + 8  < dst_end)) ? tmp_data2.x : dst_data2.x;
tmp_data2.y = ((dst_index + 10 >= dst_start) && (dst_index + 10 < dst_end)) ? tmp_data2.y : dst_data2.y;
*((__global short2*)((__global char *)mat_dst + dst_index + 0)) = tmp_data0;
*((__global short2*)((__global char *)mat_dst + dst_index + 4)) = tmp_data1;
*((__global short2*)((__global char *)mat_dst + dst_index + 8)) = tmp_data2;
}
}
__kernel void merge_vector_C3_D4(__global int *mat_dst,  int dst_step,  int dst_offset,
__global int *mat_src0, int src0_step, int src0_offset,
__global int *mat_src1, int src1_step, int src1_offset,
__global int *mat_src2, int src2_step, int src2_offset, int offset_cols,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
__global int* src0 = (__global int * )((__global uchar *)mat_src0 + src0_index + (x << 2));
__global int* src1 = (__global int * )((__global uchar *)mat_src1 + src1_index + (x << 2));
__global int* src2 = (__global int * )((__global uchar *)mat_src2 + src2_index + (x << 2));
__global int* dist0 = (__global int *)((__global uchar *)mat_dst  + dst_index  + 3 * (x << 2));
__global int* dist1 = dist0 + 1;
__global int* dist2 = dist0 + 2;
int  src0_data = *src0;
int  src1_data = *src1;
int  src2_data = *src2;
*dist0 = src0_data;
*dist1 = src1_data;
*dist2 = src2_data;
}
}
__kernel void merge_vector_C3_D5(__global float *mat_dst,  int dst_step,  int dst_offset,
__global float *mat_src0, int src0_step, int src0_offset,
__global float *mat_src1, int src1_step, int src1_offset,
__global float *mat_src2, int src2_step, int src2_offset, int offset_cols,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
__global float* src0 = (__global float * )((__global uchar *)mat_src0 + src0_index + (x << 2));
__global float* src1 = (__global float * )((__global uchar *)mat_src1 + src1_index + (x << 2));
__global float* src2 = (__global float * )((__global uchar *)mat_src2 + src2_index + (x << 2));
__global float* dist0 = (__global float *)((__global uchar *)mat_dst  + dst_index  + 3 * (x << 2));
__global float* dist1 = dist0 + 1;
__global float* dist2 = dist0 + 2;
float  src0_data = *src0;
float  src1_data = *src1;
float  src2_data = *src2;
*dist0 = src0_data;
*dist1 = src1_data;
*dist2 = src2_data;
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void merge_vector_C3_D6(__global double *mat_dst,  int dst_step,  int dst_offset,
__global double *mat_src0, int src0_step, int src0_offset,
__global double *mat_src1, int src1_step, int src1_offset,
__global double *mat_src2, int src2_step, int src2_offset, int offset_cols,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
__global double* src0 = (__global double * )((__global uchar *)mat_src0 + src0_index + (x << 3));
__global double* src1 = (__global double * )((__global uchar *)mat_src1 + src1_index + (x << 3));
__global double* src2 = (__global double * )((__global uchar *)mat_src2 + src2_index + (x << 3));
__global double* dist0 = (__global double *)((__global uchar *)mat_dst  + dst_index  + 3 * (x << 3));
__global double* dist1 = dist0 + 1;
__global double* dist2 = dist0 + 2;
double  src0_data = *src0;
double  src1_data = *src1;
double  src2_data = *src2;
*dist0 = src0_data;
*dist1 = src1_data;
*dist2 = src2_data;
}
}
#endif
__kernel void merge_vector_C4_D0(__global uchar *mat_dst,  int dst_step,  int dst_offset,
__global uchar *mat_src0, int src0_step, int src0_offset,
__global uchar *mat_src1, int src1_step, int src1_offset,
__global uchar *mat_src2, int src2_step, int src2_offset,
__global uchar *mat_src3, int src3_step, int src3_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int src3_index = mad24(y, src3_step, src3_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
uchar src0 = *(mat_src0 + src0_index + x );
uchar src1 = *(mat_src1 + src1_index + x);
uchar src2 = *(mat_src2 + src2_index + x);
uchar src3 = *(mat_src3 + src3_index + x);
*((__global uchar4 *)(mat_dst  + dst_index + (x << 2))) = (uchar4)(src0, src1, src2, src3);
}
}
__kernel void merge_vector_C4_D1(__global char *mat_dst,  int dst_step,  int dst_offset,
__global char *mat_src0, int src0_step, int src0_offset,
__global char *mat_src1, int src1_step, int src1_offset,
__global char *mat_src2, int src2_step, int src2_offset,
__global char *mat_src3, int src3_step, int src3_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int src3_index = mad24(y, src3_step, src3_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
char src0 = *(mat_src0 + src0_index + x );
char src1 = *(mat_src1 + src1_index + x);
char src2 = *(mat_src2 + src2_index + x);
char src3 = *(mat_src3 + src3_index + x);
*((__global char4 *)(mat_dst  + dst_index + (x << 2))) = (char4)(src0, src1, src2, src3);
}
}
__kernel void merge_vector_C4_D2(__global ushort *mat_dst,  int dst_step,  int dst_offset,
__global ushort *mat_src0, int src0_step, int src0_offset,
__global ushort *mat_src1, int src1_step, int src1_offset,
__global ushort *mat_src2, int src2_step, int src2_offset,
__global ushort *mat_src3, int src3_step, int src3_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int src3_index = mad24(y, src3_step, src3_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
ushort src0 = *((__global ushort *)((__global uchar *)mat_src0 + src0_index + (x << 1)));
ushort src1 = *((__global ushort *)((__global uchar *)mat_src1 + src1_index + (x << 1)));
ushort src2 = *((__global ushort *)((__global uchar *)mat_src2 + src2_index + (x << 1)));
ushort src3 = *((__global ushort *)((__global uchar *)mat_src3 + src3_index + (x << 1)));
*((__global ushort4 *)((__global uchar *)mat_dst  + dst_index + (x << 3))) = (ushort4)(src0, src1, src2, src3);
}
}
__kernel void merge_vector_C4_D3(__global short *mat_dst,  int dst_step,  int dst_offset,
__global short *mat_src0, int src0_step, int src0_offset,
__global short *mat_src1, int src1_step, int src1_offset,
__global short *mat_src2, int src2_step, int src2_offset,
__global short *mat_src3, int src3_step, int src3_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int src3_index = mad24(y, src3_step, src3_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
short src0 = *((__global short *)((__global uchar *)mat_src0 + src0_index + (x << 1)));
short src1 = *((__global short *)((__global uchar *)mat_src1 + src1_index + (x << 1)));
short src2 = *((__global short *)((__global uchar *)mat_src2 + src2_index + (x << 1)));
short src3 = *((__global short *)((__global uchar *)mat_src3 + src3_index + (x << 1)));
*((__global short4 *)((__global uchar *)mat_dst  + dst_index + (x << 3))) = (short4)(src0, src1, src2, src3);
}
}
__kernel void merge_vector_C4_D4(__global int *mat_dst,  int dst_step,  int dst_offset,
__global int *mat_src0, int src0_step, int src0_offset,
__global int *mat_src1, int src1_step, int src1_offset,
__global int *mat_src2, int src2_step, int src2_offset,
__global int *mat_src3, int src3_step, int src3_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int src3_index = mad24(y, src3_step, src3_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
int src0 = *((__global int *)((__global uchar *)mat_src0 + src0_index + (x << 2)));
int src1 = *((__global int *)((__global uchar *)mat_src1 + src1_index + (x << 2)));
int src2 = *((__global int *)((__global uchar *)mat_src2 + src2_index + (x << 2)));
int src3 = *((__global int *)((__global uchar *)mat_src3 + src3_index + (x << 2)));
*((__global int4 *)((__global uchar *)mat_dst  + dst_index + (x << 4))) = (int4)(src0, src1, src2, src3);
}
}
__kernel void merge_vector_C4_D5(__global float *mat_dst,  int dst_step,  int dst_offset,
__global float *mat_src0, int src0_step, int src0_offset,
__global float *mat_src1, int src1_step, int src1_offset,
__global float *mat_src2, int src2_step, int src2_offset,
__global float *mat_src3, int src3_step, int src3_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int src3_index = mad24(y, src3_step, src3_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
float src0 = *((__global float *)((__global uchar *)mat_src0 + src0_index + (x << 2)));
float src1 = *((__global float *)((__global uchar *)mat_src1 + src1_index + (x << 2)));
float src2 = *((__global float *)((__global uchar *)mat_src2 + src2_index + (x << 2)));
float src3 = *((__global float *)((__global uchar *)mat_src3 + src3_index + (x << 2)));
*((__global float4 *)((__global uchar *)mat_dst  + dst_index + (x << 4))) = (float4)(src0, src1, src2, src3);
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void merge_vector_C4_D6(__global double *mat_dst,  int dst_step,  int dst_offset,
__global double *mat_src0, int src0_step, int src0_offset,
__global double *mat_src1, int src1_step, int src1_offset,
__global double *mat_src2, int src2_step, int src2_offset,
__global double *mat_src3, int src3_step, int src3_offset,
int rows, int cols, int dst_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
int src0_index = mad24(y, src0_step, src0_offset);
int src1_index = mad24(y, src1_step, src1_offset);
int src2_index = mad24(y, src2_step, src2_offset);
int src3_index = mad24(y, src3_step, src3_offset);
int dst_index  = mad24(y, dst_step , dst_offset);
double src0 = *((__global double *)((__global uchar *)mat_src0 + src0_index + (x << 3)));
double src1 = *((__global double *)((__global uchar *)mat_src1 + src1_index + (x << 3)));
double src2 = *((__global double *)((__global uchar *)mat_src2 + src2_index + (x << 3)));
double src3 = *((__global double *)((__global uchar *)mat_src3 + src3_index + (x << 3)));
*((__global double4 *)((__global uchar *)mat_dst  + dst_index + (x << 5))) = (double4)(src0, src1, src2, src3);
}
}
#endif
__kernel void merge_vector_C2_D0_1(int rows, int cols,
__global uchar *mat_dst,  int dst_step,
__global uchar *mat_src0, int src0_step,
__global uchar *mat_src1, int src1_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global uchar4  *src0_y = (__global uchar4 * )(mat_src0 + y * src0_step);
__global uchar4  *src1_y = (__global uchar4 * )(mat_src1 + y * src1_step);
__global uchar8 *dst_y  = (__global uchar8 *)(mat_dst  + y * dst_step);
uchar4 value1 = src0_y[x];
uchar4 value2 = src1_y[x];
uchar8 value;
value.even = value1;
value.odd = value2;
dst_y[x] = value;
}
}
__kernel void merge_vector_C2_D1_1(int rows, int cols,
__global char *mat_dst,  int dst_step,
__global char *mat_src0, int src0_step,
__global char *mat_src1, int src1_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global char4  *src0_y = (__global char4 * )(mat_src0 + y * src0_step);
__global char4  *src1_y = (__global char4 * )(mat_src1 + y * src1_step);
__global char8 *dst_y  = (__global char8 *)(mat_dst  + y * dst_step);
char4 value1 = src0_y[x];
char4 value2 = src1_y[x];
char8 value;
value.even = value1;
value.odd = value2;
dst_y[x] = value;
}
}
__kernel void merge_vector_C2_D2_1(int rows, int cols,
__global ushort *mat_dst,  int dst_step,
__global ushort *mat_src0, int src0_step,
__global ushort *mat_src1, int src1_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global ushort2  *src0_y = (__global ushort2 *)((__global uchar *)mat_src0 + y * src0_step);
__global ushort2  *src1_y = (__global ushort2 *)((__global uchar *)mat_src1 + y * src1_step);
__global ushort4  *dst_y  = (__global ushort4 *)((__global uchar *)mat_dst  + y * dst_step);
ushort2 value1 = src0_y[x];
ushort2 value2 = src1_y[x];
ushort4 value;
value.even = value1;
value.odd = value2;
dst_y[x] = value;
}
}
__kernel void merge_vector_C2_D3_1(int rows, int cols,
__global short *mat_dst,  int dst_step,
__global short *mat_src0, int src0_step,
__global short *mat_src1, int src1_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global short2  *src0_y = (__global short2 *)((__global uchar *)mat_src0 + y * src0_step);
__global short2  *src1_y = (__global short2 *)((__global uchar *)mat_src1 + y * src1_step);
__global short4 *dst_y   = (__global short4 *)((__global uchar *)mat_dst  + y * dst_step);
short2 value1 = src0_y[x];
short2 value2 = src1_y[x];
short4 value;
value.even = value1;
value.odd = value2;
dst_y[x] = value;
}
}
__kernel void merge_vector_C2_D4_1(int rows, int cols,
__global int *mat_dst,  int dst_step,
__global int *mat_src0, int src0_step,
__global int *mat_src1, int src1_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global int  *src0_y = (__global int *)((__global uchar *)mat_src0 + y * src0_step);
__global int  *src1_y = (__global int *)((__global uchar *)mat_src1 + y * src1_step);
__global int2  *dst_y  = (__global int2 *)((__global uchar *)mat_dst  + y * dst_step);
int value1 = src0_y[x];
int value2 = src1_y[x];
int2 value;
value.even = value1;
value.odd = value2;
dst_y[x] = value;
}
}
__kernel void merge_vector_C2_D5_1(int rows, int cols,
__global float *mat_dst,  int dst_step,
__global float *mat_src0, int src0_step,
__global float *mat_src1, int src1_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global float  *src0_y = (__global float *)((__global uchar *)mat_src0 + y * src0_step);
__global float  *src1_y = (__global float *)((__global uchar *)mat_src1 + y * src1_step);
__global float2  *dst_y  = (__global float2 *)((__global uchar *)mat_dst  + y * dst_step);
float value1 = src0_y[x];
float value2 = src1_y[x];
dst_y[x] = (float2)(value1, value2);
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void merge_vector_C2_D6_1(int rows, int cols,
__global double *mat_dst,  int dst_step,
__global double *mat_src0, int src0_step,
__global double *mat_src1, int src1_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global double  *src0_y = (__global double *)((__global uchar *)mat_src0 + y * src0_step);
__global double  *src1_y = (__global double *)((__global uchar *)mat_src1 + y * src1_step);
__global double2 *dst_y  = (__global double2 *)((__global uchar *)mat_dst  + y * dst_step);
double value1 = src0_y[x];
double value2 = src1_y[x];
dst_y[x] = (double2)(value1, value2);
}
}
#endif
__kernel void merge_vector_C3_D0_1(int rows, int cols,
__global uchar *mat_dst,  int dst_step,
__global uchar *mat_src0, int src0_step,
__global uchar *mat_src1, int src1_step,
__global uchar *mat_src2, int src2_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global uchar4  *src0_y = (__global uchar4 * )(mat_src0 + y * src0_step);
__global uchar4  *src1_y = (__global uchar4 * )(mat_src1 + y * src1_step);
__global uchar4  *src2_y = (__global uchar4 * )(mat_src2 + y * src0_step);
__global uchar4 *dst_y  = (__global uchar4 *)(mat_dst  + y * dst_step);
uchar4 value0 = src0_y[x];
uchar4 value1 = src1_y[x];
uchar4 value2 = src2_y[x];
dst_y[3 * x + 0] = (uchar4)(value0.s0, value1.s0, value2.s0,
value0.s1);
dst_y[3 * x + 1] = (uchar4)(value1.s1, value2.s1,
value0.s2, value1.s2);
dst_y[3 * x + 2] = (uchar4)(value2.s2,
value0.s3, value1.s3, value2.s3);
}
}
__kernel void merge_vector_C3_D1_1(int rows, int cols,
__global char *mat_dst,  int dst_step,
__global char *mat_src0, int src0_step,
__global char *mat_src1, int src1_step,
__global char *mat_src2, int src2_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global char4  *src0_y = (__global char4 * )(mat_src0 + y * src0_step);
__global char4  *src1_y = (__global char4 * )(mat_src1 + y * src1_step);
__global char4  *src2_y = (__global char4 * )(mat_src2 + y * src0_step);
__global char4 *dst_y  = (__global char4 *)(mat_dst  + y * dst_step);
char4 value0 = src0_y[x];
char4 value1 = src1_y[x];
char4 value2 = src2_y[x];
dst_y[3 * x + 0] = (char4)(value0.s0, value1.s0, value2.s0,
value0.s1);
dst_y[3 * x + 1] = (char4)(value1.s1, value2.s1,
value0.s2, value1.s2);
dst_y[3 * x + 2] = (char4)(value2.s2,
value0.s3, value1.s3, value2.s3);
}
}
__kernel void merge_vector_C3_D2_1(int rows, int cols,
__global ushort *mat_dst,  int dst_step,
__global ushort *mat_src0, int src0_step,
__global ushort *mat_src1, int src1_step,
__global ushort *mat_src2, int src2_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global ushort2  *src0_y = (__global ushort2 * )((__global char *)mat_src0 + y * src0_step);
__global ushort2  *src1_y = (__global ushort2 * )((__global char *)mat_src1 + y * src1_step);
__global ushort2  *src2_y = (__global ushort2 * )((__global char *)mat_src2 + y * src0_step);
__global ushort2 *dst_y  = (__global ushort2 *)((__global char *)mat_dst  + y * dst_step);
ushort2 value0 = src0_y[x];
ushort2 value1 = src1_y[x];
ushort2 value2 = src2_y[x];
dst_y[3 * x + 0] = (ushort2)(value0.x, value1.x);
dst_y[3 * x + 1] = (ushort2)(value2.x, value0.y);
dst_y[3 * x + 2] = (ushort2)(value1.y, value2.y);
}
}
__kernel void merge_vector_C3_D3_1(int rows, int cols,
__global short *mat_dst,  int dst_step,
__global short *mat_src0, int src0_step,
__global short *mat_src1, int src1_step,
__global short *mat_src2, int src2_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global short2  *src0_y = (__global short2 * )((__global char *)mat_src0 + y * src0_step);
__global short2  *src1_y = (__global short2 * )((__global char *)mat_src1 + y * src1_step);
__global short2  *src2_y = (__global short2 * )((__global char *)mat_src2 + y * src0_step);
__global short2 *dst_y  = (__global short2 *)((__global char *)mat_dst  + y * dst_step);
short2 value0 = src0_y[x];
short2 value1 = src1_y[x];
short2 value2 = src2_y[x];
dst_y[3 * x + 0] = (short2)(value0.x, value1.x);
dst_y[3 * x + 1] = (short2)(value2.x, value0.y);
dst_y[3 * x + 2] = (short2)(value1.y, value2.y);
}
}
__kernel void merge_vector_C3_D4_1(int rows, int cols,
__global int *mat_dst,  int dst_step,
__global int *mat_src0, int src0_step,
__global int *mat_src1, int src1_step,
__global int *mat_src2, int src2_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global int  *src0_y = (__global int * )((__global char *)mat_src0 + y * src0_step);
__global int  *src1_y = (__global int * )((__global char *)mat_src1 + y * src1_step);
__global int  *src2_y = (__global int * )((__global char *)mat_src2 + y * src0_step);
__global int *dst_y  = (__global int *)((__global char *)mat_dst  + y * dst_step);
int value0 = src0_y[x];
int value1 = src1_y[x];
int value2 = src2_y[x];
dst_y[3 * x + 0] = value0;
dst_y[3 * x + 1] = value1;
dst_y[3 * x + 2] = value2;
}
}
__kernel void merge_vector_C3_D5_1(int rows, int cols,
__global float *mat_dst,  int dst_step,
__global float *mat_src0, int src0_step,
__global float *mat_src1, int src1_step,
__global float *mat_src2, int src2_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global float  *src0_y = (__global float * )((__global char *)mat_src0 + y * src0_step);
__global float  *src1_y = (__global float * )((__global char *)mat_src1 + y * src1_step);
__global float  *src2_y = (__global float * )((__global char *)mat_src2 + y * src0_step);
__global float *dst_y  = (__global float *)((__global char *)mat_dst  + y * dst_step);
float value0 = src0_y[x];
float value1 = src1_y[x];
float value2 = src2_y[x];
dst_y[3 * x + 0] = value0;
dst_y[3 * x + 1] = value1;
dst_y[3 * x + 2] = value2;
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void merge_vector_C3_D6_1(int rows, int cols,
__global double *mat_dst,  int dst_step,
__global double *mat_src0, int src0_step,
__global double *mat_src1, int src1_step,
__global double *mat_src2, int src2_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global double  *src0_y = (__global double * )((__global char *)mat_src0 + y * src0_step);
__global double  *src1_y = (__global double * )((__global char *)mat_src1 + y * src1_step);
__global double  *src2_y = (__global double * )((__global char *)mat_src2 + y * src0_step);
__global double *dst_y  = (__global double *)((__global char *)mat_dst  + y * dst_step);
double value0 = src0_y[x];
double value1 = src1_y[x];
double value2 = src2_y[x];
dst_y[3 * x + 0] = value0;
dst_y[3 * x + 1] = value1;
dst_y[3 * x + 2] = value2;
}
}
#endif
__kernel void merge_vector_C4_D0_1(int rows, int cols,
__global uchar *mat_dst,  int dst_step,
__global uchar *mat_src0, int src0_step,
__global uchar *mat_src1, int src1_step,
__global uchar *mat_src2, int src2_step,
__global uchar *mat_src3, int src3_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global uchar4  *src0_y = (__global uchar4 * )(mat_src0 + y * src0_step);
__global uchar4  *src1_y = (__global uchar4 * )(mat_src1 + y * src1_step);
__global uchar4  *src2_y = (__global uchar4 * )(mat_src2 + y * src0_step);
__global uchar4  *src3_y = (__global uchar4 * )(mat_src3 + y * src1_step);
__global uchar16 *dst_y  = (__global uchar16 *)(mat_dst  + y * dst_step);
uchar4 value0 = src0_y[x];
uchar4 value1 = src1_y[x];
uchar4 value2 = src2_y[x];
uchar4 value3 = src3_y[x];
dst_y[x] = (uchar16)(value0.x, value1.x, value2.x, value3.x,
value0.y, value1.y, value2.y, value3.y,
value0.z, value1.z, value2.z, value3.z,
value0.w, value1.w, value2.w, value3.w);
}
}
__kernel void merge_vector_C4_D1_1(int rows, int cols,
__global char *mat_dst,  int dst_step,
__global char *mat_src0, int src0_step,
__global char *mat_src1, int src1_step,
__global char *mat_src2, int src2_step,
__global char *mat_src3, int src3_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global char4  *src0_y = (__global char4 * )(mat_src0 + y * src0_step);
__global char4  *src1_y = (__global char4 * )(mat_src1 + y * src1_step);
__global char4  *src2_y = (__global char4 * )(mat_src2 + y * src0_step);
__global char4  *src3_y = (__global char4 * )(mat_src3 + y * src1_step);
__global char16 *dst_y  = (__global char16 *)(mat_dst  + y * dst_step);
char4 value0 = src0_y[x];
char4 value1 = src1_y[x];
char4 value2 = src2_y[x];
char4 value3 = src3_y[x];
dst_y[x] = (char16)(value0.x, value1.x, value2.x, value3.x,
value0.y, value1.y, value2.y, value3.y,
value0.z, value1.z, value2.z, value3.z,
value0.w, value1.w, value2.w, value3.w);
}
}
__kernel void merge_vector_C4_D2_1(int rows, int cols,
__global ushort *mat_dst,  int dst_step,
__global ushort *mat_src0, int src0_step,
__global ushort *mat_src1, int src1_step,
__global ushort *mat_src2, int src2_step,
__global ushort *mat_src3, int src3_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global ushort2  *src0_y = (__global ushort2 * )((__global uchar*)mat_src0 + y * src0_step);
__global ushort2  *src1_y = (__global ushort2 * )((__global uchar*)mat_src1 + y * src1_step);
__global ushort2  *src2_y = (__global ushort2 * )((__global uchar*)mat_src2 + y * src0_step);
__global ushort2  *src3_y = (__global ushort2 * )((__global uchar*)mat_src3 + y * src1_step);
__global ushort8 *dst_y  = (__global ushort8 *)((__global uchar*)mat_dst  + y * dst_step);
ushort2 value0 = src0_y[x];
ushort2 value1 = src1_y[x];
ushort2 value2 = src2_y[x];
ushort2 value3 = src3_y[x];
dst_y[x] = (ushort8)(value0.x, value1.x, value2.x, value3.x,
value0.y, value1.y, value2.y, value3.y);
}
}
__kernel void merge_vector_C4_D3_1(int rows, int cols,
__global short *mat_dst,  int dst_step,
__global short *mat_src0, int src0_step,
__global short *mat_src1, int src1_step,
__global short *mat_src2, int src2_step,
__global short *mat_src3, int src3_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global short2  *src0_y = (__global short2 * )((__global uchar*)mat_src0 + y * src0_step);
__global short2  *src1_y = (__global short2 * )((__global uchar*)mat_src1 + y * src1_step);
__global short2  *src2_y = (__global short2 * )((__global uchar*)mat_src2 + y * src0_step);
__global short2  *src3_y = (__global short2 * )((__global uchar*)mat_src3 + y * src1_step);
__global short8 *dst_y  = (__global short8 *)((__global uchar*)mat_dst  + y * dst_step);
short2 value0 = src0_y[x];
short2 value1 = src1_y[x];
short2 value2 = src2_y[x];
short2 value3 = src3_y[x];
dst_y[x] = (short8)(value0.x, value1.x, value2.x, value3.x,
value0.y, value1.y, value2.y, value3.y);
}
}
__kernel void merge_vector_C4_D4_1(int rows, int cols,
__global int *mat_dst,  int dst_step,
__global int *mat_src0, int src0_step,
__global int *mat_src1, int src1_step,
__global int *mat_src2, int src2_step,
__global int *mat_src3, int src3_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global int *src0_y = (__global int * )((__global uchar*)mat_src0 + y * src0_step);
__global int *src1_y = (__global int * )((__global uchar*)mat_src1 + y * src1_step);
__global int *src2_y = (__global int * )((__global uchar*)mat_src2 + y * src0_step);
__global int *src3_y = (__global int * )((__global uchar*)mat_src3 + y * src1_step);
__global int4 *dst_y  = (__global int4 *)((__global uchar*)mat_dst  + y * dst_step);
int value0 = src0_y[x];
int value1 = src1_y[x];
int value2 = src2_y[x];
int value3 = src3_y[x];
dst_y[x] = (int4)(value0, value1, value2, value3);
}
}
__kernel void merge_vector_C4_D5_1(int rows, int cols,
__global float *mat_dst,  int dst_step,
__global float *mat_src0, int src0_step,
__global float *mat_src1, int src1_step,
__global float *mat_src2, int src2_step,
__global float *mat_src3, int src3_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global float *src0_y = (__global float * )((__global uchar*)mat_src0 + y * src0_step);
__global float *src1_y = (__global float * )((__global uchar*)mat_src1 + y * src1_step);
__global float *src2_y = (__global float * )((__global uchar*)mat_src2 + y * src0_step);
__global float *src3_y = (__global float * )((__global uchar*)mat_src3 + y * src1_step);
__global float4 *dst_y  = (__global float4 *)((__global uchar*)mat_dst  + y * dst_step);
float value0 = src0_y[x];
float value1 = src1_y[x];
float value2 = src2_y[x];
float value3 = src3_y[x];
dst_y[x] = (float4)(value0, value1, value2, value3);
}
}
#if defined (DOUBLE_SUPPORT)
__kernel void merge_vector_C4_D6_1(int rows, int cols,
__global double *mat_dst,  int dst_step,
__global double *mat_src0, int src0_step,
__global double *mat_src1, int src1_step,
__global double *mat_src2, int src2_step,
__global double *mat_src3, int src3_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ((x < cols) && (y < rows))
{
__global double *src0_y = (__global double * )((__global uchar*)mat_src0 + y * src0_step);
__global double *src1_y = (__global double * )((__global uchar*)mat_src1 + y * src1_step);
__global double *src2_y = (__global double * )((__global uchar*)mat_src2 + y * src0_step);
__global double *src3_y = (__global double * )((__global uchar*)mat_src3 + y * src1_step);
__global double4 *dst_y  = (__global double4 *)((__global uchar*)mat_dst  + y * dst_step);
double value0 = src0_y[x];
double value1 = src1_y[x];
double value2 = src2_y[x];
double value3 = src3_y[x];
dst_y[x] = (double4)(value0, value1, value2, value3);
}
}
#endif
131c0e27e47b2163871bfd35ae8f659bmoments#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
typedef double T;
#else
typedef long T;
#endif
#define DST_ROW_00     0
#define DST_ROW_10     1
#define DST_ROW_01     2
#define DST_ROW_20     3
#define DST_ROW_11     4
#define DST_ROW_02     5
#define DST_ROW_30     6
#define DST_ROW_21     7
#define DST_ROW_12     8
#define DST_ROW_03     9
__kernel void icvContourMoments(int contour_total,
__global float* reader_oclmat_data,
__global T* dst_a,
int dst_step)
{
T xi_1, yi_1, xi_12, yi_12, xi, yi, xi2, yi2, dxy, xii_1, yii_1;
int idx = get_global_id(0);
if (idx < 0 || idx >= contour_total)
return;
xi_1 = (T)(*(reader_oclmat_data + (get_global_id(0) << 1)));
yi_1 = (T)(*(reader_oclmat_data + (get_global_id(0) << 1) + 1));
xi_12 = xi_1 * xi_1;
yi_12 = yi_1 * yi_1;
if(idx == contour_total - 1)
{
xi = (T)(*(reader_oclmat_data));
yi = (T)(*(reader_oclmat_data + 1));
}
else
{
xi = (T)(*(reader_oclmat_data + (idx + 1) * 2));
yi = (T)(*(reader_oclmat_data + (idx + 1) * 2 + 1));
}
xi2 = xi * xi;
yi2 = yi * yi;
dxy = xi_1 * yi - xi * yi_1;
xii_1 = xi_1 + xi;
yii_1 = yi_1 + yi;
dst_step /= sizeof(T);
*( dst_a + DST_ROW_00 * dst_step + idx) = dxy;
*( dst_a + DST_ROW_10 * dst_step + idx) = dxy * xii_1;
*( dst_a + DST_ROW_01 * dst_step + idx) = dxy * yii_1;
*( dst_a + DST_ROW_20 * dst_step + idx) = dxy * (xi_1 * xii_1 + xi2);
*( dst_a + DST_ROW_11 * dst_step + idx) = dxy * (xi_1 * (yii_1 + yi_1) + xi * (yii_1 + yi));
*( dst_a + DST_ROW_02 * dst_step + idx) = dxy * (yi_1 * yii_1 + yi2);
*( dst_a + DST_ROW_30 * dst_step + idx) = dxy * xii_1 * (xi_12 + xi2);
*( dst_a + DST_ROW_03 * dst_step + idx) = dxy * yii_1 * (yi_12 + yi2);
*( dst_a + DST_ROW_21 * dst_step + idx) =
dxy * (xi_12 * (3 * yi_1 + yi) + 2 * xi * xi_1 * yii_1 +
xi2 * (yi_1 + 3 * yi));
*( dst_a + DST_ROW_12 * dst_step + idx) =
dxy * (yi_12 * (3 * xi_1 + xi) + 2 * yi * yi_1 * xii_1 +
yi2 * (xi_1 + 3 * xi));
}
#if defined (DOUBLE_SUPPORT)
#define WT double
#define WT4 double4
#define convert_T4 convert_double4
#define convert_T convert_double
#else
#define WT float
#define WT4 float4
#define convert_T4 convert_float4
#define convert_T convert_float
#endif
#ifdef CV_8UC1
#define TT uchar
#elif defined CV_16UC1
#define TT ushort
#elif defined CV_16SC1
#define TT short
#elif defined CV_32FC1
#define TT float
#elif defined CV_64FC1
#ifdef DOUBLE_SUPPORT
#define TT double
#else
#define TT float
#endif
#endif
__kernel void CvMoments(__global TT* src_data, int src_rows, int src_cols, int src_step,
__global WT* dst_m,
int dst_cols, int dst_step, int binary)
{
int dy = get_global_id(1);
int ly = get_local_id(1);
int gidx = get_group_id(0);
int gidy = get_group_id(1);
int x_rest = src_cols % 256;
int y_rest = src_rows % 256;
__local int codxy[256];
codxy[ly] = ly;
barrier(CLK_LOCAL_MEM_FENCE);
WT4 x0 = (WT4)(0.f);
WT4 x1 = (WT4)(0.f);
WT4 x2 = (WT4)(0.f);
WT4 x3 = (WT4)(0.f);
__global TT* row = src_data + gidy * src_step + ly * src_step + gidx * 256;
WT4 p;
WT4 x;
WT4 xp;
WT4 xxp;
WT py = 0.f, sy = 0.f;
if(dy < src_rows)
{
if((x_rest > 0) && (gidx == ((int)get_num_groups(0) - 1)))
{
int i;
for(i = 0; i < x_rest - 4; i += 4)
{
p = convert_T4(vload4(0, row + i));
x = convert_T4(vload4(0, codxy + i));
xp = x * p;
xxp = xp * x;
x0 += p;
x1 += xp;
x2 += xxp;
x3 += convert_T4(xxp * x);
}
x0.s0 = x0.s0 + x0.s1 + x0.s2 + x0.s3;
x1.s0 = x1.s0 + x1.s1 + x1.s2 + x1.s3;
x2.s0 = x2.s0 + x2.s1 + x2.s2 + x2.s3;
x3.s0 = x3.s0 + x3.s1 + x3.s2 + x3.s3;
WT x0_ = 0;
WT x1_ = 0;
WT x2_ = 0;
WT x3_ = 0;
for(; i < x_rest; i++)
{
WT p_ = 0;
p_ = row[i];
WT x_ = convert_T(codxy[i]);
WT xp_ = x_ * p_;
WT xxp_ = xp_ * x_;
x0_ += p_;
x1_ += xp_;
x2_ += xxp_;
x3_ += xxp_ * x_;
}
x0.s0 += x0_;
x1.s0 += x1_;
x2.s0 += x2_;
x3.s0 += x3_;
}else
{
for(int i = 0; i < 256; i += 4)
{
p = convert_T4(vload4(0, row + i));
x = convert_T4(vload4(0, codxy + i));
xp = x * p;
xxp = xp * x;
x0 += p;
x1 += xp;
x2 += xxp;
x3 += convert_T4(xxp * x);
}
x0.s0 = x0.s0 + x0.s1 + x0.s2 + x0.s3;
x1.s0 = x1.s0 + x1.s1 + x1.s2 + x1.s3;
x2.s0 = x2.s0 + x2.s1 + x2.s2 + x2.s3;
x3.s0 = x3.s0 + x3.s1 + x3.s2 + x3.s3;
}
py = ly * x0.s0;
sy = ly * ly;
}
__local WT mom[10][256];
if((y_rest > 0) && (gidy == ((int)get_num_groups(1) - 1)))
{
if(ly < y_rest)
{
mom[9][ly] = py * sy;
mom[8][ly] = x1.s0 * sy;
mom[7][ly] = x2.s0 * ly;
mom[6][ly] = x3.s0;
mom[5][ly] = x0.s0 * sy;
mom[4][ly] = x1.s0 * ly;
mom[3][ly] = x2.s0;
mom[2][ly] = py;
mom[1][ly] = x1.s0;
mom[0][ly] = x0.s0;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 10)
for(int i = 1; i < y_rest; i++)
mom[ly][0] = mom[ly][i] + mom[ly][0];
}
else
{
mom[9][ly] = py * sy;
mom[8][ly] = x1.s0 * sy;
mom[7][ly] = x2.s0 * ly;
mom[6][ly] = x3.s0;
mom[5][ly] = x0.s0 * sy;
mom[4][ly] = x1.s0 * ly;
mom[3][ly] = x2.s0;
mom[2][ly] = py;
mom[1][ly] = x1.s0;
mom[0][ly] = x0.s0;
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 128)
{
mom[0][ly] = mom[0][ly] + mom[0][ly + 128];
mom[1][ly] = mom[1][ly] + mom[1][ly + 128];
mom[2][ly] = mom[2][ly] + mom[2][ly + 128];
mom[3][ly] = mom[3][ly] + mom[3][ly + 128];
mom[4][ly] = mom[4][ly] + mom[4][ly + 128];
mom[5][ly] = mom[5][ly] + mom[5][ly + 128];
mom[6][ly] = mom[6][ly] + mom[6][ly + 128];
mom[7][ly] = mom[7][ly] + mom[7][ly + 128];
mom[8][ly] = mom[8][ly] + mom[8][ly + 128];
mom[9][ly] = mom[9][ly] + mom[9][ly + 128];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 64)
{
mom[0][ly] = mom[0][ly] + mom[0][ly + 64];
mom[1][ly] = mom[1][ly] + mom[1][ly + 64];
mom[2][ly] = mom[2][ly] + mom[2][ly + 64];
mom[3][ly] = mom[3][ly] + mom[3][ly + 64];
mom[4][ly] = mom[4][ly] + mom[4][ly + 64];
mom[5][ly] = mom[5][ly] + mom[5][ly + 64];
mom[6][ly] = mom[6][ly] + mom[6][ly + 64];
mom[7][ly] = mom[7][ly] + mom[7][ly + 64];
mom[8][ly] = mom[8][ly] + mom[8][ly + 64];
mom[9][ly] = mom[9][ly] + mom[9][ly + 64];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 32)
{
mom[0][ly] = mom[0][ly] + mom[0][ly + 32];
mom[1][ly] = mom[1][ly] + mom[1][ly + 32];
mom[2][ly] = mom[2][ly] + mom[2][ly + 32];
mom[3][ly] = mom[3][ly] + mom[3][ly + 32];
mom[4][ly] = mom[4][ly] + mom[4][ly + 32];
mom[5][ly] = mom[5][ly] + mom[5][ly + 32];
mom[6][ly] = mom[6][ly] + mom[6][ly + 32];
mom[7][ly] = mom[7][ly] + mom[7][ly + 32];
mom[8][ly] = mom[8][ly] + mom[8][ly + 32];
mom[9][ly] = mom[9][ly] + mom[9][ly + 32];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 16)
{
mom[0][ly] = mom[0][ly] + mom[0][ly + 16];
mom[1][ly] = mom[1][ly] + mom[1][ly + 16];
mom[2][ly] = mom[2][ly] + mom[2][ly + 16];
mom[3][ly] = mom[3][ly] + mom[3][ly + 16];
mom[4][ly] = mom[4][ly] + mom[4][ly + 16];
mom[5][ly] = mom[5][ly] + mom[5][ly + 16];
mom[6][ly] = mom[6][ly] + mom[6][ly + 16];
mom[7][ly] = mom[7][ly] + mom[7][ly + 16];
mom[8][ly] = mom[8][ly] + mom[8][ly + 16];
mom[9][ly] = mom[9][ly] + mom[9][ly + 16];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 8)
{
mom[0][ly] = mom[0][ly] + mom[0][ly + 8];
mom[1][ly] = mom[1][ly] + mom[1][ly + 8];
mom[2][ly] = mom[2][ly] + mom[2][ly + 8];
mom[3][ly] = mom[3][ly] + mom[3][ly + 8];
mom[4][ly] = mom[4][ly] + mom[4][ly + 8];
mom[5][ly] = mom[5][ly] + mom[5][ly + 8];
mom[6][ly] = mom[6][ly] + mom[6][ly + 8];
mom[7][ly] = mom[7][ly] + mom[7][ly + 8];
mom[8][ly] = mom[8][ly] + mom[8][ly + 8];
mom[9][ly] = mom[9][ly] + mom[9][ly + 8];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 4)
{
mom[0][ly] = mom[0][ly] + mom[0][ly + 4];
mom[1][ly] = mom[1][ly] + mom[1][ly + 4];
mom[2][ly] = mom[2][ly] + mom[2][ly + 4];
mom[3][ly] = mom[3][ly] + mom[3][ly + 4];
mom[4][ly] = mom[4][ly] + mom[4][ly + 4];
mom[5][ly] = mom[5][ly] + mom[5][ly + 4];
mom[6][ly] = mom[6][ly] + mom[6][ly + 4];
mom[7][ly] = mom[7][ly] + mom[7][ly + 4];
mom[8][ly] = mom[8][ly] + mom[8][ly + 4];
mom[9][ly] = mom[9][ly] + mom[9][ly + 4];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 2)
{
mom[0][ly] = mom[0][ly] + mom[0][ly + 2];
mom[1][ly] = mom[1][ly] + mom[1][ly + 2];
mom[2][ly] = mom[2][ly] + mom[2][ly + 2];
mom[3][ly] = mom[3][ly] + mom[3][ly + 2];
mom[4][ly] = mom[4][ly] + mom[4][ly + 2];
mom[5][ly] = mom[5][ly] + mom[5][ly + 2];
mom[6][ly] = mom[6][ly] + mom[6][ly + 2];
mom[7][ly] = mom[7][ly] + mom[7][ly + 2];
mom[8][ly] = mom[8][ly] + mom[8][ly + 2];
mom[9][ly] = mom[9][ly] + mom[9][ly + 2];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 1)
{
mom[0][ly] = mom[0][ly] + mom[0][ly + 1];
mom[1][ly] = mom[1][ly] + mom[1][ly + 1];
mom[2][ly] = mom[2][ly] + mom[2][ly + 1];
mom[3][ly] = mom[3][ly] + mom[3][ly + 1];
mom[4][ly] = mom[4][ly] + mom[4][ly + 1];
mom[5][ly] = mom[5][ly] + mom[5][ly + 1];
mom[6][ly] = mom[6][ly] + mom[6][ly + 1];
mom[7][ly] = mom[7][ly] + mom[7][ly + 1];
mom[8][ly] = mom[8][ly] + mom[8][ly + 1];
mom[9][ly] = mom[9][ly] + mom[9][ly + 1];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(binary)
{
WT s = 1.0f/255;
if(ly < 10)
mom[ly][0] *= s;
barrier(CLK_LOCAL_MEM_FENCE);
}
WT xm = (gidx * 256) * mom[0][0];
WT ym = (gidy * 256) * mom[0][0];
if(ly == 0)
{
mom[0][1] = mom[0][0];
mom[1][1] = mom[1][0] + xm;
mom[2][1] = mom[2][0] + ym;
mom[3][1] = mom[3][0] + gidx * 256 * (mom[1][0] * 2 + xm);
mom[4][1] = mom[4][0] + gidx * 256 * (mom[2][0] + ym) + gidy * 256 * mom[1][0];
mom[5][1] = mom[5][0] + gidy * 256 * (mom[2][0] * 2 + ym);
mom[6][1] = mom[6][0] + gidx * 256 * (3 * mom[3][0] + 256 * gidx * (3 * mom[1][0] + xm));
mom[7][1] = mom[7][0] + gidx * 256 * (2 * (mom[4][0] + 256 * gidy * mom[1][0]) + 256 * gidx * (mom[2][0] + ym)) + 256 * gidy * mom[3][0];
mom[8][1] = mom[8][0] + gidy * 256 * (2 * (mom[4][0] + 256 * gidx * mom[2][0]) + 256 * gidy * (mom[1][0] + xm)) + 256 * gidx * mom[5][0];
mom[9][1] = mom[9][0] + gidy * 256 * (3 * mom[5][0] + 256 * gidy * (3 * mom[2][0] + ym));
}
barrier(CLK_LOCAL_MEM_FENCE);
if(ly < 10)
dst_m[10 * gidy * dst_step + ly * dst_step + gidx] = mom[ly][1];
}
9145a57effb2e86f802a70722027b627objdetect_hog#define CELL_WIDTH 8
#define CELL_HEIGHT 8
#define CELLS_PER_BLOCK_X 2
#define CELLS_PER_BLOCK_Y 2
#define NTHREADS 256
#define CV_PI_F 3.1415926535897932384626433832795f
#ifdef INTEL_DEVICE
#define QANGLE_TYPE    int
#define QANGLE_TYPE2  int2
#else
#define QANGLE_TYPE    uchar
#define QANGLE_TYPE2  uchar2
#endif
__kernel void compute_hists_lut_kernel(
const int cblock_stride_x, const int cblock_stride_y,
const int cnbins, const int cblock_hist_size, const int img_block_width,
const int blocks_in_group, const int blocks_total,
const int grad_quadstep, const int qangle_step,
__global const float* grad, __global const QANGLE_TYPE* qangle,
__global const float* gauss_w_lut,
__global float* block_hists, __local float* smem)
{
const int lx = get_local_id(0);
const int lp = lx / 24; 
const int gid = get_group_id(0) * blocks_in_group + lp;
const int gidY = gid / img_block_width;
const int gidX = gid - gidY * img_block_width;
const int lidX = lx - lp * 24;
const int lidY = get_local_id(1);
const int cell_x = lidX / 12;
const int cell_y = lidY;
const int cell_thread_x = lidX - cell_x * 12;
__local float* hists = smem + lp * cnbins * (CELLS_PER_BLOCK_X *
CELLS_PER_BLOCK_Y * 12 + CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y);
__local float* final_hist = hists + cnbins *
(CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12);
const int offset_x = gidX * cblock_stride_x + (cell_x << 2) + cell_thread_x;
const int offset_y = gidY * cblock_stride_y + (cell_y << 2);
__global const float* grad_ptr = (gid < blocks_total) ?
grad + offset_y * grad_quadstep + (offset_x << 1) : grad;
__global const QANGLE_TYPE* qangle_ptr = (gid < blocks_total) ?
qangle + offset_y * qangle_step + (offset_x << 1) : qangle;
__local float* hist = hists + 12 * (cell_y * CELLS_PER_BLOCK_Y + cell_x) +
cell_thread_x;
for (int bin_id = 0; bin_id < cnbins; ++bin_id)
hist[bin_id * 48] = 0.f;
const int dist_x = -4 + cell_thread_x - 4 * cell_x;
const int dist_center_x = dist_x - 4 * (1 - 2 * cell_x);
const int dist_y_begin = -4 - 4 * lidY;
for (int dist_y = dist_y_begin; dist_y < dist_y_begin + 12; ++dist_y)
{
float2 vote = (float2) (grad_ptr[0], grad_ptr[1]);
QANGLE_TYPE2 bin = (QANGLE_TYPE2) (qangle_ptr[0], qangle_ptr[1]);
grad_ptr += grad_quadstep;
qangle_ptr += qangle_step;
int dist_center_y = dist_y - 4 * (1 - 2 * cell_y);
int idx = (dist_center_y + 8) * 16 + (dist_center_x + 8);
float gaussian = gauss_w_lut[idx];
idx = (dist_y + 8) * 16 + (dist_x + 8);
float interp_weight = gauss_w_lut[256+idx];
hist[bin.x * 48] += gaussian * interp_weight * vote.x;
hist[bin.y * 48] += gaussian * interp_weight * vote.y;
}
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local float* hist_ = hist;
for (int bin_id = 0; bin_id < cnbins; ++bin_id, hist_ += 48)
{
if (cell_thread_x < 6)
hist_[0] += hist_[6];
barrier(CLK_LOCAL_MEM_FENCE);
if (cell_thread_x < 3)
hist_[0] += hist_[3];
#ifdef CPU
barrier(CLK_LOCAL_MEM_FENCE);
#endif
if (cell_thread_x == 0)
final_hist[(cell_x * 2 + cell_y) * cnbins + bin_id] =
hist_[0] + hist_[1] + hist_[2];
}
#ifdef CPU
barrier(CLK_LOCAL_MEM_FENCE);
#endif
int tid = (cell_y * CELLS_PER_BLOCK_Y + cell_x) * 12 + cell_thread_x;
if ((tid < cblock_hist_size) && (gid < blocks_total))
{
__global float* block_hist = block_hists +
(gidY * img_block_width + gidX) * cblock_hist_size;
block_hist[tid] = final_hist[tid];
}
}
__kernel void normalize_hists_36_kernel(__global float* block_hists,
const float threshold, __local float *squares)
{
const int tid = get_local_id(0);
const int gid = get_global_id(0);
const int bid = tid / 36;      
const int boffset = bid * 36;  
const int hid = tid - boffset; 
float elem = block_hists[gid];
squares[tid] = elem * elem;
barrier(CLK_LOCAL_MEM_FENCE);
__local float* smem = squares + boffset;
float sum = smem[hid];
if (hid < 18)
smem[hid] = sum = sum + smem[hid + 18];
barrier(CLK_LOCAL_MEM_FENCE);
if (hid < 9)
smem[hid] = sum = sum + smem[hid + 9];
barrier(CLK_LOCAL_MEM_FENCE);
if (hid < 4)
smem[hid] = sum + smem[hid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
sum = smem[0] + smem[1] + smem[2] + smem[3] + smem[8];
elem = elem / (sqrt(sum) + 3.6f);
elem = min(elem, threshold);
barrier(CLK_LOCAL_MEM_FENCE);
squares[tid] = elem * elem;
barrier(CLK_LOCAL_MEM_FENCE);
sum = smem[hid];
if (hid < 18)
smem[hid] = sum = sum + smem[hid + 18];
barrier(CLK_LOCAL_MEM_FENCE);
if (hid < 9)
smem[hid] = sum = sum + smem[hid + 9];
barrier(CLK_LOCAL_MEM_FENCE);
if (hid < 4)
smem[hid] = sum + smem[hid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
sum = smem[0] + smem[1] + smem[2] + smem[3] + smem[8];
block_hists[gid] = elem / (sqrt(sum) + 1e-3f);
}
float reduce_smem(volatile __local float* smem, int size)
{
unsigned int tid = get_local_id(0);
float sum = smem[tid];
if (size >= 512) { if (tid < 256) smem[tid] = sum = sum + smem[tid + 256];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 256) { if (tid < 128) smem[tid] = sum = sum + smem[tid + 128];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 128) { if (tid < 64) smem[tid] = sum = sum + smem[tid + 64];
barrier(CLK_LOCAL_MEM_FENCE); }
#ifdef CPU
if (size >= 64) { if (tid < 32) smem[tid] = sum = sum + smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 32) { if (tid < 16) smem[tid] = sum = sum + smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 16) { if (tid < 8) smem[tid] = sum = sum + smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 8) { if (tid < 4) smem[tid] = sum = sum + smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 4) { if (tid < 2) smem[tid] = sum = sum + smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE); }
if (size >= 2) { if (tid < 1) smem[tid] = sum = sum + smem[tid + 1];
barrier(CLK_LOCAL_MEM_FENCE); }
#else
if (tid < 32)
{
if (size >= 64) smem[tid] = sum = sum + smem[tid + 32];
#if WAVE_SIZE < 32
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) {
#endif
if (size >= 32) smem[tid] = sum = sum + smem[tid + 16];
if (size >= 16) smem[tid] = sum = sum + smem[tid + 8];
if (size >= 8) smem[tid] = sum = sum + smem[tid + 4];
if (size >= 4) smem[tid] = sum = sum + smem[tid + 2];
if (size >= 2) smem[tid] = sum = sum + smem[tid + 1];
}
#endif
return sum;
}
__kernel void normalize_hists_kernel(
const int nthreads, const int block_hist_size, const int img_block_width,
__global float* block_hists, const float threshold, __local float *squares)
{
const int tid = get_local_id(0);
const int gidX = get_group_id(0);
const int gidY = get_group_id(1);
__global float* hist = block_hists + (gidY * img_block_width + gidX) *
block_hist_size + tid;
float elem = 0.f;
if (tid < block_hist_size)
elem = hist[0];
squares[tid] = elem * elem;
barrier(CLK_LOCAL_MEM_FENCE);
float sum = reduce_smem(squares, nthreads);
float scale = 1.0f / (sqrt(sum) + 0.1f * block_hist_size);
elem = min(elem * scale, threshold);
barrier(CLK_LOCAL_MEM_FENCE);
squares[tid] = elem * elem;
barrier(CLK_LOCAL_MEM_FENCE);
sum = reduce_smem(squares, nthreads);
scale = 1.0f / (sqrt(sum) + 1e-3f);
if (tid < block_hist_size)
hist[0] = elem * scale;
}
__kernel void classify_hists_180_kernel(
const int cdescr_width, const int cdescr_height, const int cblock_hist_size,
const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float * block_hists, __global const float* coefs,
float free_coef, float threshold, __global uchar* labels)
{
const int tid = get_local_id(0);
const int gidX = get_group_id(0);
const int gidY = get_group_id(1);
__global const float* hist = block_hists + (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
float product = 0.f;
for (int i = 0; i < cdescr_height; i++)
{
product += coefs[i * cdescr_width + tid] *
hist[i * img_block_width * cblock_hist_size + tid];
}
__local float products[180];
products[tid] = product;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 90) products[tid] = product = product + products[tid + 90];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 45) products[tid] = product = product + products[tid + 45];
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local float* smem = products;
#ifdef CPU
if (tid < 13) smem[tid] = product = product + smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) smem[tid] = product = product + smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<8) smem[tid] = product = product + smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<4) smem[tid] = product = product + smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<2) smem[tid] = product = product + smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE);
#else
if (tid < 13)
{
smem[tid] = product = product + smem[tid + 32];
}
#if WAVE_SIZE < 32
barrier(CLK_LOCAL_MEM_FENCE);
#endif
if (tid < 16)
{
smem[tid] = product = product + smem[tid + 16];
smem[tid] = product = product + smem[tid + 8];
smem[tid] = product = product + smem[tid + 4];
smem[tid] = product = product + smem[tid + 2];
}
#endif
if (tid == 0){
product = product + smem[tid + 1];
labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
}
}
__kernel void classify_hists_252_kernel(
const int cdescr_width, const int cdescr_height, const int cblock_hist_size,
const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float * block_hists, __global const float* coefs,
float free_coef, float threshold, __global uchar* labels)
{
const int tid = get_local_id(0);
const int gidX = get_group_id(0);
const int gidY = get_group_id(1);
__global const float* hist = block_hists + (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
float product = 0.f;
if (tid < cdescr_width)
{
for (int i = 0; i < cdescr_height; i++)
product += coefs[i * cdescr_width + tid] *
hist[i * img_block_width * cblock_hist_size + tid];
}
__local float products[NTHREADS];
products[tid] = product;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 128) products[tid] = product = product + products[tid + 128];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 64) products[tid] = product = product + products[tid + 64];
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local float* smem = products;
#ifdef CPU
if(tid<32) smem[tid] = product = product + smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<16) smem[tid] = product = product + smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<8) smem[tid] = product = product + smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<4) smem[tid] = product = product + smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<2) smem[tid] = product = product + smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE);
#else
if (tid < 32)
{
smem[tid] = product = product + smem[tid + 32];
#if WAVE_SIZE < 32
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) {
#endif
smem[tid] = product = product + smem[tid + 16];
smem[tid] = product = product + smem[tid + 8];
smem[tid] = product = product + smem[tid + 4];
smem[tid] = product = product + smem[tid + 2];
}
#endif
if (tid == 0){
product = product + smem[tid + 1];
labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
}
}
__kernel void classify_hists_kernel(
const int cdescr_size, const int cdescr_width, const int cblock_hist_size,
const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float * block_hists, __global const float* coefs,
float free_coef, float threshold, __global uchar* labels)
{
const int tid = get_local_id(0);
const int gidX = get_group_id(0);
const int gidY = get_group_id(1);
__global const float* hist = block_hists + (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
float product = 0.f;
for (int i = tid; i < cdescr_size; i += NTHREADS)
{
int offset_y = i / cdescr_width;
int offset_x = i - offset_y * cdescr_width;
product += coefs[i] *
hist[offset_y * img_block_width * cblock_hist_size + offset_x];
}
__local float products[NTHREADS];
products[tid] = product;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 128) products[tid] = product = product + products[tid + 128];
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 64) products[tid] = product = product + products[tid + 64];
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local float* smem = products;
#ifdef CPU
if(tid<32) smem[tid] = product = product + smem[tid + 32];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<16) smem[tid] = product = product + smem[tid + 16];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<8) smem[tid] = product = product + smem[tid + 8];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<4) smem[tid] = product = product + smem[tid + 4];
barrier(CLK_LOCAL_MEM_FENCE);
if(tid<2) smem[tid] = product = product + smem[tid + 2];
barrier(CLK_LOCAL_MEM_FENCE);
#else
if (tid < 32)
{
smem[tid] = product = product + smem[tid + 32];
#if WAVE_SIZE < 32
} barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16) {
#endif
smem[tid] = product = product + smem[tid + 16];
smem[tid] = product = product + smem[tid + 8];
smem[tid] = product = product + smem[tid + 4];
smem[tid] = product = product + smem[tid + 2];
}
#endif
if (tid == 0){
smem[tid] = product = product + smem[tid + 1];
labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold);
}
}
__kernel void extract_descrs_by_rows_kernel(
const int cblock_hist_size, const int descriptors_quadstep,
const int cdescr_size, const int cdescr_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float* block_hists, __global float* descriptors)
{
int tid = get_local_id(0);
int gidX = get_group_id(0);
int gidY = get_group_id(1);
__global const float* hist = block_hists + (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
__global float* descriptor = descriptors +
(gidY * get_num_groups(0) + gidX) * descriptors_quadstep;
for (int i = tid; i < cdescr_size; i += NTHREADS)
{
int offset_y = i / cdescr_width;
int offset_x = i - offset_y * cdescr_width;
descriptor[i] = hist[offset_y * img_block_width * cblock_hist_size + offset_x];
}
}
__kernel void extract_descrs_by_cols_kernel(
const int cblock_hist_size, const int descriptors_quadstep, const int cdescr_size,
const int cnblocks_win_x, const int cnblocks_win_y, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
__global const float* block_hists, __global float* descriptors)
{
int tid = get_local_id(0);
int gidX = get_group_id(0);
int gidY = get_group_id(1);
__global const float* hist = block_hists +  (gidY * win_block_stride_y *
img_block_width + gidX * win_block_stride_x) * cblock_hist_size;
__global float* descriptor = descriptors +
(gidY * get_num_groups(0) + gidX) * descriptors_quadstep;
for (int i = tid; i < cdescr_size; i += NTHREADS)
{
int block_idx = i / cblock_hist_size;
int idx_in_block = i - block_idx * cblock_hist_size;
int y = block_idx / cnblocks_win_x;
int x = block_idx - y * cnblocks_win_x;
descriptor[(x * cnblocks_win_y + y) * cblock_hist_size + idx_in_block] =
hist[(y * img_block_width  + x) * cblock_hist_size + idx_in_block];
}
}
__kernel void compute_gradients_8UC4_kernel(
const int height, const int width,
const int img_step, const int grad_quadstep, const int qangle_step,
const __global uchar4 * img, __global float * grad, __global QANGLE_TYPE * qangle,
const float angle_scale, const char correct_gamma, const int cnbins)
{
const int x = get_global_id(0);
const int tid = get_local_id(0);
const int gSizeX = get_local_size(0);
const int gidY = get_group_id(1);
__global const uchar4* row = img + gidY * img_step;
__local float sh_row[(NTHREADS + 2) * 3];
uchar4 val;
if (x < width)
val = row[x];
else
val = row[width - 2];
sh_row[tid + 1] = val.x;
sh_row[tid + 1 + (NTHREADS + 2)] = val.y;
sh_row[tid + 1 + 2 * (NTHREADS + 2)] = val.z;
if (tid == 0)
{
val = row[max(x - 1, 1)];
sh_row[0] = val.x;
sh_row[(NTHREADS + 2)] = val.y;
sh_row[2 * (NTHREADS + 2)] = val.z;
}
if (tid == gSizeX - 1)
{
val = row[min(x + 1, width - 2)];
sh_row[gSizeX + 1] = val.x;
sh_row[gSizeX + 1 + (NTHREADS + 2)] = val.y;
sh_row[gSizeX + 1 + 2 * (NTHREADS + 2)] = val.z;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (x < width)
{
float3 a = (float3) (sh_row[tid], sh_row[tid + (NTHREADS + 2)],
sh_row[tid + 2 * (NTHREADS + 2)]);
float3 b = (float3) (sh_row[tid + 2], sh_row[tid + 2 + (NTHREADS + 2)],
sh_row[tid + 2 + 2 * (NTHREADS + 2)]);
float3 dx;
if (correct_gamma == 1)
dx = sqrt(b) - sqrt(a);
else
dx = b - a;
float3 dy = (float3) 0.f;
if (gidY > 0 && gidY < height - 1)
{
a = convert_float3(img[(gidY - 1) * img_step + x].xyz);
b = convert_float3(img[(gidY + 1) * img_step + x].xyz);
if (correct_gamma == 1)
dy = sqrt(b) - sqrt(a);
else
dy = b - a;
}
float best_dx = dx.x;
float best_dy = dy.x;
float mag0 = dx.x * dx.x + dy.x * dy.x;
float mag1 = dx.y * dx.y + dy.y * dy.y;
if (mag0 < mag1)
{
best_dx = dx.y;
best_dy = dy.y;
mag0 = mag1;
}
mag1 = dx.z * dx.z + dy.z * dy.z;
if (mag0 < mag1)
{
best_dx = dx.z;
best_dy = dy.z;
mag0 = mag1;
}
mag0 = sqrt(mag0);
float ang = (atan2(best_dy, best_dx) + CV_PI_F) * angle_scale - 0.5f;
int hidx = (int)floor(ang);
ang -= hidx;
hidx = (hidx + cnbins) % cnbins;
qangle[(gidY * qangle_step + x) << 1] = hidx;
qangle[((gidY * qangle_step + x) << 1) + 1] = (hidx + 1) % cnbins;
grad[(gidY * grad_quadstep + x) << 1] = mag0 * (1.f - ang);
grad[((gidY * grad_quadstep + x) << 1) + 1] = mag0 * ang;
}
}
__kernel void compute_gradients_8UC1_kernel(
const int height, const int width,
const int img_step, const int grad_quadstep, const int qangle_step,
__global const uchar * img, __global float * grad, __global QANGLE_TYPE * qangle,
const float angle_scale, const char correct_gamma, const int cnbins)
{
const int x = get_global_id(0);
const int tid = get_local_id(0);
const int gSizeX = get_local_size(0);
const int gidY = get_group_id(1);
__global const uchar* row = img + gidY * img_step;
__local float sh_row[NTHREADS + 2];
if (x < width)
sh_row[tid + 1] = row[x];
else
sh_row[tid + 1] = row[width - 2];
if (tid == 0)
sh_row[0] = row[max(x - 1, 1)];
if (tid == gSizeX - 1)
sh_row[gSizeX + 1] = row[min(x + 1, width - 2)];
barrier(CLK_LOCAL_MEM_FENCE);
if (x < width)
{
float dx;
if (correct_gamma == 1)
dx = sqrt(sh_row[tid + 2]) - sqrt(sh_row[tid]);
else
dx = sh_row[tid + 2] - sh_row[tid];
float dy = 0.f;
if (gidY > 0 && gidY < height - 1)
{
float a = (float) img[ (gidY + 1) * img_step + x ];
float b = (float) img[ (gidY - 1) * img_step + x ];
if (correct_gamma == 1)
dy = sqrt(a) - sqrt(b);
else
dy = a - b;
}
float mag = sqrt(dx * dx + dy * dy);
float ang = (atan2(dy, dx) + CV_PI_F) * angle_scale - 0.5f;
int hidx = (int)floor(ang);
ang -= hidx;
hidx = (hidx + cnbins) % cnbins;
qangle[ (gidY * qangle_step + x) << 1 ]     = hidx;
qangle[ ((gidY * qangle_step + x) << 1) + 1 ] = (hidx + 1) % cnbins;
grad[ (gidY * grad_quadstep + x) << 1 ]       = mag * (1.f - ang);
grad[ ((gidY * grad_quadstep + x) << 1) + 1 ]   = mag * ang;
}
}
9fce6b301b44c107c666aacfb0b0eaf2operator_convertTo#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void convert_to(
__global const srcT* restrict srcMat,
__global dstT* dstMat,
int cols1, int rows,
int sstep1, int soffset1,
int dstep1, int doffset1,
float alpha, float beta)
{
int x = get_global_id(0);
int y = get_global_id(1);
int srcidx = mad24(y, sstep1, x + soffset1);
int dstidx = mad24(y, dstep1, x + doffset1);
if ( (x < cols1) && (y < rows) )
{
float temp_src = convert_float(srcMat[srcidx]);
dstMat[dstidx] = convertToDstType(temp_src*alpha+beta);
}
}
3200cfd5ac5217f6e3d92e8af4e31487operator_copyToM#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void copy_to_with_mask(
__global const GENTYPE* restrict srcMat,
__global GENTYPE* dstMat,
__global const uchar* restrict maskMat,
int cols,
int rows,
int srcStep_in_pixel,
int srcoffset_in_pixel,
int dstStep_in_pixel,
int dstoffset_in_pixel,
int maskStep,
int maskoffset)
{
int x=get_global_id(0);
int y=get_global_id(1);
if (x < cols && y < rows)
{
int maskidx = mad24(y,maskStep,x+ maskoffset);
if ( maskMat[maskidx])
{
int srcidx = mad24(y,srcStep_in_pixel,x+ srcoffset_in_pixel);
int dstidx = mad24(y,dstStep_in_pixel,x+ dstoffset_in_pixel);
dstMat[dstidx] = srcMat[srcidx];
}
}
}
c6b06af957485640edb1940b4f8853fcoperator_setTo#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void set_to_without_mask_C1_D0(__global uchar * scalar,__global uchar * dstMat,
int cols,int rows,int dstStep_in_pixel,int offset_in_pixel)
{
int x=get_global_id(0)<<2;
int y=get_global_id(1);
int idx = mad24(y,dstStep_in_pixel,x+ offset_in_pixel);
uchar4 out;
out.x = out.y = out.z = out.w = scalar[0];
if ( (x+3 < cols) && (y < rows)&& ((offset_in_pixel&3) == 0))
{
*(__global uchar4*)(dstMat+idx) = out;
}
else
{
if((x+3 < cols) && (y < rows))
{
dstMat[idx] = out.x;
dstMat[idx+1] = out.y;
dstMat[idx+2] = out.z;
dstMat[idx+3] = out.w;
}
if((x+2 < cols) && (y < rows))
{
dstMat[idx] = out.x;
dstMat[idx+1] = out.y;
dstMat[idx+2] = out.z;
}
else if((x+1 < cols) && (y < rows))
{
dstMat[idx] = out.x;
dstMat[idx+1] = out.y;
}
else if((x < cols) && (y < rows))
{
dstMat[idx] = out.x;
}
}
}
__kernel void set_to_without_mask(__global GENTYPE * scalar,__global GENTYPE * dstMat,
int cols, int rows, int dstStep_in_pixel, int offset_in_pixel)
{
int x = get_global_id(0);
int y = get_global_id(1);
if ( (x < cols) & (y < rows))
{
int idx = mad24(y, dstStep_in_pixel, x + offset_in_pixel);
dstMat[idx] = scalar[0];
}
}
a19d7ffed8829cb4d606327c9d41db89operator_setToM#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
__kernel void set_to_with_mask(
__global GENTYPE * scalar,
__global GENTYPE * dstMat,
int cols,
int rows,
int dstStep_in_pixel,
int dstoffset_in_pixel,
__global const uchar * restrict maskMat,
int maskStep,
int maskoffset)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (x < cols && y < rows)
{
int maskidx = mad24(y,maskStep,x+ maskoffset);
if (maskMat[maskidx])
{
int dstidx = mad24(y,dstStep_in_pixel,x+ dstoffset_in_pixel);
dstMat[dstidx] = scalar[0];
}
}
}
9a888db2d6921485d70785f94e5a9393optical_flow_farneback#define tx  (int)get_local_id(0)
#define ty  get_local_id(1)
#define bx  get_group_id(0)
#define bdx (int)get_local_size(0)
#define BORDER_SIZE 5
#define MAX_KSIZE_HALF 100
#ifndef polyN
#define polyN 5
#endif
__kernel void polynomialExpansion(__global float * dst,
__global __const float * src,
__global __const float * c_g,
__global __const float * c_xg,
__global __const float * c_xxg,
__local float * smem,
const float4 ig,
const int height, const int width,
int dstStep, int srcStep)
{
const int y = get_global_id(1);
const int x = bx * (bdx - 2*polyN) + tx - polyN;
dstStep /= sizeof(*dst);
srcStep /= sizeof(*src);
int xWarped;
__local float *row = smem + tx;
if (y < height && y >= 0)
{
xWarped = min(max(x, 0), width - 1);
row[0] = src[mad24(y, srcStep, xWarped)] * c_g[0];
row[bdx] = 0.f;
row[2*bdx] = 0.f;
#pragma unroll
for (int k = 1; k <= polyN; ++k)
{
float t0 = src[mad24(max(y - k, 0), srcStep, xWarped)];
float t1 = src[mad24(min(y + k, height - 1), srcStep, xWarped)];
row[0] += c_g[k] * (t0 + t1);
row[bdx] += c_xg[k] * (t1 - t0);
row[2*bdx] += c_xxg[k] * (t0 + t1);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (y < height && y >= 0 && tx >= polyN && tx + polyN < bdx && x < width)
{
float b1 = c_g[0] * row[0];
float b3 = c_g[0] * row[bdx];
float b5 = c_g[0] * row[2*bdx];
float b2 = 0, b4 = 0, b6 = 0;
#pragma unroll
for (int k = 1; k <= polyN; ++k)
{
b1 += (row[k] + row[-k]) * c_g[k];
b4 += (row[k] + row[-k]) * c_xxg[k];
b2 += (row[k] - row[-k]) * c_xg[k];
b3 += (row[k + bdx] + row[-k + bdx]) * c_g[k];
b6 += (row[k + bdx] - row[-k + bdx]) * c_xg[k];
b5 += (row[k + 2*bdx] + row[-k + 2*bdx]) * c_g[k];
}
dst[mad24(y, dstStep, xWarped)] = b3*ig.s0;
dst[mad24(height + y, dstStep, xWarped)] = b2*ig.s0;
dst[mad24(2*height + y, dstStep, xWarped)] = b1*ig.s1 + b5*ig.s2;
dst[mad24(3*height + y, dstStep, xWarped)] = b1*ig.s1 + b4*ig.s2;
dst[mad24(4*height + y, dstStep, xWarped)] = b6*ig.s3;
}
}
int idx_row_low(const int y, const int last_row)
{
return abs(y) % (last_row + 1);
}
int idx_row_high(const int y, const int last_row)
{
return abs(last_row - abs(last_row - y)) % (last_row + 1);
}
int idx_row(const int y, const int last_row)
{
return idx_row_low(idx_row_high(y, last_row), last_row);
}
int idx_col_low(const int x, const int last_col)
{
return abs(x) % (last_col + 1);
}
int idx_col_high(const int x, const int last_col)
{
return abs(last_col - abs(last_col - x)) % (last_col + 1);
}
int idx_col(const int x, const int last_col)
{
return idx_col_low(idx_col_high(x, last_col), last_col);
}
__kernel void gaussianBlur(__global float * dst,
__global const float * src,
__global const float * c_gKer,
__local float * smem,
const int height,  const int width,
int dstStep, int srcStep,
const int ksizeHalf)
{
const int y = get_global_id(1);
const int x = get_global_id(0);
dstStep /= sizeof(*dst);
srcStep /= sizeof(*src);
__local float *row = smem + ty * (bdx + 2*ksizeHalf);
if (y < height)
{
for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx)
{
int xExt = (int)(bx * bdx) + i - ksizeHalf;
xExt = idx_col(xExt, width - 1);
row[i] = src[mad24(y, srcStep, xExt)] * c_gKer[0];
for (int j = 1; j <= ksizeHalf; ++j)
row[i] += (src[mad24(idx_row_low(y - j, height - 1), srcStep, xExt)]
+ src[mad24(idx_row_high(y + j, height - 1), srcStep, xExt)]) * c_gKer[j];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (y < height && y >= 0 && x < width && x >= 0)
{
row += tx + ksizeHalf;
float res = row[0] * c_gKer[0];
for (int i = 1; i <= ksizeHalf; ++i)
res += (row[-i] + row[i]) * c_gKer[i];
dst[mad24(y, dstStep, x)] = res;
}
}
__constant float c_border[BORDER_SIZE + 1] = { 0.14f, 0.14f, 0.4472f, 0.4472f, 0.4472f, 1.f };
__kernel void updateMatrices(__global float * M,
__global const float * flowx, __global const float * flowy,
__global const float * R0, __global const float * R1,
const int height, const int width,
int mStep, int xStep,  int yStep, int R0Step, int R1Step)
{
const int y = get_global_id(1);
const int x = get_global_id(0);
mStep /= sizeof(*M);
xStep /= sizeof(*flowx);
yStep /= sizeof(*flowy);
R0Step /= sizeof(*R0);
R1Step /= sizeof(*R1);
if (y < height && y >= 0 && x < width && x >= 0)
{
float dx = flowx[mad24(y, xStep, x)];
float dy = flowy[mad24(y, yStep, x)];
float fx = x + dx;
float fy = y + dy;
int x1 = convert_int(floor(fx));
int y1 = convert_int(floor(fy));
fx -= x1;
fy -= y1;
float r2, r3, r4, r5, r6;
if (x1 >= 0 && y1 >= 0 && x1 < width - 1 && y1 < height - 1)
{
float a00 = (1.f - fx) * (1.f - fy);
float a01 = fx * (1.f - fy);
float a10 = (1.f - fx) * fy;
float a11 = fx * fy;
r2 = a00 * R1[mad24(y1, R1Step, x1)] +
a01 * R1[mad24(y1, R1Step, x1 + 1)] +
a10 * R1[mad24(y1 + 1, R1Step, x1)] +
a11 * R1[mad24(y1 + 1, R1Step, x1 + 1)];
r3 = a00 * R1[mad24(height + y1, R1Step, x1)] +
a01 * R1[mad24(height + y1, R1Step, x1 + 1)] +
a10 * R1[mad24(height + y1 + 1, R1Step, x1)] +
a11 * R1[mad24(height + y1 + 1, R1Step, x1 + 1)];
r4 = a00 * R1[mad24(2*height + y1, R1Step, x1)] +
a01 * R1[mad24(2*height + y1, R1Step, x1 + 1)] +
a10 * R1[mad24(2*height + y1 + 1, R1Step, x1)] +
a11 * R1[mad24(2*height + y1 + 1, R1Step, x1 + 1)];
r5 = a00 * R1[mad24(3*height + y1, R1Step, x1)] +
a01 * R1[mad24(3*height + y1, R1Step, x1 + 1)] +
a10 * R1[mad24(3*height + y1 + 1, R1Step, x1)] +
a11 * R1[mad24(3*height + y1 + 1, R1Step, x1 + 1)];
r6 = a00 * R1[mad24(4*height + y1, R1Step, x1)] +
a01 * R1[mad24(4*height + y1, R1Step, x1 + 1)] +
a10 * R1[mad24(4*height + y1 + 1, R1Step, x1)] +
a11 * R1[mad24(4*height + y1 + 1, R1Step, x1 + 1)];
r4 = (R0[mad24(2*height + y, R0Step, x)] + r4) * 0.5f;
r5 = (R0[mad24(3*height + y, R0Step, x)] + r5) * 0.5f;
r6 = (R0[mad24(4*height + y, R0Step, x)] + r6) * 0.25f;
}
else
{
r2 = r3 = 0.f;
r4 = R0[mad24(2*height + y, R0Step, x)];
r5 = R0[mad24(3*height + y, R0Step, x)];
r6 = R0[mad24(4*height + y, R0Step, x)] * 0.5f;
}
r2 = (R0[mad24(y, R0Step, x)] - r2) * 0.5f;
r3 = (R0[mad24(height + y, R0Step, x)] - r3) * 0.5f;
r2 += r4*dy + r6*dx;
r3 += r6*dy + r5*dx;
float scale =
c_border[min(x, BORDER_SIZE)] *
c_border[min(y, BORDER_SIZE)] *
c_border[min(width - x - 1, BORDER_SIZE)] *
c_border[min(height - y - 1, BORDER_SIZE)];
r2 *= scale;
r3 *= scale;
r4 *= scale;
r5 *= scale;
r6 *= scale;
M[mad24(y, mStep, x)] = r4*r4 + r6*r6;
M[mad24(height + y, mStep, x)] = (r4 + r5)*r6;
M[mad24(2*height + y, mStep, x)] = r5*r5 + r6*r6;
M[mad24(3*height + y, mStep, x)] = r4*r2 + r6*r3;
M[mad24(4*height + y, mStep, x)] = r6*r2 + r5*r3;
}
}
__kernel void boxFilter5(__global float * dst,
__global const float * src,
__local float * smem,
const int height,  const int width,
int dstStep, int srcStep,
const int ksizeHalf)
{
const int y = get_global_id(1);
const int x = get_global_id(0);
const float boxAreaInv = 1.f / ((1 + 2*ksizeHalf) * (1 + 2*ksizeHalf));
const int smw = bdx + 2*ksizeHalf;
__local float *row = smem + 5 * ty * smw;
dstStep /= sizeof(*dst);
srcStep /= sizeof(*src);
if (y < height)
{
for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx)
{
int xExt = (int)(bx * bdx) + i - ksizeHalf;
xExt = min(max(xExt, 0), width - 1);
#pragma unroll
for (int k = 0; k < 5; ++k)
row[k*smw + i] = src[mad24(k*height + y, srcStep, xExt)];
for (int j = 1; j <= ksizeHalf; ++j)
#pragma unroll
for (int k = 0; k < 5; ++k)
row[k*smw + i] +=
src[mad24(k*height + max(y - j, 0), srcStep, xExt)] +
src[mad24(k*height + min(y + j, height - 1), srcStep, xExt)];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (y < height && y >= 0 && x < width && x >= 0)
{
row += tx + ksizeHalf;
float res[5];
#pragma unroll
for (int k = 0; k < 5; ++k)
res[k] = row[k*smw];
for (int i = 1; i <= ksizeHalf; ++i)
#pragma unroll
for (int k = 0; k < 5; ++k)
res[k] += row[k*smw - i] + row[k*smw + i];
#pragma unroll
for (int k = 0; k < 5; ++k)
dst[mad24(k*height + y, dstStep, x)] = res[k] * boxAreaInv;
}
}
__kernel void updateFlow(__global float4 * flowx, __global float4 * flowy,
__global const float4 * M,
const int height, const int width,
int xStep, int yStep, int mStep)
{
const int y = get_global_id(1);
const int x = get_global_id(0);
xStep /= sizeof(*flowx);
yStep /= sizeof(*flowy);
mStep /= sizeof(*M);
if (y < height && y >= 0 && x < width && x >= 0)
{
float4 g11 = M[mad24(y, mStep, x)];
float4 g12 = M[mad24(height + y, mStep, x)];
float4 g22 = M[mad24(2*height + y, mStep, x)];
float4 h1 =  M[mad24(3*height + y, mStep, x)];
float4 h2 =  M[mad24(4*height + y, mStep, x)];
float4 detInv = (float4)(1.f) / (g11*g22 - g12*g12 + (float4)(1e-3f));
flowx[mad24(y, xStep, x)] = (g11*h2 - g12*h1) * detInv;
flowy[mad24(y, yStep, x)] = (g22*h1 - g12*h2) * detInv;
}
}
__kernel void gaussianBlur5(__global float * dst,
__global const float * src,
__global const float * c_gKer,
__local float * smem,
const int height,  const int width,
int dstStep, int srcStep,
const int ksizeHalf)
{
const int y = get_global_id(1);
const int x = get_global_id(0);
const int smw = bdx + 2*ksizeHalf;
__local volatile float *row = smem + 5 * ty * smw;
dstStep /= sizeof(*dst);
srcStep /= sizeof(*src);
if (y < height)
{
for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx)
{
int xExt = (int)(bx * bdx) + i - ksizeHalf;
xExt = idx_col(xExt, width - 1);
#pragma unroll
for (int k = 0; k < 5; ++k)
row[k*smw + i] = src[mad24(k*height + y, srcStep, xExt)] * c_gKer[0];
for (int j = 1; j <= ksizeHalf; ++j)
#pragma unroll
for (int k = 0; k < 5; ++k)
row[k*smw + i] +=
(src[mad24(k*height + idx_row_low(y - j, height - 1), srcStep, xExt)] +
src[mad24(k*height + idx_row_high(y + j, height - 1), srcStep, xExt)]) * c_gKer[j];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (y < height && y >= 0 && x < width && x >= 0)
{
row += tx + ksizeHalf;
float res[5];
#pragma unroll
for (int k = 0; k < 5; ++k)
res[k] = row[k*smw] * c_gKer[0];
for (int i = 1; i <= ksizeHalf; ++i)
#pragma unroll
for (int k = 0; k < 5; ++k)
res[k] += (row[k*smw - i] + row[k*smw + i]) * c_gKer[i];
#pragma unroll
for (int k = 0; k < 5; ++k)
dst[mad24(k*height + y, dstStep, x)] = res[k];
}
}
4ea04a3f4d11332d5b2176a64df4ffacpyr_downint idx_row_low(int y, int last_row)
{
return abs(y) % (last_row + 1);
}
int idx_row_high(int y, int last_row)
{
return abs(last_row - (int)abs(last_row - y)) % (last_row + 1);
}
int idx_row(int y, int last_row)
{
return idx_row_low(idx_row_high(y, last_row), last_row);
}
int idx_col_low(int x, int last_col)
{
return abs(x) % (last_col + 1);
}
int idx_col_high(int x, int last_col)
{
return abs(last_col - (int)abs(last_col - x)) % (last_col + 1);
}
int idx_col(int x, int last_col)
{
return idx_col_low(idx_col_high(x, last_col), last_col);
}
__kernel void pyrDown_C1_D0(__global uchar * srcData, int srcStep, int srcRows, int srcCols, __global uchar *dst, int dstStep, int dstCols)
{
const int x = get_global_id(0);
const int y = get_group_id(1);
__local float smem[256 + 4];
float sum;
const int src_y = 2*y;
const int last_row = srcRows - 1;
const int last_col = srcCols - 1;
if (src_y >= 2 && src_y < srcRows - 2 && x >= 2 && x < srcCols - 2)
{
sum =       0.0625f * (((srcData + (src_y - 2) * srcStep))[x]);
sum = sum + 0.25f   * (((srcData + (src_y - 1) * srcStep))[x]);
sum = sum + 0.375f  * (((srcData + (src_y    ) * srcStep))[x]);
sum = sum + 0.25f   * (((srcData + (src_y + 1) * srcStep))[x]);
sum = sum + 0.0625f * (((srcData + (src_y + 2) * srcStep))[x]);
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
sum =       0.0625f * (((srcData + (src_y - 2) * srcStep))[left_x]);
sum = sum + 0.25f   * (((srcData + (src_y - 1) * srcStep))[left_x]);
sum = sum + 0.375f  * (((srcData + (src_y    ) * srcStep))[left_x]);
sum = sum + 0.25f   * (((srcData + (src_y + 1) * srcStep))[left_x]);
sum = sum + 0.0625f * (((srcData + (src_y + 2) * srcStep))[left_x]);
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
sum =       0.0625f * (((srcData + (src_y - 2) * srcStep))[right_x]);
sum = sum + 0.25f   * (((srcData + (src_y - 1) * srcStep))[right_x]);
sum = sum + 0.375f  * (((srcData + (src_y    ) * srcStep))[right_x]);
sum = sum + 0.25f   * (((srcData + (src_y + 1) * srcStep))[right_x]);
sum = sum + 0.0625f * (((srcData + (src_y + 2) * srcStep))[right_x]);
smem[4 + get_local_id(0)] = sum;
}
}
else
{
int col = idx_col(x, last_col);
sum =       0.0625f * (((srcData + idx_row(src_y - 2, last_row) * srcStep))[col]);
sum = sum + 0.25f   * (((srcData + idx_row(src_y - 1, last_row) * srcStep))[col]);
sum = sum + 0.375f  * (((srcData + idx_row(src_y    , last_row) * srcStep))[col]);
sum = sum + 0.25f   * (((srcData + idx_row(src_y + 1, last_row) * srcStep))[col]);
sum = sum + 0.0625f * (((srcData + idx_row(src_y + 2, last_row) * srcStep))[col]);
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
col = idx_col(left_x, last_col);
sum =       0.0625f * (((srcData + idx_row(src_y - 2, last_row) * srcStep))[col]);
sum = sum + 0.25f   * (((srcData + idx_row(src_y - 1, last_row) * srcStep))[col]);
sum = sum + 0.375f  * (((srcData + idx_row(src_y    , last_row) * srcStep))[col]);
sum = sum + 0.25f   * (((srcData + idx_row(src_y + 1, last_row) * srcStep))[col]);
sum = sum + 0.0625f * (((srcData + idx_row(src_y + 2, last_row) * srcStep))[col]);
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
col = idx_col(right_x, last_col);
sum =       0.0625f * (((srcData + idx_row(src_y - 2, last_row) * srcStep))[col]);
sum = sum + 0.25f   * (((srcData + idx_row(src_y - 1, last_row) * srcStep))[col]);
sum = sum + 0.375f  * (((srcData + idx_row(src_y    , last_row) * srcStep))[col]);
sum = sum + 0.25f   * (((srcData + idx_row(src_y + 1, last_row) * srcStep))[col]);
sum = sum + 0.0625f * (((srcData + idx_row(src_y + 2, last_row) * srcStep))[col]);
smem[4 + get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 128)
{
const int tid2 = get_local_id(0) * 2;
sum =       0.0625f * smem[2 + tid2 - 2];
sum = sum + 0.25f   * smem[2 + tid2 - 1];
sum = sum + 0.375f  * smem[2 + tid2    ];
sum = sum + 0.25f   * smem[2 + tid2 + 1];
sum = sum + 0.0625f * smem[2 + tid2 + 2];
const int dst_x = (get_group_id(0) * get_local_size(0) + tid2) / 2;
if (dst_x < dstCols)
dst[y * dstStep + dst_x] = convert_uchar_sat_rte(sum);
}
}
__kernel void pyrDown_C4_D0(__global uchar4 * srcData, int srcStep, int srcRows, int srcCols, __global uchar4 *dst, int dstStep, int dstCols)
{
const int x = get_global_id(0);
const int y = get_group_id(1);
__local float4 smem[256 + 4];
float4 sum;
const int src_y = 2*y;
const int last_row = srcRows - 1;
const int last_col = srcCols - 1;
float4 co1 = 0.375f;
float4 co2 = 0.25f;
float4 co3 = 0.0625f;
if (src_y >= 2 && src_y < srcRows - 2 && x >= 2 && x < srcCols - 2)
{
sum =       co3 * convert_float4((((srcData + (src_y - 2) * srcStep / 4))[x]));
sum = sum + co2 * convert_float4((((srcData + (src_y - 1) * srcStep / 4))[x]));
sum = sum + co1 * convert_float4((((srcData + (src_y    ) * srcStep / 4))[x]));
sum = sum + co2 * convert_float4((((srcData + (src_y + 1) * srcStep / 4))[x]));
sum = sum + co3 * convert_float4((((srcData + (src_y + 2) * srcStep / 4))[x]));
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
sum =       co3 * convert_float4((((srcData + (src_y - 2) * srcStep / 4))[left_x]));
sum = sum + co2 * convert_float4((((srcData + (src_y - 1) * srcStep / 4))[left_x]));
sum = sum + co1 * convert_float4((((srcData + (src_y    ) * srcStep / 4))[left_x]));
sum = sum + co2 * convert_float4((((srcData + (src_y + 1) * srcStep / 4))[left_x]));
sum = sum + co3 * convert_float4((((srcData + (src_y + 2) * srcStep / 4))[left_x]));
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
sum =       co3 * convert_float4((((srcData + (src_y - 2) * srcStep / 4))[right_x]));
sum = sum + co2 * convert_float4((((srcData + (src_y - 1) * srcStep / 4))[right_x]));
sum = sum + co1 * convert_float4((((srcData + (src_y    ) * srcStep / 4))[right_x]));
sum = sum + co2 * convert_float4((((srcData + (src_y + 1) * srcStep / 4))[right_x]));
sum = sum + co3 * convert_float4((((srcData + (src_y + 2) * srcStep / 4))[right_x]));
smem[4 + get_local_id(0)] = sum;
}
}
else
{
int col = idx_col(x, last_col);
sum =       co3 * convert_float4((((srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]));
sum = sum + co2 * convert_float4((((srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]));
sum = sum + co1 * convert_float4((((srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]));
sum = sum + co2 * convert_float4((((srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]));
sum = sum + co3 * convert_float4((((srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]));
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
col = idx_col(left_x, last_col);
sum =       co3 * convert_float4((((srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]));
sum = sum + co2 * convert_float4((((srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]));
sum = sum + co1 * convert_float4((((srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]));
sum = sum + co2 * convert_float4((((srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]));
sum = sum + co3 * convert_float4((((srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]));
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
col = idx_col(right_x, last_col);
sum =       co3 * convert_float4((((srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]));
sum = sum + co2 * convert_float4((((srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]));
sum = sum + co1 * convert_float4((((srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]));
sum = sum + co2 * convert_float4((((srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]));
sum = sum + co3 * convert_float4((((srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]));
smem[4 + get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 128)
{
const int tid2 = get_local_id(0) * 2;
sum =       co3 * smem[2 + tid2 - 2];
sum = sum + co2 * smem[2 + tid2 - 1];
sum = sum + co1 * smem[2 + tid2    ];
sum = sum + co2 * smem[2 + tid2 + 1];
sum = sum + co3 * smem[2 + tid2 + 2];
const int dst_x = (get_group_id(0) * get_local_size(0) + tid2) / 2;
if (dst_x < dstCols)
dst[y * dstStep / 4 + dst_x] = convert_uchar4_sat_rte(sum);
}
}
__kernel void pyrDown_C1_D2(__global ushort * srcData, int srcStep, int srcRows, int srcCols, __global ushort *dst, int dstStep, int dstCols)
{
const int x = get_global_id(0);
const int y = get_group_id(1);
__local float smem[256 + 4];
float sum;
const int src_y = 2*y;
const int last_row = srcRows - 1;
const int last_col = srcCols - 1;
if (src_y >= 2 && src_y < srcRows - 2 && x >= 2 && x < srcCols - 2)
{
sum =       0.0625f * ((__global ushort*)((__global char*)srcData + (src_y - 2) * srcStep))[x];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + (src_y - 1) * srcStep))[x];
sum = sum + 0.375f  * ((__global ushort*)((__global char*)srcData + (src_y    ) * srcStep))[x];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + (src_y + 1) * srcStep))[x];
sum = sum + 0.0625f * ((__global ushort*)((__global char*)srcData + (src_y + 2) * srcStep))[x];
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
sum =       0.0625f * ((__global ushort*)((__global char*)srcData + (src_y - 2) * srcStep))[left_x];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + (src_y - 1) * srcStep))[left_x];
sum = sum + 0.375f  * ((__global ushort*)((__global char*)srcData + (src_y    ) * srcStep))[left_x];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + (src_y + 1) * srcStep))[left_x];
sum = sum + 0.0625f * ((__global ushort*)((__global char*)srcData + (src_y + 2) * srcStep))[left_x];
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
sum =       0.0625f * ((__global ushort*)((__global char*)srcData + (src_y - 2) * srcStep))[right_x];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + (src_y - 1) * srcStep))[right_x];
sum = sum + 0.375f  * ((__global ushort*)((__global char*)srcData + (src_y    ) * srcStep))[right_x];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + (src_y + 1) * srcStep))[right_x];
sum = sum + 0.0625f * ((__global ushort*)((__global char*)srcData + (src_y + 2) * srcStep))[right_x];
smem[4 + get_local_id(0)] = sum;
}
}
else
{
int col = idx_col(x, last_col);
sum =       0.0625f * ((__global ushort*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global ushort*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global ushort*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
col = idx_col(left_x, last_col);
sum =       0.0625f * ((__global ushort*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global ushort*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global ushort*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
col = idx_col(right_x, last_col);
sum =       0.0625f * ((__global ushort*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global ushort*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global ushort*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global ushort*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[4 + get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 128)
{
const int tid2 = get_local_id(0) * 2;
sum =       0.0625f * smem[2 + tid2 - 2];
sum = sum + 0.25f   * smem[2 + tid2 - 1];
sum = sum + 0.375f  * smem[2 + tid2    ];
sum = sum + 0.25f   * smem[2 + tid2 + 1];
sum = sum + 0.0625f * smem[2 + tid2 + 2];
const int dst_x = (get_group_id(0) * get_local_size(0) + tid2) / 2;
if (dst_x < dstCols)
dst[y * dstStep / 2 + dst_x] = convert_ushort_sat_rte(sum);
}
}
__kernel void pyrDown_C4_D2(__global ushort4 * srcData, int srcStep, int srcRows, int srcCols, __global ushort4 *dst, int dstStep, int dstCols)
{
const int x = get_global_id(0);
const int y = get_group_id(1);
__local float4 smem[256 + 4];
float4 sum;
const int src_y = 2*y;
const int last_row = srcRows - 1;
const int last_col = srcCols - 1;
float4 co1 = 0.375f;
float4 co2 = 0.25f;
float4 co3 = 0.0625f;
if (src_y >= 2 && src_y < srcRows - 2 && x >= 2 && x < srcCols - 2)
{
sum =       co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[x]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[x]);
sum = sum + co1 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[x]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[x]);
sum = sum + co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[x]);
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
sum =       co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[left_x]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[left_x]);
sum = sum + co1 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[left_x]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[left_x]);
sum = sum + co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[left_x]);
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
sum =       co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[right_x]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[right_x]);
sum = sum + co1 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[right_x]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[right_x]);
sum = sum + co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[right_x]);
smem[4 + get_local_id(0)] = sum;
}
}
else
{
int col = idx_col(x, last_col);
sum =       co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]);
sum = sum + co1 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]);
sum = sum + co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]);
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
col = idx_col(left_x, last_col);
sum =       co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]);
sum = sum + co1 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]);
sum = sum + co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]);
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
col = idx_col(right_x, last_col);
sum =       co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]);
sum = sum + co1 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]);
sum = sum + co3 * convert_float4(((__global ushort4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]);
smem[4 + get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 128)
{
const int tid2 = get_local_id(0) * 2;
sum =       co3 * smem[2 + tid2 - 2];
sum = sum + co2 * smem[2 + tid2 - 1];
sum = sum + co1 * smem[2 + tid2    ];
sum = sum + co2 * smem[2 + tid2 + 1];
sum = sum + co3 * smem[2 + tid2 + 2];
const int dst_x = (get_group_id(0) * get_local_size(0) + tid2) / 2;
if (dst_x < dstCols)
dst[y * dstStep / 8 + dst_x] = convert_ushort4_sat_rte(sum);
}
}
__kernel void pyrDown_C1_D3(__global short * srcData, int srcStep, int srcRows, int srcCols, __global short *dst, int dstStep, int dstCols)
{
const int x = get_global_id(0);
const int y = get_group_id(1);
__local float smem[256 + 4];
float sum;
const int src_y = 2*y;
const int last_row = srcRows - 1;
const int last_col = srcCols - 1;
if (src_y >= 2 && src_y < srcRows - 2 && x >= 2 && x < srcCols - 2)
{
sum =       0.0625f * ((__global short*)((__global char*)srcData + (src_y - 2) * srcStep))[x];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + (src_y - 1) * srcStep))[x];
sum = sum + 0.375f  * ((__global short*)((__global char*)srcData + (src_y    ) * srcStep))[x];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + (src_y + 1) * srcStep))[x];
sum = sum + 0.0625f * ((__global short*)((__global char*)srcData + (src_y + 2) * srcStep))[x];
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
sum =       0.0625f * ((__global short*)((__global char*)srcData + (src_y - 2) * srcStep))[left_x];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + (src_y - 1) * srcStep))[left_x];
sum = sum + 0.375f  * ((__global short*)((__global char*)srcData + (src_y    ) * srcStep))[left_x];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + (src_y + 1) * srcStep))[left_x];
sum = sum + 0.0625f * ((__global short*)((__global char*)srcData + (src_y + 2) * srcStep))[left_x];
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
sum =       0.0625f * ((__global short*)((__global char*)srcData + (src_y - 2) * srcStep))[right_x];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + (src_y - 1) * srcStep))[right_x];
sum = sum + 0.375f  * ((__global short*)((__global char*)srcData + (src_y    ) * srcStep))[right_x];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + (src_y + 1) * srcStep))[right_x];
sum = sum + 0.0625f * ((__global short*)((__global char*)srcData + (src_y + 2) * srcStep))[right_x];
smem[4 + get_local_id(0)] = sum;
}
}
else
{
int col = idx_col(x, last_col);
sum =       0.0625f * ((__global short*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global short*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global short*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
col = idx_col(left_x, last_col);
sum =       0.0625f * ((__global short*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global short*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global short*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
col = idx_col(right_x, last_col);
sum =       0.0625f * ((__global short*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global short*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global short*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global short*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[4 + get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 128)
{
const int tid2 = get_local_id(0) * 2;
sum =       0.0625f * smem[2 + tid2 - 2];
sum = sum + 0.25f   * smem[2 + tid2 - 1];
sum = sum + 0.375f  * smem[2 + tid2    ];
sum = sum + 0.25f   * smem[2 + tid2 + 1];
sum = sum + 0.0625f * smem[2 + tid2 + 2];
const int dst_x = (get_group_id(0) * get_local_size(0) + tid2) / 2;
if (dst_x < dstCols)
dst[y * dstStep / 2 + dst_x] = convert_short_sat_rte(sum);
}
}
__kernel void pyrDown_C4_D3(__global short4 * srcData, int srcStep, int srcRows, int srcCols, __global short4 *dst, int dstStep, int dstCols)
{
const int x = get_global_id(0);
const int y = get_group_id(1);
__local float4 smem[256 + 4];
float4 sum;
const int src_y = 2*y;
const int last_row = srcRows - 1;
const int last_col = srcCols - 1;
float4 co1 = 0.375f;
float4 co2 = 0.25f;
float4 co3 = 0.0625f;
if (src_y >= 2 && src_y < srcRows - 2 && x >= 2 && x < srcCols - 2)
{
sum =       co3 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[x]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[x]);
sum = sum + co1 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[x]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[x]);
sum = sum + co3 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[x]);
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
sum =       co3 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[left_x]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[left_x]);
sum = sum + co1 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[left_x]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[left_x]);
sum = sum + co3 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[left_x]);
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
sum =       co3 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[right_x]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[right_x]);
sum = sum + co1 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[right_x]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[right_x]);
sum = sum + co3 * convert_float4(((__global short4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[right_x]);
smem[4 + get_local_id(0)] = sum;
}
}
else
{
int col = idx_col(x, last_col);
sum =       co3 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]);
sum = sum + co1 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]);
sum = sum + co3 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]);
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
col = idx_col(left_x, last_col);
sum =       co3 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]);
sum = sum + co1 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]);
sum = sum + co3 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]);
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
col = idx_col(right_x, last_col);
sum =       co3 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col]);
sum = sum + co1 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col]);
sum = sum + co2 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col]);
sum = sum + co3 * convert_float4(((__global short4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col]);
smem[4 + get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 128)
{
const int tid2 = get_local_id(0) * 2;
sum =       co3 * smem[2 + tid2 - 2];
sum = sum + co2 * smem[2 + tid2 - 1];
sum = sum + co1 * smem[2 + tid2    ];
sum = sum + co2 * smem[2 + tid2 + 1];
sum = sum + co3 * smem[2 + tid2 + 2];
const int dst_x = (get_group_id(0) * get_local_size(0) + tid2) / 2;
if (dst_x < dstCols)
dst[y * dstStep / 8 + dst_x] = convert_short4_sat_rte(sum);
}
}
__kernel void pyrDown_C1_D5(__global float * srcData, int srcStep, int srcRows, int srcCols, __global float *dst, int dstStep, int dstCols)
{
const int x = get_global_id(0);
const int y = get_group_id(1);
__local float smem[256 + 4];
float sum;
const int src_y = 2*y;
const int last_row = srcRows - 1;
const int last_col = srcCols - 1;
if (src_y >= 2 && src_y < srcRows - 2 && x >= 2 && x < srcCols - 2)
{
sum =       0.0625f * ((__global float*)((__global char*)srcData + (src_y - 2) * srcStep))[x];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + (src_y - 1) * srcStep))[x];
sum = sum + 0.375f  * ((__global float*)((__global char*)srcData + (src_y    ) * srcStep))[x];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + (src_y + 1) * srcStep))[x];
sum = sum + 0.0625f * ((__global float*)((__global char*)srcData + (src_y + 2) * srcStep))[x];
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
sum =       0.0625f * ((__global float*)((__global char*)srcData + (src_y - 2) * srcStep))[left_x];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + (src_y - 1) * srcStep))[left_x];
sum = sum + 0.375f  * ((__global float*)((__global char*)srcData + (src_y    ) * srcStep))[left_x];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + (src_y + 1) * srcStep))[left_x];
sum = sum + 0.0625f * ((__global float*)((__global char*)srcData + (src_y + 2) * srcStep))[left_x];
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
sum =       0.0625f * ((__global float*)((__global char*)srcData + (src_y - 2) * srcStep))[right_x];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + (src_y - 1) * srcStep))[right_x];
sum = sum + 0.375f  * ((__global float*)((__global char*)srcData + (src_y    ) * srcStep))[right_x];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + (src_y + 1) * srcStep))[right_x];
sum = sum + 0.0625f * ((__global float*)((__global char*)srcData + (src_y + 2) * srcStep))[right_x];
smem[4 + get_local_id(0)] = sum;
}
}
else
{
int col = idx_col(x, last_col);
sum =       0.0625f * ((__global float*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global float*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global float*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
col = idx_col(left_x, last_col);
sum =       0.0625f * ((__global float*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global float*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global float*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
col = idx_col(right_x, last_col);
sum =       0.0625f * ((__global float*)((__global char*)srcData + idx_row(src_y - 2, last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + idx_row(src_y - 1, last_row) * srcStep))[col];
sum = sum + 0.375f  * ((__global float*)((__global char*)srcData + idx_row(src_y    , last_row) * srcStep))[col];
sum = sum + 0.25f   * ((__global float*)((__global char*)srcData + idx_row(src_y + 1, last_row) * srcStep))[col];
sum = sum + 0.0625f * ((__global float*)((__global char*)srcData + idx_row(src_y + 2, last_row) * srcStep))[col];
smem[4 + get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 128)
{
const int tid2 = get_local_id(0) * 2;
sum =       0.0625f * smem[2 + tid2 - 2];
sum = sum + 0.25f   * smem[2 + tid2 - 1];
sum = sum + 0.375f  * smem[2 + tid2    ];
sum = sum + 0.25f   * smem[2 + tid2 + 1];
sum = sum + 0.0625f * smem[2 + tid2 + 2];
const int dst_x = (get_group_id(0) * get_local_size(0) + tid2) / 2;
if (dst_x < dstCols)
dst[y * dstStep / 4 + dst_x] = sum;
}
}
__kernel void pyrDown_C4_D5(__global float4 * srcData, int srcStep, int srcRows, int srcCols, __global float4 *dst, int dstStep, int dstCols)
{
const int x = get_global_id(0);
const int y = get_group_id(1);
__local float4 smem[256 + 4];
float4 sum;
const int src_y = 2*y;
const int last_row = srcRows - 1;
const int last_col = srcCols - 1;
float4 co1 = 0.375f;
float4 co2 = 0.25f;
float4 co3 = 0.0625f;
if (src_y >= 2 && src_y < srcRows - 2 && x >= 2 && x < srcCols - 2)
{
sum =       co3 * ((__global float4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[x];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[x];
sum = sum + co1 * ((__global float4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[x];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[x];
sum = sum + co3 * ((__global float4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[x];
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
sum =       co3 * ((__global float4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[left_x];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[left_x];
sum = sum + co1 * ((__global float4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[left_x];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[left_x];
sum = sum + co3 * ((__global float4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[left_x];
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
sum =       co3 * ((__global float4*)((__global char4*)srcData + (src_y - 2) * srcStep / 4))[right_x];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + (src_y - 1) * srcStep / 4))[right_x];
sum = sum + co1 * ((__global float4*)((__global char4*)srcData + (src_y    ) * srcStep / 4))[right_x];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + (src_y + 1) * srcStep / 4))[right_x];
sum = sum + co3 * ((__global float4*)((__global char4*)srcData + (src_y + 2) * srcStep / 4))[right_x];
smem[4 + get_local_id(0)] = sum;
}
}
else
{
int col = idx_col(x, last_col);
sum =       co3 * ((__global float4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col];
sum = sum + co1 * ((__global float4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col];
sum = sum + co3 * ((__global float4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col];
smem[2 + get_local_id(0)] = sum;
if (get_local_id(0) < 2)
{
const int left_x = x - 2;
col = idx_col(left_x, last_col);
sum =       co3 * ((__global float4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col];
sum = sum + co1 * ((__global float4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col];
sum = sum + co3 * ((__global float4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col];
smem[get_local_id(0)] = sum;
}
if (get_local_id(0) > 253)
{
const int right_x = x + 2;
col = idx_col(right_x, last_col);
sum =       co3 * ((__global float4*)((__global char4*)srcData + idx_row(src_y - 2, last_row) * srcStep / 4))[col];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + idx_row(src_y - 1, last_row) * srcStep / 4))[col];
sum = sum + co1 * ((__global float4*)((__global char4*)srcData + idx_row(src_y    , last_row) * srcStep / 4))[col];
sum = sum + co2 * ((__global float4*)((__global char4*)srcData + idx_row(src_y + 1, last_row) * srcStep / 4))[col];
sum = sum + co3 * ((__global float4*)((__global char4*)srcData + idx_row(src_y + 2, last_row) * srcStep / 4))[col];
smem[4 + get_local_id(0)] = sum;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 128)
{
const int tid2 = get_local_id(0) * 2;
sum =       co3 * smem[2 + tid2 - 2];
sum = sum + co2 * smem[2 + tid2 - 1];
sum = sum + co1 * smem[2 + tid2    ];
sum = sum + co2 * smem[2 + tid2 + 1];
sum = sum + co3 * smem[2 + tid2 + 2];
const int dst_x = (get_group_id(0) * get_local_size(0) + tid2) / 2;
if (dst_x < dstCols)
dst[y * dstStep / 16 + dst_x] = sum;
}
}
a852332a866d8849b7d1bfdd7017b87fpyr_up__kernel void pyrUp(__global Type* src, __global Type* dst,
int srcRows, int dstRows, int srcCols, int dstCols,
int srcOffset, int dstOffset, int srcStep, int dstStep)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
const int lsizex = get_local_size(0);
const int lsizey = get_local_size(1);
const int tidx = get_local_id(0);
const int tidy = get_local_id(1);
__local floatType s_srcPatch[10][10];
__local floatType s_dstPatch[20][16];
if( tidx < 10 && tidy < 10 )
{
int srcx = mad24((int)get_group_id(0), lsizex>>1, tidx) - 1;
int srcy = mad24((int)get_group_id(1), lsizey>>1, tidy) - 1;
srcx = abs(srcx);
srcx = min(srcCols - 1,srcx);
srcy = abs(srcy);
srcy = min(srcRows -1 ,srcy);
s_srcPatch[tidy][tidx] = convertToFloat(src[srcx + srcy * srcStep]);
}
barrier(CLK_LOCAL_MEM_FENCE);
floatType sum = (floatType)0;
const floatType evenFlag = (floatType)((tidx & 1) == 0);
const floatType oddFlag = (floatType)((tidx & 1) != 0);
const bool  eveny = ((tidy & 1) == 0);
const floatType co1 = (floatType)0.375f;
const floatType co2 = (floatType)0.25f;
const floatType co3 = (floatType)0.0625f;
if(eveny)
{
sum =       ( evenFlag* co3 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx - 1) >> 1)];
sum = sum + ( evenFlag* co1 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx    ) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 1) >> 1)];
sum = sum + ( evenFlag* co3 ) * s_srcPatch[1 + (tidy >> 1)][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[2 + tidy][tidx] = sum;
if (tidy < 2)
{
sum = 0;
if (eveny)
{
sum =       (evenFlag * co3 ) * s_srcPatch[lsizey-16][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[lsizey-16][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * co1 ) * s_srcPatch[lsizey-16][1 + ((tidx    ) >> 1)];
sum = sum + ( oddFlag * co2 ) * s_srcPatch[lsizey-16][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * co3 ) * s_srcPatch[lsizey-16][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[tidy][tidx] = sum;
}
if (tidy > 13)
{
sum = 0;
if (eveny)
{
sum =       (evenFlag * co3) * s_srcPatch[lsizey-7][1 + ((tidx - 2) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[lsizey-7][1 + ((tidx - 1) >> 1)];
sum = sum + (evenFlag * co1) * s_srcPatch[lsizey-7][1 + ((tidx    ) >> 1)];
sum = sum + ( oddFlag * co2) * s_srcPatch[lsizey-7][1 + ((tidx + 1) >> 1)];
sum = sum + (evenFlag * co3) * s_srcPatch[lsizey-7][1 + ((tidx + 2) >> 1)];
}
s_dstPatch[4 + tidy][tidx] = sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum =       co3 * s_dstPatch[2 + tidy - 2][tidx];
sum = sum + co2 * s_dstPatch[2 + tidy - 1][tidx];
sum = sum + co1 * s_dstPatch[2 + tidy    ][tidx];
sum = sum + co2 * s_dstPatch[2 + tidy + 1][tidx];
sum = sum + co3 * s_dstPatch[2 + tidy + 2][tidx];
if ((x < dstCols) && (y < dstRows))
dst[x + y * dstStep] = convertToType(4.0f * sum);
}
66198bcfcc6a241a5e4d6cadf0cd3dc0pyrlk#define  BUFFER  64
#define  BUFFER2  BUFFER>>1
#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
#ifdef CPU
void reduce3(float val1, float val2, float val3,  __local float* smem1,  __local float* smem2,  __local float* smem3, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
smem3[tid] = val3;
barrier(CLK_LOCAL_MEM_FENCE);
for(int i = BUFFER2; i > 0; i >>= 1)
{
if(tid < i)
{
smem1[tid] += smem1[tid + i];
smem2[tid] += smem2[tid + i];
smem3[tid] += smem3[tid + i];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
void reduce2(float val1, float val2, volatile __local float* smem1, volatile __local float* smem2, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
barrier(CLK_LOCAL_MEM_FENCE);
for(int i = BUFFER2; i > 0; i >>= 1)
{
if(tid < i)
{
smem1[tid] += smem1[tid + i];
smem2[tid] += smem2[tid + i];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
void reduce1(float val1, volatile __local float* smem1, int tid)
{
smem1[tid] = val1;
barrier(CLK_LOCAL_MEM_FENCE);
for(int i = BUFFER2; i > 0; i >>= 1)
{
if(tid < i)
{
smem1[tid] += smem1[tid + i];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
#else
void reduce3(float val1, float val2, float val3,
__local volatile float* smem1, __local volatile float* smem2, __local volatile float* smem3, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
smem3[tid] = val3;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 32)
{
smem1[tid] += smem1[tid + 32];
smem2[tid] += smem2[tid + 32];
smem3[tid] += smem3[tid + 32];
#if WAVE_SIZE < 32
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
#endif
smem1[tid] += smem1[tid + 16];
smem2[tid] += smem2[tid + 16];
smem3[tid] += smem3[tid + 16];
#if WAVE_SIZE <16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
smem1[tid] += smem1[tid + 8];
smem2[tid] += smem2[tid + 8];
smem3[tid] += smem3[tid + 8];
smem1[tid] += smem1[tid + 4];
smem2[tid] += smem2[tid + 4];
smem3[tid] += smem3[tid + 4];
smem1[tid] += smem1[tid + 2];
smem2[tid] += smem2[tid + 2];
smem3[tid] += smem3[tid + 2];
smem1[tid] += smem1[tid + 1];
smem2[tid] += smem2[tid + 1];
smem3[tid] += smem3[tid + 1];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
void reduce2(float val1, float val2, __local volatile float* smem1, __local volatile float* smem2, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 32)
{
smem1[tid] += smem1[tid + 32];
smem2[tid] += smem2[tid + 32];
#if WAVE_SIZE < 32
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
#endif
smem1[tid] += smem1[tid + 16];
smem2[tid] += smem2[tid + 16];
#if WAVE_SIZE <16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
smem1[tid] += smem1[tid + 8];
smem2[tid] += smem2[tid + 8];
smem1[tid] += smem1[tid + 4];
smem2[tid] += smem2[tid + 4];
smem1[tid] += smem1[tid + 2];
smem2[tid] += smem2[tid + 2];
smem1[tid] += smem1[tid + 1];
smem2[tid] += smem2[tid + 1];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
void reduce1(float val1, __local volatile float* smem1, int tid)
{
smem1[tid] = val1;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 32)
{
smem1[tid] += smem1[tid + 32];
#if WAVE_SIZE < 32
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
#endif
smem1[tid] += smem1[tid + 16];
#if WAVE_SIZE <16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
smem1[tid] += smem1[tid + 8];
smem1[tid] += smem1[tid + 4];
smem1[tid] += smem1[tid + 2];
smem1[tid] += smem1[tid + 1];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
#endif
#define SCALE (1.0f / (1 << 20))
#define  THRESHOLD  0.01f
__constant sampler_t sampler    = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_LINEAR;
void SetPatch(image2d_t I, float x, float y,
float* Pch, float* Dx, float* Dy,
float* A11, float* A12, float* A22)
{
*Pch = read_imagef(I, sampler, (float2)(x, y)).x;
float dIdx = 3.0f * read_imagef(I, sampler, (float2)(x + 1, y - 1)).x + 10.0f * read_imagef(I, sampler, (float2)(x + 1, y)).x + 3.0f * read_imagef(I, sampler, (float2)(x + 1, y + 1)).x -
(3.0f * read_imagef(I, sampler, (float2)(x - 1, y - 1)).x + 10.0f * read_imagef(I, sampler, (float2)(x - 1, y)).x + 3.0f * read_imagef(I, sampler, (float2)(x - 1, y + 1)).x);
float dIdy = 3.0f * read_imagef(I, sampler, (float2)(x - 1, y + 1)).x + 10.0f * read_imagef(I, sampler, (float2)(x, y + 1)).x + 3.0f * read_imagef(I, sampler, (float2)(x + 1, y + 1)).x -
(3.0f * read_imagef(I, sampler, (float2)(x - 1, y - 1)).x + 10.0f * read_imagef(I, sampler, (float2)(x, y - 1)).x + 3.0f * read_imagef(I, sampler, (float2)(x + 1, y - 1)).x);
*Dx = dIdx;
*Dy = dIdy;
*A11 += dIdx * dIdx;
*A12 += dIdx * dIdy;
*A22 += dIdy * dIdy;
}
void GetPatch(image2d_t J, float x, float y,
float* Pch, float* Dx, float* Dy,
float* b1, float* b2)
{
float J_val = read_imagef(J, sampler, (float2)(x, y)).x;
float diff = (J_val - *Pch) * 32.0f;
*b1 += diff**Dx;
*b2 += diff**Dy;
}
void GetError(image2d_t J, const float x, const float y, const float* Pch, float* errval)
{
float diff = read_imagef(J, sampler, (float2)(x,y)).x-*Pch;
*errval += fabs(diff);
}
void SetPatch4(image2d_t I, const float x, const float y,
float4* Pch, float4* Dx, float4* Dy,
float* A11, float* A12, float* A22)
{
*Pch = read_imagef(I, sampler, (float2)(x, y));
float4 dIdx = 3.0f * read_imagef(I, sampler, (float2)(x + 1, y - 1)) + 10.0f * read_imagef(I, sampler, (float2)(x + 1, y)) + 3.0f * read_imagef(I, sampler, (float2)(x + 1, y + 1)) -
(3.0f * read_imagef(I, sampler, (float2)(x - 1, y - 1)) + 10.0f * read_imagef(I, sampler, (float2)(x - 1, y)) + 3.0f * read_imagef(I, sampler, (float2)(x - 1, y + 1)));
float4 dIdy = 3.0f * read_imagef(I, sampler, (float2)(x - 1, y + 1)) + 10.0f * read_imagef(I, sampler, (float2)(x, y + 1)) + 3.0f * read_imagef(I, sampler, (float2)(x + 1, y + 1)) -
(3.0f * read_imagef(I, sampler, (float2)(x - 1, y - 1)) + 10.0f * read_imagef(I, sampler, (float2)(x, y - 1)) + 3.0f * read_imagef(I, sampler, (float2)(x + 1, y - 1)));
*Dx = dIdx;
*Dy = dIdy;
float4 sqIdx = dIdx * dIdx;
*A11 += sqIdx.x + sqIdx.y + sqIdx.z;
sqIdx = dIdx * dIdy;
*A12 += sqIdx.x + sqIdx.y + sqIdx.z;
sqIdx = dIdy * dIdy;
*A22 += sqIdx.x + sqIdx.y + sqIdx.z;
}
void GetPatch4(image2d_t J, const float x, const float y,
const float4* Pch, const float4* Dx, const float4* Dy,
float* b1, float* b2)
{
float4 J_val = read_imagef(J, sampler, (float2)(x, y));
float4 diff = (J_val - *Pch) * 32.0f;
float4 xdiff = diff* *Dx;
*b1 += xdiff.x + xdiff.y + xdiff.z;
xdiff = diff* *Dy;
*b2 += xdiff.x + xdiff.y + xdiff.z;
}
void GetError4(image2d_t J, const float x, const float y, const float4* Pch, float* errval)
{
float4 diff = read_imagef(J, sampler, (float2)(x,y))-*Pch;
*errval += fabs(diff.x) + fabs(diff.y) + fabs(diff.z);
}
#define  GRIDSIZE  3
__kernel void lkSparse_C1_D5(image2d_t I, image2d_t J,
__global const float2* prevPts, int prevPtsStep, __global float2* nextPts, int nextPtsStep, __global uchar* status, __global float* err,
const int level, const int rows, const int cols, int PATCH_X, int PATCH_Y, int cn, int c_winSize_x, int c_winSize_y, int c_iters, char calcErr)
{
__local float smem1[BUFFER];
__local float smem2[BUFFER];
__local float smem3[BUFFER];
unsigned int xid=get_local_id(0);
unsigned int yid=get_local_id(1);
unsigned int gid=get_group_id(0);
unsigned int xsize=get_local_size(0);
unsigned int ysize=get_local_size(1);
int xBase, yBase, k;
float2 c_halfWin = (float2)((c_winSize_x - 1)>>1, (c_winSize_y - 1)>>1);
const int tid = mad24(yid, xsize, xid);
float2 prevPt = prevPts[gid] / (float2)(1 << level);
if (prevPt.x < 0 || prevPt.x >= cols || prevPt.y < 0 || prevPt.y >= rows)
{
if (tid == 0 && level == 0)
{
status[gid] = 0;
}
return;
}
prevPt -= c_halfWin;
float A11 = 0;
float A12 = 0;
float A22 = 0;
float I_patch[GRIDSIZE][GRIDSIZE];
float dIdx_patch[GRIDSIZE][GRIDSIZE];
float dIdy_patch[GRIDSIZE][GRIDSIZE];
yBase=yid;
{
xBase=xid;
SetPatch(I, prevPt.x + xBase + 0.5f, prevPt.y + yBase + 0.5f,
&I_patch[0][0], &dIdx_patch[0][0], &dIdy_patch[0][0],
&A11, &A12, &A22);
xBase+=xsize;
SetPatch(I, prevPt.x + xBase + 0.5f, prevPt.y + yBase + 0.5f,
&I_patch[0][1], &dIdx_patch[0][1], &dIdy_patch[0][1],
&A11, &A12, &A22);
xBase+=xsize;
if(xBase= cols || prevPt.y < -c_halfWin.y || prevPt.y >= rows)
{
if (tid == 0 && level == 0)
status[gid] = 0;
return;
}
float b1 = 0;
float b2 = 0;
yBase=yid;
{
xBase=xid;
GetPatch(J, prevPt.x + xBase + 0.5f, prevPt.y + yBase + 0.5f,
&I_patch[0][0], &dIdx_patch[0][0], &dIdy_patch[0][0],
&b1, &b2);
xBase+=xsize;
GetPatch(J, prevPt.x + xBase + 0.5f, prevPt.y + yBase + 0.5f,
&I_patch[0][1], &dIdx_patch[0][1], &dIdy_patch[0][1],
&b1, &b2);
xBase+=xsize;
if(xBase>1, (c_winSize_y - 1)>>1);
const int tid = mad24(yid, xsize, xid);
float2 nextPt = prevPts[gid]/(float2)(1<= cols || nextPt.y < 0 || nextPt.y >= rows)
{
if (tid == 0 && level == 0)
{
status[gid] = 0;
}
return;
}
nextPt -= c_halfWin;
float A11 = 0.0f;
float A12 = 0.0f;
float A22 = 0.0f;
float4 I_patch[8];
float4 dIdx_patch[8];
float4 dIdy_patch[8];
float4 I_add,Dx_add,Dy_add;
yBase=yid;
{
xBase=xid;
SetPatch4(I, nextPt.x + xBase + 0.5f, nextPt.y + yBase + 0.5f,
&I_patch[0], &dIdx_patch[0], &dIdy_patch[0],
&A11, &A12, &A22);
xBase+=xsize;
SetPatch4(I, nextPt.x + xBase + 0.5f, nextPt.y + yBase + 0.5f,
&I_patch[1], &dIdx_patch[1], &dIdy_patch[1],
&A11, &A12, &A22);
xBase+=xsize;
if(xBase= cols || nextPt.y < -c_halfWin.y || nextPt.y >= rows)
{
if (tid == 0 && level == 0)
status[gid] = 0;
return;
}
float b1 = 0;
float b2 = 0;
yBase=yid;
{
xBase=xid;
GetPatch4(J, nextPt.x + xBase + 0.5f, nextPt.y + yBase + 0.5f,
&I_patch[0], &dIdx_patch[0], &dIdy_patch[0],
&b1, &b2);
xBase+=xsize;
GetPatch4(J, nextPt.x + xBase + 0.5f, nextPt.y + yBase + 0.5f,
&I_patch[1], &dIdx_patch[1], &dIdy_patch[1],
&b1, &b2);
xBase+=xsize;
if(xBase= cols || y >= rows)
return;
int A11i = 0;
int A12i = 0;
int A22i = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int dIdx = dIdx_patch[(get_local_id(1) + i) * patchWidth + (get_local_id(0) + j)];
int dIdy = dIdy_patch[(get_local_id(1) + i) * patchWidth + (get_local_id(0) + j)];
A11i += dIdx * dIdx;
A12i += dIdx * dIdy;
A22i += dIdy * dIdy;
}
}
float A11 = A11i;
float A12 = A12i;
float A22 = A22i;
float D = A11 * A22 - A12 * A12;
if (D < 1.192092896e-07f)
{
return;
}
D = 1.f / D;
A11 *= D;
A12 *= D;
A22 *= D;
float2 nextPt;
nextPt.x = x + prevU[y/2 * prevUStep / 4 + x/2] * 2.0f;
nextPt.y = y + prevV[y/2 * prevVStep / 4 + x/2] * 2.0f;
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < 0 || nextPt.x >= cols || nextPt.y < 0 || nextPt.y >= rows)
{
return;
}
int b1 = 0;
int b2 = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int iI = I_patch[(get_local_id(1) + i) * patchWidth + get_local_id(0) + j];
int iJ = read_imagei(J, sampler, (float2)(nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f)).x;
int diff = (iJ - iI) * 32;
int dIdx = dIdx_patch[(get_local_id(1) + i) * patchWidth + (get_local_id(0) + j)];
int dIdy = dIdy_patch[(get_local_id(1) + i) * patchWidth + (get_local_id(0) + j)];
b1 += diff * dIdx;
b2 += diff * dIdy;
}
}
float2 delta;
delta.x = A12 * b2 - A22 * b1;
delta.y = A12 * b1 - A11 * b2;
nextPt.x += delta.x;
nextPt.y += delta.y;
if (fabs(delta.x) < 0.01f && fabs(delta.y) < 0.01f)
break;
}
u[y * uStep / 4 + x] = nextPt.x - x;
v[y * vStep / 4 + x] = nextPt.y - y;
if (calcErr)
{
int errval = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int iI = I_patch[(get_local_id(1) + i) * patchWidth + get_local_id(0) + j];
int iJ = read_imagei(J, sampler, (float2)(nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f)).x;
errval += abs(iJ - iI);
}
}
}
}
516e74880f1fd439f623e7b83bfb3b37split_mat#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#if DATA_DEPTH == 0
#define BASE_TYPE uchar
#elif DATA_DEPTH == 1
#error data_depth char, use uchar datatype instead
#elif DATA_DEPTH == 2
#define BASE_TYPE ushort
#elif DATA_DEPTH == 3
#error data_depth short, use ushort datatype instead
#elif DATA_DEPTH == 4
#define BASE_TYPE int
#elif DATA_DEPTH == 5
#define BASE_TYPE float
#elif DATA_DEPTH == 6
#define BASE_TYPE double
#else
#error data_depth
#endif
#if DATA_CHAN == 2
#define SRC_VEC_SIZE 2
#elif DATA_CHAN == 3
#define SRC_VEC_SIZE 4
#elif DATA_CHAN == 4
#define SRC_VEC_SIZE 4
#else
#error data_chan
#endif
#define __CAT(x, y) x##y
#define CAT(x, y) __CAT(x, y)
#define uchar1 uchar
#define char1 char
#define ushort1 ushort
#define short1 short
#define int1 int
#define float1 float
#define double1 double
#define TYPE BASE_TYPE
#define SRC_TYPE CAT(BASE_TYPE, SRC_VEC_SIZE)
#define DST_VEC_TYPE CAT(BASE_TYPE, VEC_SIZE)
#define vstore1 vstore
#define VSTORE CAT(vstore, VEC_SIZE)
#define VSTORE_ALIGNED(ptr, v) *((__global DST_VEC_TYPE*)(ptr)) = (v)
#define VSTORE_UNALIGNED(ptr, v) VSTORE((v), 0, (__global TYPE*)(ptr))
#ifdef DST0_ALIGNED
#define VSTORE_dst0 VSTORE_ALIGNED
#else
#define VSTORE_dst0 VSTORE_UNALIGNED
#endif
#ifdef DST1_ALIGNED
#define VSTORE_dst1 VSTORE_ALIGNED
#else
#define VSTORE_dst1 VSTORE_UNALIGNED
#endif
#ifdef DST2_ALIGNED
#define VSTORE_dst2 VSTORE_ALIGNED
#else
#define VSTORE_dst2 VSTORE_UNALIGNED
#endif
#ifdef DST3_ALIGNED
#define VSTORE_dst3 VSTORE_ALIGNED
#else
#define VSTORE_dst3 VSTORE_UNALIGNED
#endif
__kernel void split_vector(
__global SRC_TYPE* src, int srcStepBytes, int2 srcOffset,
__global TYPE* dst0, int dst0StepBytes, int2 dst0Offset,
__global TYPE* dst1, int dst1StepBytes, int2 dst1Offset,
#if DATA_CHAN > 2
__global TYPE* dst2, int dst2StepBytes, int2 dst2Offset,
#endif
#if DATA_CHAN > 3
__global TYPE* dst3, int dst3StepBytes, int2 dst3Offset,
#endif
int2 size)
{
int x = get_global_id(0) * VEC_SIZE;
int y = get_global_id(1);
if (x < size.x && y < size.y)
{
SRC_TYPE srcData[VEC_SIZE];
int xOffsetLimitBytes = srcOffset.x + size.x * sizeof(SRC_TYPE);
int xOffsetBytes = srcOffset.x + x * sizeof(SRC_TYPE);
int yOffsetBytes = (srcOffset.y + y) * srcStepBytes;
#pragma unroll
for (int i = 0; i < VEC_SIZE; i++, xOffsetBytes += sizeof(SRC_TYPE))
{
srcData[i] = (xOffsetBytes >= xOffsetLimitBytes) ? (SRC_TYPE)0 :
*(__global SRC_TYPE*)((__global char*)src + yOffsetBytes + xOffsetBytes);
}
#if VEC_SIZE == 1
TYPE dstC0 = srcData[0].s0;
TYPE dstC1 = srcData[0].s1;
#if DATA_CHAN > 2
TYPE dstC2 = srcData[0].s2;
#endif
#if DATA_CHAN > 3
TYPE dstC3 = srcData[0].s3;
#endif
# define VEC_TO_ARRAY(v, a) TYPE a[1] = {v};
#elif VEC_SIZE == 2
DST_VEC_TYPE dstC0 = (DST_VEC_TYPE)(srcData[0].s0, srcData[1].s0);
DST_VEC_TYPE dstC1 = (DST_VEC_TYPE)(srcData[0].s1, srcData[1].s1);
#if DATA_CHAN > 2
DST_VEC_TYPE dstC2 = (DST_VEC_TYPE)(srcData[0].s2, srcData[1].s2);
#endif
#if DATA_CHAN > 3
DST_VEC_TYPE dstC3 = (DST_VEC_TYPE)(srcData[0].s3, srcData[1].s3);
#endif
# define VEC_TO_ARRAY(v, a) TYPE a[2] = {v.s0, v.s1};
#elif VEC_SIZE == 4
DST_VEC_TYPE dstC0 = (DST_VEC_TYPE)(srcData[0].s0, srcData[1].s0, srcData[2].s0, srcData[3].s0);
DST_VEC_TYPE dstC1 = (DST_VEC_TYPE)(srcData[0].s1, srcData[1].s1, srcData[2].s1, srcData[3].s1);
#if DATA_CHAN > 2
DST_VEC_TYPE dstC2 = (DST_VEC_TYPE)(srcData[0].s2, srcData[1].s2, srcData[2].s2, srcData[3].s2);
#endif
#if DATA_CHAN > 3
DST_VEC_TYPE dstC3 = (DST_VEC_TYPE)(srcData[0].s3, srcData[1].s3, srcData[2].s3, srcData[3].s3);
#endif
# define VEC_TO_ARRAY(v, a) TYPE a[4] = {v.s0, v.s1, v.s2, v.s3};
#endif
#ifndef BYPASS_VSTORE
#define BYPASS_VSTORE false
#endif
#define WRITE_VEC_DST(dst, vecValue) \
{ \
int dst ## xOffsetLimitBytes = dst ## Offset.x + size.x * sizeof(TYPE); \
int dst ## xOffsetBytes = dst ## Offset.x + x * sizeof(TYPE); \
int dst ## yOffsetBytes = (dst ## Offset.y + y) * dst ## StepBytes; \
if (!BYPASS_VSTORE && dst ## xOffsetBytes + (int)sizeof(DST_VEC_TYPE) <= dst ## xOffsetLimitBytes) \
{ \
VSTORE_ ## dst(((__global char*)dst + dst ## yOffsetBytes + dst ## xOffsetBytes), vecValue); \
} \
else \
{ \
VEC_TO_ARRAY(vecValue, vecValue##Array); \
for (int i = 0; i < VEC_SIZE; i++, dst ## xOffsetBytes += sizeof(TYPE)) \
{ \
if (dst ## xOffsetBytes + (int)sizeof(TYPE) <= dst ## xOffsetLimitBytes) \
*(__global TYPE*)((__global char*)dst + dst ## yOffsetBytes + dst ## xOffsetBytes) = vecValue##Array[i]; \
else \
break; \
} \
} \
}
WRITE_VEC_DST(dst0, dstC0);
WRITE_VEC_DST(dst1, dstC1);
#if DATA_CHAN > 2
WRITE_VEC_DST(dst2, dstC2);
#endif
#if DATA_CHAN > 3
WRITE_VEC_DST(dst3, dstC3);
#endif
}
}
8689026266644305bc75b3f61ac6d027stereobm#define ROWSperTHREAD 21
#define BLOCK_W       128
#define N_DISPARITIES 8
#define STEREO_MIND 0
#define STEREO_DISP_STEP N_DISPARITIES
#ifndef radius
#define radius 64
#endif
unsigned int CalcSSD(__local unsigned int *col_ssd)
{
unsigned int cache = col_ssd[0];
#pragma unroll
for(int i = 1; i <= (radius << 1); i++)
cache += col_ssd[i];
return cache;
}
uint2 MinSSD(__local unsigned int *col_ssd)
{
unsigned int ssd[N_DISPARITIES];
const int win_size = (radius << 1);
ssd[0] = CalcSSD(col_ssd + 0 * (BLOCK_W + win_size));
ssd[1] = CalcSSD(col_ssd + 1 * (BLOCK_W + win_size));
ssd[2] = CalcSSD(col_ssd + 2 * (BLOCK_W + win_size));
ssd[3] = CalcSSD(col_ssd + 3 * (BLOCK_W + win_size));
ssd[4] = CalcSSD(col_ssd + 4 * (BLOCK_W + win_size));
ssd[5] = CalcSSD(col_ssd + 5 * (BLOCK_W + win_size));
ssd[6] = CalcSSD(col_ssd + 6 * (BLOCK_W + win_size));
ssd[7] = CalcSSD(col_ssd + 7 * (BLOCK_W + win_size));
unsigned int mssd = min(min(min(ssd[0], ssd[1]), min(ssd[4], ssd[5])), min(min(ssd[2], ssd[3]), min(ssd[6], ssd[7])));
int bestIdx = 0;
for (int i = 0; i < N_DISPARITIES; i++)
{
if (mssd == ssd[i])
bestIdx = i;
}
return (uint2)(mssd, bestIdx);
}
void StepDown(int idx1, int idx2, __global unsigned char* imageL,
__global unsigned char* imageR, int d,   __local unsigned int *col_ssd)
{
uint8 imgR1 = convert_uint8(vload8(0, imageR + (idx1 - d - 7)));
uint8 imgR2 = convert_uint8(vload8(0, imageR + (idx2 - d - 7)));
uint8 diff1 = (uint8)(imageL[idx1]) - imgR1;
uint8 diff2 = (uint8)(imageL[idx2]) - imgR2;
uint8 res = diff2 * diff2 - diff1 * diff1;
const int win_size = (radius << 1);
col_ssd[0 * (BLOCK_W + win_size)] += res.s7;
col_ssd[1 * (BLOCK_W + win_size)] += res.s6;
col_ssd[2 * (BLOCK_W + win_size)] += res.s5;
col_ssd[3 * (BLOCK_W + win_size)] += res.s4;
col_ssd[4 * (BLOCK_W + win_size)] += res.s3;
col_ssd[5 * (BLOCK_W + win_size)] += res.s2;
col_ssd[6 * (BLOCK_W + win_size)] += res.s1;
col_ssd[7 * (BLOCK_W + win_size)] += res.s0;
}
void InitColSSD(int x_tex, int y_tex, int im_pitch, __global unsigned char* imageL,
__global unsigned char* imageR, int d,
__local unsigned int *col_ssd)
{
uint8 leftPixel1;
uint8 diffa = 0;
int idx = y_tex * im_pitch + x_tex;
const int win_size = (radius << 1);
for(int i = 0; i < (win_size + 1); i++)
{
leftPixel1 = (uint8)(imageL[idx]);
uint8 imgR = convert_uint8(vload8(0, imageR + (idx - d - 7)));
uint8 res = leftPixel1 - imgR;
diffa += res * res;
idx += im_pitch;
}
col_ssd[0 * (BLOCK_W + win_size)] = diffa.s7;
col_ssd[1 * (BLOCK_W + win_size)] = diffa.s6;
col_ssd[2 * (BLOCK_W + win_size)] = diffa.s5;
col_ssd[3 * (BLOCK_W + win_size)] = diffa.s4;
col_ssd[4 * (BLOCK_W + win_size)] = diffa.s3;
col_ssd[5 * (BLOCK_W + win_size)] = diffa.s2;
col_ssd[6 * (BLOCK_W + win_size)] = diffa.s1;
col_ssd[7 * (BLOCK_W + win_size)] = diffa.s0;
}
__kernel void stereoKernel(__global unsigned char *left, __global unsigned char *right,
__global unsigned int *cminSSDImage, int cminSSD_step,
__global unsigned char *disp, int disp_step,int cwidth, int cheight,
int img_step, int maxdisp,
__local unsigned int *col_ssd_cache)
{
__local unsigned int *col_ssd = col_ssd_cache + get_local_id(0);
__local unsigned int *col_ssd_extra = get_local_id(0) < (radius << 1) ? col_ssd + BLOCK_W : 0;
int X = get_group_id(0) * BLOCK_W + get_local_id(0) + maxdisp + radius;
#define Y (int)(get_group_id(1) * ROWSperTHREAD + radius)
__global unsigned int* minSSDImage = cminSSDImage + X + Y * cminSSD_step;
__global unsigned char* disparImage = disp + X + Y * disp_step;
int end_row = ROWSperTHREAD < (cheight - Y) ? ROWSperTHREAD:(cheight - Y);
int y_tex;
int x_tex = X - radius;
for(int d = STEREO_MIND; d < maxdisp; d += STEREO_DISP_STEP)
{
y_tex = Y - radius;
InitColSSD(x_tex, y_tex, img_step, left, right, d, col_ssd);
if (col_ssd_extra > 0)
if (x_tex + BLOCK_W < cwidth)
InitColSSD(x_tex + BLOCK_W, y_tex, img_step, left, right, d, col_ssd_extra);
barrier(CLK_LOCAL_MEM_FENCE);
uint2 minSSD = MinSSD(col_ssd);
if (X < cwidth - radius && Y < cheight - radius)
{
if (minSSD.x < minSSDImage[0])
{
disparImage[0] = (unsigned char)(d + minSSD.y);
minSSDImage[0] = minSSD.x;
}
}
for(int row = 1; row < end_row; row++)
{
int idx1 = y_tex * img_step + x_tex;
int idx2 = min(y_tex + ((radius << 1) + 1), cheight - 1) * img_step + x_tex;
barrier(CLK_LOCAL_MEM_FENCE);
StepDown(idx1, idx2, left, right, d, col_ssd);
if (col_ssd_extra > 0)
if (x_tex + BLOCK_W < cwidth)
StepDown(idx1, idx2, left + BLOCK_W, right + BLOCK_W, d, col_ssd_extra);
barrier(CLK_LOCAL_MEM_FENCE);
uint2 minSSD = MinSSD(col_ssd);
if (X < cwidth - radius && row < cheight - radius - Y)
{
int idx = row * cminSSD_step;
if (minSSD.x < minSSDImage[idx])
{
disparImage[disp_step * row] = (unsigned char)(d + minSSD.y);
minSSDImage[idx] = minSSD.x;
}
}
y_tex++;
}
}
}
__kernel void prefilter_xsobel(__global unsigned char *input, __global unsigned char *output,
int rows, int cols, int prefilterCap)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < cols && y < rows)
{
int cov = input[(y-1) * cols + (x-1)] * (-1) + input[(y-1) * cols + (x+1)] * (1) +
input[(y)   * cols + (x-1)] * (-2) + input[(y)   * cols + (x+1)] * (2) +
input[(y+1) * cols + (x-1)] * (-1) + input[(y+1) * cols + (x+1)] * (1);
cov = min(min(max(-prefilterCap, cov), prefilterCap) + prefilterCap, 255);
output[y * cols + x] = cov & 0xFF;
}
}
float sobel(__global unsigned char *input, int x, int y, int rows, int cols)
{
float conv = 0;
int y1 = y==0? 0 : y-1;
int x1 = x==0? 0 : x-1;
if(x < cols && y < rows && x > 0 && y > 0)
{
conv = (float)input[(y1)  * cols + (x1)] * (-1) + (float)input[(y1)  * cols + (x+1)] * (1) +
(float)input[(y)   * cols + (x1)] * (-2) + (float)input[(y)   * cols + (x+1)] * (2) +
(float)input[(y+1) * cols + (x1)] * (-1) + (float)input[(y+1) * cols + (x+1)] * (1);
}
return fabs(conv);
}
float CalcSums(__local float *cols, __local float *cols_cache, int winsz)
{
unsigned int cache = cols[0];
for(int i = 1; i <= winsz; i++)
cache += cols[i];
return cache;
}
#define RpT (2 * ROWSperTHREAD)
__kernel void textureness_kernel(__global unsigned char *disp, int disp_rows, int disp_cols,
int disp_step, __global unsigned char *input, int input_rows,
int input_cols,int winsz, float threshold,
__local float *cols_cache)
{
int winsz2 = winsz/2;
int n_dirty_pixels = (winsz2) * 2;
int local_id_x = get_local_id(0);
int group_size_x = get_local_size(0);
int group_id_y = get_group_id(1);
__local float *cols = cols_cache + group_size_x + local_id_x;
__local float *cols_extra = local_id_x < n_dirty_pixels ? cols + group_size_x : 0;
int x = get_global_id(0);
int beg_row = group_id_y * RpT;
int end_row = min(beg_row + RpT, disp_rows);
int y = beg_row;
float sum = 0;
float sum_extra = 0;
for(int i = y - winsz2; i <= y + winsz2; ++i)
{
sum += sobel(input, x - winsz2, i, input_rows, input_cols);
if (cols_extra)
sum_extra += sobel(input, x + group_size_x - winsz2, i, input_rows, input_cols);
}
*cols = sum;
if (cols_extra)
*cols_extra = sum_extra;
barrier(CLK_LOCAL_MEM_FENCE);
float sum_win = CalcSums(cols, cols_cache + local_id_x, winsz) * 255;
if (sum_win < threshold)
disp[y * disp_step + x] = 0;
barrier(CLK_LOCAL_MEM_FENCE);
for(int y = beg_row + 1; y < end_row; ++y)
{
sum = sum - sobel(input, x - winsz2, y - winsz2 - 1, input_rows, input_cols) +
sobel(input, x - winsz2, y + winsz2, input_rows, input_cols);
*cols = sum;
if (cols_extra)
{
sum_extra = sum_extra - sobel(input, x + group_size_x - winsz2, y - winsz2 - 1,input_rows, input_cols)
+ sobel(input, x + group_size_x - winsz2, y + winsz2, input_rows, input_cols);
*cols_extra = sum_extra;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (x < disp_cols)
{
float sum_win = CalcSums(cols, cols_cache + local_id_x, winsz) * 255;
if (sum_win < threshold)
disp[y * disp_step + x] = 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
5dcc7a3ab0f93796613f382a3c29d5fcstereobp#if defined (DOUBLE_SUPPORT)
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#endif
#ifdef T_FLOAT
#define T float
#define T4 float4
#else
#define T short
#define T4 short4
#endif
T saturate_cast(float v){
#ifdef T_SHORT
return convert_short_sat_rte(v);
#else
return v;
#endif
}
T4 saturate_cast4(float4 v){
#ifdef T_SHORT
return convert_short4_sat_rte(v);
#else
return v;
#endif
}
#define FLOAT_MAX 3.402823466e+38f
typedef struct
{
int   cndisp;
float cmax_data_term;
float cdata_weight;
float cmax_disc_term;
float cdisc_single_jump;
}con_srtuct_t;
float pix_diff_1(const uchar4 l, __global const uchar *rs)
{
return abs((int)(l.x) - *rs);
}
float pix_diff_4(const uchar4 l, __global const uchar *rs)
{
uchar4 r;
r = *((__global uchar4 *)rs);
const float tr = 0.299f;
const float tg = 0.587f;
const float tb = 0.114f;
float val;
val  = tb * abs((int)l.x - r.x);
val += tg * abs((int)l.y - r.y);
val += tr * abs((int)l.z - r.z);
return val;
}
float pix_diff_3(const uchar4 l, __global const uchar *rs)
{
return pix_diff_4(l, rs);
}
#ifndef CN
#define CN 4
#endif
#ifndef CNDISP
#define CNDISP 64
#endif
#define CAT(X,Y) X##Y
#define CAT2(X,Y) CAT(X,Y)
#define PIX_DIFF CAT2(pix_diff_, CN)
__kernel void comp_data(__global uchar *left,  int left_rows,  int left_cols,  int left_step,
__global uchar *right, int right_step,
__global T *data, int data_step,
__constant con_srtuct_t *con_st)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y > 0 && y < (left_rows - 1) && x > 0 && x < (left_cols - 1))
{
data_step /= sizeof(T);
const __global uchar* ls = left  + y * left_step  + x * CN;
const __global uchar* rs = right + y * right_step + x * CN;
__global T *ds = data + y * data_step + x;
const unsigned int disp_step = data_step * left_rows;
const float weightXterm = con_st -> cdata_weight * con_st -> cmax_data_term;
const uchar4 ls_data = vload4(0, ls);
for (int disp = 0; disp < con_st -> cndisp; disp++)
{
if (x - disp >= 1)
{
float val = 0;
val = PIX_DIFF(ls_data, rs - disp * CN);
ds[disp * disp_step] =  saturate_cast(fmin(con_st -> cdata_weight * val, weightXterm));
}
else
{
ds[disp * disp_step] =  saturate_cast(weightXterm);
}
}
}
}
__kernel void data_step_down(__global T *src, int src_rows,
__global T *dst, int dst_rows, int dst_cols,
int src_step, int dst_step,
int cndisp)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
src_step /= sizeof(T);
dst_step /= sizeof(T);
int4 coor_step = (int4)(src_rows * src_step);
int4 coor = (int4)(min(2*y+0, src_rows-1) * src_step + 2*x+0,
min(2*y+1, src_rows-1) * src_step + 2*x+0,
min(2*y+0, src_rows-1) * src_step + 2*x+1,
min(2*y+1, src_rows-1) * src_step + 2*x+1);
for (int d = 0; d < cndisp; ++d)
{
float dst_reg;
dst_reg  = src[coor.x];
dst_reg += src[coor.y];
dst_reg += src[coor.z];
dst_reg += src[coor.w];
coor += coor_step;
dst[(d * dst_rows + y) * dst_step + x] = saturate_cast(dst_reg);
}
}
}
__kernel void level_up_message(__global T *src, int src_rows, int src_step,
__global T *dst, int dst_rows, int dst_cols, int dst_step,
int cndisp)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (x < dst_cols && y < dst_rows)
{
src_step /= sizeof(T);
dst_step /= sizeof(T);
const int dst_disp_step = dst_step * dst_rows;
const int src_disp_step = src_step * src_rows;
__global T       *dstr = dst + y * dst_step + x;
__global const T *srcr = src + (y / 2 * src_step) + (x / 2);
for (int d = 0; d < cndisp; ++d)
dstr[d * dst_disp_step] = srcr[d * src_disp_step];
}
}
void message(__global T *us_, __global T *ds_, __global T *ls_, __global T *rs_,
const __global T *dt,
int u_step, int msg_disp_step, int data_disp_step,
float4 cmax_disc_term, float4 cdisc_single_jump)
{
__global T *us = us_ + u_step;
__global T *ds = ds_ - u_step;
__global T *ls = ls_ + 1;
__global T *rs = rs_ - 1;
float4 minimum = (float4)(FLOAT_MAX);
T4 t_dst[CNDISP];
float4 dst_reg;
float4 prev;
float4 cur;
T t_us = us[0];
T t_ds = ds[0];
T t_ls = ls[0];
T t_rs = rs[0];
T t_dt = dt[0];
prev = (float4)(t_us + t_ls + t_rs + t_dt,
t_ds + t_ls + t_rs + t_dt,
t_us + t_ds + t_rs + t_dt,
t_us + t_ds + t_ls + t_dt);
minimum = min(prev, minimum);
t_dst[0] = saturate_cast4(prev);
for(int i = 1, idx = msg_disp_step; i < CNDISP; ++i, idx+=msg_disp_step)
{
t_us = us[idx];
t_ds = ds[idx];
t_ls = ls[idx];
t_rs = rs[idx];
t_dt = dt[data_disp_step * i];
dst_reg = (float4)(t_us + t_ls + t_rs + t_dt,
t_ds + t_ls + t_rs + t_dt,
t_us + t_ds + t_rs + t_dt,
t_us + t_ds + t_ls + t_dt);
minimum = min(dst_reg, minimum);
prev += cdisc_single_jump;
prev = min(prev, dst_reg);
t_dst[i] = saturate_cast4(prev);
}
minimum += cmax_disc_term;
float4 sum = (float4)(0);
prev = convert_float4(t_dst[CNDISP - 1]);
for (int disp = CNDISP - 2; disp >= 0; disp--)
{
prev += cdisc_single_jump;
cur = convert_float4(t_dst[disp]);
prev = min(prev, cur);
cur = min(prev, minimum);
sum += cur;
t_dst[disp] = saturate_cast4(cur);
}
dst_reg = convert_float4(t_dst[CNDISP - 1]);
dst_reg = min(dst_reg, minimum);
t_dst[CNDISP - 1] = saturate_cast4(dst_reg);
sum += dst_reg;
sum /= (float4)(CNDISP);
#pragma unroll
for(int i = 0, idx = 0; i < CNDISP; ++i, idx+=msg_disp_step)
{
T4 dst = t_dst[i];
us_[idx] = dst.x - sum.x;
ds_[idx] = dst.y - sum.y;
rs_[idx] = dst.z - sum.z;
ls_[idx] = dst.w - sum.w;
}
}
__kernel void one_iteration(__global T *u,    int u_step,
__global T *data, int data_step,
__global T *d,    __global T *l, __global T *r,
int t, int cols, int rows,
float cmax_disc_term, float cdisc_single_jump)
{
const int y = get_global_id(1);
const int x = ((get_global_id(0)) << 1) + ((y + t) & 1);
if ((y > 0) && (y < rows - 1) && (x > 0) && (x < cols - 1))
{
u_step    /= sizeof(T);
data_step /= sizeof(T);
__global T *us = u + y * u_step + x;
__global T *ds = d + y * u_step + x;
__global T *ls = l + y * u_step + x;
__global T *rs = r + y * u_step + x;
const __global  T *dt = data + y * data_step + x;
int msg_disp_step = u_step * rows;
int data_disp_step = data_step * rows;
message(us, ds, ls, rs, dt,
u_step, msg_disp_step, data_disp_step,
(float4)(cmax_disc_term), (float4)(cdisc_single_jump));
}
}
__kernel void output(const __global T *u, int u_step,
const __global T *d, const __global T *l,
const __global T *r, const __global T *data,
__global T *disp, int disp_rows, int disp_cols, int disp_step,
int cndisp)
{
const int x = get_global_id(0);
const int y = get_global_id(1);
if (y > 0 && y < disp_rows - 1 && x > 0 && x < disp_cols - 1)
{
u_step    /= sizeof(T);
disp_step /= sizeof(T);
const __global T *us = u + (y + 1) * u_step + x;
const __global T *ds = d + (y - 1) * u_step + x;
const __global T *ls = l + y * u_step + (x + 1);
const __global T *rs = r + y * u_step + (x - 1);
const __global T *dt = data + y * u_step + x;
int disp_steps = disp_rows * u_step;
int best = 0;
float best_val = FLOAT_MAX;
for (int d = 0; d < cndisp; ++d)
{
float val;
val  = us[d * disp_steps];
val += ds[d * disp_steps];
val += ls[d * disp_steps];
val += rs[d * disp_steps];
val += dt[d * disp_steps];
if (val < best_val)
{
best_val = val;
best = d;
}
}
(disp + y * disp_step)[x] = convert_short_sat(best);
}
}
c1f26bdbf3637f3bf83d2d9b7adc3c2bstereocsbp__kernel void get_first_k_initial_global_0(__global short *data_cost_selected_, __global short *selected_disp_pyr,
__global short *ctemp, int h, int w, int nr_plane,
int cmsg_step1, int cdisp_step1, int cndisp)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
__global short *selected_disparity = selected_disp_pyr      + y * cmsg_step1 + x;
__global short *data_cost_selected = data_cost_selected_    + y * cmsg_step1 + x;
__global short *data_cost          = ctemp + y * cmsg_step1 + x;
for(int i = 0; i < nr_plane; i++)
{
short minimum = SHRT_MAX;
int id = 0;
for(int d = 0; d < cndisp; d++)
{
short cur = data_cost[d * cdisp_step1];
if(cur < minimum)
{
minimum = cur;
id = d;
}
}
data_cost_selected[i  * cdisp_step1] = minimum;
selected_disparity[i  * cdisp_step1] = id;
data_cost         [id * cdisp_step1] = SHRT_MAX;
}
}
}
__kernel void get_first_k_initial_global_1(__global  float *data_cost_selected_, __global float *selected_disp_pyr,
__global  float *ctemp, int h, int w, int nr_plane,
int cmsg_step1, int cdisp_step1, int cndisp)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
__global   float *selected_disparity = selected_disp_pyr      + y * cmsg_step1 + x;
__global   float *data_cost_selected = data_cost_selected_    + y * cmsg_step1 + x;
__global   float *data_cost          = ctemp + y * cmsg_step1 + x;
for(int i = 0; i < nr_plane; i++)
{
float minimum = FLT_MAX;
int id = 0;
for(int d = 0; d < cndisp; d++)
{
float cur = data_cost[d * cdisp_step1];
if(cur < minimum)
{
minimum = cur;
id = d;
}
}
data_cost_selected[i  * cdisp_step1] = minimum;
selected_disparity[i  * cdisp_step1] = id;
data_cost         [id * cdisp_step1] = FLT_MAX;
}
}
}
__kernel void get_first_k_initial_local_0(__global  short *data_cost_selected_, __global short *selected_disp_pyr,
__global  short *ctemp,int h, int w, int nr_plane,
int cmsg_step1, int cdisp_step1, int cndisp)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
__global short *selected_disparity = selected_disp_pyr   + y * cmsg_step1 + x;
__global short *data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
__global short *data_cost = ctemp + y * cmsg_step1 + x;
int nr_local_minimum = 0;
short prev = data_cost[0 * cdisp_step1];
short cur  = data_cost[1 * cdisp_step1];
short next = data_cost[2 * cdisp_step1];
for (int d = 1; d < cndisp - 1 && nr_local_minimum < nr_plane; d++)
{
if (cur < prev && cur < next)
{
data_cost_selected[nr_local_minimum * cdisp_step1] = cur;
selected_disparity[nr_local_minimum * cdisp_step1] = d;
data_cost[d * cdisp_step1] = SHRT_MAX;
nr_local_minimum++;
}
prev = cur;
cur = next;
next = data_cost[(d + 1) * cdisp_step1];
}
for (int i = nr_local_minimum; i < nr_plane; i++)
{
short minimum = SHRT_MAX;
int id = 0;
for (int d = 0; d < cndisp; d++)
{
cur = data_cost[d * cdisp_step1];
if (cur < minimum)
{
minimum = cur;
id = d;
}
}
data_cost_selected[i * cdisp_step1] = minimum;
selected_disparity[i * cdisp_step1] = id;
data_cost[id * cdisp_step1] = SHRT_MAX;
}
}
}
__kernel void get_first_k_initial_local_1(__global float *data_cost_selected_, __global float *selected_disp_pyr,
__global float *ctemp,int h, int w, int nr_plane,
int cmsg_step1,  int cdisp_step1, int cndisp)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
__global float *selected_disparity = selected_disp_pyr   + y * cmsg_step1 + x;
__global float *data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
__global float *data_cost = ctemp + y * cmsg_step1 + x;
int nr_local_minimum = 0;
float prev = data_cost[0 * cdisp_step1];
float cur  = data_cost[1 * cdisp_step1];
float next = data_cost[2 * cdisp_step1];
for (int d = 1; d < cndisp - 1 && nr_local_minimum < nr_plane; d++)
{
if (cur < prev && cur < next)
{
data_cost_selected[nr_local_minimum * cdisp_step1] = cur;
selected_disparity[nr_local_minimum * cdisp_step1] = d;
data_cost[d * cdisp_step1] = FLT_MAX ;
nr_local_minimum++;
}
prev = cur;
cur = next;
next = data_cost[(d + 1) * cdisp_step1];
}
for (int i = nr_local_minimum; i < nr_plane; i++)
{
float minimum = FLT_MAX;
int id = 0;
for (int d = 0; d < cndisp; d++)
{
cur = data_cost[d * cdisp_step1];
if (cur < minimum)
{
minimum = cur;
id = d;
}
}
data_cost_selected[i * cdisp_step1] = minimum;
selected_disparity[i * cdisp_step1] = id;
data_cost[id * cdisp_step1] = FLT_MAX;
}
}
}
float compute_3(__global uchar* left, __global uchar* right,
float cdata_weight,  float cmax_data_term)
{
float tb = 0.114f * abs((int)left[0] - right[0]);
float tg = 0.587f * abs((int)left[1] - right[1]);
float tr = 0.299f * abs((int)left[2] - right[2]);
return fmin(cdata_weight * (tr + tg + tb), cdata_weight * cmax_data_term);
}
float compute_1(__global uchar* left, __global uchar* right,
float cdata_weight,  float cmax_data_term)
{
return fmin(cdata_weight * abs((int)*left - (int)*right), cdata_weight * cmax_data_term);
}
short round_short(float v)
{
return convert_short_sat_rte(v);
}
__kernel void init_data_cost_0(__global short *ctemp, __global uchar *cleft, __global uchar *cright,
int h, int w, int level, int channels,
int cmsg_step1, float cdata_weight, float cmax_data_term, int cdisp_step1,
int cth, int cimg_step, int cndisp)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
int y0 = y << level;
int yt = (y + 1) << level;
int x0 = x << level;
int xt = (x + 1) << level;
__global short *data_cost = ctemp + y * cmsg_step1 + x;
for(int d = 0; d < cndisp; ++d)
{
float val = 0.0f;
for(int yi = y0; yi < yt; yi++)
{
for(int xi = x0; xi < xt; xi++)
{
int xr = xi - d;
if(d < cth || xr < 0)
val += cdata_weight * cmax_data_term;
else
{
__global uchar *lle = cleft  + yi * cimg_step + xi * channels;
__global uchar *lri = cright + yi * cimg_step + xr * channels;
if(channels == 1)
val += compute_1(lle, lri, cdata_weight, cmax_data_term);
else
val += compute_3(lle, lri, cdata_weight, cmax_data_term);
}
}
}
data_cost[cdisp_step1 * d] = round_short(val);
}
}
}
__kernel void init_data_cost_1(__global float *ctemp, __global uchar *cleft, __global uchar *cright,
int h, int w, int level, int channels,
int cmsg_step1, float cdata_weight, float cmax_data_term, int cdisp_step1,
int cth, int cimg_step, int cndisp)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
int y0 = y << level;
int yt = (y + 1) << level;
int x0 = x << level;
int xt = (x + 1) << level;
__global float *data_cost = ctemp + y * cmsg_step1 + x;
for(int d = 0; d < cndisp; ++d)
{
float val = 0.0f;
for(int yi = y0; yi < yt; yi++)
{
for(int xi = x0; xi < xt; xi++)
{
int xr = xi - d;
if(d < cth || xr < 0)
val += cdata_weight * cmax_data_term;
else
{
__global uchar* lle = cleft  + yi * cimg_step + xi * channels;
__global uchar* lri = cright + yi * cimg_step + xr * channels;
if(channels == 1)
val += compute_1(lle, lri, cdata_weight, cmax_data_term);
else
val += compute_3(lle, lri, cdata_weight, cmax_data_term);
}
}
}
data_cost[cdisp_step1 * d] = val;
}
}
}
__kernel void init_data_cost_reduce_0(__global short *ctemp, __global uchar *cleft, __global uchar *cright,
__local float *smem, int level, int rows, int cols, int h, int winsz, int channels,
int cndisp,int cimg_step, float cdata_weight, float cmax_data_term, int cth,
int cdisp_step1, int cmsg_step1)
{
int x_out = get_group_id(0);
int y_out = get_group_id(1) % h;
int d = (get_group_id(1) / h ) * get_local_size(2) + get_local_id(2);
int tid = get_local_id(0);
if (d < cndisp)
{
int x0 = x_out << level;
int y0 = y_out << level;
int len = min(y0 + winsz, rows) - y0;
float val = 0.0f;
if (x0 + tid < cols)
{
if (x0 + tid - d < 0 || d < cth)
val = cdata_weight * cmax_data_term * len;
else
{
__global uchar* lle =  cleft + y0 * cimg_step + channels * (x0 + tid    );
__global uchar* lri = cright + y0 * cimg_step + channels * (x0 + tid - d);
for(int y = 0; y < len; ++y)
{
if(channels == 1)
val += compute_1(lle, lri, cdata_weight, cmax_data_term);
else
val += compute_3(lle, lri, cdata_weight, cmax_data_term);
lle += cimg_step;
lri += cimg_step;
}
}
}
__local float* dline = smem + winsz * get_local_id(2);
dline[tid] = val;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local float* dline = smem + winsz * get_local_id(2);
if (winsz >= 256)
{
if (tid < 128)
dline[tid] += dline[tid + 128];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local float* dline = smem + winsz * get_local_id(2);
if (winsz >= 128)
{
if (tid <  64)
dline[tid] += dline[tid + 64];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 64)
if (tid < 32)
vdline[tid] += vdline[tid + 32];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 32)
if (tid < 16)
vdline[tid] += vdline[tid + 16];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d= 16)
if (tid <  8)
vdline[tid] += vdline[tid + 8];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d= 8)
if (tid <  4)
vdline[tid] += vdline[tid + 4];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d= 4)
if (tid <  2)
vdline[tid] += vdline[tid + 2];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d= 2)
if (tid <  1)
vdline[tid] += vdline[tid + 1];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local float* dline = smem + winsz * get_local_id(2);
__global short* data_cost = ctemp + y_out * cmsg_step1 + x_out;
if (tid == 0)
data_cost[cdisp_step1 * d] = convert_short_sat_rte(dline[0]);
}
}
__kernel void init_data_cost_reduce_1(__global float *ctemp, __global uchar *cleft, __global uchar *cright,
__local float *smem, int level, int rows, int cols, int h, int winsz, int channels,
int cndisp,int cimg_step, float cdata_weight, float cmax_data_term, int cth,
int cdisp_step1, int cmsg_step1)
{
int x_out = get_group_id(0);
int y_out = get_group_id(1) % h;
int d = (get_group_id(1) / h ) * get_local_size(2) + get_local_id(2);
int tid = get_local_id(0);
if (d < cndisp)
{
int x0 = x_out << level;
int y0 = y_out << level;
int len = min(y0 + winsz, rows) - y0;
float val = 0.0f;
if (x0 + tid < cols)
{
if (x0 + tid - d < 0 || d < cth)
val = cdata_weight * cmax_data_term * len;
else
{
__global uchar* lle =  cleft + y0 * cimg_step + channels * (x0 + tid    );
__global uchar* lri = cright + y0 * cimg_step + channels * (x0 + tid - d);
for(int y = 0; y < len; ++y)
{
if(channels == 1)
val += compute_1(lle, lri, cdata_weight, cmax_data_term);
else
val += compute_3(lle, lri, cdata_weight, cmax_data_term);
lle += cimg_step;
lri += cimg_step;
}
}
}
__local float* dline = smem + winsz * get_local_id(2);
dline[tid] = val;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local float* dline = smem + winsz * get_local_id(2);
if (winsz >= 256)
if (tid < 128)
dline[tid] += dline[tid + 128];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local float* dline = smem + winsz * get_local_id(2);
if (winsz >= 128)
if (tid < 64)
dline[tid] += dline[tid + 64];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 64)
if (tid < 32)
vdline[tid] += vdline[tid + 32];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 32)
if (tid < 16)
vdline[tid] += vdline[tid + 16];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 16)
if (tid < 8)
vdline[tid] += vdline[tid + 8];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 8)
if (tid < 4)
vdline[tid] += vdline[tid + 4];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 4)
if (tid < 2)
vdline[tid] += vdline[tid + 2];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 2)
if (tid < 1)
vdline[tid] += vdline[tid + 1];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < cndisp)
{
__global float *data_cost = ctemp + y_out * cmsg_step1 + x_out;
__local float* dline = smem + winsz * get_local_id(2);
if (tid == 0)
data_cost[cdisp_step1 * d] =  dline[0];
}
}
__kernel void compute_data_cost_0(__global const short *selected_disp_pyr, __global short *data_cost_,
__global uchar *cleft, __global uchar *cright,
int h, int w, int level, int nr_plane, int channels,
int cmsg_step1, int cmsg_step2, int cdisp_step1, int cdisp_step2, float cdata_weight,
float cmax_data_term, int cimg_step, int cth)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
int y0 = y << level;
int yt = (y + 1) << level;
int x0 = x << level;
int xt = (x + 1) << level;
__global const short *selected_disparity = selected_disp_pyr + y/2 * cmsg_step2 + x/2;
__global       short *data_cost          = data_cost_ + y * cmsg_step1 + x;
for(int d = 0; d < nr_plane; d++)
{
float val = 0.0f;
for(int yi = y0; yi < yt; yi++)
{
for(int xi = x0; xi < xt; xi++)
{
int sel_disp = selected_disparity[d * cdisp_step2];
int xr = xi - sel_disp;
if (xr < 0 || sel_disp < cth)
val += cdata_weight * cmax_data_term;
else
{
__global uchar* left_x  = cleft + yi * cimg_step + xi * channels;
__global uchar* right_x = cright + yi * cimg_step + xr * channels;
if(channels == 1)
val += compute_1(left_x, right_x, cdata_weight, cmax_data_term);
else
val += compute_3(left_x, right_x, cdata_weight, cmax_data_term);
}
}
}
data_cost[cdisp_step1 * d] = convert_short_sat_rte(val);
}
}
}
__kernel void compute_data_cost_1(__global const float *selected_disp_pyr, __global float *data_cost_,
__global uchar *cleft, __global uchar *cright,
int h, int w, int level, int nr_plane, int channels,
int cmsg_step1, int cmsg_step2, int cdisp_step1, int cdisp_step2, float cdata_weight,
float cmax_data_term, int cimg_step, int cth)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
int y0 = y << level;
int yt = (y + 1) << level;
int x0 = x << level;
int xt = (x + 1) << level;
__global const float *selected_disparity = selected_disp_pyr + y/2 * cmsg_step2 + x/2;
__global       float *data_cost          = data_cost_ + y * cmsg_step1 + x;
for(int d = 0; d < nr_plane; d++)
{
float val = 0.0f;
for(int yi = y0; yi < yt; yi++)
{
for(int xi = x0; xi < xt; xi++)
{
int sel_disp = selected_disparity[d * cdisp_step2];
int xr = xi - sel_disp;
if (xr < 0 || sel_disp < cth)
val += cdata_weight * cmax_data_term;
else
{
__global uchar* left_x  = cleft + yi * cimg_step + xi * channels;
__global uchar* right_x = cright + yi * cimg_step + xr * channels;
if(channels == 1)
val += compute_1(left_x, right_x, cdata_weight, cmax_data_term);
else
val += compute_3(left_x, right_x, cdata_weight, cmax_data_term);
}
}
}
data_cost[cdisp_step1 * d] = val;
}
}
}
__kernel void compute_data_cost_reduce_0(__global const short* selected_disp_pyr, __global short* data_cost_,
__global uchar *cleft, __global uchar *cright,__local float *smem,
int level, int rows, int cols, int h, int nr_plane,
int channels, int winsz,
int cmsg_step1, int cmsg_step2, int cdisp_step1, int cdisp_step2,
float cdata_weight,  float cmax_data_term, int cimg_step,int cth)
{
int x_out = get_group_id(0);
int y_out = get_group_id(1) % h;
int d = (get_group_id(1)/ h) * get_local_size(2) + get_local_id(2);
int tid = get_local_id(0);
__global const short* selected_disparity = selected_disp_pyr + y_out/2 * cmsg_step2 + x_out/2;
__global short* data_cost = data_cost_ + y_out * cmsg_step1 + x_out;
if (d < nr_plane)
{
int sel_disp = selected_disparity[d * cdisp_step2];
int x0 = x_out << level;
int y0 = y_out << level;
int len = min(y0 + winsz, rows) - y0;
float val = 0.0f;
if (x0 + tid < cols)
{
if (x0 + tid - sel_disp < 0 || sel_disp < cth)
val = cdata_weight * cmax_data_term * len;
else
{
__global uchar* lle =  cleft + y0 * cimg_step + channels * (x0 + tid    );
__global uchar* lri = cright + y0 * cimg_step + channels * (x0 + tid - sel_disp);
for(int y = 0; y < len; ++y)
{
if(channels == 1)
val += compute_1(lle, lri, cdata_weight, cmax_data_term);
else
val += compute_3(lle, lri, cdata_weight, cmax_data_term);
lle += cimg_step;
lri += cimg_step;
}
}
}
__local float* dline = smem + winsz * get_local_id(2);
dline[tid] = val;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 64)
{
if (tid < 32)
vdline[tid] += vdline[tid + 32];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 32)
{
if (tid < 16)
vdline[tid] += vdline[tid + 16];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 16)
{
if (tid < 8)
vdline[tid] += vdline[tid + 8];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 8)
{
if (tid < 4)
vdline[tid] += vdline[tid + 4];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 4)
{
if (tid < 2)
vdline[tid] += vdline[tid + 2];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 2)
{
if (tid < 1)
vdline[tid] += vdline[tid + 1];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (tid == 0)
data_cost[cdisp_step1 * d] = convert_short_sat_rte(vdline[0]);
}
}
__kernel void compute_data_cost_reduce_1(__global const float *selected_disp_pyr, __global float *data_cost_,
__global uchar *cleft, __global uchar *cright, __local float *smem,
int level, int rows, int cols, int h, int nr_plane,
int channels, int winsz,
int cmsg_step1, int cmsg_step2, int cdisp_step1,int cdisp_step2, float cdata_weight,
float cmax_data_term, int cimg_step, int cth)
{
int x_out = get_group_id(0);
int y_out = get_group_id(1) % h;
int d = (get_group_id(1)/ h) * get_local_size(2) + get_local_id(2);
int tid = get_local_id(0);
__global const float *selected_disparity = selected_disp_pyr + y_out/2 * cmsg_step2 + x_out/2;
__global float *data_cost = data_cost_ + y_out * cmsg_step1 + x_out;
if (d < nr_plane)
{
int sel_disp = selected_disparity[d * cdisp_step2];
int x0 = x_out << level;
int y0 = y_out << level;
int len = min(y0 + winsz, rows) - y0;
float val = 0.0f;
if (x0 + tid < cols)
{
if (x0 + tid - sel_disp < 0 || sel_disp < cth)
val = cdata_weight * cmax_data_term * len;
else
{
__global uchar* lle =  cleft + y0 * cimg_step + channels * (x0 + tid    );
__global uchar* lri = cright + y0 * cimg_step + channels * (x0 + tid - sel_disp);
for(int y = 0; y < len; ++y)
{
if(channels == 1)
val += compute_1(lle, lri, cdata_weight, cmax_data_term);
else
val += compute_3(lle, lri, cdata_weight, cmax_data_term);
lle += cimg_step;
lri += cimg_step;
}
}
}
__local float* dline = smem + winsz * get_local_id(2);
dline[tid] = val;
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 64)
{
if (tid < 32)
vdline[tid] += vdline[tid + 32];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 32)
{
if (tid < 16)
vdline[tid] += vdline[tid + 16];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >= 16)
{
if (tid <  8)
vdline[tid] += vdline[tid + 8];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >=  8)
{
if (tid <  4)
vdline[tid] += vdline[tid + 4];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >=  4)
{
if (tid <  2)
vdline[tid] += vdline[tid + 2];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (winsz >=  2)
{
if (tid <  1)
vdline[tid] += vdline[tid + 1];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if(d < nr_plane)
{
__local volatile float* vdline = smem + winsz * get_local_id(2);
if (tid == 0)
data_cost[cdisp_step1 * d] = vdline[0];
}
}
void get_first_k_element_increase_0(__global short* u_new, __global short *d_new, __global short *l_new,
__global short *r_new, __global const short *u_cur, __global const short *d_cur,
__global const short *l_cur, __global const short *r_cur,
__global short *data_cost_selected, __global short *disparity_selected_new,
__global short *data_cost_new, __global const short* data_cost_cur,
__global const short *disparity_selected_cur,
int nr_plane, int nr_plane2,
int cdisp_step1, int cdisp_step2)
{
for(int i = 0; i < nr_plane; i++)
{
short minimum = SHRT_MAX;
int id = 0;
for(int j = 0; j < nr_plane2; j++)
{
short cur = data_cost_new[j * cdisp_step1];
if(cur < minimum)
{
minimum = cur;
id = j;
}
}
data_cost_selected[i * cdisp_step1] = data_cost_cur[id * cdisp_step1];
disparity_selected_new[i * cdisp_step1] = disparity_selected_cur[id * cdisp_step2];
u_new[i * cdisp_step1] = u_cur[id * cdisp_step2];
d_new[i * cdisp_step1] = d_cur[id * cdisp_step2];
l_new[i * cdisp_step1] = l_cur[id * cdisp_step2];
r_new[i * cdisp_step1] = r_cur[id * cdisp_step2];
data_cost_new[id * cdisp_step1] = SHRT_MAX;
}
}
__kernel void init_message_0(__global short *u_new_, __global short *d_new_, __global short *l_new_,
__global short *r_new_, __global  short *u_cur_, __global const short *d_cur_,
__global const short *l_cur_, __global const short *r_cur_, __global short *ctemp,
__global short *selected_disp_pyr_new, __global const short *selected_disp_pyr_cur,
__global short *data_cost_selected_, __global const short *data_cost_,
int h, int w, int nr_plane, int h2, int w2, int nr_plane2,
int cdisp_step1, int cdisp_step2, int cmsg_step1, int cmsg_step2)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y < h && x < w)
{
__global const short *u_cur = u_cur_ + min(h2-1, y/2 + 1) * cmsg_step2 + x/2;
__global const short *d_cur = d_cur_ + max(0, y/2 - 1)    * cmsg_step2 + x/2;
__global const short *l_cur = l_cur_ + y/2                * cmsg_step2 + min(w2-1, x/2 + 1);
__global const short *r_cur = r_cur_ + y/2                * cmsg_step2 + max(0, x/2 - 1);
__global short *data_cost_new = ctemp + y * cmsg_step1 + x;
__global const short *disparity_selected_cur = selected_disp_pyr_cur + y/2 * cmsg_step2 + x/2;
__global const short *data_cost = data_cost_ + y * cmsg_step1 + x;
for(int d = 0; d < nr_plane2; d++)
{
int idx2 = d * cdisp_step2;
short val  = data_cost[d * cdisp_step1] + u_cur[idx2] + d_cur[idx2] + l_cur[idx2] + r_cur[idx2];
data_cost_new[d * cdisp_step1] = val;
}
__global short *data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
__global short *disparity_selected_new = selected_disp_pyr_new + y * cmsg_step1 + x;
__global short *u_new = u_new_ + y * cmsg_step1 + x;
__global short *d_new = d_new_ + y * cmsg_step1 + x;
__global short *l_new = l_new_ + y * cmsg_step1 + x;
__global short *r_new = r_new_ + y * cmsg_step1 + x;
u_cur = u_cur_ + y/2 * cmsg_step2 + x/2;
d_cur = d_cur_ + y/2 * cmsg_step2 + x/2;
l_cur = l_cur_ + y/2 * cmsg_step2 + x/2;
r_cur = r_cur_ + y/2 * cmsg_step2 + x/2;
get_first_k_element_increase_0(u_new, d_new, l_new, r_new, u_cur, d_cur, l_cur, r_cur,
data_cost_selected, disparity_selected_new, data_cost_new,
data_cost, disparity_selected_cur, nr_plane, nr_plane2,
cdisp_step1, cdisp_step2);
}
}
__kernel void init_message_1(__global float *u_new_, __global float *d_new_, __global float *l_new_,
__global float *r_new_, __global const float *u_cur_, __global const float *d_cur_,
__global const float *l_cur_, __global const float *r_cur_, __global float *ctemp,
__global float *selected_disp_pyr_new, __global const float *selected_disp_pyr_cur,
__global float *data_cost_selected_, __global const float *data_cost_,
int h, int w, int nr_plane, int h2, int w2, int nr_plane2,
int cdisp_step1, int cdisp_step2, int cmsg_step1, int cmsg_step2)
{
int x = get_global_id(0);
int y = get_global_id(1);
__global const float *u_cur = u_cur_ + min(h2-1, y/2 + 1) * cmsg_step2 + x/2;
__global const float *d_cur = d_cur_ + max(0, y/2 - 1)    * cmsg_step2 + x/2;
__global const float *l_cur = l_cur_ + y/2                * cmsg_step2 + min(w2-1, x/2 + 1);
__global const float *r_cur = r_cur_ + y/2                * cmsg_step2 + max(0, x/2 - 1);
__global float *data_cost_new = ctemp + y * cmsg_step1 + x;
__global const float *disparity_selected_cur = selected_disp_pyr_cur + y/2 * cmsg_step2 + x/2;
__global const float *data_cost = data_cost_ + y * cmsg_step1 + x;
if (y < h && x < w)
{
for(int d = 0; d < nr_plane2; d++)
{
int idx2 = d * cdisp_step2;
float val  = data_cost[d * cdisp_step1] + u_cur[idx2] + d_cur[idx2] + l_cur[idx2] + r_cur[idx2];
data_cost_new[d * cdisp_step1] = val;
}
}
__global float *data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
__global float *disparity_selected_new = selected_disp_pyr_new + y * cmsg_step1 + x;
__global float *u_new = u_new_ + y * cmsg_step1 + x;
__global float *d_new = d_new_ + y * cmsg_step1 + x;
__global float *l_new = l_new_ + y * cmsg_step1 + x;
__global float *r_new = r_new_ + y * cmsg_step1 + x;
barrier(CLK_LOCAL_MEM_FENCE);
if(y < h && x < w)
{
u_cur = u_cur_ + y/2 * cmsg_step2 + x/2;
d_cur = d_cur_ + y/2 * cmsg_step2 + x/2;
l_cur = l_cur_ + y/2 * cmsg_step2 + x/2;
r_cur = r_cur_ + y/2 * cmsg_step2 + x/2;
for(int i = 0; i < nr_plane; i++)
{
float minimum = FLT_MAX;
int id = 0;
for(int j = 0; j < nr_plane2; j++)
{
float cur = data_cost_new[j * cdisp_step1];
if(cur < minimum)
{
minimum = cur;
id = j;
}
}
data_cost_selected[i * cdisp_step1] = data_cost[id * cdisp_step1];
disparity_selected_new[i * cdisp_step1] = disparity_selected_cur[id * cdisp_step2];
u_new[i * cdisp_step1] = u_cur[id * cdisp_step2];
d_new[i * cdisp_step1] = d_cur[id * cdisp_step2];
l_new[i * cdisp_step1] = l_cur[id * cdisp_step2];
r_new[i * cdisp_step1] = r_cur[id * cdisp_step2];
data_cost_new[id * cdisp_step1] = FLT_MAX;
}
}
}
void message_per_pixel_0(__global const short *data, __global short *msg_dst, __global const short *msg1,
__global const short *msg2, __global const short *msg3,
__global const short *dst_disp, __global const short *src_disp,
int nr_plane, __global short *temp,
float cmax_disc_term, int cdisp_step1, float cdisc_single_jump)
{
short minimum = SHRT_MAX;
for(int d = 0; d < nr_plane; d++)
{
int idx = d * cdisp_step1;
short val  = data[idx] + msg1[idx] + msg2[idx] + msg3[idx];
if(val < minimum)
minimum = val;
msg_dst[idx] = val;
}
float sum = 0;
for(int d = 0; d < nr_plane; d++)
{
float cost_min = minimum + cmax_disc_term;
short src_disp_reg = src_disp[d * cdisp_step1];
for(int d2 = 0; d2 < nr_plane; d2++)
cost_min = fmin(cost_min, (msg_dst[d2 * cdisp_step1] +
cdisc_single_jump * abs(dst_disp[d2 * cdisp_step1] - src_disp_reg)));
temp[d * cdisp_step1] = convert_short_sat_rte(cost_min);
sum += cost_min;
}
sum /= nr_plane;
for(int d = 0; d < nr_plane; d++)
msg_dst[d * cdisp_step1] = convert_short_sat_rte(temp[d * cdisp_step1] - sum);
}
void message_per_pixel_1(__global const float *data, __global float *msg_dst, __global const float *msg1,
__global const float *msg2, __global const float *msg3,
__global const float *dst_disp, __global const float *src_disp,
int nr_plane, __global float *temp,
float cmax_disc_term, int cdisp_step1, float cdisc_single_jump)
{
float minimum = FLT_MAX;
for(int d = 0; d < nr_plane; d++)
{
int idx = d * cdisp_step1;
float val  = data[idx] + msg1[idx] + msg2[idx] + msg3[idx];
if(val < minimum)
minimum = val;
msg_dst[idx] = val;
}
float sum = 0;
for(int d = 0; d < nr_plane; d++)
{
float cost_min = minimum + cmax_disc_term;
float src_disp_reg = src_disp[d * cdisp_step1];
for(int d2 = 0; d2 < nr_plane; d2++)
cost_min = fmin(cost_min, (msg_dst[d2 * cdisp_step1] +
cdisc_single_jump * fabs(dst_disp[d2 * cdisp_step1] - src_disp_reg)));
temp[d * cdisp_step1] = cost_min;
sum += cost_min;
}
sum /= nr_plane;
for(int d = 0; d < nr_plane; d++)
msg_dst[d * cdisp_step1] = temp[d * cdisp_step1] - sum;
}
__kernel void compute_message_0(__global short *u_, __global short *d_, __global short *l_, __global short *r_,
__global const short *data_cost_selected, __global const short *selected_disp_pyr_cur,
__global short *ctemp, int h, int w, int nr_plane, int i,
float cmax_disc_term, int cdisp_step1, int cmsg_step1, float cdisc_single_jump)
{
int y = get_global_id(1);
int x = ((get_global_id(0)) << 1) + ((y + i) & 1);
if (y > 0 && y < h - 1 && x > 0 && x < w - 1)
{
__global const short *data = data_cost_selected + y * cmsg_step1 + x;
__global short *u = u_ + y * cmsg_step1 + x;
__global short *d = d_ + y * cmsg_step1 + x;
__global short *l = l_ + y * cmsg_step1 + x;
__global short *r = r_ + y * cmsg_step1 + x;
__global const short *disp = selected_disp_pyr_cur + y * cmsg_step1 + x;
__global short *temp = ctemp + y * cmsg_step1 + x;
message_per_pixel_0(data, u, r - 1, u + cmsg_step1, l + 1, disp, disp - cmsg_step1, nr_plane, temp,
cmax_disc_term, cdisp_step1, cdisc_single_jump);
message_per_pixel_0(data, d, d - cmsg_step1, r - 1, l + 1, disp, disp + cmsg_step1, nr_plane, temp,
cmax_disc_term, cdisp_step1, cdisc_single_jump);
message_per_pixel_0(data, l, u + cmsg_step1, d - cmsg_step1, l + 1, disp, disp - 1, nr_plane, temp,
cmax_disc_term, cdisp_step1, cdisc_single_jump);
message_per_pixel_0(data, r, u + cmsg_step1, d - cmsg_step1, r - 1, disp, disp + 1, nr_plane, temp,
cmax_disc_term, cdisp_step1, cdisc_single_jump);
}
}
__kernel void compute_message_1(__global float *u_, __global float *d_, __global float *l_, __global float *r_,
__global const float *data_cost_selected, __global const float *selected_disp_pyr_cur,
__global float *ctemp, int h, int w, int nr_plane, int i,
float cmax_disc_term, int cdisp_step1, int cmsg_step1, float cdisc_single_jump)
{
int y = get_global_id(1);
int x = ((get_global_id(0)) << 1) + ((y + i) & 1);
if (y > 0 && y < h - 1 && x > 0 && x < w - 1)
{
__global const float *data = data_cost_selected + y * cmsg_step1 + x;
__global float *u = u_ + y * cmsg_step1 + x;
__global float *d = d_ + y * cmsg_step1 + x;
__global float *l = l_ + y * cmsg_step1 + x;
__global float *r = r_ + y * cmsg_step1 + x;
__global const float *disp = selected_disp_pyr_cur + y * cmsg_step1 + x;
__global float *temp = ctemp + y * cmsg_step1 + x;
message_per_pixel_1(data, u, r - 1, u + cmsg_step1, l + 1, disp, disp - cmsg_step1, nr_plane, temp,
cmax_disc_term, cdisp_step1, cdisc_single_jump);
message_per_pixel_1(data, d, d - cmsg_step1, r - 1, l + 1, disp, disp + cmsg_step1, nr_plane, temp,
cmax_disc_term, cdisp_step1, cdisc_single_jump);
message_per_pixel_1(data, l, u + cmsg_step1, d - cmsg_step1, l + 1, disp, disp - 1, nr_plane, temp,
cmax_disc_term, cdisp_step1, cdisc_single_jump);
message_per_pixel_1(data, r, u + cmsg_step1, d - cmsg_step1, r - 1, disp, disp + 1, nr_plane, temp,
cmax_disc_term, cdisp_step1, cdisc_single_jump);
}
}
__kernel void compute_disp_0(__global const short *u_, __global const short *d_, __global const short *l_,
__global const short *r_, __global const short * data_cost_selected,
__global const short *disp_selected_pyr,
__global short* disp,
int res_step, int cols, int rows, int nr_plane,
int cmsg_step1, int cdisp_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y > 0 && y < rows - 1 && x > 0 && x < cols - 1)
{
__global const short *data = data_cost_selected + y * cmsg_step1 + x;
__global const short *disp_selected = disp_selected_pyr + y * cmsg_step1 + x;
__global const short *u = u_ + (y+1) * cmsg_step1 + (x+0);
__global const short *d = d_ + (y-1) * cmsg_step1 + (x+0);
__global const short *l = l_ + (y+0) * cmsg_step1 + (x+1);
__global const short *r = r_ + (y+0) * cmsg_step1 + (x-1);
short best = 0;
short best_val = SHRT_MAX;
for (int i = 0; i < nr_plane; ++i)
{
int idx = i * cdisp_step1;
short val = data[idx]+ u[idx] + d[idx] + l[idx] + r[idx];
if (val < best_val)
{
best_val = val;
best = disp_selected[idx];
}
}
disp[res_step * y + x] = best;
}
}
__kernel void compute_disp_1(__global const float *u_, __global const float *d_, __global const float *l_,
__global const float *r_, __global const float *data_cost_selected,
__global const float *disp_selected_pyr,
__global short *disp,
int res_step, int cols, int rows, int nr_plane,
int cmsg_step1, int cdisp_step1)
{
int x = get_global_id(0);
int y = get_global_id(1);
if (y > 0 && y < rows - 1 && x > 0 && x < cols - 1)
{
__global const float *data = data_cost_selected + y * cmsg_step1 + x;
__global const float *disp_selected = disp_selected_pyr + y * cmsg_step1 + x;
__global const float *u = u_ + (y+1) * cmsg_step1 + (x+0);
__global const float *d = d_ + (y-1) * cmsg_step1 + (x+0);
__global const float *l = l_ + (y+0) * cmsg_step1 + (x+1);
__global const float *r = r_ + (y+0) * cmsg_step1 + (x-1);
short best = 0;
short best_val = SHRT_MAX;
for (int i = 0; i < nr_plane; ++i)
{
int idx = i * cdisp_step1;
float val = data[idx]+ u[idx] + d[idx] + l[idx] + r[idx];
if (val < best_val)
{
best_val = val;
best = convert_short_sat_rte(disp_selected[idx]);
}
}
disp[res_step * y + x] = best;
}
}
d203bd312e7d4f9915cdec480d0c54e1svm#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define TYPE double
#else
#define TYPE float
#endif
#if defined ADDEXP
#define EXP(X) exp(X)
#else
#define EXP(X) X
#endif
#if defined ADDPOW
#define POW(X,Y) pow(fabs(X),(Y))
#else
#define POW(X,Y) X
#endif
#define MAX_VAL   (FLT_MAX*1e-3)
#define BLOCK_SIZE 16
__kernel void svm_linear(__global float* src, int src_step, __global float* src2, int src2_step, __global TYPE* dst, int dst_step, int src_rows, int src2_cols,
int width, TYPE alpha, TYPE beta)
{
const int  col = get_global_id(0);
const int  row = get_global_id(1);
if(row < src_rows && col < src2_cols)
{
int t = 0;
TYPE temp = 0.0;
for(t = 0; t < width - BLOCK_SIZE; t += BLOCK_SIZE)
{
float16 t0 = vload16(0, src + row * src_step + t);
float16 t1 = vload16(0, src2 + col * src2_step + t);
t0 *= t1;
temp += t0.s0 + t0.s1 + t0.s2 + t0.s3 + t0.s4 + t0.s5 + t0.s6 + t0.s7 +
t0.s8 + t0.s9 + t0.sa + t0.sb + t0.sc + t0.sd + t0.se + t0.sf;
}
for(; t < width; t++)
{
temp += src[row * src_step + t] * src2[col * src2_step + t];
}
TYPE temp1 = (TYPE) (temp * alpha + beta);
if( temp1 > MAX_VAL )
{
dst[row * dst_step + col] = MAX_VAL;
}
else
{
dst[row * dst_step + col] = temp1;
}
}
}
__kernel void svm_sigmod(__global float* src, int src_step, __global float* src2, int src2_step, __global TYPE* dst, int dst_step, int src_rows, int src2_cols,
int width, TYPE alpha, TYPE beta)
{
const int  col = get_global_id(0);
const int  row = get_global_id(1);
if(row < src_rows && col < src2_cols)
{
int t = 0;
TYPE temp = 0.0;
for(t = 0; t < width - BLOCK_SIZE; t += BLOCK_SIZE)
{
float16 t0 = vload16(0, src + row * src_step + t);
float16 t1 = vload16(0, src2 + col * src2_step + t);
t0 *= t1;
temp += t0.s0 + t0.s1 + t0.s2 + t0.s3 + t0.s4 + t0.s5 + t0.s6 + t0.s7 +
t0.s8 + t0.s9 + t0.sa + t0.sb + t0.sc + t0.sd + t0.se + t0.sf;
}
for(; t < width; t++)
{
temp += src[row * src_step + t] * src2[col * src2_step + t];
}
TYPE tp = (TYPE) (temp * alpha + beta);
TYPE e = exp(-fabs(tp));
TYPE temp1;
if(tp > 0)
{
temp1 = (TYPE)((1. - e) / (1. + e));
}
else
{
temp1 = (TYPE)((e - 1.) / (e + 1.));
}
if( temp1 > MAX_VAL )
{
dst[row * dst_step + col] = MAX_VAL;
}
else
{
dst[row * dst_step + col] = temp1;
}
}
}
__kernel void svm_poly(__global float* src, int src_step, __global float* src2, int src2_step, __global TYPE* dst, int dst_step, int src_rows, int src2_cols,
int width, TYPE alpha, TYPE beta, TYPE degree)
{
const int  col = get_global_id(0);
const int  row = get_global_id(1);
if(row < src_rows && col < src2_cols)
{
int t = 0;
TYPE temp = 0.0;
for(t = 0; t < width - BLOCK_SIZE; t += BLOCK_SIZE)
{
float16 t0 = vload16(0, src + row * src_step + t);
float16 t1 = vload16(0, src2 + col * src2_step + t);
t0 *= t1;
temp += t0.s0 + t0.s1 + t0.s2 + t0.s3 + t0.s4 + t0.s5 + t0.s6 + t0.s7 +
t0.s8 + t0.s9 + t0.sa + t0.sb + t0.sc + t0.sd + t0.se + t0.sf;
}
for(; t < width; t++)
{
temp += src[row * src_step + t] * src2[col * src2_step + t];
}
TYPE temp1 = (TYPE)(POW((temp * alpha + beta), degree));
if( temp1 > MAX_VAL )
{
dst[row * dst_step + col] = MAX_VAL;
}
else
{
dst[row * dst_step + col] = temp1;
}
}
}
__kernel void svm_rbf(__global float* src, int src_step, __global float* src2, int src2_step, __global TYPE* dst, int dst_step, int src_rows, int src2_cols,
int width, TYPE gamma)
{
const int  col = get_global_id(0);
const int  row = get_global_id(1);
if(row < src_rows && col < src2_cols)
{
int t = 0;
TYPE temp = 0.0;
for(t = 0; t < width - BLOCK_SIZE; t += BLOCK_SIZE)
{
float16 t0 = vload16(0, src + row * src_step + t);
float16 t1 = vload16(0, src2 + col * src2_step + t);
t0 = (t0 - t1) * (t0 - t1);
temp += t0.s0 + t0.s1 + t0.s2 + t0.s3 + t0.s4 + t0.s5 + t0.s6 + t0.s7 +
t0.s8 + t0.s9 + t0.sa + t0.sb + t0.sc + t0.sd + t0.se + t0.sf;
}
for(; t < width; t++)
{
temp += (src[row * src_step + t] - src2[col * src2_step + t]) * (src[row * src_step + t] - src2[col * src2_step + t]);
}
TYPE temp1 = EXP((TYPE)(temp * gamma));
if( temp1 > MAX_VAL )
{
dst[row * dst_step + col] = MAX_VAL;
}
else
{
dst[row * dst_step + col] = temp1;
}
}
}
366022531756acf8bbab0f837ce1849atvl1flow__kernel void centeredGradientKernel(__global const float* src, int src_col, int src_row, int src_step,
__global float* dx, __global float* dy, int dx_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if((x < src_col)&&(y < src_row))
{
int src_x1 = (x + 1) < (src_col -1)? (x + 1) : (src_col - 1);
int src_x2 = (x - 1) > 0 ? (x -1) : 0;
dx[y * dx_step+ x] = 0.5f * (src[y * src_step + src_x1] - src[y * src_step+ src_x2]);
int src_y1 = (y+1) < (src_row - 1) ? (y + 1) : (src_row - 1);
int src_y2 = (y - 1) > 0 ? (y - 1) : 0;
dy[y * dx_step+ x] = 0.5f * (src[src_y1 * src_step + x] - src[src_y2 * src_step+ x]);
}
}
float bicubicCoeff(float x_)
{
float x = fabs(x_);
if (x <= 1.0f)
return x * x * (1.5f * x - 2.5f) + 1.0f;
else if (x < 2.0f)
return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
else
return 0.0f;
}
__kernel void warpBackwardKernel(__global const float* I0, int I0_step, int I0_col, int I0_row,
image2d_t tex_I1, image2d_t tex_I1x, image2d_t tex_I1y,
__global const float* u1, int u1_step,
__global const float* u2,
__global float* I1w,
__global float* I1wx, 
__global float* I1wy, 
__global float* grad, 
__global float* rho,
int I1w_step,
int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < I0_col&&y < I0_row)
{
float u1Val = u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
float u2Val = u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
float wx = x + u1Val;
float wy = y + u2Val;
int xmin = ceil(wx - 2.0f);
int xmax = floor(wx + 2.0f);
int ymin = ceil(wy - 2.0f);
int ymax = floor(wy + 2.0f);
float sum  = 0.0f;
float sumx = 0.0f;
float sumy = 0.0f;
float wsum = 0.0f;
sampler_t sampleri = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
for (int cy = ymin; cy <= ymax; ++cy)
{
for (int cx = xmin; cx <= xmax; ++cx)
{
float w = bicubicCoeff(wx - cx) * bicubicCoeff(wy - cy);
int2 cood = (int2)(cx, cy);
sum += w * read_imagef(tex_I1, sampleri, cood).x;
sumx += w * read_imagef(tex_I1x, sampleri, cood).x;
sumy += w * read_imagef(tex_I1y, sampleri, cood).x;
wsum += w;
}
}
float coeff = 1.0f / wsum;
float I1wVal  = sum  * coeff;
float I1wxVal = sumx * coeff;
float I1wyVal = sumy * coeff;
I1w[y * I1w_step + x]  = I1wVal;
I1wx[y * I1w_step + x] = I1wxVal;
I1wy[y * I1w_step + x] = I1wyVal;
float Ix2 = I1wxVal * I1wxVal;
float Iy2 = I1wyVal * I1wyVal;
grad[y * I1w_step + x] = Ix2 + Iy2;
float I0Val = I0[y * I0_step + x];
rho[y * I1w_step + x] = I1wVal - I1wxVal * u1Val - I1wyVal * u2Val - I0Val;
}
}
float readImage(__global float *image,  int x,  int y,  int rows,  int cols, int elemCntPerRow)
{
int i0 = clamp(x, 0, cols - 1);
int j0 = clamp(y, 0, rows - 1);
return image[j0 * elemCntPerRow + i0];
}
__kernel void warpBackwardKernelNoImage2d(__global const float* I0, int I0_step, int I0_col, int I0_row,
__global const float* tex_I1, __global const float* tex_I1x, __global const float* tex_I1y,
__global const float* u1, int u1_step,
__global const float* u2,
__global float* I1w,
__global float* I1wx, 
__global float* I1wy, 
__global float* grad, 
__global float* rho,
int I1w_step,
int u2_step,
int I1_step,
int I1x_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < I0_col&&y < I0_row)
{
float u1Val = u1[y * u1_step + x];
float u2Val = u2[y * u2_step + x];
float wx = x + u1Val;
float wy = y + u2Val;
int xmin = ceil(wx - 2.0f);
int xmax = floor(wx + 2.0f);
int ymin = ceil(wy - 2.0f);
int ymax = floor(wy + 2.0f);
float sum  = 0.0f;
float sumx = 0.0f;
float sumy = 0.0f;
float wsum = 0.0f;
for (int cy = ymin; cy <= ymax; ++cy)
{
for (int cx = xmin; cx <= xmax; ++cx)
{
float w = bicubicCoeff(wx - cx) * bicubicCoeff(wy - cy);
int2 cood = (int2)(cx, cy);
sum += w * readImage(tex_I1, cood.x, cood.y, I0_col, I0_row, I1_step);
sumx += w * readImage(tex_I1x, cood.x, cood.y, I0_col, I0_row, I1x_step);
sumy += w * readImage(tex_I1y, cood.x, cood.y, I0_col, I0_row, I1x_step);
wsum += w;
}
}
float coeff = 1.0f / wsum;
float I1wVal  = sum  * coeff;
float I1wxVal = sumx * coeff;
float I1wyVal = sumy * coeff;
I1w[y * I1w_step + x]  = I1wVal;
I1wx[y * I1w_step + x] = I1wxVal;
I1wy[y * I1w_step + x] = I1wyVal;
float Ix2 = I1wxVal * I1wxVal;
float Iy2 = I1wyVal * I1wyVal;
grad[y * I1w_step + x] = Ix2 + Iy2;
float I0Val = I0[y * I0_step + x];
rho[y * I1w_step + x] = I1wVal - I1wxVal * u1Val - I1wyVal * u2Val - I0Val;
}
}
__kernel void estimateDualVariablesKernel(__global const float* u1, int u1_col, int u1_row, int u1_step,
__global const float* u2,
__global float* p11, int p11_step,
__global float* p12,
__global float* p21,
__global float* p22,
float taut,
int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < u1_col && y < u1_row)
{
int src_x1 = (x + 1) < (u1_col - 1) ? (x + 1) : (u1_col - 1);
float u1x = u1[(y + u1_offset_y) * u1_step + src_x1 + u1_offset_x] - u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
int src_y1 = (y + 1) < (u1_row - 1) ? (y + 1) : (u1_row - 1);
float u1y = u1[(src_y1 + u1_offset_y) * u1_step + x + u1_offset_x] - u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
int src_x2 = (x + 1) < (u1_col - 1) ? (x + 1) : (u1_col - 1);
float u2x = u2[(y + u2_offset_y) * u2_step + src_x2 + u2_offset_x] - u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
int src_y2 = (y + 1) <  (u1_row - 1) ? (y + 1) : (u1_row - 1);
float u2y = u2[(src_y2 + u2_offset_y) * u2_step + x + u2_offset_x] - u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
float g1 = hypot(u1x, u1y);
float g2 = hypot(u2x, u2y);
float ng1 = 1.0f + taut * g1;
float ng2 = 1.0f + taut * g2;
p11[y * p11_step + x] = (p11[y * p11_step + x] + taut * u1x) / ng1;
p12[y * p11_step + x] = (p12[y * p11_step + x] + taut * u1y) / ng1;
p21[y * p11_step + x] = (p21[y * p11_step + x] + taut * u2x) / ng2;
p22[y * p11_step + x] = (p22[y * p11_step + x] + taut * u2y) / ng2;
}
}
float divergence(__global const float* v1, __global const float* v2, int y, int x, int v1_step, int v2_step)
{
if (x > 0 && y > 0)
{
float v1x = v1[y * v1_step + x] - v1[y * v1_step + x - 1];
float v2y = v2[y * v2_step + x] - v2[(y - 1) * v2_step + x];
return v1x + v2y;
}
else
{
if (y > 0)
return v1[y * v1_step + 0] + v2[y * v2_step + 0] - v2[(y - 1) * v2_step + 0];
else
{
if (x > 0)
return v1[0 * v1_step + x] - v1[0 * v1_step + x - 1] + v2[0 * v2_step + x];
else
return v1[0 * v1_step + 0] + v2[0 * v2_step + 0];
}
}
}
__kernel void estimateUKernel(__global const float* I1wx, int I1wx_col, int I1wx_row, int I1wx_step,
__global const float* I1wy, 
__global const float* grad, 
__global const float* rho_c, 
__global const float* p11, 
__global const float* p12, 
__global const float* p21, 
__global const float* p22, 
__global float* u1, int u1_step,
__global float* u2,
__global float* error, float l_t, float theta, int u2_step,
int u1_offset_x,
int u1_offset_y,
int u2_offset_x,
int u2_offset_y,
char calc_error)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < I1wx_col && y < I1wx_row)
{
float I1wxVal = I1wx[y * I1wx_step + x];
float I1wyVal = I1wy[y * I1wx_step + x];
float gradVal = grad[y * I1wx_step + x];
float u1OldVal = u1[(y + u1_offset_y) * u1_step + x + u1_offset_x];
float u2OldVal = u2[(y + u2_offset_y) * u2_step + x + u2_offset_x];
float rho = rho_c[y * I1wx_step + x] + (I1wxVal * u1OldVal + I1wyVal * u2OldVal);
float d1 = 0.0f;
float d2 = 0.0f;
if (rho < -l_t * gradVal)
{
d1 = l_t * I1wxVal;
d2 = l_t * I1wyVal;
}
else if (rho > l_t * gradVal)
{
d1 = -l_t * I1wxVal;
d2 = -l_t * I1wyVal;
}
else if (gradVal > 1.192092896e-07f)
{
float fi = -rho / gradVal;
d1 = fi * I1wxVal;
d2 = fi * I1wyVal;
}
float v1 = u1OldVal + d1;
float v2 = u2OldVal + d2;
float div_p1 = divergence(p11, p12, y, x, I1wx_step, I1wx_step);
float div_p2 = divergence(p21, p22, y, x, I1wx_step, I1wx_step);
float u1NewVal = v1 + theta * div_p1;
float u2NewVal = v2 + theta * div_p2;
u1[(y + u1_offset_y) * u1_step + x + u1_offset_x] = u1NewVal;
u2[(y + u2_offset_y) * u2_step + x + u2_offset_x] = u2NewVal;
if(calc_error)
{
float n1 = (u1OldVal - u1NewVal) * (u1OldVal - u1NewVal);
float n2 = (u2OldVal - u2NewVal) * (u2OldVal - u2NewVal);
error[y * I1wx_step + x] = n1 + n2;
}
}
}
15d604b56daef7cecff8d9ada2970732contrastThresholdsigmaFeature2D.SIFTFeature2D.SURF_OCLimage is empty or has incorrect depth (!=CV_8U)/Users/jason/Projects/opencv/opencv-2.4.11/modules/nonfree/src/sift.cppmask has incorrect type (!=CV_8UC1)firstOctave >= -1 && actualNLayers <= nOctaveLayersoctave >= firstOctave && layer <= nOctaveLayers+2calcDescriptors!img.empty() && img.depth() == CV_8U/Users/jason/Projects/opencv/opencv-2.4.11/modules/nonfree/src/surf.cppmask.empty() || (mask.type() == CV_8U && mask.size() == img.size())hessianThreshold >= 0nOctaves > 0nOctaveLayers > 0nOriSamples <= nOriSampleBoundSURFInvokerwinbuf->cols >= win_size*win_size/Users/jason/Projects/opencv/opencv-2.4.11/modules/nonfree/src/precomp.hppkeypointsGPU.type() == CV_32FC1 && keypointsGPU.rows == ROWS_COUNTdownloadKeypoints/Users/jason/Projects/opencv/opencv-2.4.11/modules/nonfree/src/surf_ocl.cppdescriptorsGPU.type() == CV_32FdownloadDescriptorsdescriptors.type() == CV_32FicvCalcLayerDetAndTraceicvFindMaximaInLayer_withmaskicvFindMaximaInLayericvInterpolateKeypointicvCalcOrientationicvSetUprightcompute_descriptors64normalize_descriptors64compute_descriptors128normalize_descriptors128!img.empty() && img.type() == CV_8UC1SURF_OCL_Invokermask.empty() || (mask.size() == img.size() && mask.type() == CV_8UC1)surf_.nOctaves > 0 && surf_.nOctaveLayers > 0img_rows - min_size >= 0img_cols - min_size >= 0layer_rows - 2 * min_margin > 0layer_cols - 2 * min_margin > 0Masked SURF detector is not implemented yet-D ORI_LOCAL_SIZE=-D ORI_SEARCH_INC=clReleaseKernel(kernel) == CL_SUCCESSopenCLExecuteKernelSURF-D WAVE_SIZE=surf#ifdef DISABLE_IMAGE2D
#define IMAGE_INT32 __global uint  *
#define IMAGE_INT8  __global uchar *
#else
#define IMAGE_INT32 image2d_t
#define IMAGE_INT8  image2d_t
#endif
uint read_sumTex(IMAGE_INT32 img, sampler_t sam, int2 coord, int rows, int cols, int elemPerRow)
{
#ifdef DISABLE_IMAGE2D
int x = clamp(coord.x, 0, cols);
int y = clamp(coord.y, 0, rows);
return img[elemPerRow * y + x];
#else
return read_imageui(img, sam, coord).x;
#endif
}
uchar read_imgTex(IMAGE_INT8 img, sampler_t sam, float2 coord, int rows, int cols, int elemPerRow)
{
#ifdef DISABLE_IMAGE2D
int x = clamp(round(coord.x), 0, cols - 1);
int y = clamp(round(coord.y), 0, rows - 1);
return img[elemPerRow * y + x];
#else
return (uchar)read_imageui(img, sam, coord).x;
#endif
}
#if defined (DOUBLE_SUPPORT)
#ifdef cl_khr_fp64
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#elif defined (cl_amd_fp64)
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#endif
#define F double
#else
#define F float
#endif
__constant sampler_t sampler    = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
#ifndef FLT_EPSILON
#define FLT_EPSILON (1e-15)
#endif
#ifndef CV_PI_F
#define CV_PI_F 3.14159265f
#endif
float icvCalcHaarPatternSum_2(
IMAGE_INT32 sumTex,
__constant float2 *src,
int oldSize,
int newSize,
int y, int x,
int rows, int cols, int elemPerRow)
{
float ratio = (float)newSize / oldSize;
F d = 0;
int2 dx1 = convert_int2(round(ratio * src[0]));
int2 dy1 = convert_int2(round(ratio * src[1]));
int2 dx2 = convert_int2(round(ratio * src[2]));
int2 dy2 = convert_int2(round(ratio * src[3]));
F t = 0;
t += read_sumTex( sumTex, sampler, (int2)(x + dx1.x, y + dy1.x), rows, cols, elemPerRow );
t -= read_sumTex( sumTex, sampler, (int2)(x + dx1.x, y + dy2.x), rows, cols, elemPerRow );
t -= read_sumTex( sumTex, sampler, (int2)(x + dx2.x, y + dy1.x), rows, cols, elemPerRow );
t += read_sumTex( sumTex, sampler, (int2)(x + dx2.x, y + dy2.x), rows, cols, elemPerRow );
d += t * src[4].x / ((dx2.x - dx1.x) * (dy2.x - dy1.x));
t = 0;
t += read_sumTex( sumTex, sampler, (int2)(x + dx1.y, y + dy1.y), rows, cols, elemPerRow );
t -= read_sumTex( sumTex, sampler, (int2)(x + dx1.y, y + dy2.y), rows, cols, elemPerRow );
t -= read_sumTex( sumTex, sampler, (int2)(x + dx2.y, y + dy1.y), rows, cols, elemPerRow );
t += read_sumTex( sumTex, sampler, (int2)(x + dx2.y, y + dy2.y), rows, cols, elemPerRow );
d += t * src[4].y / ((dx2.y - dx1.y) * (dy2.y - dy1.y));
return (float)d;
}
__inline int calcSize(int octave, int layer)
{
const int HAAR_SIZE0 = 9;
const int HAAR_SIZE_INC = 6;
return (HAAR_SIZE0 + HAAR_SIZE_INC * layer) << octave;
}
F calcAxisAlignedDerivative(
int plus1a_A, int plus1a_B, int plus1a_C, int plus1a_D, F plus1a_scale,
int plus1b_A, int plus1b_B, int plus1b_C, int plus1b_D, F plus1b_scale,
int minus2_A, int minus2_B, int minus2_C, int minus2_D, F minus2_scale)
{
F plus1a = plus1a_A - plus1a_B - plus1a_C + plus1a_D;
F plus1b = plus1b_A - plus1b_B - plus1b_C + plus1b_D;
F minus2 = minus2_A - minus2_B - minus2_C + minus2_D;
return (plus1a / plus1a_scale -
2.0f * minus2 / minus2_scale +
plus1b / plus1b_scale);
}
__kernel void icvCalcLayerDetAndTrace(
IMAGE_INT32 sumTex,
__global float * det,
__global float * trace,
int det_step,
int trace_step,
int c_img_rows,
int c_img_cols,
int c_nOctaveLayers,
int c_octave,
int c_layer_rows,
int sumTex_step
)
{
det_step   /= sizeof(*det);
trace_step /= sizeof(*trace);
sumTex_step/= sizeof(uint);
const int gridDim_y  = get_num_groups(1) / (c_nOctaveLayers + 2);
const int blockIdx_y = get_group_id(1) % gridDim_y;
const int blockIdx_z = get_group_id(1) / gridDim_y;
const int j = get_local_id(0) + get_group_id(0) * get_local_size(0);
const int i = get_local_id(1) + blockIdx_y * get_local_size(1);
const int layer = blockIdx_z;
const int size = calcSize(c_octave, layer);
const int samples_i = 1 + ((c_img_rows - size) >> c_octave);
const int samples_j = 1 + ((c_img_cols - size) >> c_octave);
const int margin = (size >> 1) >> c_octave;
if (size <= c_img_rows && size <= c_img_cols && i < samples_i && j < samples_j)
{
int x = j << c_octave;
int y = i << c_octave;
float ratio = (float)size / 9;
int r1 = round(ratio);
int r2 = round(ratio * 2.0f);
int r3 = round(ratio * 3.0f);
int r4 = round(ratio * 4.0f);
int r5 = round(ratio * 5.0f);
int r6 = round(ratio * 6.0f);
int r7 = round(ratio * 7.0f);
int r8 = round(ratio * 8.0f);
int r9 = round(ratio * 9.0f);
F d = 0;
{
int t02 = read_sumTex( sumTex, sampler, (int2)(x, y + r2), c_img_rows, c_img_cols, sumTex_step );
int t07 = read_sumTex( sumTex, sampler, (int2)(x, y + r7), c_img_rows, c_img_cols, sumTex_step );
int t32 = read_sumTex( sumTex, sampler, (int2)(x + r3, y + r2), c_img_rows, c_img_cols, sumTex_step );
int t37 = read_sumTex( sumTex, sampler, (int2)(x + r3, y + r7), c_img_rows, c_img_cols, sumTex_step );
int t62 = read_sumTex( sumTex, sampler, (int2)(x + r6, y + r2), c_img_rows, c_img_cols, sumTex_step );
int t67 = read_sumTex( sumTex, sampler, (int2)(x + r6, y + r7), c_img_rows, c_img_cols, sumTex_step );
int t92 = read_sumTex( sumTex, sampler, (int2)(x + r9, y + r2), c_img_rows, c_img_cols, sumTex_step );
int t97 = read_sumTex( sumTex, sampler, (int2)(x + r9, y + r7), c_img_rows, c_img_cols, sumTex_step );
d = calcAxisAlignedDerivative(t02, t07, t32, t37, (r3) * (r7 - r2),
t62, t67, t92, t97, (r9 - r6) * (r7 - r2),
t32, t37, t62, t67, (r6 - r3) * (r7 - r2));
}
const float dx  = (float)d;
d = 0;
{
int t20 = read_sumTex( sumTex, sampler, (int2)(x + r2, y), c_img_rows, c_img_cols, sumTex_step );
int t23 = read_sumTex( sumTex, sampler, (int2)(x + r2, y + r3), c_img_rows, c_img_cols, sumTex_step );
int t70 = read_sumTex( sumTex, sampler, (int2)(x + r7, y), c_img_rows, c_img_cols, sumTex_step );
int t73 = read_sumTex( sumTex, sampler, (int2)(x + r7, y + r3), c_img_rows, c_img_cols, sumTex_step );
int t26 = read_sumTex( sumTex, sampler, (int2)(x + r2, y + r6), c_img_rows, c_img_cols, sumTex_step );
int t76 = read_sumTex( sumTex, sampler, (int2)(x + r7, y + r6), c_img_rows, c_img_cols, sumTex_step );
int t29 = read_sumTex( sumTex, sampler, (int2)(x + r2, y + r9), c_img_rows, c_img_cols, sumTex_step );
int t79 = read_sumTex( sumTex, sampler, (int2)(x + r7, y + r9), c_img_rows, c_img_cols, sumTex_step );
d = calcAxisAlignedDerivative(t20, t23, t70, t73, (r7 - r2) * (r3),
t26, t29, t76, t79, (r7 - r2) * (r9 - r6),
t23, t26, t73, t76, (r7 - r2) * (r6 - r3));
}
const float dy  = (float)d;
d = 0;
{
F t = 0;
t += read_sumTex( sumTex, sampler, (int2)(x + r1, y + r1), c_img_rows, c_img_cols, sumTex_step );
t -= read_sumTex( sumTex, sampler, (int2)(x + r1, y + r4), c_img_rows, c_img_cols, sumTex_step );
t -= read_sumTex( sumTex, sampler, (int2)(x + r4, y + r1), c_img_rows, c_img_cols, sumTex_step );
t += read_sumTex( sumTex, sampler, (int2)(x + r4, y + r4), c_img_rows, c_img_cols, sumTex_step );
d += t / ((r4 - r1) * (r4 - r1));
t = 0;
t += read_sumTex( sumTex, sampler, (int2)(x + r5, y + r1), c_img_rows, c_img_cols, sumTex_step );
t -= read_sumTex( sumTex, sampler, (int2)(x + r5, y + r4), c_img_rows, c_img_cols, sumTex_step );
t -= read_sumTex( sumTex, sampler, (int2)(x + r8, y + r1), c_img_rows, c_img_cols, sumTex_step );
t += read_sumTex( sumTex, sampler, (int2)(x + r8, y + r4), c_img_rows, c_img_cols, sumTex_step );
d -= t / ((r8 - r5) * (r4 - r1));
t = 0;
t += read_sumTex( sumTex, sampler, (int2)(x + r1, y + r5), c_img_rows, c_img_cols, sumTex_step );
t -= read_sumTex( sumTex, sampler, (int2)(x + r1, y + r8), c_img_rows, c_img_cols, sumTex_step );
t -= read_sumTex( sumTex, sampler, (int2)(x + r4, y + r5), c_img_rows, c_img_cols, sumTex_step );
t += read_sumTex( sumTex, sampler, (int2)(x + r4, y + r8), c_img_rows, c_img_cols, sumTex_step );
d -= t / ((r4 - r1) * (r8 - r5));
t = 0;
t += read_sumTex( sumTex, sampler, (int2)(x + r5, y + r5), c_img_rows, c_img_cols, sumTex_step );
t -= read_sumTex( sumTex, sampler, (int2)(x + r5, y + r8), c_img_rows, c_img_cols, sumTex_step );
t -= read_sumTex( sumTex, sampler, (int2)(x + r8, y + r5), c_img_rows, c_img_cols, sumTex_step );
t += read_sumTex( sumTex, sampler, (int2)(x + r8, y + r8), c_img_rows, c_img_cols, sumTex_step );
d += t / ((r8 - r5) * (r8 - r5));
}
const float dxy = (float)d;
det  [j + margin + det_step   * (layer * c_layer_rows + i + margin)] = dx * dy - 0.81f * dxy * dxy;
trace[j + margin + trace_step * (layer * c_layer_rows + i + margin)] = dx + dy;
}
}
__constant float c_DM[5] = {0, 0, 9, 9, 1};
bool within_check(IMAGE_INT32 maskSumTex, int sum_i, int sum_j, int size, int rows, int cols, int step)
{
float ratio = (float)size / 9.0f;
float d = 0;
int dx1 = round(ratio * c_DM[0]);
int dy1 = round(ratio * c_DM[1]);
int dx2 = round(ratio * c_DM[2]);
int dy2 = round(ratio * c_DM[3]);
float t = 0;
t += read_sumTex(maskSumTex, sampler, (int2)(sum_j + dx1, sum_i + dy1), rows, cols, step);
t -= read_sumTex(maskSumTex, sampler, (int2)(sum_j + dx1, sum_i + dy2), rows, cols, step);
t -= read_sumTex(maskSumTex, sampler, (int2)(sum_j + dx2, sum_i + dy1), rows, cols, step);
t += read_sumTex(maskSumTex, sampler, (int2)(sum_j + dx2, sum_i + dy2), rows, cols, step);
d += t * c_DM[4] / ((dx2 - dx1) * (dy2 - dy1));
return (d >= 0.5f);
}
__kernel
void icvFindMaximaInLayer_withmask(
__global const float * det,
__global const float * trace,
__global int4 * maxPosBuffer,
volatile __global int* maxCounter,
int counter_offset,
int det_step,
int trace_step,
int c_img_rows,
int c_img_cols,
int c_nOctaveLayers,
int c_octave,
int c_layer_rows,
int c_layer_cols,
int c_max_candidates,
float c_hessianThreshold,
IMAGE_INT32 maskSumTex,
int mask_step
)
{
volatile __local  float N9[768];
det_step   /= sizeof(*det);
trace_step /= sizeof(*trace);
maxCounter += counter_offset;
mask_step  /= sizeof(uint);
const int gridDim_y  = get_num_groups(1) / c_nOctaveLayers;
const int blockIdx_y = get_group_id(1)   % gridDim_y;
const int blockIdx_z = get_group_id(1)   / gridDim_y;
const int layer = blockIdx_z + 1;
const int size = calcSize(c_octave, layer);
const int margin = ((calcSize(c_octave, layer + 1) >> 1) >> c_octave) + 1;
const int j = get_local_id(0) + get_group_id(0) * (get_local_size(0) - 2) + margin - 1;
const int i = get_local_id(1) + blockIdx_y * (get_local_size(1) - 2) + margin - 1;
const int zoff = get_local_size(0) * get_local_size(1);
const int localLin = get_local_id(0) + get_local_id(1) * get_local_size(0) + zoff;
N9[localLin - zoff] =
det[det_step *
(c_layer_rows * (layer - 1) + min(max(i, 0), c_img_rows - 1))
+ min(max(j, 0), c_img_cols - 1)];
N9[localLin       ] =
det[det_step *
(c_layer_rows * (layer    ) + min(max(i, 0), c_img_rows - 1))
+ min(max(j, 0), c_img_cols - 1)];
N9[localLin + zoff] =
det[det_step *
(c_layer_rows * (layer + 1) + min(max(i, 0), c_img_rows - 1))
+ min(max(j, 0), c_img_cols - 1)];
barrier(CLK_LOCAL_MEM_FENCE);
if (i < c_layer_rows - margin
&& j < c_layer_cols - margin
&& get_local_id(0) > 0
&& get_local_id(0) < get_local_size(0) - 1
&& get_local_id(1) > 0
&& get_local_id(1) < get_local_size(1) - 1
)
{
float val0 = N9[localLin];
if (val0 > c_hessianThreshold)
{
const int sum_i = (i - ((size >> 1) >> c_octave)) << c_octave;
const int sum_j = (j - ((size >> 1) >> c_octave)) << c_octave;
if (within_check(maskSumTex, sum_i, sum_j, size, c_img_rows, c_img_cols, mask_step))
{
const bool condmax = val0 > N9[localLin - 1 - get_local_size(0) - zoff]
&&                   val0 > N9[localLin     - get_local_size(0) - zoff]
&&                   val0 > N9[localLin + 1 - get_local_size(0) - zoff]
&&                   val0 > N9[localLin - 1                     - zoff]
&&                   val0 > N9[localLin                         - zoff]
&&                   val0 > N9[localLin + 1                     - zoff]
&&                   val0 > N9[localLin - 1 + get_local_size(0) - zoff]
&&                   val0 > N9[localLin     + get_local_size(0) - zoff]
&&                   val0 > N9[localLin + 1 + get_local_size(0) - zoff]
&&                   val0 > N9[localLin - 1 - get_local_size(0)]
&&                   val0 > N9[localLin     - get_local_size(0)]
&&                   val0 > N9[localLin + 1 - get_local_size(0)]
&&                   val0 > N9[localLin - 1                    ]
&&                   val0 > N9[localLin + 1                    ]
&&                   val0 > N9[localLin - 1 + get_local_size(0)]
&&                   val0 > N9[localLin     + get_local_size(0)]
&&                   val0 > N9[localLin + 1 + get_local_size(0)]
&&                   val0 > N9[localLin - 1 - get_local_size(0) + zoff]
&&                   val0 > N9[localLin     - get_local_size(0) + zoff]
&&                   val0 > N9[localLin + 1 - get_local_size(0) + zoff]
&&                   val0 > N9[localLin - 1                     + zoff]
&&                   val0 > N9[localLin                         + zoff]
&&                   val0 > N9[localLin + 1                     + zoff]
&&                   val0 > N9[localLin - 1 + get_local_size(0) + zoff]
&&                   val0 > N9[localLin     + get_local_size(0) + zoff]
&&                   val0 > N9[localLin + 1 + get_local_size(0) + zoff]
;
if(condmax)
{
int ind = atomic_inc(maxCounter);
if (ind < c_max_candidates)
{
const int laplacian = (int) copysign(1.0f, trace[trace_step* (layer * c_layer_rows + i) + j]);
maxPosBuffer[ind] = (int4)(j, i, layer, laplacian);
}
}
}
}
}
}
__kernel
void icvFindMaximaInLayer(
__global float * det,
__global float * trace,
__global int4 * maxPosBuffer,
volatile __global  int* maxCounter,
int counter_offset,
int det_step,
int trace_step,
int c_img_rows,
int c_img_cols,
int c_nOctaveLayers,
int c_octave,
int c_layer_rows,
int c_layer_cols,
int c_max_candidates,
float c_hessianThreshold
)
{
volatile __local  float N9[768];
det_step   /= sizeof(float);
trace_step /= sizeof(float);
maxCounter += counter_offset;
const int gridDim_y  = get_num_groups(1) / c_nOctaveLayers;
const int blockIdx_y = get_group_id(1)   % gridDim_y;
const int blockIdx_z = get_group_id(1)   / gridDim_y;
const int layer = blockIdx_z + 1;
const int size = calcSize(c_octave, layer);
const int margin = ((calcSize(c_octave, layer + 1) >> 1) >> c_octave) + 1;
const int j = get_local_id(0) + get_group_id(0) * (get_local_size(0) - 2) + margin - 1;
const int i = get_local_id(1) + blockIdx_y      * (get_local_size(1) - 2) + margin - 1;
const int zoff     = get_local_size(0) * get_local_size(1);
const int localLin = get_local_id(0) + get_local_id(1) * get_local_size(0) + zoff;
int l_x = min(max(j, 0), c_img_cols - 1);
int l_y = c_layer_rows * layer + min(max(i, 0), c_img_rows - 1);
N9[localLin - zoff] =
det[det_step * (l_y - c_layer_rows) + l_x];
N9[localLin       ] =
det[det_step * (l_y               ) + l_x];
N9[localLin + zoff] =
det[det_step * (l_y + c_layer_rows) + l_x];
barrier(CLK_LOCAL_MEM_FENCE);
if (i < c_layer_rows - margin
&& j < c_layer_cols - margin
&& get_local_id(0) > 0
&& get_local_id(0) < get_local_size(0) - 1
&& get_local_id(1) > 0
&& get_local_id(1) < get_local_size(1) - 1
)
{
float val0 = N9[localLin];
if (val0 > c_hessianThreshold)
{
const bool condmax = val0 > N9[localLin - 1 - get_local_size(0) - zoff]
&&                   val0 > N9[localLin     - get_local_size(0) - zoff]
&&                   val0 > N9[localLin + 1 - get_local_size(0) - zoff]
&&                   val0 > N9[localLin - 1                     - zoff]
&&                   val0 > N9[localLin                         - zoff]
&&                   val0 > N9[localLin + 1                     - zoff]
&&                   val0 > N9[localLin - 1 + get_local_size(0) - zoff]
&&                   val0 > N9[localLin     + get_local_size(0) - zoff]
&&                   val0 > N9[localLin + 1 + get_local_size(0) - zoff]
&&                   val0 > N9[localLin - 1 - get_local_size(0)]
&&                   val0 > N9[localLin     - get_local_size(0)]
&&                   val0 > N9[localLin + 1 - get_local_size(0)]
&&                   val0 > N9[localLin - 1                    ]
&&                   val0 > N9[localLin + 1                    ]
&&                   val0 > N9[localLin - 1 + get_local_size(0)]
&&                   val0 > N9[localLin     + get_local_size(0)]
&&                   val0 > N9[localLin + 1 + get_local_size(0)]
&&                   val0 > N9[localLin - 1 - get_local_size(0) + zoff]
&&                   val0 > N9[localLin     - get_local_size(0) + zoff]
&&                   val0 > N9[localLin + 1 - get_local_size(0) + zoff]
&&                   val0 > N9[localLin - 1                     + zoff]
&&                   val0 > N9[localLin                         + zoff]
&&                   val0 > N9[localLin + 1                     + zoff]
&&                   val0 > N9[localLin - 1 + get_local_size(0) + zoff]
&&                   val0 > N9[localLin     + get_local_size(0) + zoff]
&&                   val0 > N9[localLin + 1 + get_local_size(0) + zoff]
;
if(condmax)
{
int ind = atomic_inc(maxCounter);
if (ind < c_max_candidates)
{
const int laplacian = (int) copysign(1.0f, trace[trace_step* (layer * c_layer_rows + i) + j]);
maxPosBuffer[ind] = (int4)(j, i, layer, laplacian);
}
}
}
}
}
inline bool solve3x3_float(const float4 *A, const float *b, float *x)
{
float det = A[0].x * (A[1].y * A[2].z - A[1].z * A[2].y)
- A[0].y * (A[1].x * A[2].z - A[1].z * A[2].x)
+ A[0].z * (A[1].x * A[2].y - A[1].y * A[2].x);
if (det != 0)
{
F invdet = 1.0 / det;
x[0] = invdet *
(b[0]    * (A[1].y * A[2].z - A[1].z * A[2].y) -
A[0].y * (b[1]    * A[2].z - A[1].z * b[2]   ) +
A[0].z * (b[1]    * A[2].y - A[1].y * b[2]   ));
x[1] = invdet *
(A[0].x * (b[1]    * A[2].z - A[1].z * b[2]   ) -
b[0]    * (A[1].x * A[2].z - A[1].z * A[2].x) +
A[0].z * (A[1].x * b[2]    - b[1]    * A[2].x));
x[2] = invdet *
(A[0].x * (A[1].y * b[2]    - b[1]    * A[2].y) -
A[0].y * (A[1].x * b[2]    - b[1]    * A[2].x) +
b[0]    * (A[1].x * A[2].y - A[1].y * A[2].x));
return true;
}
return false;
}
#define X_ROW          0
#define Y_ROW          1
#define LAPLACIAN_ROW  2
#define OCTAVE_ROW     3
#define SIZE_ROW       4
#define ANGLE_ROW      5
#define HESSIAN_ROW    6
#define ROWS_COUNT     7
__kernel
void icvInterpolateKeypoint(
__global const float * det,
__global const int4 * maxPosBuffer,
__global float * keypoints,
volatile __global  int * featureCounter,
int det_step,
int keypoints_step,
int c_img_rows,
int c_img_cols,
int c_octave,
int c_layer_rows,
int c_max_features
)
{
det_step /= sizeof(*det);
keypoints_step /= sizeof(*keypoints);
__global float * featureX       = keypoints + X_ROW * keypoints_step;
__global float * featureY       = keypoints + Y_ROW * keypoints_step;
__global int * featureLaplacian = (__global int *)keypoints + LAPLACIAN_ROW * keypoints_step;
__global int * featureOctave    = (__global int *)keypoints + OCTAVE_ROW * keypoints_step;
__global float * featureSize    = keypoints + SIZE_ROW * keypoints_step;
__global float * featureHessian = keypoints + HESSIAN_ROW * keypoints_step;
const int4 maxPos = maxPosBuffer[get_group_id(0)];
const int j = maxPos.x - 1 + get_local_id(0);
const int i = maxPos.y - 1 + get_local_id(1);
const int layer = maxPos.z - 1 + get_local_id(2);
volatile __local  float N9[3][3][3];
N9[get_local_id(2)][get_local_id(1)][get_local_id(0)] =
det[det_step * (c_layer_rows * layer + i) + j];
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) == 0 && get_local_id(1) == 0 && get_local_id(2) == 0)
{
float dD[3];
dD[0] = -0.5f * (N9[1][1][2] - N9[1][1][0]);
dD[1] = -0.5f * (N9[1][2][1] - N9[1][0][1]);
dD[2] = -0.5f * (N9[2][1][1] - N9[0][1][1]);
float4 H[3];
H[0].x = N9[1][1][0] - 2.0f * N9[1][1][1] + N9[1][1][2];
H[0].y= 0.25f * (N9[1][2][2] - N9[1][2][0] - N9[1][0][2] + N9[1][0][0]);
H[0].z= 0.25f * (N9[2][1][2] - N9[2][1][0] - N9[0][1][2] + N9[0][1][0]);
H[1].x = H[0].y;
H[1].y = N9[1][0][1] - 2.0f * N9[1][1][1] + N9[1][2][1];
H[1].z= 0.25f * (N9[2][2][1] - N9[2][0][1] - N9[0][2][1] + N9[0][0][1]);
H[2].x = H[0].z;
H[2].y = H[1].z;
H[2].z = N9[0][1][1] - 2.0f * N9[1][1][1] + N9[2][1][1];
float x[3];
if (solve3x3_float(H, dD, x))
{
if (fabs(x[0]) <= 1.f && fabs(x[1]) <= 1.f && fabs(x[2]) <= 1.f)
{
const int size = calcSize(c_octave, maxPos.z);
const int sum_i = (maxPos.y - ((size >> 1) >> c_octave)) << c_octave;
const int sum_j = (maxPos.x - ((size >> 1) >> c_octave)) << c_octave;
const float center_i = sum_i + (float)(size - 1) / 2;
const float center_j = sum_j + (float)(size - 1) / 2;
const float px = center_j + x[0] * (1 << c_octave);
const float py = center_i + x[1] * (1 << c_octave);
const int ds = size - calcSize(c_octave, maxPos.z - 1);
const float psize = round(size + x[2] * ds);
const float s = psize * 1.2f / 9.0f;
const int grad_wav_size = 2 * round(2.0f * s);
if ((c_img_rows + 1) >= grad_wav_size && (c_img_cols + 1) >= grad_wav_size)
{
int ind = atomic_inc(featureCounter);
if (ind < c_max_features)
{
featureX[ind] = px;
featureY[ind] = py;
featureLaplacian[ind] = maxPos.w;
featureOctave[ind] = c_octave;
featureSize[ind] = psize;
featureHessian[ind] = N9[1][1][1];
}
}
}
}
}
}
#define ORI_WIN       60
#define ORI_SAMPLES     113
#define ORI_RESPONSE_REDUCTION_WIDTH     48
#define ORI_RESPONSE_ARRAY_SIZE           (ORI_RESPONSE_REDUCTION_WIDTH * 2)
__constant float c_aptX[ORI_SAMPLES] = {-6, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6};
__constant float c_aptY[ORI_SAMPLES] = {0, -3, -2, -1, 0, 1, 2, 3, -4, -3, -2, -1, 0, 1, 2, 3, 4, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -4, -3, -2, -1, 0, 1, 2, 3, 4, -3, -2, -1, 0, 1, 2, 3, 0};
__constant float c_aptW[ORI_SAMPLES] = {0.001455130288377404f, 0.001707611023448408f, 0.002547456417232752f, 0.003238451667129993f, 0.0035081731621176f,
0.003238451667129993f, 0.002547456417232752f, 0.001707611023448408f, 0.002003900473937392f, 0.0035081731621176f, 0.005233579315245152f,
0.00665318313986063f, 0.00720730796456337f, 0.00665318313986063f, 0.005233579315245152f, 0.0035081731621176f,
0.002003900473937392f, 0.001707611023448408f, 0.0035081731621176f, 0.006141661666333675f, 0.009162282571196556f,
0.01164754293859005f, 0.01261763460934162f, 0.01164754293859005f, 0.009162282571196556f, 0.006141661666333675f,
0.0035081731621176f, 0.001707611023448408f, 0.002547456417232752f, 0.005233579315245152f, 0.009162282571196556f,
0.01366852037608624f, 0.01737609319388866f, 0.0188232995569706f, 0.01737609319388866f, 0.01366852037608624f,
0.009162282571196556f, 0.005233579315245152f, 0.002547456417232752f, 0.003238451667129993f, 0.00665318313986063f,
0.01164754293859005f, 0.01737609319388866f, 0.02208934165537357f, 0.02392910048365593f, 0.02208934165537357f,
0.01737609319388866f, 0.01164754293859005f, 0.00665318313986063f, 0.003238451667129993f, 0.001455130288377404f,
0.0035081731621176f, 0.00720730796456337f, 0.01261763460934162f, 0.0188232995569706f, 0.02392910048365593f,
0.02592208795249462f, 0.02392910048365593f, 0.0188232995569706f, 0.01261763460934162f, 0.00720730796456337f,
0.0035081731621176f, 0.001455130288377404f, 0.003238451667129993f, 0.00665318313986063f, 0.01164754293859005f,
0.01737609319388866f, 0.02208934165537357f, 0.02392910048365593f, 0.02208934165537357f, 0.01737609319388866f,
0.01164754293859005f, 0.00665318313986063f, 0.003238451667129993f, 0.002547456417232752f, 0.005233579315245152f,
0.009162282571196556f, 0.01366852037608624f, 0.01737609319388866f, 0.0188232995569706f, 0.01737609319388866f,
0.01366852037608624f, 0.009162282571196556f, 0.005233579315245152f, 0.002547456417232752f, 0.001707611023448408f,
0.0035081731621176f, 0.006141661666333675f, 0.009162282571196556f, 0.01164754293859005f, 0.01261763460934162f,
0.01164754293859005f, 0.009162282571196556f, 0.006141661666333675f, 0.0035081731621176f, 0.001707611023448408f,
0.002003900473937392f, 0.0035081731621176f, 0.005233579315245152f, 0.00665318313986063f, 0.00720730796456337f,
0.00665318313986063f, 0.005233579315245152f, 0.0035081731621176f, 0.002003900473937392f, 0.001707611023448408f,
0.002547456417232752f, 0.003238451667129993f, 0.0035081731621176f, 0.003238451667129993f, 0.002547456417232752f,
0.001707611023448408f, 0.001455130288377404f
};
__constant float2 c_NX[5] = { (float2)(0, 2), (float2)(0, 0), (float2)(2, 4), (float2)(4, 4), (float2)(-1, 1) };
__constant float2 c_NY[5] = { (float2)(0, 0), (float2)(0, 2), (float2)(4, 4), (float2)(2, 4), (float2)(1, -1) };
void reduce_32_sum(volatile __local  float * data, volatile float* partial_reduction, int tid)
{
#define op(A, B) (*A)+(B)
data[tid] = *partial_reduction;
barrier(CLK_LOCAL_MEM_FENCE);
#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
if (tid < 16)
{
data[tid] = *partial_reduction = op(partial_reduction, data[tid + 16]);
#if WAVE_SIZE < 16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
data[tid] = *partial_reduction = op(partial_reduction, data[tid + 8]);
#if WAVE_SIZE < 8
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 4)
{
#endif
data[tid] = *partial_reduction = op(partial_reduction, data[tid + 4]);
#if WAVE_SIZE < 4
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 2)
{
#endif
data[tid] = *partial_reduction = op(partial_reduction, data[tid + 2 ]);
#if WAVE_SIZE < 2
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 1)
{
#endif
data[tid] = *partial_reduction = op(partial_reduction, data[tid + 1 ]);
}
#undef WAVE_SIZE
#undef op
}
__kernel
void icvCalcOrientation(
IMAGE_INT32 sumTex,
__global float * keypoints,
int keypoints_step,
int c_img_rows,
int c_img_cols,
int sum_step
)
{
keypoints_step /= sizeof(*keypoints);
sum_step       /= sizeof(uint);
__global float* featureX    = keypoints + X_ROW * keypoints_step;
__global float* featureY    = keypoints + Y_ROW * keypoints_step;
__global float* featureSize = keypoints + SIZE_ROW * keypoints_step;
__global float* featureDir  = keypoints + ANGLE_ROW * keypoints_step;
__local  float s_X[ORI_SAMPLES];
__local  float s_Y[ORI_SAMPLES];
__local  float s_angle[ORI_SAMPLES];
__local  float s_sumx[ORI_RESPONSE_ARRAY_SIZE];
__local  float s_sumy[ORI_RESPONSE_ARRAY_SIZE];
__local  float s_mod[ORI_RESPONSE_ARRAY_SIZE];
const float s = featureSize[get_group_id(0)] * 1.2f / 9.0f;
const int grad_wav_size = 2 * round(2.0f * s);
if ((c_img_rows + 1) < grad_wav_size || (c_img_cols + 1) < grad_wav_size)
return;
const int tid = get_local_id(0);
if (tid < ORI_RESPONSE_ARRAY_SIZE - ORI_LOCAL_SIZE) {
s_mod[tid + ORI_LOCAL_SIZE] = 0.0f;
}
float ratio = (float)grad_wav_size / 4;
int r2 = round(ratio * 2.0);
int r4 = round(ratio * 4.0);
for (int i = tid; i < ORI_SAMPLES; i += ORI_LOCAL_SIZE )
{
float X = 0.0f, Y = 0.0f, angle = 0.0f;
const float margin = (float)(grad_wav_size - 1) / 2.0f;
const int x = round(featureX[get_group_id(0)] + c_aptX[i] * s - margin);
const int y = round(featureY[get_group_id(0)] + c_aptY[i] * s - margin);
if (y >= 0 && y < (c_img_rows + 1) - grad_wav_size &&
x >= 0 && x < (c_img_cols + 1) - grad_wav_size)
{
float apt = c_aptW[i];
float t00 = read_sumTex( sumTex, sampler, (int2)(x, y), c_img_rows, c_img_cols, sum_step);
float t02 = read_sumTex( sumTex, sampler, (int2)(x, y + r2), c_img_rows, c_img_cols, sum_step);
float t04 = read_sumTex( sumTex, sampler, (int2)(x, y + r4), c_img_rows, c_img_cols, sum_step);
float t20 = read_sumTex( sumTex, sampler, (int2)(x + r2, y), c_img_rows, c_img_cols, sum_step);
float t24 = read_sumTex( sumTex, sampler, (int2)(x + r2, y + r4), c_img_rows, c_img_cols, sum_step);
float t40 = read_sumTex( sumTex, sampler, (int2)(x + r4, y), c_img_rows, c_img_cols, sum_step);
float t42 = read_sumTex( sumTex, sampler, (int2)(x + r4, y + r2), c_img_rows, c_img_cols, sum_step);
float t44 = read_sumTex( sumTex, sampler, (int2)(x + r4, y + r4), c_img_rows, c_img_cols, sum_step);
F t = t00 - t04 - t20 + t24;
X -= t / ((r2) * (r4));
t = t20 - t24 - t40 + t44;
X += t / ((r4 - r2) * (r4));
t = t00 - t02 - t40 + t42;
Y += t / ((r2) * (r4));
t = t02 - t04 - t42 + t44;
Y -= t  / ((r4) * (r4 - r2));
X = apt*X;
Y = apt*Y;
angle = atan2(Y, X);
if (angle < 0)
angle += 2.0f * CV_PI_F;
angle *= 180.0f / CV_PI_F;
}
s_X[i] = X;
s_Y[i] = Y;
s_angle[i] = angle;
}
barrier(CLK_LOCAL_MEM_FENCE);
float bestx = 0, besty = 0, best_mod = 0;
float sumx = 0.0f, sumy = 0.0f;
const int dir = tid * ORI_SEARCH_INC;
#pragma unroll
for (int i = 0; i < ORI_SAMPLES; ++i) {
int angle = round(s_angle[i]);
int d = abs(angle - dir);
if (d < ORI_WIN / 2 || d > 360 - ORI_WIN / 2)
{
sumx += s_X[i];
sumy += s_Y[i];
}
}
s_sumx[tid] = sumx;
s_sumy[tid] = sumy;
s_mod[tid] = sumx*sumx + sumy*sumy;
barrier(CLK_LOCAL_MEM_FENCE);
for(int t = ORI_RESPONSE_REDUCTION_WIDTH; t >= 3; t /= 2) {
if (tid < t) {
if (s_mod[tid] < s_mod[tid + t]) {
s_mod[tid] = s_mod[tid + t];
s_sumx[tid] = s_sumx[tid + t];
s_sumy[tid] = s_sumy[tid + t];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0)
{
int bestIdx = 0;
if (s_mod[1] > s_mod[bestIdx])
bestIdx = 1;
if (s_mod[2] > s_mod[bestIdx])
bestIdx = 2;
float kp_dir = atan2(s_sumy[bestIdx], s_sumx[bestIdx]);
if (kp_dir < 0)
kp_dir += 2.0f * CV_PI_F;
kp_dir *= 180.0f / CV_PI_F;
kp_dir = 360.0f - kp_dir;
if (fabs(kp_dir - 360.f) < FLT_EPSILON)
kp_dir = 0.f;
featureDir[get_group_id(0)] = kp_dir;
}
}
__kernel
void icvSetUpright(
__global float * keypoints,
int keypoints_step,
int nFeatures
)
{
keypoints_step /= sizeof(*keypoints);
__global float* featureDir  = keypoints + ANGLE_ROW * keypoints_step;
if(get_global_id(0) <= nFeatures)
{
featureDir[get_global_id(0)] = 270.0f;
}
}
#undef ORI_SEARCH_INC
#undef ORI_WIN
#undef ORI_SAMPLES
#define PATCH_SZ 20
__constant float c_DW[PATCH_SZ * PATCH_SZ] =
{
3.695352233989979e-006f, 8.444558261544444e-006f, 1.760426494001877e-005f, 3.34794785885606e-005f, 5.808438800158911e-005f, 9.193058212986216e-005f, 0.0001327334757661447f, 0.0001748319627949968f, 0.0002100782439811155f, 0.0002302826324012131f, 0.0002302826324012131f, 0.0002100782439811155f, 0.0001748319627949968f, 0.0001327334757661447f, 9.193058212986216e-005f, 5.808438800158911e-005f, 3.34794785885606e-005f, 1.760426494001877e-005f, 8.444558261544444e-006f, 3.695352233989979e-006f,
8.444558261544444e-006f, 1.929736572492402e-005f, 4.022897701361217e-005f, 7.650675252079964e-005f, 0.0001327334903180599f, 0.0002100782585330308f, 0.0003033203829545528f, 0.0003995231236331165f, 0.0004800673632416874f, 0.0005262381164357066f, 0.0005262381164357066f, 0.0004800673632416874f, 0.0003995231236331165f, 0.0003033203829545528f, 0.0002100782585330308f, 0.0001327334903180599f, 7.650675252079964e-005f, 4.022897701361217e-005f, 1.929736572492402e-005f, 8.444558261544444e-006f,
1.760426494001877e-005f, 4.022897701361217e-005f, 8.386484114453197e-005f, 0.0001594926579855382f, 0.0002767078403849155f, 0.0004379475140012801f, 0.0006323281559161842f, 0.0008328808471560478f, 0.001000790391117334f, 0.001097041997127235f, 0.001097041997127235f, 0.001000790391117334f, 0.0008328808471560478f, 0.0006323281559161842f, 0.0004379475140012801f, 0.0002767078403849155f, 0.0001594926579855382f, 8.386484114453197e-005f, 4.022897701361217e-005f, 1.760426494001877e-005f,
3.34794785885606e-005f, 7.650675252079964e-005f, 0.0001594926579855382f, 0.0003033203247468919f, 0.0005262380582280457f, 0.0008328807889483869f, 0.001202550483867526f, 0.001583957928232849f, 0.001903285388834775f, 0.002086334861814976f, 0.002086334861814976f, 0.001903285388834775f, 0.001583957928232849f, 0.001202550483867526f, 0.0008328807889483869f, 0.0005262380582280457f, 0.0003033203247468919f, 0.0001594926579855382f, 7.650675252079964e-005f, 3.34794785885606e-005f,
5.808438800158911e-005f, 0.0001327334903180599f, 0.0002767078403849155f, 0.0005262380582280457f, 0.0009129836107604206f, 0.001444985857233405f, 0.002086335094645619f, 0.002748048631474376f, 0.00330205773934722f, 0.003619635012000799f, 0.003619635012000799f, 0.00330205773934722f, 0.002748048631474376f, 0.002086335094645619f, 0.001444985857233405f, 0.0009129836107604206f, 0.0005262380582280457f, 0.0002767078403849155f, 0.0001327334903180599f, 5.808438800158911e-005f,
9.193058212986216e-005f, 0.0002100782585330308f, 0.0004379475140012801f, 0.0008328807889483869f, 0.001444985857233405f, 0.002286989474669099f, 0.00330205773934722f, 0.004349356517195702f, 0.00522619066759944f, 0.005728822201490402f, 0.005728822201490402f, 0.00522619066759944f, 0.004349356517195702f, 0.00330205773934722f, 0.002286989474669099f, 0.001444985857233405f, 0.0008328807889483869f, 0.0004379475140012801f, 0.0002100782585330308f, 9.193058212986216e-005f,
0.0001327334757661447f, 0.0003033203829545528f, 0.0006323281559161842f, 0.001202550483867526f, 0.002086335094645619f, 0.00330205773934722f, 0.004767658654600382f, 0.006279794964939356f, 0.007545807864516974f, 0.008271530270576477f, 0.008271530270576477f, 0.007545807864516974f, 0.006279794964939356f, 0.004767658654600382f, 0.00330205773934722f, 0.002086335094645619f, 0.001202550483867526f, 0.0006323281559161842f, 0.0003033203829545528f, 0.0001327334757661447f,
0.0001748319627949968f, 0.0003995231236331165f, 0.0008328808471560478f, 0.001583957928232849f, 0.002748048631474376f, 0.004349356517195702f, 0.006279794964939356f, 0.008271529339253902f, 0.009939077310264111f, 0.01089497376233339f, 0.01089497376233339f, 0.009939077310264111f, 0.008271529339253902f, 0.006279794964939356f, 0.004349356517195702f, 0.002748048631474376f, 0.001583957928232849f, 0.0008328808471560478f, 0.0003995231236331165f, 0.0001748319627949968f,
0.0002100782439811155f, 0.0004800673632416874f, 0.001000790391117334f, 0.001903285388834775f, 0.00330205773934722f, 0.00522619066759944f, 0.007545807864516974f, 0.009939077310264111f, 0.01194280479103327f, 0.01309141051024199f, 0.01309141051024199f, 0.01194280479103327f, 0.009939077310264111f, 0.007545807864516974f, 0.00522619066759944f, 0.00330205773934722f, 0.001903285388834775f, 0.001000790391117334f, 0.0004800673632416874f, 0.0002100782439811155f,
0.0002302826324012131f, 0.0005262381164357066f, 0.001097041997127235f, 0.002086334861814976f, 0.003619635012000799f, 0.005728822201490402f, 0.008271530270576477f, 0.01089497376233339f, 0.01309141051024199f, 0.01435048412531614f, 0.01435048412531614f, 0.01309141051024199f, 0.01089497376233339f, 0.008271530270576477f, 0.005728822201490402f, 0.003619635012000799f, 0.002086334861814976f, 0.001097041997127235f, 0.0005262381164357066f, 0.0002302826324012131f,
0.0002302826324012131f, 0.0005262381164357066f, 0.001097041997127235f, 0.002086334861814976f, 0.003619635012000799f, 0.005728822201490402f, 0.008271530270576477f, 0.01089497376233339f, 0.01309141051024199f, 0.01435048412531614f, 0.01435048412531614f, 0.01309141051024199f, 0.01089497376233339f, 0.008271530270576477f, 0.005728822201490402f, 0.003619635012000799f, 0.002086334861814976f, 0.001097041997127235f, 0.0005262381164357066f, 0.0002302826324012131f,
0.0002100782439811155f, 0.0004800673632416874f, 0.001000790391117334f, 0.001903285388834775f, 0.00330205773934722f, 0.00522619066759944f, 0.007545807864516974f, 0.009939077310264111f, 0.01194280479103327f, 0.01309141051024199f, 0.01309141051024199f, 0.01194280479103327f, 0.009939077310264111f, 0.007545807864516974f, 0.00522619066759944f, 0.00330205773934722f, 0.001903285388834775f, 0.001000790391117334f, 0.0004800673632416874f, 0.0002100782439811155f,
0.0001748319627949968f, 0.0003995231236331165f, 0.0008328808471560478f, 0.001583957928232849f, 0.002748048631474376f, 0.004349356517195702f, 0.006279794964939356f, 0.008271529339253902f, 0.009939077310264111f, 0.01089497376233339f, 0.01089497376233339f, 0.009939077310264111f, 0.008271529339253902f, 0.006279794964939356f, 0.004349356517195702f, 0.002748048631474376f, 0.001583957928232849f, 0.0008328808471560478f, 0.0003995231236331165f, 0.0001748319627949968f,
0.0001327334757661447f, 0.0003033203829545528f, 0.0006323281559161842f, 0.001202550483867526f, 0.002086335094645619f, 0.00330205773934722f, 0.004767658654600382f, 0.006279794964939356f, 0.007545807864516974f, 0.008271530270576477f, 0.008271530270576477f, 0.007545807864516974f, 0.006279794964939356f, 0.004767658654600382f, 0.00330205773934722f, 0.002086335094645619f, 0.001202550483867526f, 0.0006323281559161842f, 0.0003033203829545528f, 0.0001327334757661447f,
9.193058212986216e-005f, 0.0002100782585330308f, 0.0004379475140012801f, 0.0008328807889483869f, 0.001444985857233405f, 0.002286989474669099f, 0.00330205773934722f, 0.004349356517195702f, 0.00522619066759944f, 0.005728822201490402f, 0.005728822201490402f, 0.00522619066759944f, 0.004349356517195702f, 0.00330205773934722f, 0.002286989474669099f, 0.001444985857233405f, 0.0008328807889483869f, 0.0004379475140012801f, 0.0002100782585330308f, 9.193058212986216e-005f,
5.808438800158911e-005f, 0.0001327334903180599f, 0.0002767078403849155f, 0.0005262380582280457f, 0.0009129836107604206f, 0.001444985857233405f, 0.002086335094645619f, 0.002748048631474376f, 0.00330205773934722f, 0.003619635012000799f, 0.003619635012000799f, 0.00330205773934722f, 0.002748048631474376f, 0.002086335094645619f, 0.001444985857233405f, 0.0009129836107604206f, 0.0005262380582280457f, 0.0002767078403849155f, 0.0001327334903180599f, 5.808438800158911e-005f,
3.34794785885606e-005f, 7.650675252079964e-005f, 0.0001594926579855382f, 0.0003033203247468919f, 0.0005262380582280457f, 0.0008328807889483869f, 0.001202550483867526f, 0.001583957928232849f, 0.001903285388834775f, 0.002086334861814976f, 0.002086334861814976f, 0.001903285388834775f, 0.001583957928232849f, 0.001202550483867526f, 0.0008328807889483869f, 0.0005262380582280457f, 0.0003033203247468919f, 0.0001594926579855382f, 7.650675252079964e-005f, 3.34794785885606e-005f,
1.760426494001877e-005f, 4.022897701361217e-005f, 8.386484114453197e-005f, 0.0001594926579855382f, 0.0002767078403849155f, 0.0004379475140012801f, 0.0006323281559161842f, 0.0008328808471560478f, 0.001000790391117334f, 0.001097041997127235f, 0.001097041997127235f, 0.001000790391117334f, 0.0008328808471560478f, 0.0006323281559161842f, 0.0004379475140012801f, 0.0002767078403849155f, 0.0001594926579855382f, 8.386484114453197e-005f, 4.022897701361217e-005f, 1.760426494001877e-005f,
8.444558261544444e-006f, 1.929736572492402e-005f, 4.022897701361217e-005f, 7.650675252079964e-005f, 0.0001327334903180599f, 0.0002100782585330308f, 0.0003033203829545528f, 0.0003995231236331165f, 0.0004800673632416874f, 0.0005262381164357066f, 0.0005262381164357066f, 0.0004800673632416874f, 0.0003995231236331165f, 0.0003033203829545528f, 0.0002100782585330308f, 0.0001327334903180599f, 7.650675252079964e-005f, 4.022897701361217e-005f, 1.929736572492402e-005f, 8.444558261544444e-006f,
3.695352233989979e-006f, 8.444558261544444e-006f, 1.760426494001877e-005f, 3.34794785885606e-005f, 5.808438800158911e-005f, 9.193058212986216e-005f, 0.0001327334757661447f, 0.0001748319627949968f, 0.0002100782439811155f, 0.0002302826324012131f, 0.0002302826324012131f, 0.0002100782439811155f, 0.0001748319627949968f, 0.0001327334757661447f, 9.193058212986216e-005f, 5.808438800158911e-005f, 3.34794785885606e-005f, 1.760426494001877e-005f, 8.444558261544444e-006f, 3.695352233989979e-006f
};
inline uchar readerGet(
IMAGE_INT8 src,
const float centerX, const float centerY, const float win_offset, const float cos_dir, const float sin_dir,
int i, int j, int rows, int cols, int elemPerRow
)
{
float pixel_x = centerX + (win_offset + j) * cos_dir + (win_offset + i) * sin_dir;
float pixel_y = centerY - (win_offset + j) * sin_dir + (win_offset + i) * cos_dir;
return read_imgTex(src, sampler, (float2)(pixel_x, pixel_y), rows, cols, elemPerRow);
}
inline float linearFilter(
IMAGE_INT8 src,
const float centerX, const float centerY, const float win_offset, const float cos_dir, const float sin_dir,
float y, float x, int rows, int cols, int elemPerRow
)
{
x -= 0.5f;
y -= 0.5f;
float out = 0.0f;
const int x1 = round(x);
const int y1 = round(y);
const int x2 = x1 + 1;
const int y2 = y1 + 1;
uchar src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y1, x1, rows, cols, elemPerRow);
out = out + src_reg * ((x2 - x) * (y2 - y));
src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y1, x2, rows, cols, elemPerRow);
out = out + src_reg * ((x - x1) * (y2 - y));
src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y2, x1, rows, cols, elemPerRow);
out = out + src_reg * ((x2 - x) * (y - y1));
src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y2, x2, rows, cols, elemPerRow);
out = out + src_reg * ((x - x1) * (y - y1));
return out;
}
void calc_dx_dy(
IMAGE_INT8 imgTex,
volatile __local  float *s_dx_bin,
volatile __local  float *s_dy_bin,
volatile __local  float *s_PATCH,
__global const float* featureX,
__global const float* featureY,
__global const float* featureSize,
__global const float* featureDir,
int rows,
int cols,
int elemPerRow
)
{
const float centerX = featureX[get_group_id(0)];
const float centerY = featureY[get_group_id(0)];
const float size = featureSize[get_group_id(0)];
float descriptor_dir = 360.0f - featureDir[get_group_id(0)];
if(fabs(descriptor_dir - 360.0f) < FLT_EPSILON)
{
descriptor_dir = 0.0f;
}
descriptor_dir *= (float)(CV_PI_F / 180.0f);
const float s = size * 1.2f / 9.0f;
const int win_size = (int)((PATCH_SZ + 1) * s);
float sin_dir;
float cos_dir;
sin_dir = sincos(descriptor_dir, &cos_dir);
const float win_offset = -(float)(win_size - 1) / 2;
const int xBlock = (get_group_id(1) & 3);
const int yBlock = (get_group_id(1) >> 2);
const int xIndex = xBlock * 5 + get_local_id(0);
const int yIndex = yBlock * 5 + get_local_id(1);
const float icoo = ((float)yIndex / (PATCH_SZ + 1)) * win_size;
const float jcoo = ((float)xIndex / (PATCH_SZ + 1)) * win_size;
s_PATCH[get_local_id(1) * 6 + get_local_id(0)] = linearFilter(imgTex, centerX, centerY, win_offset, cos_dir, sin_dir, icoo, jcoo, rows, cols, elemPerRow);
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < 5 && get_local_id(1) < 5)
{
const int tid = get_local_id(1) * 5 + get_local_id(0);
const float dw = c_DW[yIndex * PATCH_SZ + xIndex];
const float vx = (
s_PATCH[      get_local_id(1) * 6 + get_local_id(0) + 1] -
s_PATCH[      get_local_id(1) * 6 + get_local_id(0)    ] +
s_PATCH[(get_local_id(1) + 1) * 6 + get_local_id(0) + 1] -
s_PATCH[(get_local_id(1) + 1) * 6 + get_local_id(0)    ])
* dw;
const float vy = (
s_PATCH[(get_local_id(1) + 1) * 6 + get_local_id(0)    ] -
s_PATCH[      get_local_id(1) * 6 + get_local_id(0)    ] +
s_PATCH[(get_local_id(1) + 1) * 6 + get_local_id(0) + 1] -
s_PATCH[      get_local_id(1) * 6 + get_local_id(0) + 1])
* dw;
s_dx_bin[tid] = vx;
s_dy_bin[tid] = vy;
}
}
void reduce_sum25(
volatile __local  float* sdata1,
volatile __local  float* sdata2,
volatile __local  float* sdata3,
volatile __local  float* sdata4,
int tid
)
{
#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
if (tid < 9)
{
sdata1[tid] += sdata1[tid + 16];
sdata2[tid] += sdata2[tid + 16];
sdata3[tid] += sdata3[tid + 16];
sdata4[tid] += sdata4[tid + 16];
#if WAVE_SIZE < 16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
sdata1[tid] += sdata1[tid + 8];
sdata2[tid] += sdata2[tid + 8];
sdata3[tid] += sdata3[tid + 8];
sdata4[tid] += sdata4[tid + 8];
#if WAVE_SIZE < 8
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 4)
{
#endif
sdata1[tid] += sdata1[tid + 4];
sdata2[tid] += sdata2[tid + 4];
sdata3[tid] += sdata3[tid + 4];
sdata4[tid] += sdata4[tid + 4];
#if WAVE_SIZE < 4
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 2)
{
#endif
sdata1[tid] += sdata1[tid + 2];
sdata2[tid] += sdata2[tid + 2];
sdata3[tid] += sdata3[tid + 2];
sdata4[tid] += sdata4[tid + 2];
#if WAVE_SIZE < 2
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 1)
{
#endif
sdata1[tid] += sdata1[tid + 1];
sdata2[tid] += sdata2[tid + 1];
sdata3[tid] += sdata3[tid + 1];
sdata4[tid] += sdata4[tid + 1];
}
#undef WAVE_SIZE
}
__kernel
void compute_descriptors64(
IMAGE_INT8 imgTex,
__global float * descriptors,
__global const float * keypoints,
int descriptors_step,
int keypoints_step,
int rows,
int cols,
int img_step
)
{
descriptors_step /= sizeof(float);
keypoints_step   /= sizeof(float);
__global const float * featureX    = keypoints + X_ROW * keypoints_step;
__global const float * featureY    = keypoints + Y_ROW * keypoints_step;
__global const float * featureSize = keypoints + SIZE_ROW * keypoints_step;
__global const float * featureDir  = keypoints + ANGLE_ROW * keypoints_step;
volatile __local  float sdx[25];
volatile __local  float sdy[25];
volatile __local  float sdxabs[25];
volatile __local  float sdyabs[25];
volatile __local  float s_PATCH[6*6];
calc_dx_dy(imgTex, sdx, sdy, s_PATCH, featureX, featureY, featureSize, featureDir, rows, cols, img_step);
barrier(CLK_LOCAL_MEM_FENCE);
const int tid = get_local_id(1) * get_local_size(0) + get_local_id(0);
if (tid < 25)
{
sdxabs[tid] = fabs(sdx[tid]);
sdyabs[tid] = fabs(sdy[tid]);
}
barrier(CLK_LOCAL_MEM_FENCE);
reduce_sum25(sdx, sdy, sdxabs, sdyabs, tid);
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 25)
{
__global float* descriptors_block = descriptors + descriptors_step * get_group_id(0) + (get_group_id(1) << 2);
if (tid == 0)
{
descriptors_block[0] = sdx[0];
descriptors_block[1] = sdy[0];
descriptors_block[2] = sdxabs[0];
descriptors_block[3] = sdyabs[0];
}
}
}
__kernel
void compute_descriptors128(
IMAGE_INT8 imgTex,
__global float * descriptors,
__global float * keypoints,
int descriptors_step,
int keypoints_step,
int rows,
int cols,
int img_step
)
{
descriptors_step /= sizeof(*descriptors);
keypoints_step   /= sizeof(*keypoints);
__global float * featureX   = keypoints + X_ROW * keypoints_step;
__global float * featureY   = keypoints + Y_ROW * keypoints_step;
__global float* featureSize = keypoints + SIZE_ROW * keypoints_step;
__global float* featureDir  = keypoints + ANGLE_ROW * keypoints_step;
volatile __local  float sdx[25];
volatile __local  float sdy[25];
volatile __local  float sd1[25];
volatile __local  float sd2[25];
volatile __local  float sdabs1[25];
volatile __local  float sdabs2[25];
volatile __local  float s_PATCH[6*6];
calc_dx_dy(imgTex, sdx, sdy, s_PATCH, featureX, featureY, featureSize, featureDir, rows, cols, img_step);
barrier(CLK_LOCAL_MEM_FENCE);
const int tid = get_local_id(1) * get_local_size(0) + get_local_id(0);
if (tid < 25)
{
if (sdy[tid] >= 0)
{
sd1[tid] = sdx[tid];
sdabs1[tid] = fabs(sdx[tid]);
sd2[tid] = 0;
sdabs2[tid] = 0;
}
else
{
sd1[tid] = 0;
sdabs1[tid] = 0;
sd2[tid] = sdx[tid];
sdabs2[tid] = fabs(sdx[tid]);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
reduce_sum25(sd1, sd2, sdabs1, sdabs2, tid);
barrier(CLK_LOCAL_MEM_FENCE);
__global float* descriptors_block = descriptors + descriptors_step * get_group_id(0) + (get_group_id(1) << 3);
if (tid < 25)
{
if (tid == 0)
{
descriptors_block[0] = sd1[0];
descriptors_block[1] = sdabs1[0];
descriptors_block[2] = sd2[0];
descriptors_block[3] = sdabs2[0];
}
if (sdx[tid] >= 0)
{
sd1[tid] = sdy[tid];
sdabs1[tid] = fabs(sdy[tid]);
sd2[tid] = 0;
sdabs2[tid] = 0;
}
else
{
sd1[tid] = 0;
sdabs1[tid] = 0;
sd2[tid] = sdy[tid];
sdabs2[tid] = fabs(sdy[tid]);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
reduce_sum25(sd1, sd2, sdabs1, sdabs2, tid);
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 25)
{
if (tid == 0)
{
descriptors_block[4] = sd1[0];
descriptors_block[5] = sdabs1[0];
descriptors_block[6] = sd2[0];
descriptors_block[7] = sdabs2[0];
}
}
}
void reduce_sum128(volatile __local  float* smem, int tid)
{
#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
if (tid < 64)
{
smem[tid] += smem[tid + 64];
#if WAVE_SIZE < 64
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 32)
{
#endif
smem[tid] += smem[tid + 32];
#if WAVE_SIZE < 32
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
#endif
smem[tid] += smem[tid + 16];
#if WAVE_SIZE < 16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
smem[tid] += smem[tid + 8];
#if WAVE_SIZE < 8
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 4)
{
#endif
smem[tid] += smem[tid + 4];
#if WAVE_SIZE < 4
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 2)
{
#endif
smem[tid] += smem[tid + 2];
#if WAVE_SIZE < 2
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 1)
{
#endif
smem[tid] += smem[tid + 1];
}
}
void reduce_sum64(volatile __local  float* smem, int tid)
{
#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
if (tid < 32)
{
smem[tid] += smem[tid + 32];
#if WAVE_SIZE < 32
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 16)
{
#endif
smem[tid] += smem[tid + 16];
#if WAVE_SIZE < 16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
smem[tid] += smem[tid + 8];
#if WAVE_SIZE < 8
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 4)
{
#endif
smem[tid] += smem[tid + 4];
#if WAVE_SIZE < 4
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 2)
{
#endif
smem[tid] += smem[tid + 2];
#if WAVE_SIZE < 2
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 1)
{
#endif
smem[tid] += smem[tid + 1];
}
}
__kernel
void normalize_descriptors128(__global float * descriptors, int descriptors_step)
{
descriptors_step /= sizeof(*descriptors);
__global float* descriptor_base = descriptors + descriptors_step * get_group_id(0);
volatile __local  float sqDesc[128];
const float lookup = descriptor_base[get_local_id(0)];
sqDesc[get_local_id(0)] = lookup * lookup;
barrier(CLK_LOCAL_MEM_FENCE);
reduce_sum128(sqDesc, get_local_id(0));
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local  float len;
if (get_local_id(0) == 0)
{
len = sqrt(sqDesc[0]);
}
barrier(CLK_LOCAL_MEM_FENCE);
descriptor_base[get_local_id(0)] = lookup / len;
}
__kernel
void normalize_descriptors64(__global float * descriptors, int descriptors_step)
{
descriptors_step /= sizeof(*descriptors);
__global float* descriptor_base = descriptors + descriptors_step * get_group_id(0);
volatile __local  float sqDesc[64];
const float lookup = descriptor_base[get_local_id(0)];
sqDesc[get_local_id(0)] = lookup * lookup;
barrier(CLK_LOCAL_MEM_FENCE);
reduce_sum64(sqDesc, get_local_id(0));
barrier(CLK_LOCAL_MEM_FENCE);
volatile __local  float len;
if (get_local_id(0) == 0)
{
len = sqrt(sqDesc[0]);
}
barrier(CLK_LOCAL_MEM_FENCE);
descriptor_base[get_local_id(0)] = lookup / len;
}
54ec12032d35474b210ae136c256f5b0Iteration: , normError:  (V[] failedmove failed: lambda = , e2 = ) > decreasing lambda to imagePoints.size() == (size_t)num_cameras && visibility.size() == (size_t)num_cameras && R.size() == (size_t)num_cameras && T.size() == (size_t)num_cameras && (distCoeffs.size() == (size_t)num_cameras || distCoeffs.size() == 0)bundleAdjust/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/ba.cpp_points.size() == ptparams.size() && _points.type() == ptparams.type()BasicRetinaFilter::resize( BasicRetinaFilter::spatial constant of the low pass filter must be superior to zero !!! correcting parameter setting to 0,001BasicRetinaFilter::spatial filtering coefficient must be superior to zero, correcting value to 0.01BasicRetinaFilter::setProgressiveFilterConstants_CustomAccuracy: error: input accuracy map does not match filter size, init skeptBasicRetinaFilter::runProgressiveFilter: cannot perform filtering, no progressive filter settled up/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/bowmsctrainer.cpptempledge_img.channels()==1matchEdgeImage/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/chamfermatching.cppIndex too big.
img.type() == CV_8UC1 && templ.type() == CV_8UC1chamerMatching!imgDescriptor.empty()/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/chowliutree.cppimgDescriptors[0].cols == imgDescriptor.colsimgDescriptors[0].type() == imgDescriptor.type()!imgDescriptors.empty()makereduceEdgesToMinSpan(edges)cv::LUT only supports tables of size 256./Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/colormap.cppUnknown colormap id; use one of COLORMAP_*applyColorMapinterp1cv::argsort only sorts 1D matrices.argsortcv::sortRowsByIndices only works on integer indices!sortMatrixRowsByIndicesDetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork: Cannot load a cascade from the file 'SeparateDetectionWork/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/detection_based_tracker.cppERROR in DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork in pthread_mutex_init(&mutex, NULL) is %dERROR in DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork in pthread_cond_init(&objectDetectorRun,, NULL) is %dERROR in DetectionBasedTracker::SeparateDetectionWork::SeparateDetectionWork in pthread_cond_init(&objectDetectorThreadStartStop,, NULL) is %d


ATTENTION!!! dangerous algorithm error: destructor DetectionBasedTracker::DetectionBasedTracker::~SeparateDetectionWork is called before stopping the workthreadDetectionBasedTracker::SeparateDetectionWork::run() --- startDetectionBasedTracker::SeparateDetectionWork::run is called while the previous run is not stoppedDetectionBasedTracker::SeparateDetectionWork::run --- end
 %s: ERROR: UNKNOWN Exception caught

workcycleObjectDetectorFunction
 %s: ERROR: Exception caught: 
'%s'


 %s: ERROR: OpenCV Exception caught: 
'%s'

DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- startstateThread==STATE_THREAD_WORKING_SLEEPINGworkcycleObjectDetectorDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- before waitingDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- after waitingDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- next stepDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle from inner part of lock just before waitingDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle just after waitingDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- imageSeparateDetecting is empty, continueDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- start handling imageSeparateDetecting, img.size=%dx%d, img.data=0x%pDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- end handling imageSeparateDetectingDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- go out from the workcycle just after detectingDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- objects num==%d, t_ms=%.4fDetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector: ReturningSimpleHighguiDemoCore::stop is called but the SimpleHighguiDemoCore pthread is not activeDetectionBasedTracker::SeparateDetectionWork::stop: before going to sleep to wait for the signal from the workthreadDetectionBasedTracker::SeparateDetectionWork::stop: after receiving the signal from the workthread, stateThread=%dDetectionBasedTracker::SeparateDetectionWork::resetTrackingDetectionBasedTracker::SeparateDetectionWork::resetTracking: since workthread is detecting objects at the moment, we should make cascadeInThread stop detecting and forget the detecting resultsDetectionBasedTracker::SeparateDetectionWork::resetTracking: since workthread is NOT detecting objects at the moment, we should NOT make any additional actionsDetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: shouldCommunicateWithDetectingThread=%dDetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: lastBigDetectionDuration=%f msDetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: shouldSendNewDataToWorkThread was 1, now it is %d, since time_from_previous_launch_in_ms=%.2f, minDetectionPeriod=%dDetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: result: shouldHandleResult=%d(params.minObjectSize > 0) && (params.maxObjectSize >= 0) && (params.scaleFactor > 1.0) && (params.maxTrackLifetime >= 0)DetectionBasedTrackerDetectionBasedTracker::DetectionBasedTracker: Cannot load a cascade from the file 'imageGray.type()==CV_8UC1processDetectionBasedTracker::process: time from the previous call is %f msDetectionBasedTracker::process: get _rectsWhereRegions were got from resultDetectDetectionBasedTracker::process: get _rectsWhereRegions from previous positionsDetectionBasedTracker::process: ERROR: ATTENTION: strange algorithm's behavior: trackedObjects[i].rect() is emptyDetectionBasedTracker::process: tracked objects num==%dDetectionBasedTracker::process: rectsWhereRegions.size()=%dDetectionBasedTracker::process: detectedObjectsInRegions.size()=%dDetectionBasedTracker::process: found a object with SIZE %d x %d, rect={%d, %d, %d x %d}DetectionBasedTracker::updateTrackedObjects: N1=%d, N2=%dDetectionBasedTracker::updateTrackedObjects: i=%dnumpositions > 0updateTrackedObjectsDetectionBasedTracker::updateTrackedObjects: prevRect[%d]={%d, %d, %d x %d}DetectionBasedTracker::updateTrackedObjects: j=%dDetectionBasedTracker::updateTrackedObjects: j=%d is rejected, because it has correspondence=%dDetectionBasedTracker::updateTrackedObjects: j=%d is rejected, because it is intersected with another rectangleDetectionBasedTracker::updateTrackedObjects: detectedObjects[%d]={%d, %d, %d x %d}DetectionBasedTracker::updateTrackedObjects: There is intersection between prevRect and detectedRect, r={%d, %d, %d x %d}DetectionBasedTracker::updateTrackedObjects: The area of intersection is %d, it is better than bestArea=%dDetectionBasedTracker::updateTrackedObjects: The best correspondence for i=%d is j=%dDetectionBasedTracker::updateTrackedObjects: Found intersection between rectangles j=%d and bestIndex=%d, rectangle j=%d is marked as intersectedDetectionBasedTracker::updateTrackedObjects: There is no correspondence for i=%d DetectionBasedTracker::updateTrackedObjects: start second cycleDetectionBasedTracker::updateTrackedObjects: add positionDetectionBasedTracker::updateTrackedObjects: new objectDetectionBasedTracker::updateTrackedObjects: was auxiliary intersectionnumpos > 0DetectionBasedTracker::updateTrackedObjects: deleted object {%d, %d, %d x %d}DetectionBasedTracker::calcTrackedObjectPositionToShow: ERROR: wrong i=%dDetectionBasedTracker::calcTrackedObjectPositionToShow: trackedObjects[%d].numDetectedFrames=%d <= numStepsToWaitBeforeFirstShow=%d --- return empty Rect()DetectionBasedTracker::calcTrackedObjectPositionToShow: ERROR: no positions for i=%dDetectionBasedTracker::calcTrackedObjectPositionToShow: Result for i=%d: {%d, %d, %d x %d}DetectionBasedTracker::detectInRegion: Empty intersectionDetectionBasedTracker::detectInRegion: img1.size()=%d x %d, d=%dDetectionBasedTracker::setParameters: ERROR: wrong parameters valueThis FaceRecognizer (%s) does not support updating, you have to use FaceRecognizer::train to update it.update/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/facerec.cppFile can't be opened for writing!base != 0setLabelsInfogetLabelInfogetLabelsByStringEmpty training data was given. You'll need more than one sample to learn a model.Labels must be given as integer (CV_32SC1). Expected %d, but was %d.In the Eigenfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.This Eigenfaces model is not computed yet. Did you call Eigenfaces::train?Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.num_componentseigenvaluesprojectionslabelslabelsInfoIn the Fisherfaces method all input samples (training images) must be of equal size! Expected %d pixels, but was %d pixels.Expected the labels in a matrix with one row or column! Given dimensions are rows=%s, cols=%d.This Fisherfaces model is not computed yet. Did you call Fisherfaces::train?neighborsgrid_xgrid_yhistogramsThe images are expected as InputArray::STD_VECTOR_MAT (a std::vector) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.This LBPH model is not computed yet. Did you call the train method?ncomponentsThe data is expected as InputArray::STD_VECTOR_MAT (a std::vector) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).asRowMatrixWrong number of elements in matrix #%d! Expected %d was %d.Using Original Local Binary Patterns for feature extraction only works on single-channel images (given %d). Please pass the image data as a grayscale image!elbpThis type is not implemented yet.histcFaceRecognizer.EigenfacesFaceRecognizer.FisherfacesFaceRecognizer.LBPHlabel(type()==0) || (DataType<_Tp>::type == type())DataType<_Tp>::type == type() && cols == 1OpenCV has been compiled without SIFT supportCvFeatureTracker/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/featuretracker.cppOpenCV has been compiled without SURF supportUnknown feature typebgr_subset.total() == countgenerateColors/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/gencolors.cppdownsamplePointssrc.cols == 1 || src.rows == 1src.total() >= countsrc.type() == CV_8UC3Such big matrix cann't be created.ImageLogPolProjection::no projection setted up... performing default retina projection... take careImageLogPolProjection::initLogRetinaSampling: could not initialize logPolar projection for a log projection system
 -> you probably chose the wrong init function, use initLogPolarCortexSampling() insteadImageLogPolProjection::initLogRetinaSampling: reduction factor must be superior to 0, skeeping initialisation...ImageLogPolProjection::could not initialize log projection for a logPolar projection system
 -> you probably chose the wrong init function, use initLogRetinaSampling() insteadImageLogPolProjection::reduction factor must be superior to 0, skeeping initialisation...*..Wrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).subspaceProject/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/lda.cppWrong mean shape for the given data matrix. Expected %d, but was %d.subspaceReconstructWrong mean shape for the given eigenvector matrix. Expected %d, but was %d.At least two classes are needed to perform a LDA. Reason: Only one class was given!ldaThe number of samples must equal the number of labels. Given %d labels, %d samples. Warning: Less observations than feature dimension given!Computation will probably fail.InputArray Datatype %d is not supported.Wrong shape of input matrix! Expected a matrix with one row or column.cv::sortColumnsByIndices only works on integer indices!sortMatrixColumnsByIndicesflags & MEAN_FIELD || flags & SAMPLEDFabMap/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/openfabmap.cppflags & NAIVE_BAYES || flags & CHOW_LIUclTree.type() == CV_64FC1!queryImgDescriptor.empty()addTraining!queryImgDescriptors[i].empty()queryImgDescriptors[i].rows == 1queryImgDescriptors[i].cols == clTree.colsqueryImgDescriptors[i].type() == CV_32F!testImgDescriptor.empty()!(flags & MOTION_MODEL)!_testImgDescriptors[i].empty()_testImgDescriptors[i].rows == 1_testImgDescriptors[i].cols == clTree.cols_testImgDescriptors[i].type() == CV_32F!trainingImgDescriptors.empty()getNewPlaceLikelihoodnumSamples > 0!matches.empty()normaliseDistributionflags & SAMPLEDFabMap2getLikelihoods(src_x.rows>0)&&(src_y.rows>0)&&(src_x.cols==1)&&(src_y.cols==1) &&(dst.cols==1)&&(dst.rows==(order+1))&&(order>=1)polyfit/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/polyfit.cppRetina::setup: wrong/unappropriate xml parameter file : error report :`n=>Retina::setup: resetting retina with default parameters=> keeping current parametersRetina::setup: provided parameters file could not be open... skeeping configurationOPLandIPLparvocolorModenormaliseOutputphotoreceptorsLocalAdaptationSensitivityphotoreceptorsTemporalConstantphotoreceptorsSpatialConstanthorizontalCellsGainhcellsTemporalConstanthcellsSpatialConstantganglionCellsSensitivityIPLmagnoparasolCells_betaparasolCells_tauparasolCells_kamacrinCellsTemporalCutFrequencyV0CompressionParameterlocalAdaptintegration_taulocalAdaptintegration_kCurrent Retina instance setup :
OPLandIPLparvo
==> colorMode : 
==> normalizeParvoOutput :
==> photoreceptorsLocalAdaptationSensitivity : 
==> photoreceptorsTemporalConstant : 
==> photoreceptorsSpatialConstant : 
==> horizontalCellsGain : 
==> hcellsTemporalConstant : 
==> hcellsSpatialConstant : 
==> parvoGanglionCellsSensitivity : }

IPLmagno
==> normaliseOutput : 
==> parasolCells_beta : 
==> parasolCells_tau : 
==> parasolCells_k : 
==> amacrinCellsTemporalCutFrequency : 
==> V0CompressionParameter : 
==> localAdaptintegration_tau : 
==> localAdaptintegration_k : Retina cannot be applied, wrong input buffer sizeRetina::runRetina.hBad retina size setup : size height and with must be superior to zeroRetina::setupRetina cannot be applied, input buffer is emptyinput image must be single channel (gray levels), bgr format (color) or bgra (color with transparency which won't be considered_convertCvMat2ValarrayBuffer/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/retina.cppColor channels proportions: pR, pG, pB= RetinaColor::No or wrong color sampling method, skeepingRetinaColor::applyKrauskopfLMS2Acr1cr2Transform: input buffer does not match retina buffer size, conversion abortedTemplateBuffer::TemplateBuffer::normalizeGrayOutputCentredSigmoide error: 2nd parameter (sensitivity) must not equal 0, copying original data...RetinaFilter::Problem initializing photoreceptors log sampling, could not setup retina filterRetinaFilter::checkInput: input buffer does not match retina buffer size, conversion abortedRetinaFilter::checkInput: input size= / retina size=not working, sorry!image0.empty()RGBDOdometry/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/rgbdodometry.cppimage0.type() == CV_8UC1depth0.type() == CV_32FC1 && depth0.size() == image0.size()image1.size() == image0.size()image1.type() == CV_8UC1depth1.type() == CV_32FC1 && depth1.size() == image0.size()validMask0.empty() || (validMask0.type() == CV_8UC1 && validMask0.size() == image0.size())validMask1.empty() || (validMask1.type() == CV_8UC1 && validMask1.size() == image0.size())cameraMatrix.type() == CV_32FC1 && cameraMatrix.size() == Size(3,3)iterCounts.empty() || minGradientMagnitudes.empty() || minGradientMagnitudes.size() == iterCounts.size()initRt.empty() || (initRt.type()==CV_64FC1 && initRt.size()==Size(4,4) )level_dI_dx1.type() == CV_16Slevel_dI_dy1.type() == CV_16ScameraMatrix.type() == CV_64FC1cvtDepth2CloudK.type() == CV_64FC1computeCorrespK_inv.type() == CV_64FC1Rt.type() == CV_64FC1Unsupported value of transformation type flag.computeKsiksi.size() == Size(1,6) && ksi.type() == CV_64FC1computeProjectiveMatriximg.depth() == CV_8U/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/selfsimilarity.cppestimateResolution/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/spinimages.cpp#VRML V2.0 utf8Shapegeometry PointSetcoord Coordinatepoint[color Colorcolor[Comparing scene spinimage  of Spin correlation time  = Matches number = Matches number [filtered by similarity measure] = Matches number [filtered by geometric consistency] = grouping ...G = sec/Users/jason/Projects/opencv/opencv-2.4.11/modules/contrib/src/stereovar.cppdisp.size() == left.size() && disp.type() == CV_8UC1H.type() == CV_64F && H.size() == Size(3, 3)focalsFromHomography/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/autocalib.cppm >= 1calibrateRotatingCameraHs[i].size() == Size(3, 3) && Hs[i].type() == CV_64Funsupported blending methodcreateDefault/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/blenders.cppimg.type() == CV_16SC3feedmask.type() == CV_8Uweight_type == CV_32F || weight_type == CV_16SMultiBandBlenderimg.type() == CV_16SC3 || img.type() == CV_8UC3src.type() == CV_16SC3normalizeUsingWeightMapweight.type() == CV_16SC1createWeightMapunsupported exposure compensation method/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/exposure_compensate.cppcorners.size() == images.size() && images.size() == masks.size()image.type() == CV_8UC3SurfFeaturesFinder/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/matchers.cpp(image.type() == CV_8UC3) || (image.type() == CV_8UC1)find(image.type() == CV_8UC3) || (image.type() == CV_8UC4) || (image.type() == CV_8UC1)mask.empty() || (mask.type() == CV_8U && mask.cols == num_images && mask.rows)features1.descriptors.type() == features2.descriptors.type()matchfeatures2.descriptors.depth() == CV_8U || features2.descriptors.depth() == CV_32Frvec.type() == CV_32FsetUpInitialCameraParams/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/motion_estimators.cppunsupported kind of wave correctionwaveCorrectgraph matches_graph{
/\" -- "[label="Nm=, Ni=, C="];
";
centers.size() > 0 && centers.size() <= 2findMaxSpanningTreeimage1.size() == mask1.size()/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/seam_finders.cppimage2.size() == mask2.size()image1.channels() == 3 || image1.channels() == 4computeGradientsimage2.channels() == 3 || image2.channels() == 4costFunction() == COLOR_GRADstates_[comp1] & INTERSgetSeamTipsstates_[comp] & INTERScomputeCostsboth images must have CV_32FC3(4) or CV_8UC3(4) typeestimateSeamseam.front() == p1seam.back() == p2src[i].channels() == 3unsupported pixel similarity measurefindInPair/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/../../imgproc/src/gcgraph.hppimgs.size() == imgs_.size()composePanorama/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/stitcher.cppmask.type() == CV_8U && mask.size() == Size(3, 3)setRefinementMask/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hppsrc_br.x - src_tl.x + 1 == src.cols && src_br.y - src_tl.y + 1 == src.rowswarpBackward/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/include/opencv2/stitching/detail/warpers_inl.hppsizes.size() == corners.size()resultRoi/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/util.cppsetCameraParams/Users/jason/Projects/opencv/opencv-2.4.11/modules/stitching/src/warpers.cpp(T.size() == Size(1, 3) || T.size() == Size(3, 1)) && T.type() == CV_32FSuperResolution.BTVL1scaleScale factor.Iteration count.Asymptotic value of steepest descent method.Weight parameter to balance data term and smoothness term.Parameter of spacial distribution in Bilateral-TV.btvKernelSizeKernel size of Bilateral-TV filter.blurKernelSizeGaussian blur kernel size.blurSigmaGaussian blur sigma.temporalAreaRadiusRadius of the temporal search area.opticalFlowDense optical flow algorithm.scale_ > 1/Users/jason/Projects/opencv/opencv-2.4.11/modules/superres/src/btv_l1.cppiterations_ > 0tau_ > 0.0alpha_ > 0.0btvKernelSize_ > 0blurKernelSize_ > 0blurSigma_ >= 0.0src.channels() == 1 || src.channels() == 3 || src.channels() == 4upscaleThe called functionality is disabled for current build or platformcreateSuperResolution_BTVL1_GPU/Users/jason/Projects/opencv/opencv-2.4.11/modules/superres/src/btv_l1_gpu.cppbuildMotionMapsKernelupscaleKerneldiffSignKernelcalcBtvRegularizationKernelSuperResolution.BTVL1_OCL/Users/jason/Projects/opencv/opencv-2.4.11/modules/superres/src/btv_l1_ocl.cppbtvKernelSize_ > 0 && btvKernelSize_ <= 16vc_.isOpened()reset/Users/jason/Projects/opencv/opencv-2.4.11/modules/superres/src/frame_source.cppreader_.isOpened()convertToCn/Users/jason/Projects/opencv/opencv-2.4.11/modules/superres/src/input_array_utility.cppcn == 1 || cn == 3 || cn == 4src.depth() <= CV_64FconvertToDepthdepth == CV_8U || depth == CV_32FDenseOpticalFlowExt.FarnebackpyrScalenumLevelsnumIterspolyNpolySigmaframe1.type() == frame0.type()/Users/jason/Projects/opencv/opencv-2.4.11/modules/superres/src/optical_flow.cppframe1.size() == frame0.size()DenseOpticalFlowExt.SimplelayersaveragingBlockSizemaxFlowsigmaDistsigmaColorpostProcessWindowsigmaDistFixsigmaColorFixoccThrupscaleAveragingRadiusupscaleSigmaDistupscaleSigmaColorspeedUpThrDenseOpticalFlowExt.DualTVL1DenseOpticalFlowExt.Brox_GPUFlow smoothnessGradient constancy importancePyramid scale factorinnerIterationsNumber of lagged non-linearity iterations (inner loop)outerIterationsNumber of warping iterations (number of pyramid levels)solverIterationsNumber of linear system solver iterationsDenseOpticalFlowExt.PyrLK_GPUmaxLevelDenseOpticalFlowExt.Farneback_GPUDenseOpticalFlowExt.DualTVL1_GPUDenseOpticalFlowExt.PyrLK_OCL_frame1.type() == _frame0.type()_frame1.size() == _frame0.size()DenseOpticalFlowExt.DualTVL1_OCLDenseOpticalFlowExt.FarneBack_OCLsuperres_btvl1__kernel void buildMotionMapsKernel(__global float* forwardMotionX,
__global float* forwardMotionY,
__global float* backwardMotionX,
__global float* backwardMotionY,
__global float* forwardMapX,
__global float* forwardMapY,
__global float* backwardMapX,
__global float* backwardMapY,
int forwardMotionX_row,
int forwardMotionX_col,
int forwardMotionX_step,
int forwardMotionY_step,
int backwardMotionX_step,
int backwardMotionY_step,
int forwardMapX_step,
int forwardMapY_step,
int backwardMapX_step,
int backwardMapY_step
)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < forwardMotionX_col && y < forwardMotionX_row)
{
float fx = forwardMotionX[y * forwardMotionX_step + x];
float fy = forwardMotionY[y * forwardMotionY_step + x];
float bx = backwardMotionX[y * backwardMotionX_step + x];
float by = backwardMotionY[y * backwardMotionY_step + x];
forwardMapX[y * forwardMapX_step + x] = x + bx;
forwardMapY[y * forwardMapY_step + x] = y + by;
backwardMapX[y * backwardMapX_step + x] = x + fx;
backwardMapY[y * backwardMapY_step + x] = y + fy;
}
}
__kernel void upscaleKernel(__global float* src,
__global float* dst,
int src_step,
int dst_step,
int src_row,
int src_col,
int scale,
int channels
)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < src_col && y < src_row)
{
if(channels == 1)
{
dst[y * scale * dst_step + x * scale] = src[y * src_step + x];
}
else
{
vstore4(vload4(0, src + y * channels * src_step + 4 * x), 0, dst + y * channels * scale * dst_step + 4 * x * scale);
}
}
}
float diffSign(float a, float b)
{
return a > b ? 1.0f : a < b ? -1.0f : 0.0f;
}
float4 diffSign4(float4 a, float4 b)
{
float4 pos;
pos.x = a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f;
pos.y = a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f;
pos.z = a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f;
pos.w = 0.0f;
return pos;
}
__kernel void diffSignKernel(__global float* src1,
__global float* src2,
__global float* dst,
int src1_row,
int src1_col,
int dst_step,
int src1_step,
int src2_step)
{
int x = get_global_id(0);
int y = get_global_id(1);
if(x < src1_col && y < src1_row)
{
dst[y * dst_step + x] = diffSign(src1[y * src1_step + x], src2[y * src2_step + x]);
}
}
__kernel void calcBtvRegularizationKernel(__global float* src,
__global float* dst,
int src_step,
int dst_step,
int src_row,
int src_col,
int ksize,
int channels,
__constant float* c_btvRegWeights
)
{
int x = get_global_id(0) + ksize;
int y = get_global_id(1) + ksize;
if ((y < src_row - ksize) && (x < src_col - ksize))
{
if(channels == 1)
{
const float srcVal = src[y * src_step + x];
float dstVal = 0.0f;
for (int m = 0, count = 0; m <= ksize; ++m)
{
for (int l = ksize; l + m >= 0; --l, ++count)
{
dstVal = dstVal + c_btvRegWeights[count] * (diffSign(srcVal, src[(y + m) * src_step + (x + l)]) - diffSign(src[(y - m) * src_step + (x - l)], srcVal));
}
}
dst[y * dst_step + x] = dstVal;
}
else
{
float4 srcVal = vload4(0, src + y * src_step + 4 * x);
float4 dstVal = 0.f;
for (int m = 0, count = 0; m <= ksize; ++m)
{
for (int l = ksize; l + m >= 0; --l, ++count)
{
float4 src1;
src1.x = src[(y + m) * src_step + 4 * (x + l) + 0];
src1.y = src[(y + m) * src_step + 4 * (x + l) + 1];
src1.z = src[(y + m) * src_step + 4 * (x + l) + 2];
src1.w = src[(y + m) * src_step + 4 * (x + l) + 3];
float4 src2;
src2.x = src[(y - m) * src_step + 4 * (x - l) + 0];
src2.y = src[(y - m) * src_step + 4 * (x - l) + 1];
src2.z = src[(y - m) * src_step + 4 * (x - l) + 2];
src2.w = src[(y - m) * src_step + 4 * (x - l) + 3];
dstVal = dstVal + c_btvRegWeights[count] * (diffSign4(srcVal, src1) - diffSign4(src2, srcVal));
}
}
vstore4(dstVal, 0, dst + y * dst_step + 4 * x);
}
}
}
30f28e5e4fff61c587f608005cd5fe53BGR2BGRABGRA2BGRBGR2RGBARGBA2BGRBGR2RGBBGRA2RGBABGR2GRAYRGB2GRAYGRAY2BGRGRAY2BGRABGRA2GRAYRGBA2GRAYBGR2BGR565RGB2BGR565BGR5652BGRBGR5652RGBBGRA2BGR565RGBA2BGR565BGR5652BGRABGR5652RGBAGRAY2BGR565BGR5652GRAYBGR2BGR555RGB2BGR555BGR5552BGRBGR5552RGBBGRA2BGR555RGBA2BGR555BGR5552BGRABGR5552RGBAGRAY2BGR555BGR5552GRAYBGR2XYZXYZ2BGRBGR2YCrCbYCrCb2BGRBGR2HSVRGB2HSVBGR2LabRGB2LabBayerBG2BGRBayerGB2BGRBayerRG2BGRBayerGR2BGRBGR2LuvRGB2LuvBGR2HLSRGB2HLSHSV2BGRHSV2RGBLab2BGRLab2RGBLuv2BGRLuv2RGBHLS2BGRHLS2RGBBayerBG2BGR_VNGBayerGB2BGR_VNGBayerRG2BGR_VNGBayerGR2BGR_VNGBGR2HSV_FULLRGB2HSV_FULLBGR2HLS_FULLRGB2HLS_FULLHSV2BGR_FULLHSV2RGB_FULLHLS2BGR_FULLHLS2RGB_FULLLBGR2LabLRGB2LabLBGR2LuvLRGB2LuvLab2LBGRLab2LRGBLuv2LBGRLuv2LRGBBGR2YUVRGB2YUVYUV2BGRYUV2RGBBayerBG2GRAYBayerGB2GRAYBayerRG2GRAYBayerGR2GRAYYUV2RGB_NV12YUV2BGR_NV12YUV2RGB_NV21YUV2BGR_NV21YUV2RGBA_NV12YUV2BGRA_NV12YUV2RGBA_NV21YUV2BGRA_NV21YUV2RGB_YV12YUV2BGR_YV12YUV2RGB_IYUVYUV2BGR_IYUVYUV2RGBA_YV12YUV2BGRA_YV12YUV2RGBA_IYUVYUV2BGRA_IYUVYUV2GRAY_420YUV2RGB_UYVYYUV2BGR_UYVYYUV2RGB_VYUYYUV2BGR_VYUYYUV2RGBA_UYVYYUV2BGRA_UYVYYUV2RGBA_VYUYYUV2BGRA_VYUYYUV2RGB_YUY2YUV2BGR_YUY2YUV2RGB_YVYUYUV2BGR_YVYUYUV2RGBA_YUY2YUV2BGRA_YUY2YUV2RGBA_YVYUYUV2BGRA_YVYUYUV2GRAY_UYVYYUV2GRAY_YUY2mRGBA2RGBACOLORCVT_MAX[----------]
[ GPU INFO ] 	Run on OS Apple x64.
[----------]Incorrect device number - Device ] is NOT compatible with current GPU module buildMatrices "" and "" have different sizes : "" [] vs "" have different types : "The max difference between matrices "" is  at (, which exceeds "", where "" at () evaluates to , "" evaluates to sub matrixwhole matrixinversedirectgoldactualdiffKeyPoints size mistmach
" : KeyPoints differ at " vs "" : 
pt :  vs size : angle : response : octave : class_id : (null)%s  (test case #%d): unknown exception was thrown (the function has likely crashed)
%s (test case #%d): the error code %d is different from the expected %d
%s  (test case #%d): no expected exception was thrown
OkGeneric/UnknownNo test dataInvalid test datacvError invokedHardware/OS exceptionInvalid memory accessArithmetic exceptionCorrupted memblock (beginning)Corrupted memblock (end)Memory leakInvalid function outputUnexpected outputBad accuracyInfinite loop(?)Incorrect handling of bad argumentsOPENCV_TEST_DATA_PATH%s%s%s//Users/jason/Projects/opencv/opencv-2.4.11/modules/ts/src/ts.cppSucceeded%08x%08x
-----------------------------------
	SUM: 
-----------------------------------
	LOG: 
-----------------------------------
	CONSOLE: 
-----------------------------------
Failed
	failure reason: 
	test case #
	seed: !img.empty() && img.depth() == CV_8U && ch <= 4fillGradientsmoothBorderOpenCV Error: %s (%s) in %s, file %s, line %d
min_log_array_sizemax_log_array_sizetest_case_countinputinput/outputref input/outputref outputtemporarymask%s array %d type=%sC%d, size=(%s)
8u8s16u16s32s32f64f(typeMask & DEPTH_MASK_ALL) != 0randomType/Users/jason/Projects/opencv/opencv-2.4.11/modules/ts/src/ts_func.cppval != -1getMinValgetMaxVal!a.empty() || !b.empty()a.size == b.sizeplanes[0].rows == 1src.size == mask.size && mask.type() == CV_8Udst.size == mask.size && mask.type() == CV_8Udst.size == src.size && src.depth() == dst.depth() && 0 <= coi && coi < dst.channels()extractsrc.dims == 2randUnikernel.type() == CV_8Uerodedilatekernel.type() == CV_32F || kernel.type() == CV_64Fmask.empty() || (src.size == mask.size && mask.type() == CV_8U)temp.depth() == CV_8Usrc1.type() == src2.type() && src1.size == src2.sizemask.empty() || (src1.size == mask.size && mask.type() == CV_8U)op == '&' || op == '|' || op == '^'logicOpop == '&' || op == '|' || op == '^' || op == '~'src1.type() == src2.type() && src1.channels() == 1 && src1.size == src2.sizecmpUlpscheckarr.type() == refarr.type() && arr.size == refarr.sizecmpEps%s: Too big difference (=%g)%s: Invalid output%s: Invalid reference output%s at element %d
%s at (%s)
a.type() == b.type() && a.dims == 2 && b.dims == 2 && cn <= 2c.dims == 2 && c.type() == a.type() && c_rows == a_rows && c_cols == b_colsscn <= 4 && dcn <= 4 && (mattype == CV_32F || mattype == CV_64F) && (!haveShift || (shift.type() == mattype && (shift.rows == 1 || shift.cols == 1)))mask.empty() || (mask.type() == CV_8U && mask.size == src.size)patchZeros(_aperture_size == -1 || (_aperture_size >= 1 && _aperture_size % 2 == 1)) && dx >= 0 && dy >= 0 && dx + dy <= 3calcSobelKernel2D?-dim ( x The reference and the actual output arrays have different type or size:
 ~ too big relative difference ( > ) between  '' and '' at .

': .
cv_versionOpenCV version: Version controlcv_vcs_versionOpenCV VCS version: inner versioncv_inner_vcs_versionInner VCS version: cv_build_typeBuild type: cv_parallel_frameworkParallel framework:  sse sse2 sse3 ssse3cv_cpu_featuresCPU features: mask >= 0minmaxsrc1.empty() || (src1.type() == src2.type() && src1.size == src2.size)muldivd == (int)ofs.size()getSubArrayUnknown comparison operationcompare_compareS_!m.loc || ((int)m.loc->size() == m.m->dims && m.m->dims <= 2);
auto/Users/jason/Projects/opencv/opencv-2.4.11/modules/ts/src/ts_gtest.cppCondition range > 0 failed. Cannot generate a number in the range [0, 0).Condition range <= kMaxRange failed. Generation of a number in [0, ) was requested, but this can only generate numbers in [0, ).Value of: 
  Actual: 
Expected:  (ignoring case)
Which is: )The difference between  and  is , which exceeds , where
 evaluates to ,
, and
Expected: () != (), actual: ) <= () < () >= () > (), actual: "") (ignoring case), actual: "(Invalid Unicode 0x\0All tests in the same test case must use the same test fixture
class, so mixing TEST_F and TEST in the same test case is
illegal.  In test case test  is defined using TEST_F but
 is defined using TEST.  You probably
want to change the TEST to TEST_F or move it to another test
case.class.  However, in test case you defined test  and test using two different test fixture classes.  This can happen if
the two classes are from different namespaces or translation
units and have the same name.  You should probably rename one
of the classes to put the tests into different test cases.SetUp()the test bodyTearDown()Attempted redefinition of test case , you tried
to define a test using a fixture class different from the one
used earlier. This can happen if the two fixture classes are
from different namespaces and have the same name. You should
probably rename one of the classes to put the tests into different
test cases.%s %sthe test fixture's constructorthe test fixture's destructorSetUpTestCase()TearDownTestCase()TERMxtermxterm-colorxterm-256colorscreenscreen-256colorlinuxcygwinyestruet
Repeating all tests (iteration %d) . . .

Note: %s filter = %s
Google TestNote: %s parameter filter = %s
Note: This is test shard %d of %s.
Note: Randomizing tests' orders with a seed of %d .
[==========] Running %s from %s.
[----------] testtests%s from %s, where %s = %s
[ RUN      ] [       OK ] [  FAILED  ]  (%s ms)
%s from %s (%s ms total)

%s.%s%s from %s ran. (%s ms total)[  PASSED  ] %s.
%s, listed below:

%2d FAILED %s
TESTTESTS  YOU HAVE %d DISABLED %s

XML output file may not be null
wUnable to open file "%s"
<>&'"&#x;]]>]]>. ="testcase    
      
 />
    
testsuite  <failuresdisablederrors  
timestampAllTestsclose_fd)chdir("") failed: execve(, ...) in  failed: Only one  capturer can exist at a time./tmp/captured_stream.XXXXXXr-byte object < ... %02Xread_fd_ == -1)
not a substring of Which is: L"C++ exception with description "Unknown C++ exception thrown in pthread_key_create(&key, &DeleteThreadLocalValue)pthread_key_delete(key_)Condition 0 <= begin && begin <= size failed. Invalid shuffle range start : must be in range [0, Condition begin <= end && end <= size failed. Invalid shuffle range finish : must be in range [--help-h-?/?gtest_internal_gtest-@, 0x\'\\\a\b\f\r\t\v\xL' (no terminating NUL)" \"Global test environment set-up.Global test environment tear-down-pt-size-angle-response-octave-class_id-queryIdx-trainIdx-imgIdx-distanceSubsequent initialization of Regression utility is not allowed.perf_newFailed to open sanity data for reading: %sCould not open "%s" file for writingundefinedminmaxlastvalrng1cnrng2expect_minactual_min/Users/jason/Projects/opencv/opencv-2.4.11/modules/ts/src/ts_perf.cpp has unexpected minimal valueexpect_maxactual_max has unexpected maximal valueexpect_colsactual.size.p[1] has unexpected number of columnsexpect_rowsactual.size.p[0] has unexpected number of rowsexpect_lastactual_last has unexpected value of the last elementexpect_rng1actual_rng1 has unexpected value of the [] elementexpect_rng2actual_rng2kindlenidxexpected_kindarray.kind()  Argument "" has unexpected kindexpected_typearray.type()" has unexpected typeexpected_length(int)array.total()  Vector "" has unexpected length(size_t)26actual.total() * (size_t)actual.channels()  "]" has unexpected number of elements%s[%d]actual.empty()  expected empty expected.size()actual.size()] has unexpected size Expected:  Actual:  Absolute difference (=) between argument "]" and expected value is greater than   Relative difference ( allowed) between argument " in  pointsarray.total() * (size_t)array.channels()" has unexpected number of elementsArgument "" has unexpected size  Difference (=) between argument1 "" and expected value is greater than   Can not check regression for CV_USRTYPE1 data type for   No regression data for  argumentplain{   |perf_max_outliers           |8        |percent of allowed outliers}{   |perf_min_samples            |10       |minimal required numer of samples}{   |perf_force_samples          |100      |force set maximum number of samples for all tests}{   |perf_seed                   |809564   |seed for random numbers generator}{   |perf_threads                |-1       |the number of worker threads, if parallel execution is enabled}{   |perf_write_sanity           |false    |create new records for sanity checks}{   |perf_verify_sanity          |false    |fail tests having no regression data for sanity checks}{   |perf_impl                   ||the implementation variant of functions under test}{   |perf_list_impls             |false    |list available implementation variants and exit}{   |perf_run_cpu                |false    |deprecated, equivalent to --perf_impl=plain}{   |perf_strategy               |default  |specifies performance measuring strategy: default, base or simple (weak restrictions)}{   |perf_time_limit             |3.0      |default time limit for a single test (in seconds)}{   |perf_max_deviation          |1.0      |}{h  |help                        |false    |print help info}helpperf_run_cpuperf_implperf_strategydefaultbasesimpleNo such strategy: %s
perf_max_outliersperf_min_samplesperf_max_deviationperf_seedperf_time_limitperf_force_samplesperf_write_sanityperf_verify_sanityperf_threadsperf_list_implsAvailable implementation variants:No such implementation: %s
[----------]
[   INFO   ] 	Implementation variant: %s.
[----------]
cv_implementationcv_num_threadscompensation < 0.01 * cv::getTickFrequency()_calibrateTime compensation is %.0f  Uninitialized input/output parameters are not allowed for performance tests  next() is called before stopTimer()  stopTimer() is called before startTimer()/next()metrics.samples <= (unsigned int)currentItercalcMetricsm.samples1u  No time measurements was performed.
startTimer() and stopTimer() commands are required for performance tests.param_min_samples  Only a few samples are collected.
Please increase number of iterations or/and time limit to get reliable performance measurements.1.2 * sinh(m.gstddev * param_max_deviation)  Test results are not reliable ((mean-sigma,mean+sigma) deviation interval is greater than measured time interval).m.outliersstd::max((unsigned int)cvCeil(m.samples * param_max_outliers / 100.), 1u)  Test results are not reliable (too many outliers).    samples = %d, mean = %.2f, stddev = %.2f (%.1f%%)
custom_statusskippedbytesInbytesOuttermsamplesoutliersfrequency%.0fgmeangstddev%.6fstddevtype      = %11sparams    = %11stermination reason:  reached maximum number of iterationstermination reason:  reached time limittermination reason:  aborted by the performance testing frameworktermination reason:  unhandled exceptiontermination reason:  unknownbytesIn   =%11lubytesOut  =%11lusamples   =%11usamples   =%11u of %uoutliers  =%11ufrequency =%11.0fmin       =%11.0f = %.2fmsmedian    =%11.0f = %.2fmsgmean     =%11.0f = %.2fmsgstddev   =%11.8f = %.2fms for 97%% dispersion intervalmean      =%11.0f = %.2fmsstddev    =%11.0f = %.2fms	Test was skippedTest was skippedThe test has no sanity checks. There should be at least one check at the end of performance test.[ VALUE    ] 	%s
[ TYPE     ] 	%s
  Bad path to test resource  Requested file "" does not exist.Expected: PerfTestBody() doesn't throw an exception.
  Actual: it throws...Expected: PerfTestBody() doesn't throw an exception.
  Actual: it throws std::exception:
  Expected: PerfTestBody() doesn't throw an exception.
  Actual: it throws cv::Exception:
  pts.size() == (size_t)desc.rowscuda8U8S16U16S32S32F64FUSRTYPE1INVALID_TYPEframe.type() == CV_8UC3deblur/Users/jason/Projects/opencv/opencv-2.4.11/modules/videostab/src/deblurring.cppcan't open file: points0.size() == points1.size()estimateGlobalMotionLeastSquares/Users/jason/Projects/opencv/opencv-2.4.11/modules/videostab/src/global_motion.cppestimateGlobalMotionRobustinpaint/Users/jason/Projects/opencv/opencv-2.4.11/modules/videostab/src/inpainting.cppmask.size() == frame.size() && mask.type() == CV_8UflowX.type() == CV_32F && flowX.size() == mask0.size()calcFlowMaskflowY.type() == CV_32F && flowY.size() == mask0.size()errors.type() == CV_32F && errors.size() == mask0.size()mask0.type() == CV_8Umask1.type() == CV_8U && mask1.size() == mask0.size()flowMask.type() == CV_8UcompleteFrameAccordingToFlowflowX.type() == CV_32F && flowX.size() == flowMask.size()flowY.type() == CV_32F && flowY.size() == flowMask.size()frame1.type() == CV_8UC3 && frame1.size() == flowMask.size()mask1.type() == CV_8U && mask1.size() == flowMask.size()frame0.type() == CV_8UC3 && frame0.size() == flowMask.size()mask0.type() == CV_8U && mask0.size() == flowMask.size()frame0.type() == CV_8UC3 && frame1.type() == CV_8UC3alignementErrormask0.type() == CV_8U && mask0.size() == frame0.size()M.size() == Size(3,3) && M.type() == CV_32F/Users/jason/Projects/opencv/opencv-2.4.11/modules/videostab/include/opencv2/videostab/fast_marching_inl.hppensureInclusionConstraint/Users/jason/Projects/opencv/opencv-2.4.11/modules/videostab/src/motion_stabilizing.cppestimateOptimalTrimRatiogpu::getCudaEnabledDeviceCount() > 0DensePyrLkOptFlowEstimatorGpu/Users/jason/Projects/opencv/opencv-2.4.11/modules/videostab/src/optical_flow.cppprocessing framesfirst pass: estimating motionsestimated trim ratio: %f
Bogus message code %dSorry, there are legal restrictions on arithmetic codingALIGN_TYPE is wrong, please fixMAX_ALLOC_CHUNK is wrong, please fixBogus buffer control modeInvalid component ID %d in SOSInvalid crop requestDCT coefficient out of rangeIDCT output block size %d not supportedBogus Huffman table definitionBogus input colorspaceBogus JPEG colorspaceBogus marker lengthWrong JPEG library version: library is %d, caller expects %dSampling factors too large for interleaved scanInvalid memory pool code %dUnsupported JPEG data precision %dInvalid progressive parameters Ss=%d Se=%d Ah=%d Al=%dInvalid progressive parameters at scan script entry %dBogus sampling factorsInvalid scan script at entry %dImproper call to JPEG library in state %dJPEG parameter struct mismatch: library thinks size is %u, caller expects %uBogus virtual array accessBuffer passed to JPEG library is too smallSuspension not allowed hereCCIR601 sampling not implemented yetToo many color components: %d, max %dUnsupported color conversion requestBogus DAC index %dBogus DAC value 0x%xBogus DHT index %dBogus DQT index %dEmpty JPEG image (DNL not supported)Read from EMS failedWrite to EMS failedDidn't expect more than one scanInput file read errorOutput file write error --- out of disk space?Fractional sampling not implemented yetHuffman code size table overflowMissing Huffman code table entryMaximum supported image dimension is %u pixelsEmpty input filePremature end of input fileCannot transcode due to multiple use of quantization table %dScan script does not transmit all dataInvalid color quantization mode changeNot implemented yetRequested feature was omitted at compile timeBacking store not supportedHuffman table 0x%02x was not definedJPEG datastream contains no imageQuantization table 0x%02x was not definedNot a JPEG file: starts with 0x%02x 0x%02xInsufficient memory (case %d)Cannot quantize more than %d color componentsCannot quantize to fewer than %d colorsCannot quantize to more than %d colorsInvalid JPEG file structure: two SOF markersInvalid JPEG file structure: missing SOS markerUnsupported JPEG process: SOF type 0x%02xInvalid JPEG file structure: two SOI markersInvalid JPEG file structure: SOS before SOFFailed to create temporary file %sRead failed on temporary fileSeek failed on temporary fileWrite failed on temporary file --- out of disk space?Application transferred too few scanlinesUnsupported marker type 0x%02xVirtual array controller messed upImage too wide for this implementationRead from XMS failedWrite to XMS failedCopyright (C) 1998, Thomas G. Lane6b  27-Mar-1998Caution: quantization tables are too coarse for baseline JPEGAdobe APP14 marker: version %d, flags 0x%04x 0x%04x, transform %dUnknown APP0 marker (not JFIF), length %uUnknown APP14 marker (not Adobe), length %uDefine Arithmetic Table 0x%02x: 0x%02xDefine Huffman Table 0x%02xDefine Quantization Table %d  precision %dDefine Restart Interval %uFreed EMS handle %uObtained EMS handle %uEnd Of Image        %3d %3d %3d %3d %3d %3d %3d %3dJFIF APP0 marker: version %d.%02d, density %dx%d  %dWarning: thumbnail image size does not match data length %uJFIF extension marker: type 0x%02x, length %u    with %d x %d thumbnail imageMiscellaneous marker 0x%02x, length %uUnexpected marker 0x%02x        %4u %4u %4u %4u %4u %4u %4u %4uQuantizing to %d = %d*%d*%d colorsQuantizing to %d colorsSelected %d colors for quantizationAt marker 0x%02x, recovery action %dRST%dSmoothing not supported with nonstandard sampling ratiosStart Of Frame 0x%02x: width=%u, height=%u, components=%d    Component %d: %dhx%dv q=%dStart of ImageStart Of Scan: %d components    Component %d: dc=%d ac=%d  Ss=%d, Se=%d, Ah=%d, Al=%dClosed temporary file %sOpened temporary file %sJFIF extension marker: JPEG-compressed thumbnail image, length %uJFIF extension marker: palette thumbnail image, length %uJFIF extension marker: RGB thumbnail image, length %uUnrecognized component IDs %d %d %d, assuming YCbCrFreed XMS handle %uObtained XMS handle %uUnknown Adobe color transform code %dInconsistent progression sequence for component %d coefficient %dCorrupt JPEG data: %u extraneous bytes before marker 0x%02xCorrupt JPEG data: premature end of data segmentCorrupt JPEG data: bad Huffman codeWarning: unknown JFIF revision number %d.%02dPremature end of JPEG fileCorrupt JPEG data: found marker 0x%02x instead of RST%dInvalid SOS parameters for sequential JPEGApplication transferred too many scanlinesJPEGMEM%ld%cToo many bytes for PNG signaturePotential overflow in png_zalloc()Application built with libpng- but running with Unknown freer parameter in png_data_freerIgnoring invalid time value +0000
libpng version 1.5.12 - July 11, 2012
Copyright (c) 1998-2012 Glenn Randers-Pehrson
Copyright (c) 1996-1997 Andreas Dilger
Copyright (c) 1995-1996 Guy Eric Schalnat, Group 42, Inc.
 libpng version 1.5.12 - July 11, 2012

Ignoring attempt to set negative chromaticity valueInvalid cHRM white pointInvalid cHRM red pointInvalid cHRM green pointInvalid cHRM blue pointIgnoring attempt to set cHRM RGB triangle with zero areaextreme cHRM chunk cannot be converted to tristimulus valuesinternal error in png_XYZ_from_xyImage width is zero in IHDRImage height is zero in IHDRImage width exceeds user limit in IHDRImage height exceeds user limit in IHDRInvalid image width in IHDRInvalid image height in IHDRWidth is too large for libpng to process pixelsInvalid bit depth in IHDRInvalid color type in IHDRInvalid color type/bit depth combination in IHDRUnknown interlace method in IHDRUnknown compression method in IHDRMNG features are not allowed in a PNG datastreamUnknown filter method in IHDRInvalid filter method in IHDRInvalid IHDR dataASCII conversion buffer too smallfixed point overflow ignoredgamma table being rebuilt0123456789ABCDEF123456789fixed point overflow in libpng error: %slibpng warning: %ssCAL widthsCAL heightOut of Memoryzlib memory errorzlib stream errorzlib version errorUnknown zlib errorMissing IHDR before IDATMissing PLTE before IDATInvalid attempt to read row dataNot enough image dataExtra compressed dataDecompression errorbad adaptive filter valuesequential row overflowinternal sequential row size calculation errorInterlace handling should be turned on when using png_read_imageRead palette index exceeding num_paletteToo many IDATs foundImage is too high to process with png_read_png()Call to NULL read functionRead ErrorCan't set both read_data_fn and write_data_fn in the same structureCan't discard critical data on CRC errorApplication must supply a known background gammapng_set_backgroundoutput gamma out of expected rangeinvalid alpha modeconflicting calls to set alpha mode and backgroundinvalid error action to rgb_to_grayignoring out of range rgb_to_gray coefficientsrgb to gray red coefficientrgb to gray green coefficientlibpng does not support gamma+background+rgb_to_grayinvalid background gamma typeNULL row bufferUninitialized rowpng_do_rgb_to_gray found nongray pixelpng_do_quantize returned rowbytes=0png_do_encode_alpha: unexpected callgamma valuePNG unsigned integer out of rangeNot a PNG filePNG file corrupted by ASCII conversionCRC errorinvalid chunklengthExceeded size limit while expanding chunkpng_inflate logic errorNot enough memory to decompress chunkUnknown compression type @1Out of place IHDRInvalid IHDR chunkMissing IHDR before PLTEInvalid PLTE after IDATDuplicate PLTE chunkIgnoring PLTE chunk in grayscale PNGInvalid palette chunkTruncating incorrect tRNS chunk lengthTruncating incorrect info tRNS chunk lengthNo image in fileIncorrect IEND chunk lengthMissing IHDR before gAMAInvalid gAMA after IDATOut of place gAMA chunkDuplicate gAMA chunkIncorrect gAMA chunk lengthIgnoring gAMA chunk with out of range gammaIgnoring incorrect gAMA value @1 when sRGB is also presentMissing IHDR before sBITInvalid sBIT after IDATOut of place sBIT chunkDuplicate sBIT chunkIncorrect sBIT chunk lengthMissing IHDR before cHRMInvalid cHRM after IDATOut of place cHRM chunkDuplicate cHRM chunkIncorrect cHRM chunk lengthIgnoring cHRM chunk with negative chromaticitiesIgnoring incorrect cHRM white(@1,@2) r(@3,@4)g(@5,@6)b(@7,@8) when sRGB is also presentinternal error handling cHRM coefficientsinternal error handling cHRM->XYZMissing IHDR before sRGBInvalid sRGB after IDATOut of place sRGB chunkDuplicate sRGB chunkIncorrect sRGB chunk lengthUnknown sRGB intentIgnoring incorrect cHRM value when sRGB is also presentMissing IHDR before iCCPInvalid iCCP after IDATOut of place iCCP chunkDuplicate iCCP chunkMalformed iCCP chunkIgnoring nonzero compression type in iCCP chunkProfile size field missing from iCCP chunkIgnoring iCCP chunk with declared size = @1 and actual length = @2No space in chunk cache for sPLTMissing IHDR before sPLTInvalid sPLT after IDATmalformed sPLT chunksPLT chunk has bad lengthsPLT chunk requires too much memoryMissing IHDR before tRNSInvalid tRNS after IDATDuplicate tRNS chunkIncorrect tRNS chunk lengthMissing PLTE before tRNSZero length tRNS chunktRNS chunk not allowed with alpha channelMissing IHDR before bKGDInvalid bKGD after IDATMissing PLTE before bKGDDuplicate bKGD chunkIncorrect bKGD chunk lengthIncorrect bKGD chunk index valueMissing IHDR before hISTInvalid hIST after IDATMissing PLTE before hISTDuplicate hIST chunkIncorrect hIST chunk lengthMissing IHDR before pHYsInvalid pHYs after IDATDuplicate pHYs chunkIncorrect pHYs chunk lengthMissing IHDR before oFFsInvalid oFFs after IDATDuplicate oFFs chunkIncorrect oFFs chunk lengthMissing IHDR before pCALInvalid pCAL after IDATDuplicate pCAL chunkNo memory for pCAL purposeInvalid pCAL dataInvalid pCAL parameters for equation typeUnrecognized equation type for pCAL chunkNo memory for pCAL paramsMissing IHDR before sCALInvalid sCAL after IDATDuplicate sCAL chunksCAL chunk too shortOut of memory while processing sCAL chunkInvalid sCAL ignored: invalid unitInvalid sCAL chunk ignored: bad width formatInvalid sCAL chunk ignored: non-positive widthInvalid sCAL chunk ignored: bad height formatInvalid sCAL chunk ignored: non-positive heightOut of place tIME chunkDuplicate tIME chunkIncorrect tIME chunk lengthNo space in chunk cache for tEXtMissing IHDR before tEXtNo memory to process text chunkNot enough memory to process text chunkInsufficient memory to process text chunkNo space in chunk cache for zTXtMissing IHDR before zTXtOut of memory processing zTXt chunkTruncated zTXt chunkUnknown compression type in zTXt chunkNot enough memory to process zTXt chunkInsufficient memory to store zTXt chunkNo space in chunk cache for iTXtMissing IHDR before iTXtNo memory to process iTXt chunkTruncated iTXt chunkUnknown iTXt compression type or methodMalformed iTXt chunkNot enough memory to process iTXt chunkInsufficient memory to store iTXt chunkNo space in chunk cache for unknown chunkunknown critical chunkerror in user chunkinvalid chunk typeinternal row logic errorinternal row size calculation errorinternal row width errorinvalid user transform pixel depthDecompression ErrorExtra compression dataRow has too many bytes to allocate in memoryBuffer error in compressed datastreamData error in compressed datastreamIncomplete compressed datastreamXYZ values out of representable rangecHRM White XcHRM White YcHRM Red XcHRM Red YcHRM Green XcHRM Green YcHRM Blue XcHRM Blue YcHRM Red ZOut of range gamma value ignoredpng_set_gAMAInvalid palette size, hIST allocation skippedInsufficient memory for hIST chunk dataInvalid pCAL equation typeInvalid format for pCAL parameterInsufficient memory for pCAL purposeInsufficient memory for pCAL unitsInsufficient memory for pCAL paramsInsufficient memory for pCAL parameterInvalid sCAL unitInvalid sCAL widthInvalid sCAL heightMemory allocation failed while processing sCALInvalid sCAL width ignoredInvalid sCAL height ignoredInvalid palette lengthInsufficient memory to process iCCP chunkInsufficient memory to process iCCP profileInsufficient memory to store texttext compression mode is out of rangetRNS chunk has out-of-range samples for bit_depthNo memory for sPLT palettesOut of memory while processing sPLT chunkOut of memory while processing unknown chunkAttempt to set buffer size beyond max ignoredCall to NULL write functionWrite ErrorWriting zero-length unknown chunkValid palette required for paletted imagesNo IDATs written into fileWrote palette index exceeding num_palettepng_write_info was never called before png_write_rowinternal write transform logic errorzlib errorUnknown row filter for method 0Can't add Up filter after startingCan't add Average filter after startingCan't add Paeth filter after startingUnknown custom filter methodOnly compression windows <= 32k supported by PNGOnly compression windows >= 256 supported by PNGCompression window is being reset to 512Only compression method 8 is supported by PNGText compression window is being reset to 512Unknown filter heuristic methodInvalid bit depth for grayscale imageInvalid bit depth for RGB imageInvalid bit depth for paletted imageInvalid bit depth for grayscale+alpha imageInvalid bit depth for RGBA imageInvalid image color type specifiedInvalid compression type specifiedInvalid filter type specifiedInvalid interlace type specifiedInvalid number of colors in paletteIgnoring request to write a PLTE chunk in grayscale PNGInvalid zlib compression method or flags in IDATInvalid sRGB rendering intent specifiedUnknown compression type in iCCP chunkEmbedded profile length in iCCP chunk is negativeEmbedded profile length too large in iCCP chunkTruncating profile to actual length in iCCP chunkInvalid sBIT depth specifiedInvalid number of transparent colors specifiedIgnoring attempt to write tRNS chunk out-of-range for bit_depthIgnoring attempt to write 16-bit tRNS chunk when bit_depth is 8Can't write tRNS with an alpha channelInvalid background palette indexIgnoring attempt to write 16-bit bKGD chunk when bit_depth is 8Ignoring attempt to write bKGD chunk out-of-range for bit_depthInvalid number of histogram entries specifiedzero length keywordOut of memory while procesing keywordinvalid keyword character 0x@1trailing spaces removed from keywordleading spaces removed from keywordextra interior spaces removed from keywordZero length keywordkeyword length must be 1 - 79 charactersEmpty language field in iTXt chunkUnrecognized unit type for oFFs chunkCan't write sCAL (buffer too small)Unrecognized unit type for pHYs chunkInvalid time specified for tIME chunklength exceeds PNG maximaInvalid zlib compression method or flags in non-IDAT chunkendtextIDATinvalid zlib statezlib failed to initialize compressor () version error) stream error) memory error) unknown errorzstream already in use (internal error)versionstreammemory[no zlib message]zlib failed to reset compressor: @1(@2): @3zstream not in use (internal error)Failed to allocate memory for %s (%ld elements of %ld bytes each)No space for "TransferFunction" tagTag NoneLZWPackBitsThunderScanNeXTJPEGOld-style JPEGCCITT RLECCITT RLE/WCCITT Group 3CCITT Group 4ISO JBIGDeflateAdobeDeflatePixarLogSGILogSGILog24LZMA%s compression support is not configuredscanlineCompression algorithm does not support random accessTIFFRegisterCODECNo space to register compression scheme %sTIFFUnRegisterCODECCannot remove compression scheme %s; not registered%s %s encoding is not implementedCompression scheme %u %s encoding is not implemented%s %s decoding is not implementedCompression scheme %u %s decoding is not implementedCan not unlink directory in read-only fileDirectory %d does not existError writing directory linkTIFFSetField%s: Unknown %stag %upseudo-%s: Cannot modify tag "%s" while writingNonstandard tile width %d, convert fileNonstandard tile length %d, convert file%s: Sorry, cannot nest SubIFDs%s: Invalid %stag "%s" (not supported by codec)Unknown%s: Failed to allocate space for list of custom values%s: Bad field type %d for "%s"%s: Null count for "%s" (type %d, writecount %d, passcount %d)custom tag binary objectDotRange%s: Bad value %u for "%s" tag%s: Invalid InkNames value; expecting %d names, found %d_TIFFVGetFieldError fetching directory countError fetching directory linkSanity check on directory count failed%s: Error fetching directory count%s: Error fetching directory link_TIFFSetupFieldsSetting up field info failedFailed to allocate fields array%s: 
field[%2d] %5lu, %2d, %2d, %d, %2d, %5s, %5s, %s
TRUETIFFFieldWithTagInternal error, unknown tag 0x%xTIFFFieldWithNameInternal error, unknown tag %sTag %dSubfileTypeOldSubfileTypeImageWidthImageLengthBitsPerSamplePhotometricInterpretationThreshholdingCellWidthCellLengthFillOrderDocumentNameImageDescriptionMakeModelStripOffsetsOrientationSamplesPerPixelRowsPerStripStripByteCountsMinSampleValueMaxSampleValueXResolutionYResolutionPlanarConfigurationPageNameXPositionYPositionFreeOffsetsFreeByteCountsGrayResponseUnitGrayResponseCurveResolutionUnitPageNumberColorResponseUnitTransferFunctionSoftwareDateTimeArtistHostComputerWhitePointPrimaryChromaticitiesColorMapHalftoneHintsTileWidthTileLengthTileOffsetsTileByteCountsSubIFDInkNamesNumberOfInksTargetPrinterExtraSamplesSampleFormatSMinSampleValueSMaxSampleValueClipPathXClipPathUnitsYClipPathUnitsYCbCrCoefficientsYCbCrSubsamplingYCbCrPositioningReferenceBlackWhiteXMLPacketMatteingDataTypeImageDepthTileDepthImageFullWidthImageFullLengthTextureFormatTextureWrapModesFieldOfViewCotangentMatrixWorldToScreenMatrixWorldToCameraCopyrightRichTIFFIPTCPhotoshopEXIFIFDOffsetICC ProfileGPSIFDOffsetFaxRecvParamsFaxSubAddressFaxRecvTimeFaxDcsStoNitsInteroperabilityIFDOffsetDNGVersionDNGBackwardVersionUniqueCameraModelLocalizedCameraModelCFAPlaneColorCFALayoutLinearizationTableBlackLevelRepeatDimBlackLevelBlackLevelDeltaHBlackLevelDeltaVWhiteLevelDefaultScaleBestQualityScaleDefaultCropOriginDefaultCropSizeColorMatrix1ColorMatrix2CameraCalibration1CameraCalibration2ReductionMatrix1ReductionMatrix2AnalogBalanceAsShotNeutralAsShotWhiteXYBaselineExposureBaselineNoiseBaselineSharpnessBayerGreenSplitLinearResponseLimitCameraSerialNumberLensInfoChromaBlurRadiusAntiAliasStrengthShadowScaleDNGPrivateDataMakerNoteSafetyCalibrationIlluminant1CalibrationIlluminant2RawDataUniqueIDOriginalRawFileNameOriginalRawFileDataActiveAreaMaskedAreasAsShotICCProfileAsShotPreProfileMatrixCurrentICCProfileCurrentPreProfileMatrixPerSampleExposureTimeFNumberExposureProgramSpectralSensitivityISOSpeedRatingsOptoelectricConversionFactorExifVersionDateTimeOriginalDateTimeDigitizedComponentsConfigurationCompressedBitsPerPixelShutterSpeedValueApertureValueBrightnessValueExposureBiasValueMaxApertureValueSubjectDistanceMeteringModeLightSourceFlashFocalLengthSubjectAreaMakerNoteUserCommentSubSecTimeSubSecTimeOriginalSubSecTimeDigitizedFlashpixVersionColorSpacePixelXDimensionPixelYDimensionRelatedSoundFileFlashEnergySpatialFrequencyResponseFocalPlaneXResolutionFocalPlaneYResolutionFocalPlaneResolutionUnitSubjectLocationExposureIndexSensingMethodFileSourceSceneTypeCFAPatternCustomRenderedExposureModeWhiteBalanceDigitalZoomRatioFocalLengthIn35mmFilmSceneCaptureTypeGainControlContrastSaturationSharpnessDeviceSettingDescriptionSubjectDistanceRangeImageUniqueIDFailed to read directory at offset %lluUnknown field with tag %d (0x%x) encounteredRegistering anonymous field with tag %d (0x%x) failedPlanarconfig tag value assumed incorrect, assuming data is contig instead of chunkyCannot handle zero number of %sstripsunknown tagnamePhotometric tag is missing, assuming data is YCbCrPhotometric tag value assumed incorrect, assuming data is YCbCr instead of RGBBitsPerSample tag is missing, assuming 8 bits per sampleSamplesPerPixel tag is missing, assuming correct SamplesPerPixel value is 3SamplesPerPixel tag is missing, applying correct SamplesPerPixel value of 3ColormapTIFF directory is missing required "StripByteCounts" field, calculating from imagelengthBogus "StripByteCounts" field, ignoring and calculating from imagelengthWrong "StripByteCounts" field, ignoring and calculating from imagelengthCannot handle zero scanline sizeCannot handle zero tile sizeCannot handle zero strip sizeFailed to read custom directory at offset %lluWrong data type %d for "%s"; tag ignoredIncorrect count for "%s"Incompatible type for "%s"IO error during reading of "%s"Incorrect value for "%s"Cannot handle different values per sample for "%s"Sanity check on size of "%s" value failedOut of memory reading of "%s"Incorrect count for "%s"; tag ignoredIncompatible type for "%s"; tag ignoredIO error during reading of "%s"; tag ignoredIncorrect value for "%s"; tag ignoredCannot handle different values per sample for "%s"; tag ignoredSanity check on size of "%s" value failed; tag ignoredOut of memory reading of "%s"; tag ignoredReadDirEntryArrayInvalid TIFF directory; tags are not sorted in ascending orderfor "StripByteCounts" arrayCannot determine size of unknown tag type %dTIFF directory is missing required "%s" fieldfor IFD listincorrect count for field "%s" (%llu, expecting %u); tag ignoredincorrect count for field "%s" (%llu, expecting %u); tag trimmed%s: Seek error accessing TIFF directory%s: Can not read TIFF directory countSanity check on directory count failed, this is probably not a valid IFD offsetto read TIFF directory%.100s: Can not read TIFF directoryCan not read TIFF directory countSanity check on directory count failed, zero tag directories not supportedCan not read TIFF directoryNo definition found for tag %dASCII value for tag "%s" contains null byte in value; value incorrectly truncated during reading due to implementation limitationsASCII value for tag "%s" does not end in null bytefor strip arrayfor chopped "StripByteCounts" arrayfor chopped "StripOffsets" arrayError updating TIFF headerSanity check on tag count failed, likely corrupt TIFFMemory mapped files not currently supported for this operation.Attempt to reset field on directory not already on disk.%s: Can not read TIFF directory entry.%s: Could not find tag %d.for field buffer.Value exceeds 32bit range of output type.%s: Can not write TIFF directory entry.Error post-encoding before directory writeError flushing data before directory writeIO error writing directoryIO error writing tag dataAttempt to write value larger than 0xFFFFFFFF in Classic TIFF file.Error writing SubIFD directory linkNot enough data for scanline %lu, expected a request for at most %lld bytes, got a request for %lld bytesTIFFInitCCITTFax3Merging CCITT Fax 3 codec-specific tags failedTIFFInitCCITTFax4Merging CCITT Fax 4 codec-specific tags failedMerging common CCITT Fax codec-specific tags failedNo space for state blockFaxModeFaxFillFuncBadFaxLinesCleanFaxDataConsecutiveBadFaxLines  Group 4 Options:%suncompressed data  Group 3 Options:%s2-d encoding+%sEOL padding (%lu = 0x%lx)
  Fax Data: clean receiver regenerated uncorrected errors (%u = 0x%x)
  Bad Fax Lines: %lu
  Consecutive Bad Fax Lines: %lu
Bits/sample must be 1 for Group 3/4 encoding/decodingRow pixels integer overflow (rowpixels %u)for Group 3/4 run arraysNo space for Group 3/4 reference lineFractional scanlines cannot be readBad code word at line %u of %s %u (x %u)Premature EOF at line %u of %s %u (x %u)%s at line %u of %s %u (got %u, expected %u)Premature EOLLine length mismatchUncompressed data (not supported) at line %u of %s %u (x %u)Fractional scanlines cannot be writtenGroup3OptionsGroup4OptionsSorry, requested compression method is not configuredSorry, can not handle images with %d-bit samplesMissing needed %s tagSorry, can not handle contiguous data with %s=%d, and %s=%d and Bits/Sample=%dSamples/pixelSorry, can not handle RGB image with %s=%dColor channelsSorry, can not handle separated image with %s=%dInkSetSorry, LogL data must have %s=%dCompressionSorry, LogLuv data must have %s=%d or %dSorry, can not handle LogLuv images with %s=%dPlanarconfigurationSorry, can not handle image with %s=%dMissing required "Colormap" tagOut of memory for colormap copySorry, can not handle imageNo "get" routine setupNo "put" routine setupl; probably can not handle image formatCan't use TIFFReadRGBAStrip() with tiled file.Row passed to TIFFReadRGBAStrip() must be first in a strip.Can't use TIFFReadRGBATile() with stripped file.Row/col passed to TIFFReadRGBATile() must be topleft corner of a tile.No space for tile bufferNo space for strip bufferOut of memoryAssuming 8-bit colormapNo space for photometric conversion tableNo space for B&W mapping tableNo space for Palette mapping tableNo space for YCbCr->RGB conversion stateNo space for CIE L*a*b*->RGB conversion state.Failed to initialize CIE L*a*b*->RGB conversion state.Integer overflow in %sgtTileSeparategtStripSeparateMerging SGILog codec-specific tags failed%s: No space for LogLuv state blockSGILogDataFmtSGILogEncodeInappropriate photometric interpretation %d for SGILog compression; %smust be either LogLUV or LogLSGILog compression cannot handle non-contiguous dataNo support for converting user data format to LogLuvNo space for SGILog translation bufferNot enough data at row %lu (short %llu pixels)No support for converting user data format to LogLSGILog compression supported only for %s, or raw dataY, LXYZ, LuvUnknown data format %d for LogLuv compressionUnknown encoding %d for LogLuv compressionNo space for LZW state blockNo space for LZW code tableOld-style LZW codes, convert fileLZWDecode: Strip %d not terminated with EOI codeLZWDecode: Corrupted LZW table at scanline %dCorrupted LZW table at scanline %dWrong length of decoded string: data probably corrupted at scanline %dNot enough data at scanline %d (short %llu bytes)Bogus encoding, loop in the code table; scanline %dNo space for LZW hash tableNot enough data for scanline %ld"%s": Bad mode%s: Out of memory (TIFF structure)One of the client procedures is NULL pointer.Cannot read TIFF headerError writing TIFF headerNot a TIFF or MDI file, bad magic number %d (0x%x)Not a TIFF file, bad version number %d (0x%x)Not a TIFF file, bad BigTIFF offsetsize %d (0x%x)Not a TIFF file, bad BigTIFF unused %d (0x%x)Discarding %lu bytes to avoid buffer overrunNot enough data for scanline %luMerging PixarLog codec-specific tags failedNo space for PixarLog state blockPixarLog compression can't handle bits depth/data format combination (depth: %d)ZLib cannot deal with buffers this size%d bit input not supported in PixarLogDecoding error at scanline %lu, %sZLib error: %sNot enough data at scanline %lu (short %llu bytes)stride %lu is not a multiple of sample count, %lu, data truncated.Unsupported bits/sample: %dPixarLog compression can't handle %d bit linear encodingsEncoder error: %sTIFFPredictorInitMerging Predictor codec-specific tags failed  Predictor: none horizontal differencing floating point predictor %u (0x%x)
Horizontal differencing "Predictor" not supported with %d-bit samplesFloating point "Predictor" not supported with %d data format"Predictor" value %d not supportedOut of memory allocating %zd byte temp buffer.%lu: Strip out of range, max %luCompression scheme does not support access to raw uncompressed data%llu: Invalid strip byte count, strip %luInvalid strip byte count %llu, strip %luRead error on strip %lu; got %llu bytes, expected %lluData buffer too small to hold strip %lu%lu: Tile out of range, max %lu%llu: Invalid tile byte count, tile %luData buffer too small to hold tile %luNo space for data buffer at scanline %lu%lu: Row out of range, max %lu%lu: Sample out of range, max %luSeek error at scanline %lu, strip %luRead error at scanline %lu; got %llu bytes, expected %lluRead error at scanline %lu, strip %lu; got %llu bytes, expected %lluSeek error at row %lu, col %lu, tile %luRead error at row %lu, col %lu; got %llu bytes, expected %lluRead error at row %lu, col %lu, tile %lu; got %llu bytes, expected %lluFile not open for readingCan not read tiles from a stripped imageCan not read scanlines from a tiled imageTIFFNumberOfStripsInvalid td_samplesperpixel valueInvalid YCbCr subsampling (%dx%d)Invalid YCbCr subsamplingWrong bitspersample value (%d), Thunder decoder only supports 4bits per sample.%s data at scanline %lu (%llu != %llu)Not enoughToo much%lu: Col out of range, max %lu%lu: Depth out of range, max %luTIFFNumberOfTilesTIFFTileRowSizeCan not change "ImageLength" when using separate planesCan not grow image by strips when using separate planesTile %lu out of range, max %luFile not open for writingCan not write tiles to a stripped imageCan not write scanlines to a tiled imageMust set "ImageWidth" before writing dataMust set "PlanarConfiguration" before writing dataNo space for %s arraystilestripNo space for output bufferNo space to expand strip arraysSeek error at scanline %luMaximum TIFF file size exceededWrite error at scanline %luMerging Deflate codec-specific tags failedNo space for ZIP state block%s: %s%s: Cannot open%s: Warning, %04x:cannot get header
cannot get tab table
numattrs=%d
attrno=%d; attrname="%s"(0x%08x); attrtype="%s"(0x%08x)
refcnt = %d; type = 0x%08x %s
(%f, %f, %f)
number of entires = %d
gamma = %f
entry[%d] = %f
ascii = "%s"
uclangcode = %d; uclen = %d
sccode = %d
maclen = %d
string = "%s"
numinchans=%d, numoutchans=%d, clutlen=%d
e[%d][%d]=%f numintabents=%d, numouttabents=%d
prec=%d, sgnd=%d, cmpttype=%d
 f(%d,%d)=%ldJPEG-2000 JP2 File Format Syntax (ISO/IEC 15444-1)jpcJPEG-2000 Code Stream Syntax (ISO/IEC 15444-1)%d %d%ld%d %d
%s%4ld%s/tmp.XXXXXXXXXX/var/tmp/%08x: %02x   1.900.1JPFTYPJP2HIHDRBPCCCOLRPCLRCMAPCDEFRESRESCRESDJP2CJP2IXMLUUIDUINFULSTURLwarning: cannot handle large 64-bit box length
cannot copy box data
cannot parse box data
JP2 box: type=%c%s%c (0x%08x); length=%d
method=%d; pri=%d; approx=%d
csid=%d
channo=%d; type=%d; assoc=%d
numchans = %d
cmptno=%d; map=%d; pcol=%d
numents=%d; numchans=%d
LUT[%d][%d]=%d
error: cannot get box
error: expecting signature box
incorrect magic number
expecting file type box
box type %s
error: no code stream found
error: cannot decode code stream
error: missing IHDR box
warning: number of components mismatch
error: no components
warning: component data type mismatch
error: unsupported compression type
warning: superfluous BPCC box
error: no COLR box
ICC Profile CS %08x
warning: missing PCLR box or superfluous CMAP box
warning: missing CMAP box or superfluous PCLR box
error: invalid component number in CMAP box
error: invalid CMAP LUT index
error: no memory
%s
_jp2overhead=%lu
warning: trailing garbage in marker segment (%ld bytes)
type = 0x%04x (%s); len = %d;SOCSOTSODEOCSIZCODCOCRGNQCDQCCPOCTLMPLMPPMPPTSOPEPHCRGCOMUNKNOWNtileno = %d; len = %d; partno = %d; numparts = %d
caps = 0x%02x;
width = %d; height = %d; xoff = %d; yoff = %d;
tilewidth = %d; tileheight = %d; tilexoff = %d; tileyoff = %d;
prec[%d] = %d; sgnd[%d] = %d; hsamp[%d] = %d; vsamp[%d] = %d
csty = 0x%02x;
numdlvls = %d; qmfbid = %d; mctrans = %d
prg = %d; numlyrs = %d;
cblkwidthval = %d; cblkheightval = %d; cblksty = 0x%02x;
prcwidth[%d] = %d, prcheight[%d] = %d
compno = %d; csty = 0x%02x; numdlvls = %d;
cblkwidthval = %d; cblkheightval = %d; cblksty = 0x%02x; qmfbid = %d;
compno = %d; roisty = %d; roishift = %d
qntsty = %d; numguard = %d; numstepsizes = %d
expn[%d] = 0x%04x; mant[%d] = 0x%04x;
compno = %d; qntsty = %d; numguard = %d; numstepsizes = %d
po[%d] = %d; cs[%d] = %d; ce[%d] = %d; rs[%d] = %d; re[%d] = %d; le[%d] = %d
ind=%d; len = %d;
data =
seqno = %d;
hoff[%d] = %d; voff[%d] = %d
regid = %d;
data = %02x maxlyrsmaxpktsdebugwarning: ignoring invalid option %s
cannot get marker segment
unexpected marker segment type
invalid tile number in SOT marker segment
jpc_dec_decodepkts failed
warning: ignoring trailing garbage (%lu bytes)
read error
warning: not enough tile data (%lu bytes)
RESOLUTION LEVEL %d
xs =%d, ys = %d, xe = %d, ye = %d, w = %d, h = %d
BAND %d
CODE BLOCK GROUP %d
CODE BLOCK %d
jpc_dec_decodecblks failed
write component failed
warning: possibly corrupt code stream
invalid component number in COC marker segment
invalid component number in RGN marker segment
invalid component number in QCC marker segment
warning: ignoring unknown marker segment
imgareatlximgareatlytilegrdtlxtilegrdtlytilewidthtileheightprcwidthprcheightcblkwidthcblkheightmodeprgnomctnumrlvlssopephlazytermallsegsymvcausalptermresetprobnumgbitsrateilyrrates_jp2overheadlrcprlcprpclpcrlcprlrealinvalid JP encoder options
cannot write EOI marker
start=%d end=%d type=%d term=%d lyrno=%d firstchar=%02x size=%ld pos=%ld
lyrno = %02d
lyrno=%02d cmptno=%02d rlvlno=%02d bandno=%02d prcno=%02d cblkno=%03d passno=%03d
min rdslope = %f max rdslope = %f
maxlen=%08ld actuallen=%08ld thresh=%f
warning: empty layer generated
success %d goodthresh %f
  tcmpt %5d %5d %5d %5d
    rlvl %5d %5d %5d %5d
      band %5d %5d %5d %5d
        prc %5d %5d %5d %5d (%5d %5d)
         cblk %5d %5d %5d %5d
unsupported image type
ignoring invalid mode %s
ignoring invalid progression order %s
warning: invalid intermediate layer rates specifier ignored (%s)
warning: overriding imgareatlx value
warning: overriding imgareatly value
warning: color space apparently not RGB
invalid tile width %lu
invalid tile height %lu
invalid tile grid offset (%lu, %lu)
cannot use real mode for lossless coding
invalid precinct width
invalid precinct height
invalid code block width %d
invalid code block height %d
code block size too large
too many layers
must be at least one resolution level
invalid number of guard bits
warning: specified rate is unreasonably large (%lu > %lu)
intermediate layer rates must increase monotonically
warning: intermediate layer rates must be less than overall rate
cannot write SOC marker
cannot write SIZ marker
Creator: JasPer Version %scannot write COM marker
cannot write COD marker
error: too few guard bits (need at least %d)
cannot open tmp file
cannot write SOT marker
cannot write SOD marker
dotile failed
MQDEC A = %08lx, C = %08lx, CT=%08lx, CTX = %d, IND %d, MPS %d, QEVAL %x
AREG = %08x, CREG = %08x, CTREG = %d
IND = %02d, MPS = %d, QEVAL = %04x
warning: corrupt code stream
coding pass failed passtype=%d segtype=%d
warning: bad termination pattern detected
warning: bad segmentation symbol
warning: stopping decode prematurely as requested
packet offset=%08ld prg=%d cmptno=%02d rlvlno=%02d prcno=%03d lyrno=%02d
missing SOP marker segment
alignment failed
hdrlen=%lu bodylen=%lu 
cannot get (EPH) marker segment
missing EPH marker segment
packet body offset=%06ld
w+node %p, parent %p, value %d, lower %d, known %d
%TCannot initialize mutex (%T).Cannot lock mutex (%T).Cannot unlock mutex (%T).Attempt to set the number of threads in a thread pool to a negative value.Cannot create new thread (%T).Cannot initialize semaphore (%T).Post operation on semaphore failed (%T).Cannot read semaphore value (%T).Cannot register image file attribute type "". The type has already been registered.Cannot create image file attribute of unknown type "Error decompressing data (input data are shorter than expected).Error decompressing data (input data are longer than expected).Integer addition overflow.box2ibox2fImage channel name cannot be an empty string.Cannot find image channel "chlistchannel nameCannot invert singular matrix.Frame buffer slice name cannot be an empty string.Cannot find frame buffer slice "Image attribute name cannot be an empty string.Cannot assign a value of type "" to image attribute "" of type "Cannot find image attribute "displayWindowdataWindowpixelAspectRatioscreenWindowCenterscreenWindowWidthchannelslineOrdercompressionInvalid display window in image header.Invalid data window in image header.The width of the data window exceeds the maximum width of pixels.Invalid pixel aspect ratio in image header.Invalid screen window width in image header.Tiled image has no tile description attribute.Invalid tile size in image header.The width of the tiles exceeds the maximum width of Invalid level mode in image header.Invalid level rounding mode in image header.Invalid line order in image header.Unknown compression type in image header." image channel is invalid.The x subsampling factor for the "" channel is not 1.The y subsampling factor for the "" channel is invalid.The minimum x coordinate of the image's data window is not a multiple of the x subsampling factor of the "" channel.The minimum y coordinate of the image's data window is not a multiple of the y subsampling factor of the "Number of pixels per row in the image's data window is not a multiple of the x subsampling factor of the "Number of pixels per column in the image's data window is not a multiple of the y subsampling factor of the "File is not an image file.Cannot read version  image files.  Current file format version is The file format version number's flag field contains unrecognized flags.attribute nameattribute type nameUnexpected type for image attribute "Invalid : it is more than  characters long.Error in Huffman-encoded data (decoded data are shorter than expected).Error in Huffman-encoded data (invalid code table size).Error in Huffman-encoded data (unexpected end of code table data).Error in Huffman-encoded data (code table is longer than expected).Error in header for Huffman-encoded data (invalid number of bits).Error in Huffman-encoded data (invalid code table entry).Error in Huffman-encoded data (decoded data are longer than expected).Error in Huffman-encoded data (invalid code).Cannot read image file "Unknown pixel data type.Tried to read a raw scanline from a tiled image.Error reading pixel data from image file "Tried to read a raw tile from a scanline-based image.Error reading tile data from image file "Cannot get a TiledInputFile pointer from an InputFile that is not tiled.Tried to read scan line outside the image file's data window.Attempt to perform a memory-mapped read on a file that is not memory mapped.Invalid key code film manufacturer code (must be between 0 and 99).Invalid key code film type (must be between 0 and 99).Invalid key code prefix (must be between 0 and 999999).Invalid key code count (must be between 0 and 9999).Invalid key code perforation offset (must be between 0 and 119).Invalid key code number of perforations per frame (must be between 1 and 15).Invalid key code number of perforations per count (must be between 20 and 120).keycodem33fm33dm44fm44dUnknown pixel type.Cannot copy the value of an image file attribute of type "" to an attribute of type "Cannot open image file "". Pixel type of "" channel of output file "" is not compatible with the frame buffer's pixel type.X and/or y subsampling factors of "" are not compatible with the frame buffer's subsampling factors.No frame buffer specified as pixel data source.Tried to write more scan lines than specified by the data window.Failed to write pixel data to image file "tilesCannot copy pixels from image file "" to image file "". The input file is tiled, but the output file is not. Try using TiledOutputFile::copyPixels instead.". The files have different data windows.Quick pixel copy from image file "" failed. The files have different line orders." failed. The files use different compression methods." failed.  The files have different channel lists." failed. "" already contains pixel data.Cannot update preview image pixels. File "" does not contain a preview image.previewCannot update preview image pixels for file "Cannot overwrite scan line . The scan line has not yet been stored in file "".Cannot determine current file position (%T).unrecognized exceptionUnexpected attribute type.Error in header for PIZ-compressed data (invalid bitmap size).Integer multiplication overflow.rationalData decoding (rle) failed.Scan line  is missing.Unexpected data block y coordinate.Unexpected data block length.chromaticitieswhiteLuminanceadoptedNeutralrenderingTransformlookModTransformxDensityownercommentscapDateutcOffsetlongitudelatitudealtitudefocusexpTimeapertureisoSpeedenvmapkeyCodetimeCodewrapmodesframesPerSecondmultiViewworldToCameraworldToNDC%T.Unexpected end of file.(string)Early end of file: read  out of  requested bytes.File output failed.stringvectortiledescExpected a tiled file but the file is not tiled." channel of input file "No frame buffer specified as pixel data destination.Tile (,) is not a valid tile.Tried to read a tile outside the image file's data window.Error calling numLevels() on image file "" (numLevels() is not defined for files with RIPMAP level mode).Error calling levelWidth() on image file "Error calling levelHeight() on image file "Error calling numXTiles() on image file "" (Argument is not in valid range).Error calling numYTiles() on image file "Error calling dataWindowForLevel() on image file "Arguments not in valid range.Error calling dataWindowForTile() on image file ") is missing.Unexpected tile x coordinate.Unexpected tile y coordinate.Unexpected tile x level number coordinate.Unexpected tile y level number coordinate.Unexpected tile block length.Argument not in valid range.Unknown LevelMode format.Cannot set hours field in time code. New value is out of range.Cannot set minutes field in time code. New value is out of range.Cannot set seconds field in time code. New value is out of range.Cannot set frame field in time code. New value is out of range.Cannot extract binary group from time code user data.  Group number is out of range.timecodev2iv2fv2dv3iv3fv3dData compression (zlib) failed.Data decompression (zlib) failed.out of memoryrequested length does not fit in intout of room to push charactersunexpected end of fileinternal error: inflate stream corruptcompressed data error1.2.7internal error: deflate stream corruptincorrect header checkunknown compression methodinvalid window sizeunknown header flags setheader crc mismatchinvalid block typeinvalid stored block lengthstoo many length or distance symbolsinvalid code lengths setinvalid bit length repeatinvalid code -- missing end-of-blockinvalid literal/lengths setinvalid distances setinvalid literal/length codeinvalid distance codeinvalid distance too far backincorrect data checkincorrect length checkneed dictionarystream endfile errorstream errordata errorinsufficient memorybuffer errorincompatible versionallocinitsharedApplicationsetActivationPolicy:closestringWithFormat:removeObjectForKey:draindictionaryWithDictionary:countByEnumeratingWithState:objects:count:cStringUsingEncoding:contentViewimageframesetImageData:autosizefirstContentslidersvalueForKey:sizesetFrameSize:setFrame:display:displaysetFirstContent:screensetFrameTopLeftPoint:createSliderWithName:maxValue:value:callback:setCallback2:setUserData:setMouseCallback:setMouseParam:sliderintValuesetIntValue:setMaxValue:UTF8StringsetAutosize:mainScreenminFrameWidthWithTitle:styleMask:visibleFrameinitWithContentRect:styleMask:backing:defer:screen:setContentView:setHasShadow:setAcceptsMouseMovedEvents:useOptimizedDrawing:setTitle:makeKeyAndOrderFront:setValue:forKey:countdatetimeIntervalSince1970distantPastnextEventMatchingMask:untilDate:inMode:dequeue:typecharacterscharacterAtIndex:sendEvent:updateWindowssleepForTimeInterval:statusnumberWithBool:dictionaryWithObject:forKey:isInFullScreenModeexitFullScreenModeWithOptions:setStatus:enterFullScreenMode:withOptions:mouseLocationconvertScreenToBase:windowmodifierFlagscvSendMouseEvent:type:flags:keyDown:cvMouseEvent:namesetStringValue:setMinValue:setNumberOfTickMarks:setAllowsTickMarkValuesOnly:setValue:setCallback:addSubview:setNeedsDisplay:rightMouseDragged:rightMouseUp:rightMouseDown:mouseMoved:otherMouseDragged:otherMouseUp:otherMouseDown:mouseDragged:mouseUp:mouseDown:mouseCallbackmouseParamsetSliders:initWithBitmapDataPlanes:pixelsWide:pixelsHigh:bitsPerSample:samplesPerPixel:hasAlpha:isPlanar:colorSpaceName:bytesPerRow:bitsPerPixel:bitmapDatareleaseaddRepresentation:setFrame:drawRect:respondsToSelector:drawInRect:fromRect:operation:fraction:setImage:initWithFrame:setEditable:setSelectable:setBezeled:setBordered:setDrawsBackground:cellsetLineBreakMode:setAutoresizingMask:setContinuous:setTarget:sliderChanged:setAction:setSlider:setName:valueuserDatacallbackcallback2doFireTimer:scheduledTimerWithTimeInterval:target:selector:userInfo:repeats:updateImagecurrentRunLoopdateWithTimeIntervalSinceNow:runUntilDate:getOutputstopRunningdeviceisOpensetDelegate:inputDevicesWithMediaType:arrayByAddingObjectsFromArray:retainobjectAtIndex:defaultInputDeviceWithMediaType:open:initWithDevice:addInput:error:numberWithDouble:numberWithUnsignedInt:dictionaryWithObjectsAndKeys:setPixelBufferAttributes:setAutomaticallyDropsLateVideoFrames:addOutput:error:startRunningconnectionsformatDescriptionattributeForKey:sizeValuedealloccaptureOutput:didOutputVideoFrame:withSampleBuffer:fromConnection:captureOutput:didDropVideoFrameWithSampleBuffer:fromConnection:newFramemCurrentImageBufferimagedatabgr_imagedatabgr_imagecurrSizestringWithCString:encoding:movieWithFile:error:setAttribute:forKey:gotoEndcurrentTimegotoBeginninggetValue:stepForwardframeImageAtTime:withAttributes:error:stepBackwardsetCurrentTime:tracksOfMediaType:medialongValuestringByExpandingTildeInPathdefaultManagerfileExistsAtPath:removeItemAtPath:error:initToWritableFile:error:initWithBitmapDataPlanes:pixelsWide:pixelsHigh:bitsPerSample:samplesPerPixel:hasAlpha:isPlanar:colorSpaceName:bitmapFormat:bytesPerRow:bitsPerPixel:numberWithInt:addImage:forDuration:withAttributes:updateMovieFileCVWindowCVViewCVSliderCaptureDelegatev32@0:8@16i24i28v24@0:8@16v44@0:8r*16i24^i28^?36@16@0:8^?16@0:8v24@0:8^?16^v16@0:8v24@0:8^v16c16@0:8v20@0:8c16i16@0:8v20@0:8i16@"NSMutableDictionary"^?^vciv32@0:8{CGSize=dd}16v48@0:8{CGRect={CGPoint=dd}{CGSize=dd}}16@"NSImage"^i16@0:8v24@0:8^i16@"NSSlider"@"NSTextField"^iv16@0:8v48@0:8@16^{__CVBuffer=}24@32@40v40@0:8@16@24@32^{_IplImage=iiiii[4c][4c]iiiii^{_IplROI}^{_IplImage}^v^{_IplTileInfo}i*i[4i][4i]*}16@0:8^{__CVBuffer=}*^{_IplImage=iiiii[4c][4c]iiiii^{_IplROI}^{_IplImage}^v^{_IplTileInfo}i*i[4i][4i]*}Q
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?@__ZTSN2cv12ColumnFilterINS_4CastIisEENS_11ColumnNoVecEEE???@__ZTSN2cv12ColumnFilterINS_4CastIisEENS_25SymmColumnSmallVec_32s16sEEE??????????
?@__ZTSN2cv12DTRowInvokerE??????????
?@__ZTSN2cv12RemapInvokerE??$?@__ZTSN2cv13WarperCreatorE???
?@__ZTSN2cv14ChamferMatcher10ImageRangeE??????????
?@__ZTSN2cv14ChamferMatcher13ImageIteratorE??@__ZTSN2cv14HOGConfInvokerE??????????
?@__ZTSN2cv14MorphRowFilterINS_5MaxOpIdEENS_13MorphRowNoVecEEE??????????
?@__ZTSN2cv14MorphRowFilterINS_5MaxOpIfEENS_12MorphRowFVecINS_7VMax32fEEEEE??????????
?@__ZTSN2cv14MorphRowFilterINS_5MaxOpIhEENS_12MorphRowIVecINS_6VMax8uEEEEE??????????
?@__ZTSN2cv14MorphRowFilterINS_5MaxOpIsEENS_12MorphRowIVecINS_7VMax16sEEEEE?x?@__ZTSN2cv14MorphRowFilterINS_5MaxOpItEENS_12MorphRowIVecINS_7VMax16uEEEEE??????????
?@__ZTSN2cv14MorphRowFilterINS_5MinOpIdEENS_13MorphRowNoVecEEE??????????
?@__ZTSN2cv14MorphRowFilterINS_5MinOpIfEENS_12MorphRowFVecINS_7VMin32fEEEEE??????????
?@__ZTSN2cv14MorphRowFilterINS_5MinOpIhEENS_12MorphRowIVecINS_6VMin8uEEEEE??????????
?@__ZTSN2cv14MorphRowFilterINS_5MinOpIsEENS_12MorphRowIVecINS_7VMin16sEEEEE?x?@__ZTSN2cv14MorphRowFilterINS_5MinOpItEENS_12MorphRowIVecINS_7VMin16uEEEEE??????????
?@__ZTSN2cv14NumpyFormatterE??????????
?@__ZTSN2cv14TemplateBufferIfEE????@__ZTSN2cv15AdjusterAdapterE??????????
?@__ZTSN2cv15DTColumnInvokerE??????????
?@__ZTSN2cv15FaceRecognizer2E???
?@__ZTSN2cv15MatlabFormatterE??????????
?@__ZTSN2cv15PythonFormatterE?H?@__ZTSN2cv15SURFFindInvokerE????@__ZTSN2cv15SphericalWarperE???@__ZTSN2cv15ThresholdRunnerE??????????
?@__ZTSN2cv15resizeNNInvokerE??????????
?@__ZTSN2cv16BatchDistInvokerE??????????
?@__ZTSN2cv16DenseOpticalFlowE????@__ZTSN2cv16MorphologyRunnerE??????????
?@__ZTSN2cv16SURFBuildInvokerE???
?@__ZTSN2cv16SymmColumnFilterINS_13FixedPtCastExIihEENS_19SymmColumnVec_32s8uEEE?????????
?@__ZTSN2cv16SymmColumnFilterINS_4CastIddEENS_11ColumnNoVecEEE???@__ZTSN2cv16SymmColumnFilterINS_4CastIdhEENS_11ColumnNoVecEEE??????????
?@__ZTSN2cv16SymmColumnFilterINS_4CastIdsEENS_11ColumnNoVecEEE???@__ZTSN2cv16SymmColumnFilterINS_4CastIdtEENS_11ColumnNoVecEEE??????????
?@__ZTSN2cv16SymmColumnFilterINS_4CastIffEENS_17SymmColumnVec_32fEEE???@__ZTSN2cv16SymmColumnFilterINS_4CastIffEENS_22SymmColumnSmallVec_32fEEE??????????
?@__ZTSN2cv16SymmColumnFilterINS_4CastIfhEENS_11ColumnNoVecEEE??
?@__ZTSN2cv16SymmColumnFilterINS_4CastIfsEENS_20SymmColumnVec_32f16sEEE???@__ZTSN2cv16SymmColumnFilterINS_4CastIftEENS_11ColumnNoVecEEE??????????
?@__ZTSN2cv16SymmColumnFilterINS_4CastIisEENS_11ColumnNoVecEEE??
?@__ZTSN2cv16SymmColumnFilterINS_4CastIisEENS_25SymmColumnSmallVec_32s16sEEE??????????
?@__ZTSN2cv16TLSDataContainerE??????????
?@__ZTSN2cv17BasicRetinaFilter24Parallel_localAdaptationE????@__ZTSN2cv17BasicRetinaFilter29Parallel_verticalCausalFilterE??
?@__ZTSN2cv17BasicRetinaFilter35Parallel_horizontalAnticausalFilterE??????????
?@__ZTSN2cv17BasicRetinaFilter39Parallel_verticalCausalFilter_IrregularE??
?@__ZTSN2cv17BasicRetinaFilter40Parallel_horizontalCausalFilter_addInputE??????????
?@__ZTSN2cv17BasicRetinaFilter42Parallel_verticalAnticausalFilter_multGainE??
?@__ZTSN2cv17BasicRetinaFilter45Parallel_horizontalAnticausalFilter_IrregularE?8?@__ZTSN2cv17BasicRetinaFilterE??	?@__ZTSN2cv17MagnoRetinaFilter31Parallel_amacrineCellsComputingE?8?@__ZTSN2cv17MorphColumnFilterINS_5MaxOpIdEENS_16MorphColumnNoVecEEE??????????
?@__ZTSN2cv17MorphColumnFilterINS_5MaxOpIfEENS_15MorphColumnFVecINS_7VMax32fEEEEE??????????
?@__ZTSN2cv17MorphColumnFilterINS_5MaxOpIhEENS_15MorphColumnIVecINS_6VMax8uEEEEE??????????
?@__ZTSN2cv17MorphColumnFilterINS_5MaxOpIsEENS_15MorphColumnIVecINS_7VMax16sEEEEE??
?@__ZTSN2cv17MorphColumnFilterINS_5MaxOpItEENS_15MorphColumnIVecINS_7VMax16uEEEEE??????????
?@__ZTSN2cv17MorphColumnFilterINS_5MinOpIdEENS_16MorphColumnNoVecEEE??????????
?@__ZTSN2cv17MorphColumnFilterINS_5MinOpIfEENS_15MorphColumnFVecINS_7VMin32fEEEEE??????????
?@__ZTSN2cv17MorphColumnFilterINS_5MinOpIhEENS_15MorphColumnIVecINS_6VMin8uEEEEE??????????
?@__ZTSN2cv17MorphColumnFilterINS_5MinOpIsEENS_15MorphColumnIVecINS_7VMin16sEEEEE??
?@__ZTSN2cv17MorphColumnFilterINS_5MinOpItEENS_15MorphColumnIVecINS_7VMin16uEEEEE??????????
?@__ZTSN2cv17ParvoRetinaFilter31Parallel_OPL_OnOffWaysComputingE???
?@__ZTSN2cv17warpAffineInvokerE??????????
?@__ZTSN2cv18ResizeArea_InvokerIddEE???@__ZTSN2cv18ResizeArea_InvokerIffEE??????????
?@__ZTSN2cv18ResizeArea_InvokerIhfEE??????????
?@__ZTSN2cv18ResizeArea_InvokerIsfEE?x?@__ZTSN2cv18ResizeArea_InvokerItfEE??????????
?@__ZTSN2cv18SphericalWarperGpuE???
?@__ZTSN2cv18SymmRowSmallFilterIffNS_19SymmRowSmallVec_32fEEE?؊???????
?@__ZTSN2cv18SymmRowSmallFilterIhiNS_21SymmRowSmallVec_8u32sEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_10RGB2RGB5x5EEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_10RGB5x52RGBEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_10RGBA2mRGBAIhEEEE??9?@__ZTSN2cv20CvtColorLoop_InvokerINS_10mRGBA2RGBAIhEEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_11Gray2RGB5x5EEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_11RGB2YCrCb_fIfEEEE??
?@__ZTSN2cv20CvtColorLoop_InvokerINS_11RGB2YCrCb_iIhEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_11RGB2YCrCb_iItEEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_11RGB5x52GrayEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_11YCrCb2RGB_fIfEEEE???@__ZTSN2cv20CvtColorLoop_InvokerINS_11YCrCb2RGB_iIhEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_11YCrCb2RGB_iItEEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_7RGB2RGBIfEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_7RGB2RGBIhEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_7RGB2RGBItEEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_8Gray2RGBIfEEEE???@__ZTSN2cv20CvtColorLoop_InvokerINS_8Gray2RGBIhEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_8Gray2RGBItEEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_8RGB2GrayIfEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_8RGB2GrayIhEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_8RGB2GrayItEEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9HLS2RGB_bEEE??$?@__ZTSN2cv20CvtColorLoop_InvokerINS_9HLS2RGB_fEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9HSV2RGB_bEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_9HSV2RGB_fEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9Lab2RGB_bEEE???@__ZTSN2cv20CvtColorLoop_InvokerINS_9Lab2RGB_fEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9Luv2RGB_bEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9Luv2RGB_fEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2HLS_bEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2HLS_fEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2HSV_bEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2HSV_fEEE???@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2Lab_bEEE???@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2Lab_fEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2Luv_bEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2Luv_fEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2XYZ_fIfEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2XYZ_iIhEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_9RGB2XYZ_iItEEEE?8?@__ZTSN2cv20CvtColorLoop_InvokerINS_9XYZ2RGB_fIfEEEE??
?@__ZTSN2cv20CvtColorLoop_InvokerINS_9XYZ2RGB_iIhEEEE??????????
?@__ZTSN2cv20CvtColorLoop_InvokerINS_9XYZ2RGB_iItEEEE?8?@__ZTSN2cv21SymmColumnSmallFilterINS_13FixedPtCastExIihEENS_19SymmColumnVec_32s8uEEE????@__ZTSN2cv21SymmColumnSmallFilterINS_4CastIffEENS_22SymmColumnSmallVec_32fEEE???@__ZTSN2cv21SymmColumnSmallFilterINS_4CastIisEENS_25SymmColumnSmallVec_32s16sEEE??????????
?@__ZTSN2cv21YUV420p2RGB888InvokerILi0EEE??????????
?@__ZTSN2cv21YUV420p2RGB888InvokerILi2EEE?8?@__ZTSN2cv21YUV422toRGB888InvokerILi0ELi0ELi0EEE???@__ZTSN2cv21YUV422toRGB888InvokerILi0ELi0ELi1EEE?8?@__ZTSN2cv21YUV422toRGB888InvokerILi0ELi1ELi0EEE?8?@__ZTSN2cv21YUV422toRGB888InvokerILi0ELi1ELi1EEE?8?@__ZTSN2cv21YUV422toRGB888InvokerILi2ELi0ELi0EEE?8?@__ZTSN2cv21YUV422toRGB888InvokerILi2ELi0ELi1EEE?8?@__ZTSN2cv21YUV422toRGB888InvokerILi2ELi1ELi0EEE?8?@__ZTSN2cv21YUV422toRGB888InvokerILi2ELi1ELi1EEE?8?@__ZTSN2cv21resizeGeneric_InvokerINS_12HResizeCubicIddfEENS_12VResizeCubicIddfNS_4CastIddEENS_12VResizeNoVecEEEEE??0?@__ZTSN2cv21resizeGeneric_InvokerINS_12HResizeCubicIfffEENS_12VResizeCubicIfffNS_4CastIffEENS_19VResizeCubicVec_32fEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_12HResizeCubicIhisEENS_12VResizeCubicIhisNS_11FixedPtCastIihLi22EEENS_21VResizeCubicVec_32s8uEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_12HResizeCubicIsffEENS_12VResizeCubicIsffNS_4CastIfsEENS_21VResizeCubicVec_32f16ILi0EEEEEEE?x?@__ZTSN2cv21resizeGeneric_InvokerINS_12HResizeCubicItffEENS_12VResizeCubicItffNS_4CastIftEENS_21VResizeCubicVec_32f16ILin32768EEEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_13HResizeLinearIddfLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIddfNS_4CastIddEENS_12VResizeNoVecEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_13HResizeLinearIfffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIfffNS_4CastIffEENS_20VResizeLinearVec_32fEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_13HResizeLinearIhisLi2048ENS_12HResizeNoVecEEENS_13VResizeLinearIhisNS_11FixedPtCastIihLi22EEENS_22VResizeLinearVec_32s8uEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_13HResizeLinearIsffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIsffNS_4CastIfsEENS_22VResizeLinearVec_32f16ILi0EEEEEEE?x?@__ZTSN2cv21resizeGeneric_InvokerINS_13HResizeLinearItffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearItffNS_4CastIftEENS_22VResizeLinearVec_32f16ILin32768EEEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4IddfEENS_15VResizeLanczos4IddfNS_4CastIddEENS_12VResizeNoVecEEEEE???@__ZTSN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4IfffEENS_15VResizeLanczos4IfffNS_4CastIffEENS_12VResizeNoVecEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4IhisEENS_15VResizeLanczos4IhisNS_11FixedPtCastIihLi22EEENS_12VResizeNoVecEEEEE??????????
?@__ZTSN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4IsffEENS_15VResizeLanczos4IsffNS_4CastIfsEENS_12VResizeNoVecEEEEE?x?@__ZTSN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4ItffEENS_15VResizeLanczos4ItffNS_4CastIftEENS_12VResizeNoVecEEEEE??????????
?@__ZTSN2cv22KMeansDistanceComputerE??????????
?@__ZTSN2cv22RGB888toYUV420pInvokerILi0EEE???@__ZTSN2cv22RGB888toYUV420pInvokerILi2EEE?8?@__ZTSN2cv22YUV420sp2RGB888InvokerILi0ELi0EEE??????????
?@__ZTSN2cv22YUV420sp2RGB888InvokerILi0ELi1EEE?8?@__ZTSN2cv22YUV420sp2RGB888InvokerILi2ELi0EEE?8?@__ZTSN2cv22YUV420sp2RGB888InvokerILi2ELi1EEE?8?@__ZTSN2cv22resizeAreaFast_InvokerIddNS_19ResizeAreaFastNoVecIddEEEE??>?@__ZTSN2cv22resizeAreaFast_InvokerIffNS_19ResizeAreaFastNoVecIffEEEE??????????
?@__ZTSN2cv22resizeAreaFast_InvokerIhiNS_17ResizeAreaFastVecIhEEEE??????????
?@__ZTSN2cv22resizeAreaFast_InvokerIsfNS_17ResizeAreaFastVecIsEEEE?x?@__ZTSN2cv22resizeAreaFast_InvokerItfNS_17ResizeAreaFastVecItEEEE??????????
?@__ZTSN2cv22warpPerspectiveInvokerE??????????
?@__ZTSN2cv23YUV420p2RGBA8888InvokerILi0EEE??????????
?@__ZTSN2cv23YUV420p2RGBA8888InvokerILi2EEE?8?@__ZTSN2cv23YUV422toRGBA8888InvokerILi0ELi0ELi0EEE???@__ZTSN2cv23YUV422toRGBA8888InvokerILi0ELi0ELi1EEE?8?@__ZTSN2cv23YUV422toRGBA8888InvokerILi0ELi1ELi0EEE?8?@__ZTSN2cv23YUV422toRGBA8888InvokerILi0ELi1ELi1EEE?8?@__ZTSN2cv23YUV422toRGBA8888InvokerILi2ELi0ELi0EEE?8?@__ZTSN2cv23YUV422toRGBA8888InvokerILi2ELi0ELi1EEE?8?@__ZTSN2cv23YUV422toRGBA8888InvokerILi2ELi1ELi0EEE?8?@__ZTSN2cv23YUV422toRGBA8888InvokerILi2ELi1ELi1EEE?8?@__ZTSN2cv24CascadeClassifierInvokerE???
?@__ZTSN2cv24FindStereoCorrespInvokerE?ذ???????
?@__ZTSN2cv24KMeansPPDistanceComputerE??????????
?@__ZTSN2cv24YUV420sp2RGBA8888InvokerILi0ELi0EEE????@__ZTSN2cv24YUV420sp2RGBA8888InvokerILi0ELi1EEE?8?@__ZTSN2cv24YUV420sp2RGBA8888InvokerILi2ELi0EEE?8?@__ZTSN2cv24YUV420sp2RGBA8888InvokerILi2ELi1EEE?8?@__ZTSN2cv26BilateralFilter_8u_InvokerE??S?@__ZTSN2cv27BilateralFilter_32f_InvokerE?8?@__ZTSN2cv34adaptiveBilateralFilter_8u_InvokerE?8?@__ZTSN2cv36HaarDetectObjects_ScaleImage_InvokerE???
?@__ZTSN2cv38HaarDetectObjects_ScaleCascade_InvokerE?8?@__ZTSN2cv3ocl14BaseFilter_GPUE???@__ZTSN2cv3ocl15CvSVMKernel_oclE???@__ZTSN2cv3ocl16FilterEngine_GPUE??????????
?@__ZTSN2cv3ocl17BaseRowFilter_GPUE??@__ZTSN2cv3ocl20BaseColumnFilter_GPUE??@__ZTSN2cv5CLAHEE??????????
?@__ZTSN2cv6Mesh3D18EmptyMeshExceptionE????@__ZTSN2cv6RowSumIddEE??????????
?@__ZTSN2cv6RowSumIfdEE??????????
?@__ZTSN2cv6RowSumIhdEE??????????
?@__ZTSN2cv6RowSumIhiEE??????????
?@__ZTSN2cv6RowSumIiiEE???@__ZTSN2cv6RowSumIsdEE?8?@__ZTSN2cv6RowSumIsiEE??????????
?@__ZTSN2cv6RowSumItdEE??????????
?@__ZTSN2cv6RowSumItiEE??????????
?@__ZTSN2cv6detail10SeamFinderE???
?@__ZTSN2cv6detail14FeaturesFinderE??????????
?@__ZTSN2cv6detail14RotationWarperE???@__ZTSN2cv6detail15FeaturesMatcherE??????????
?@__ZTSN2cv6detail18RotationWarperBaseINS0_14PlaneProjectorEEE???@__ZTSN2cv6detail18RotationWarperBaseINS0_18SphericalProjectorEEE??@__ZTSN2cv6detail18RotationWarperBaseINS0_26SphericalPortraitProjectorEEE??@__ZTSN2cv6detail18SphericalWarperGpuE??????????
?@__ZTSN2cv6detail19ExposureCompensatorE??????????
?@__ZTSN2cv6detail21NoExposureCompensatorE?8?@__ZTSN2cv6detail22GraphCutSeamFinderBaseE?X?@__ZTSN2cv6detail9EstimatorE??????????
?@__ZTSN2cv7TLSDataINS_3ocl12CommandQueueEEE??????????
?@__ZTSN2cv7linemod16QuantizedPyramidE??????????
?@__ZTSN2cv7linemod8ModalityE??@__ZTSN2cv8Filter2DIdNS_4CastIddEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8Filter2DIfNS_4CastIffEENS_13FilterVec_32fEEE??????????
?@__ZTSN2cv8Filter2DIhNS_4CastIddEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8Filter2DIhNS_4CastIffEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8Filter2DIhNS_4CastIfhEENS_12FilterVec_8uEEE??????????
?@__ZTSN2cv8Filter2DIhNS_4CastIfsEENS_15FilterVec_8u16sEEE??
?@__ZTSN2cv8Filter2DIhNS_4CastIftEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8Filter2DIsNS_4CastIddEENS_11FilterNoVecEEE???@__ZTSN2cv8Filter2DIsNS_4CastIffEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8Filter2DIsNS_4CastIfsEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8Filter2DItNS_4CastIddEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8Filter2DItNS_4CastIffEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8Filter2DItNS_4CastIftEENS_11FilterNoVecEEE??????????
?@__ZTSN2cv8colormap3HSVE???
?@__ZTSN2cv8colormap3HotE??????????
?@__ZTSN2cv8colormap3JetE?x?@__ZTSN2cv8colormap4BoneE??????????
?@__ZTSN2cv8colormap4CoolE?8?@__ZTSN2cv8colormap4PinkE???@__ZTSN2cv8colormap5OceanE??????????
?@__ZTSN2cv8colormap6AutumnE??????????
?@__ZTSN2cv8colormap6SpringE???@__ZTSN2cv8colormap6SummerE?8?@__ZTSN2cv8colormap6WinterE?8?@__ZTSN2cv8colormap7RainbowE??????????
?@__ZTSN2cv8colormap8ColorMapE??????????
?@__ZTSN2cv8superres19DenseOpticalFlowExtE???@__ZTSN2cv9ColumnSumIddEE??????????
?@__ZTSN2cv9ColumnSumIdfEE??????????
?@__ZTSN2cv9ColumnSumIdhEE??????????
?@__ZTSN2cv9ColumnSumIdsEE??
?@__ZTSN2cv9ColumnSumIdtEE??????????
?@__ZTSN2cv9ColumnSumIidEE???@__ZTSN2cv9ColumnSumIifEE??????????
?@__ZTSN2cv9ColumnSumIihEE??????????
?@__ZTSN2cv9ColumnSumIiiEE??	?@__ZTSN2cv9ColumnSumIisEE??????????
?@__ZTSN2cv9ColumnSumIitEE??????????
?@__ZTSN2cv9Feature2DE???@__ZTSN2cv9FormatterE??????????
?@__ZTSN2cv9RowFilterIddNS_8RowNoVecEEE????@__ZTSN2cv9RowFilterIfdNS_8RowNoVecEEE??????????
?@__ZTSN2cv9RowFilterIffNS_10RowVec_32fEEE??????????
?@__ZTSN2cv9RowFilterIffNS_19SymmRowSmallVec_32fEEE??????????
?@__ZTSN2cv9RowFilterIhdNS_8RowNoVecEEE???@__ZTSN2cv9RowFilterIhfNS_8RowNoVecEEE??????????
?@__ZTSN2cv9RowFilterIhiNS_12RowVec_8u32sEEE??????????
?@__ZTSN2cv9RowFilterIhiNS_21SymmRowSmallVec_8u32sEEE??????????
?@__ZTSN2cv9RowFilterIsdNS_8RowNoVecEEE???@__ZTSN2cv9RowFilterIsfNS_13RowVec_16s32fEEE??????????
?@__ZTSN2cv9RowFilterItdNS_8RowNoVecEEE??????????
?@__ZTSN2cv9RowFilterItfNS_8RowNoVecEEE??????????
?@__ZTSN2cv9videostab12DeblurerBaseE???
?@__ZTSN2cv9videostab12IFrameSourceE??@__ZTSN2cv9videostab12NullDeblurerE??
?@__ZTSN2cv9videostab13InpainterBaseE??????????
?@__ZTSN2cv9videostab13NullInpainterE??
?@__ZTSN2cv9videostab14StabilizerBaseE?0?@__ZTSN2cv9videostab15NullFrameSourceE??
?@__ZTSN2cv9videostab17IMotionStabilizerE??????????
?@__ZTSN2cv9videostab22IDenseOptFlowEstimatorE??@__ZTSN2cv9videostab22IGlobalMotionEstimatorE??????????
?@__ZTSN2cv9videostab23ISparseOptFlowEstimatorE?H?@__ZTSN2cv9videostab25PyrLkOptFlowEstimatorBaseE??????????
?@__ZTSN2cv9videostab4ILogE??
?@__ZTSN2cv9videostab7NullLogE??@__ZTSN3Iex10EbadmsgExcE??
?@__ZTSN3Iex10EdeadlkExcE??????????
?@__ZTSN3Iex10EisconnExcE???@__ZTSN3Iex10EnoattrExcE???@__ZTSN3Iex10EnobufsExcE??????????
?@__ZTSN3Iex10EnodataExcE??????????
?@__ZTSN3Iex10EnoexecExcE??????????
?@__ZTSN3Iex10EnolinkExcE???@__ZTSN3Iex10EnotblkExcE??????????
?@__ZTSN3Iex10EnotdirExcE??
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??3??3qPopFrontEP5CvSeqPv??tIdentityERKNS_12_OutputArrayERKNS_7Scalar_IdEEĿpFilter2DERKNS_11_InputArrayERKNS_12_OutputArrayEiS2_S2_NS_6Point_IiEEdi???3eqRemoveEP5CvSeqi??olvePolyERKNS_11_InputArrayERKNS_12_OutputArrayEi??tartLoopEPFiiPPcEiS1_????3l??ubeRootEf??vtColorERKNS_11_InputArrayERKNS_12_OutputArrayEii??alcHistEۑolormap8ColorMap15linear_colormapERKNS_11_InputArrayES4_S4_S4_S4_??earSeqEP5CvSeqݢipLineENS_5????3etSeqElemEPK5CvSeqi??_8x32fTabEɮ??3e?olvePnPRansacERKNS_11_InputArrayES2_S2_S2_RKNS_12_OutputArrayES5_bifiS5_i??	q¤tTrackbarPosERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES8_i??RemoveSliceEP5CvSeq7CvSlice??InsertSliceEP5CvSeqiPKv????3??3Size_IiEERNS_6Point_IiEES4_ʥRect_IiEERNS_6Point_IiEES4_Х??4??4ineIteratorC??BPEvaluator??2ERKNS_3MatENS_6Point_IiEES5_ib??1ERKNS_3MatENS_6Point_IiEES5_ibŦ??4Ў4llipse2PolyENS_6Point_IiEENS_5Size_IiEEiiiiRNSt3__16vectorIS1_NS4_9allocatorIS1_EEEEܧqualizeHistERKNS_11_InputArrayERKNS_12_OutputArrayE????4??4У46Point_IiEES3_RKNS_7Scalar_IdEEiii??5Rect_IiEERKNS_7Scalar_IdEEiii??Ч4??4??4NS_6Point_IiEENS_5Size_IiEEdddRKNS_7Scalar_IdEEiii??RKNS_11RotatedRectERKNS_7Scalar_IdEEii????4??4lũndՂ	lConvexPolyER?terSpecklesERޓ	NS_3MatEPKNS_6Point_IiEEiRKNS_7Scalar_IdEEiiتKNS_12_OutputArrayERKNS_11_InputArrayERKNS_7Scalar_IdEEii???5lPolyER??ter2DERKNS_11_InputArrayERKNS_12_OutputArrayEiS2_NS_6Point_IiEEdi߇NS_3MatEPPKNS_6Point_IiEEPKiiRKNS_7Scalar_IdEEiiS3_??KNS_12_OutputArrayERKNS_11_InputArrayERKNS_7Scalar_IdEEiiNS_6Point_IiEE????5olylinesER??atchNaNsERKNS_12_OutputArrayEd??npransac9PnPSolver9syncMutexE??NS_3MatEPPKNS_6Point_IiEEPKiibRKNS_7Scalar_IdEEiii??KNS_12_OutputArrayERKNS_11_InputArrayEbRKNS_7Scalar_IdEEiii????5utTextERNS_3MatERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS_6Point_IiEEidNS_7Scalar_IdEEiib?yrDownERKNS_11_InputArrayERKNS_12_OutputArrayERKNS_5Size_IiEEi??olyfitERKNS_3MatES2_RS0_iܯ??5??5?5??5??5ontours??hessboardCorners????5??52Poly??????5??5??5??5ar?AA??
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??6ont??aceTracker????6extSizeԲhreadNumגick??rackbarPos???6ftERKNS_11_InputArrayERKNS_12_OutputArrayEii??ctERKNS_11_InputArrayERKNS_12_OutputArrayEiȵ??7ftERKNS_11_InputArrayERKNS_12_OutputArrayEii??ctERKNS_11_InputArrayERKNS_12_OutputArrayEiε??8ulSpectrumsERKNS_11_InputArrayES2_RKNS_12_OutputArrayEibµorphologyExERKNS_11_InputArrayERKNS_12_OutputArrayEiS2_NS_6Point_IiEEiiRKNS_7Scalar_IdEE????8??8??9OptimalDFTSizeEiֶGaussianKernelEidi??WindowPropertyERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEi???9??9Щ9??9DFTSize??NewCameraMatrix???9lobENSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEERNS0_6vectorIS6_NS4_IS6_EEEEb??emmERKNS_11_InputArrayES2_dS2_dRKNS_12_OutputArrayEi????;	2?9??1??6??5??4??3??7??8??5??4??6??1??2??
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LaplacianERKNS0_6GpuMatERS1_iidiRNS0_6StreamEǒ
histRangeERKNS0_6GpuMatEܦ
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resetDeviceEv??TargetArchs??s??c??polarToCartERKNS0_6GpuMatES3_RS1_S4_bRNS0_6StreamE??b??demosaicingERKNS0_6GpuMatERS1_iiRNS0_6StreamE??addWeightedERKNS0_6GpuMatEdS3_ddRS1_iRNS0_6StreamE??HoughLinesPERKNS0_6GpuMatERS1_RNS0_13HoughLinesBufEffiii?
ConvolveBuf6createENS_5Size_IiEES3_ڬ
??;deviceSupportsENS0_10FeatureSetE??solvePnPRansacERKNS_3MatES3_S3_S3_RS1_S4_bifiPNSt3__16vectorIiNS5_9allocatorIiEEEE??copyMakeBorderERKNS0_6GpuMatERS1_iiiiiRKNS_7Scalar_IdEERNS0_6StreamEأ
??;9builtWithENS0_10FeatureSetE??3hasEii??6has??17hasEqualOr??20hasEqualOrGreater????;??;PtxEii??BinEii????;??;LessPtxEii??GreaterEii????;??;PtxEii??BinEii????;??;1??6??7??8??2??0DeviceInfo??3HOGDescriptor??
2ImagePyramid8getLayerERNS0_6GpuMatENS_5Size_IiEERNS0_6StreamE??
5Video??
1??8supportsENS0_10FeatureSetE??7sharedMemPerBlockEv??1??0freeMemoryEv??2isCompatibleEv????;queryMemoryERmS2_??totalMemoryEv????;??;??;??;??;DeviceInfo5queryEv??bitwise_orERKNS0_6GpuMatE??HoughLinesERKNS0_6GpuMatERS1_??
rectStdDevERKNS0_6GpuMatES3_RS1_RKNS_5Rect_IiEERNS0_6StreamE??
evenLevelsERNS0_6GpuMatEiii¥
meanStdDevERKNS0_6GpuMatERNS_7Scalar_IdEES6_??
warpAffineERKNS0_6GpuMatERS1_RKNS_3MatENS_5Size_IiEEiiNS_7Scalar_IdEERNS0_6StreamE??
??;printCudaDeviceInfoEi??get?
createBoxFilter_GPUEiiRKNS_5Size_IiEERKNS_6Point_IiEE?
buildWarpAffineMapsERKNS_3MatEbNS_5Size_IiEERNS0_6GpuMatES7_RNS0_6StreamE??
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OpticalFlowDual_TVL1_GPU??
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sqrSumERKNS0_6GpuMatEݵ
minMaxERKNS0_6GpuMatEPdS4_S3_??
C??6??aSERKS1_??4swapERS1_??9adjustROIEiiii??5setToENS_7Scalar_IdEERKS1_??7releaseEv??2E??1E??RK??iiiPvm??NS_5Size_IiEEiPvm??S1_??NS_3MatE????;
NS_5R??RK??iiiPvm??NS_5Size_IiEEiPvm??S1_??NS_3MatE????;
NS_5R????;??;??;??;angeES4_??ect_IiEE????;angeES4_??ect_IiEE????;??;??;??;uploadERKNS_3MatE??createEiii????;??;??;??;GpuMat??StreamcvbEv??9??7reshapeEii??8downloadERNS_3MatE??6copyToERS1_??locateROIERNS_5Size_IiEERNS_6Point_IiEE??convertToERS1_idd????;??;??<2E??1E??RK??iPKiiPvPKm??PK??NS_3gpu6GpuMatE??S0_????<??<RK??iPKiiPvPKm??PK?NS_3gpu6GpuMatE??S0_ԩ??<c??get??
integralBufferedERKNS0_6GpuMatERS1_S4_RNS0_6StreamE??
PyrLKOpticalFlow¼
reateContinuousEiiiRNS0_6GpuMatE??onnectivityMaskERKNS0_6GpuMatERS1_RKNS_7Scalar_IdEES8_RNS0_6StreamE͗
??<?<ensureSizeIsEnoughEiiiRNS0_6GpuMatE??createFilter2D_GPUERKNS_3PtrINS0_14BaseFilter_GPUEEEii??
HoughLinesDownloadERKNS0_6GpuMatERKNS_12_OutputArrayES6_?
meanShiftFilteringERKNS0_6GpuMatERS1_iiNS_12TermCriteriaERNS0_6StreamE?
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buildWarpPlaneMapsENS_5Size_IiEENS_5Rect_IiEERKNS_3MatES7_S7_fRNS0_6GpuMatES9_RNS0_6StreamEߣ
??<allocMatFromBufEiiiRNS0_6GpuMatE??transformPointsERKNS0_6GpuMatERKNS_3MatES6_RS1_RNS0_6StreamE??gammaCorrectionERKNS0_6GpuMatERS1_bRNS0_6StreamE??bilateralFilterERKNS0_6GpuMatERS1_iffiRNS0_6StreamE??labelComponentsERKNS0_6GpuMatERS1_iRNS0_6StreamEԗ
BroxOpticalFlowclERKNS0_6GpuMatES4_RS2_S5_RNS0_6StreamE˷
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warpPerspectiveERKNS0_6GpuMatERS1_RKNS_3MatENS_5Size_IiEEiiNS_7Scalar_IdEERNS0_6StreamE??
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CannyERKNS0_6GpuMatE??
remapERKNS0_6GpuMatERS1_S3_S3_iiNS_7Scalar_IdEERNS0_6StreamE?
mergeE??
etToERNS0_6GpuMatENS_7Scalar_IdEE??plitERKNS0_6GpuMatE??
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fmiS0_mi??dmiS0_mi????=??=eterminantERKNS_11_InputArrayE??rawMatchesERKNS_3MatERKNSt3__16vectorINS_8KeyPointENS3_9allocatorIS5_EEEES2_SA_RKNS4_INS????=nvertERKNS_11_InputArrayERKNS_12_OutputArrayEi??m????=7computeERKNS_11_InputArrayERKNS_12_OutputArrayE??9backSubstERKNS_11_InputArrayES3_S3_S3_RKNS_12_OutputArrayE??clERKNS_11_InputArrayEi??S6_S6_i??i????>??>igenERKNS_11_InputArrayE??r??RKNS_12_OutputArrayE??bRKNS_12_OutputArrayES5_??S5_ii??ii????>??>????????@??@??@VDecompERKNS_11_InputArrayERKNS_12_OutputArrayES5_S5_i??ubdiv2D?tΐ	??@?@t??co??stroy??leteMoire͚Interlace????@??@lve??rt??bel????@Cubic??Poly??VV??Decomposite??
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RKNS_11_InputArrayES2_RKNS_12_OutputArrayE??PKfS1_Pfi????BhaseERKNS_11_InputArrayES2_RKNS_12_OutputArrayEb??yrUpERKNS_11_InputArrayERKNS_12_OutputArrayERKNS_5Size_IiEEi????CartToPolarERKNS_11_InputArrayES2_RKNS_12_OutputArrayES5_b??reateCLAHEEdNS_5Size_IiEE??o????C??CRKNS_11_InputArrayERKNS_12_OutputArrayE??PKfPfi????DRKNS_11_InputArrayERKNS_12_OutputArrayE??PKfPfi????D??DqrtERKNS_11_InputArrayERKNS_12_OutputArrayE??wapERNS_3MatES1_??ortERKNS_11_InputArrayERKNS_12_OutputArrayEi????EheckRangeERKNS_11_InputArrayEbPNS_6Point_IiEEdd??varrToMatEPKvbbi??onvexHullERKNS_11_InputArrayERKNS_12_OutputArrayEbb????E??E?E??E??E??E??E??ErtToPolar??l??nny??mShiftԩ
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0EEEbNS_3MatERNS_6Point_IiEEiiRd??1EEEbNS_3MatERNS_6Point_IiEEiiRd??2EEEbNS_3MatERNS_6Point_IiEEiiRd??3EEEbNS_3MatERNS_6Point_IiEEiiRd??4EEEbNS_3MatERNS_6Point_IiEEiiRd????G??G??G??G??G??Grans??hresholdERKNS_11_InputArrayERKNS_12_OutputArrayEddi??formERKNS_11_InputArrayERKNS_12_OutputArrayES2_??poseERKNS_11_InputArrayERKNS_12_OutputArrayEп??JperspectiveTransformERKNS_11_InputArrayERKNS_12_OutputArrayES2_??c??initWideAngleProjMapERKNS_11_InputArrayES2_NS_5Size_IiEEiiRKNS_12_OutputArrayES7_id??saveWindowParametersERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE??loadWindowParametersERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE??D??F??windowedMatchingMaskERKNSt3__16vectorINS_8KeyPointENS0_9allocatorIS2_EEEES7_ff?getValidDisparityROIENS_5Rect_IiEES1_iii??	BackgroundSubtractor??
AffineTransformPatchEP9_IplImageS1_NS_12CvAffinePoseE??ConvertImageToMatrixEP9_IplImage??OneWayDescriptorBase??PlanarObjectDetector??fastNlMeansDenoisingERKNS_11_InputArrayERKNS_12_OutputArrayEfii?LogPolar_Overlapping???J??Kc??ibrateCameraERKNS_11_InputArrayES2_NS_5Size_IiEERKNS_12_OutputArrayES7_S7_S7_iNS_12TermCriteriaE??CovarMatrixE??BackProjectE??PCAFeaturesERNSt3__16vectorIP9_IplImageNS0_9allocatorIS3_EEEERNS_11FileStorageEPKcPP5CvMatSE_??PKNS_3MatEiRS0_S3_ii??RKNS_11_InputArrayERKNS_12_OutputArrayES5_ii????K??KulTransposedERKNS_11_InputArrayERKNS_12_OutputArrayEbS2_di??atchTemplateERKNS_11_InputArrayES2_RKNS_12_OutputArrayEi??ЮLanobisERKNS_11_InputArrayES2_S2_??onobisERKNS_11_InputArrayES2_S2_????L??LdotERKNS_11_InputArrayE??invEi?mulERKNS_11_InputArrayEd????OC??clERKNS_11_InputArrayES3_ii??10computeVarERKNS_11_InputArrayES3_id??2E??1E??v??RKNS_11_InputArrayES3_i????Pv??RKNS_11_InputArrayES3_i????Pi??d????P?Pi??d????P??P??P?Q7projectERKNS_11_InputArrayE??11backProjectERKNS_11_InputArrayE??RKNS_12_OutputArrayE?????Q??QRKNS_12_OutputArrayE??????Q??QCA??ng??xMۙComputeERKNS_11_InputArrayERKNS_12_OutputArrayES5_i??ProjectERKNS_11_InputArrayES2_S2_RKNS_12_OutputArrayE????Q??Q??QCABackProjectERKNS_11_InputArrayES2_S2_RKNS_12_OutputArrayE??atchGenerator????Q??Rns??c??form??pose????RзR??Rc??ibrat??CovarMatrix??P??E??AʖBayesianProb??M??OpticalFlow??
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552BGR_8u_C2C3RPKhiPhi6CvSize??652BGR_8u_C2C3RPKhiPhi6CvSize?????
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orageE?24BriefDescriptorExtractor5writeERNS_11FileStorageE??32OpponentColorDescriptorExtractor5writeERNS_11FileStorageE??Ж???…riefDescriptorExtractor??ackgroundSubtractorMOG2ۥ
C??4readERKNS_8FileNodeE??2Ei??1Ei???????B??GenericDescriptorMatcher?riefDescriptorExtractor??ackgroundSubtractorMOG2??
1??5writeERNS_11FileStorageE??4infoEv??4descriptor??1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_??SizeEv??TypeEv??ੌ???????ୌ??????C??1??D??7scales_E??6n_rot_E??2E??1E??iif??RNSt3__16vectorIfNS1_9allocatorIfEEEERNS2_IiNS3_IiEEEEffS8_?????4generateKernelERNSt3__16vectorIfNS1_9allocatorIfEEEERNS2_IiNS3_IiEEEEffS8_??0basicSize_E?1scalerange_E???Œiif??RNSt3__16vectorIfNS1_9allocatorIfEEEERNS2_IiNS3_IiEEEEffS8_???Ҍ?ٌ?܌clERKNS_11_InputArrayES3_RNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEE??34computeDescriptorsAndOrOrientationERKNS_11_InputArrayES3_RNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERKNS_12_OutputArrayEbbb??29computeKeypointsNoOrientationERKNS_11_InputArrayES3_RNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEE??1??4infoEv??RKNS_12_OutputArrayEb????????????4descriptor??0detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_??SizeEv??TypeEv?????8_N??568_N2cv9videostab17TwoPassStabilizer9nextFrameEv??K2cv??2cv??5BRISK1??3??4S?4descriptor??1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_??SizeEv??TypeEv???????????2Ev??1Ev??0Ev??Ќ????5??1??BRISKD??FREAKD??1Ev??0Ev??Ў?5BRISKD??8superres15SuperResolution?1Ev??0Ev????????А???????C??1??D??1Ei??2Ei???6constructPyramidERKNS_3MatE??2getKeypointsEiRNSt3__16vectorINS_8KeyPointENS1_9allocatorIS3_EEEE??3safetyFactor_E??0basicSize_E????????1Ev??2Ev??????????????????C??14getAgastPointsEiRNSt3__16vectorINS_8KeyPointENS1_9allocatorIS3_EEEE??1ERK??2ERK??NS_3MatEff??S0_i???????????NS_3MatEff??S0_i????͍D??21removeBorderKeypointsERNSt3__16vectorINS_8KeyPointENS1_9allocatorIS3_EEEENS_5Size_IiEEi??6createERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??2Ev??1Ev??0Ev???͏?͏9DescriptorExtractorD??5FeatureDetectorD??1Ev??0Ev???͏?Ώ?ΏDescriptorExtractor??FastFeatureDetector??7computeERKNS??5emptyEv??_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_??t3__16vectorINS_3MatENS1_9allocatorIS3_EEEERNS2_INS2_INS_8KeyPointENS4_IS9_EEEENS4_ISB_EEEERS6_???Ώ?ҏ?֏?֏?֏OpponentColorDescriptorExtractor??evaluateGenericDescriptorMatcherERKNS_3MatES2_S2_RNSt3__16vectorINS_8KeyPointENS3_9allocatorIS5_EEEES9_PNS4_INS4_INS_6DMatchENS6_ISA_EEEENS6_ISC_EEEEPNS4_INS4_IhNS6_IhEEEENS6_ISH_EEEERNS4_INS_6Point_IfEENS6_ISM_EEEERKNS_3PtrINS_24GenericDescriptorMatcherEEE??fastNlMeansDenoisingColoredMultiERKNS_11_InputArrayERKNS_12_OutputArrayEiiffii??C??4readERKNS_8FileNodeE??1ERKNS_3PtrINS_19DescriptorExtractorEEE??2ERKNS_3PtrINS_19DescriptorExtractorEEE???܏?ߏ???1??5??1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_??4descriptor??????????writeERNS_11FileStorageE??emptyEv??ௐ???SizeEv??TypeEv?????а?ఐD??19removeInvalidPointsERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEE??6createERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??2Ev??1Ev??0Ev???А?А1Ev??0Ev???ѐ?ѐ?ѐFeatureDetector??RTreeClassifier??
6detectERKNS??5emptyEv??_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??t3__16vectorINS_3MatENS1_9allocatorIS3_EEEERNS2_INS2_INS_8KeyPointENS4_IS9_EEEENS4_ISB_EEEES8_???Ґ?ؐ?ߐ??????GridAdaptedFeatureDetectorC??c??	1ERKNS_3PtrINS_15FeatureDetectorEEEiii??2ERKNS_3PtrINS_15FeatureDetectorEEEiii????PyramidAdaptedFeatureDetectorC??DynamicAdaptedFeatureDetectorC??1ERKNS_3PtrINS_15FeatureDetectorEEEi??2ERKNS_3PtrINS_15FeatureDetectorEEEi????2Eiddibd??1Eiddibd??????10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??4infoEv????enseFeatureDetectorC??TreeBestSplitFinder??
2Efifiibb??1Efifiibb????????DenseFeatureDetector??FastFeatureDetector2??BackgroundSubtractor18getBackgroundImageERKNS_12_OutputArrayE?
OneWayDescriptorBase??PlanarObjectDetector??10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??4infoEvʃ??????5emptyEv??10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??4infoEv؃Ё????Ї?PyramidAdaptedFeatureDetector??DynamicAdaptedFeatureDetector??5emptyEv??10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??????????_6DMatchENS6_ISB_EEEERS0_RKNS_7Scalar_IdEESK_RKNS4_IcNS6_IcEEEEi??4_INS_6DMatchENS6_ISB_EEEENS6_ISD_EEEERS0_RKNS_7Scalar_IdEESM_RKNS4_INS4_IcNS6_IcEEEENS6_ISO_EEEEi????????2ERKNS_3PtrINS_15AdjusterAdapterEEEiii??1ERKNS_3PtrINS_15AdjusterAdapterEEEiii????????5emptyEv??10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??????C??6tooFewEii??7tooManyEii??2Eibii??1Eibii????????10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??4goodEv??5cloneEv??࿒????????????tar??urfAdjuster??Adjuster??DetectorCܽC??6tooFewEii??7tooManyEii??2Eddd??1Eddd???Ò?Òtar??urfAdjuster??Adjuster??Detector??10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??4goodEv??5cloneEv???Ē?ƒ?ƒ?ǒ?ǒC??6tooFewEii??7tooManyEii??2Eddd??1Eddd???Ȓ?ɒ10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??4goodEv??5cloneEv???ʒ?̒?̒?̒?͒?ΒC??22getSecondMomentsMatrixERKN2cv7Scalar_IdEE??7convertERKNSt3__16vectorI??14calcProjectionERKNSt3__16vectorIS_NS0_9allocatorIS_EEEERKN2cv4Mat_IdEERS4_??2E??1E??v??RKN2cv6Point_IfEERKNS0_7Scalar_IdEE???ߒRKN2cv6Point_IfEERKNS0_7Scalar_IdEE??v???ߒ??????6??4HarrisDetector4infoEv??7CirclesGridFinder??1Cv??	0CvKNearest??	3CvCalibFilter??
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22getSecondMomentsMatrixEv??14calcProjectionERKN2cv4Mat_IdEERS_??????N2cv8KeyPointENS0_9allocatorIS3_EEEERNS1_IS_NS4_IS_EEEE??S_NS0_9allocatorIS_EEEERNS1_IN2cv8KeyPointENS2_IS8_EEEE???????????????̓?Փ?֓?דASTXERKNS_11_InputArrayERNSt3__16vectorINS_8KeyPointENS3_9allocatorIS5_EEEEibi??REAK??????2Eib??1Eib??????astFeatureDetector2C??indOneWayDescriptorEiPKNS_16OneWayDescriptorEP9_IplImage??2Eib??1Eib????
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i??????10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??4infoEv????10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??4infoEv?????ݕ1??8EEEiPKhPKii??6EEEiPKhPKii??2EEEiPKhPKii????????????2cv??14HarrisDetector4infoEvÃ5??2??1??4́3҂BRISK4infoEv??FREAK4infoEv????????4BriefDescriptorExtractor4infoEv??0??6GridAdaptedFeatureDetector4infoEv߃??????9FastFeatureDetector4infoEv??2??8SimpleBlobDetector4infoEv????????FastFeatureDetector24infoEv??DenseFeatureDetector4infoEvу???4infoEv??10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??clERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEE?????StarDetector4infoEv??GFTTDetector4infoEv?????MSER??S??LBPH??4infoEvŁclERKNS_3MatERNSt3__16vectorINS5_INS_6Point_IiEENS4_9allocatorIS7_EEEENS8_ISA_EEEES3_??10detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_???MSER4infoEv?S?????4infoEv˂1??clERKNS_11_InputArrayES3_RNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEE?????ORB4infoEv??ocl8SURF_OCL4infoEv??ଖ4infoEv??1܅??????ූп?????֖?ؖ????????????4infoEv??5cloneEb?????4infoEv??5??15isMaskSupportedEv͟??????1΄C??D?2buildPatternEvՅ1selectPairsERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEERNS2_INS2_INS_8KeyPointENS4_IS9_EEEENS4_ISB_EEEEdb????1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_?3meanIntensityERKNS_3MatES3_ffjjj?4descriptor???—????2EbbfiRKNSt3__16vectorIiNS1_9allocatorIiEEEEۇ1EbbfiRKNSt3__16vectorIiNS1_9allocatorIiEEEE???????2Ev??1Ev??0Ev??И??1Ev??0Ev?????Л????SizeEvɈTypeEvЈ???М??Ř?Ƙ?ȘconvertERKNSt3__16vectorI??overlapERKS0_S2_??S0_NS1_9allocatorIS0_EEEERNS2_INS_6Point_IfEENS3_IS9_EEEERKNS2_IiNS3_IiEEEE??NS_6Point_IfEENS1_9allocatorIS4_EEEERNS2_IS0_NS5_IS0_EEEEffii???ј?ט?٘0retainBestERNSt3__16vectorINS_8KeyPointENS1_9allocatorIS3_EEEEi??6r??7runByKeypointSizeERNSt3__16vectorINS_8KeyPointENS1_9allocatorIS3_EEEEff܍5runByPixelsMaskERNSt3__16vectorINS_8KeyPointENS1_9allocatorIS3_EEEERKNS_3MatE??ܘunByImageBorderERNSt3__16vectorINS_8KeyPointENS1_9allocatorIS3_EEEENS_5Size_IiEEiՍemoveDuplicatedERNSt3__16vectorINS_8KeyPointENS1_9allocatorIS3_EEEE????????????Ǚ20DescriptorCollection??D??3addERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEE??5??8knnMatchERKNS_3MatERNSt3__16vectorINS5_INS_6DMatchENS4_9allocatorIS6_EEEENS7_IS9_EEEEiRKNS5_IS1_NS7_IS1_EEEEb??1??4readERKNS_8FileNodeE??6createERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE?C??DΑ3setERKNSt3__16vectorINS_3MatENS2_9allocatorIS4_EEEE??5clearEv??2E??1E??v??RKS1_???ϙv??RKS1_Ǒ?љ?ҙ?ܙ2Ev?1Ev?0Ev??ܙ?ݙ?ޙ?ޙ??20DescriptorCollection??1Ŕ5??8knnMatchERKNS_3MatES3_RNSt3__16vectorINS5_INS_6DMatchENS4_9allocatorIS6_EEEENS7_IS9_EEEEiS3_b??1??4sizeEv??3getDescriptorEi?4getDescriptorsEv??1getLocalIdxEiRiS2_??i?????????????????2Ev??1Ev??0Ev??????????????9getTrainDescriptorsEv??1radiusMatchERKNS_3MatES3_RNSt3__16vectorINS5_INS_6DMatchENS4_9allocatorIS6_EEEENS7_IS9_EEEEfS3_b??0checkMasksERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEEi?????clearEv??trainEv??matchERKNS_3MatERNSt3__16vectorINS_6DMatchENS4_9allocatorIS6_EEEERKNS5_IS1_NS7_IS1_EEEE?????emptyEv??matchERKNS_3MatES3_RNSt3__16vectorINS_6DMatchENS4_9allocatorIS6_EEEES3_??writeERNS_11FileStorageE???????????Ѓ??????????1Ԙ5isPossibleMatchERKNS_3MatEii??radiusMatchERKNS_3MatERNSt3__16vectorINS5_INS_6DMatchENS4_9allocatorIS6_EEEENS7_IS9_EEEEfRKNS5_IS1_NS7_IS1_EEEEb??isMaskedOutERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEEi??А????Х?ग़??C??1?2EibҚ1EibٚЮ?????2knnMatchImplERKNS_3MatERNSt3__16vectorINS5_INS_6DMatchENS4_9allocatorIS6_EEEENS7_IS9_EEEEiRKNS5_IS1_NS7_IS1_EEEEb؜5radiusMatchImplERKNS_3MatERNSt3__16vectorINS5_INS_6DMatchENS4_9allocatorIS6_EEEENS7_IS9_EEEEfRKNS5_IS1_NS7_IS1_EEEEbߜ?????Ј?Cϝ3addERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEE?5?4readERKNS_8FileNodeE??1۟1ERKNS_3PtrINS_5flann11IndexParamsEEERKNS1_INS2_12SearchParamsEEE۞2ERKNS_3PtrINS_5flann11IndexParamsEEERKNS1_INS2_12SearchParamsEEE?Г???????clearEv??trainEv???????writeERNS_11FileStorageEƟcloneEbԟ?ϛ??????7convertToDMatchesERKNS_17DescriptorMatcher20DescriptorCollectionERKNS_3MatES7_RNSt3__16vectorINS9_INS_6DMatchENS8_9allocatorISA_EEEENSB_ISD_EEEE?2knnMatchImplERKNS_3MatERNSt3__16vectorINS5_INS_6DMatchENS4_9allocatorIS6_EEEENS7_IS9_EEEEiRKNS5_IS1_NS7_IS1_EEEEb?5radiusMatchImplERKNS_3MatERNSt3__16vectorINS5_INS_6DMatchENS4_9allocatorIS6_EEEENS7_IS9_EEEEfRKNS5_IS1_NS7_IS1_EEEEb??????????1?C2Ev??D??3addERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEERNS2_INS2_INS_8KeyPointENS4_IS9_EEEENS4_ISB_EEEEث5??8??4readERKNS_8FileNodeE??6createERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_??8KeyPointCollection??1radiusMatchERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEfRKNS5_IS1_NS7_IS1_EEEEb??C??3addERKNSt3__16vectorINS_3MatENS2_9allocatorIS4_EEEERKNS3_INS3_INS_8KeyPointENS5_ISA_EEEENS5_ISC_EEEE٧5clearEv?2E??1E??v??RKS1_˧???vħRKS1_ҧ?????ǜ?ǜ?М1??8??5?8KeyPointCollectionũ4getTrainImagesEv߫7getTrainKeypointsEv?1radiusMatchERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_SA_RNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEfS3_b??1?9getImagesEv??8getImageEi??3keypointCountEv??0imageCountEv??2getKeypointsE??1getԪ?Ӝ?Ӝvƪiͪ?Ӝ?ӜKeyPointEi??LocalIdxEiRiS2_??i?????֜?ۜ?ܜ?ߜ?ߜ??2Evë1Evʫ0Evѫ????????????clearEv??trainEv??matchERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS_6DMatchENS7_ISB_EEEERKNS5_IS1_NS7_IS1_EEEE?????classifyERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_SA_?knnMatchERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_SA_RNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEiS3_b???matchERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_SA_RNS5_INS_6DMatchENS7_ISB_EEEES3_??writeERNS_11FileStorageE??emptyEv?????classifyERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEE?knnMatchERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEiRKNS5_IS1_NS7_IS1_EEEEb?????????????????Ќ?????????????C??D?3addERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEERNS2_INS2_INS_8KeyPointENS4_IS9_EEEENS4_ISB_EEEE??5??1??4readERKNS_8FileNodeE??2ERKNS_3PtrINS_19DescriptorExtractorEEERKNS1_INS_17DescriptorMatcherEEE״1ERKNS_3PtrINS_19DescriptorExtractorEEERKNS1_INS_17DescriptorMatcherEEE޴Д????2Ev??1Ev??0Ev????????????clearEv??trainEv?????Н?5ڶ2knnMatchImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEiRKNS5_IS1_NS7_IS1_EEEEb??isMaskSupportedEv??radiusMatchImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEfRKNS5_IS1_NS7_IS1_EEEEb??????С????VectorDescriptorMatcher5??BackgroundSubtractor׵
OneWayDescriptorMatcher5??writeERNS_11FileStorageE??emptyEv??cloneEb??ढ़??????2Eiiiddiddi߹1Eiiiddiddi???????????ٞ2Eifiiiiii??1Eifiiiiii????????4descriptorڻ0detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_ǽ1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_νSizeEv?TypeEv?????ORB1??ocl8SURF_OCL1ʤ4descriptor??1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_սSizeEv??TypeEv??????????????
RKNS_12_OutputArrayEb??????Р?Ѡ?֠2Eiiiii??1Eiiiii???ڠ?۠?ܠ??boardCorners??BoardCornerGuesses??
е??ܢChessboardCornersERKNS_11_InputArrayENS_5Size_IiEERKNS_12_OutputArrayEiݿ4QuadCornerSubpixERKNS_11_InputArrayERKNS_12_OutputArrayENS_5Size_IiEE?	????????Cv??IlmThread??LevMarq??ANN_MLP??	ERTrees??	GBTrees??	C??4??5clearEv??D??6updateERPK5CvMatRPS0_S5_??9updateAltERPK5CvMatRPS0_S5_RPd??2E??1E??v??ii14CvTermCriteriab???ţv??ii14CvTermCriteriab???ǣ?ȣinitEii14CvTermCriteriab??stepEv???У????2Ev??1Ev????????????????atMulDeriv??otionGradient??
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Uncalibrated???????????թ???????????????ժCameraMatrix2DERKNS_11_InputArrayES2_NS_5Size_IiEEd??Module_?????ഫ???????ά??????А????????less_predRKNSt3__14pairIfiEES3_??icv??TransposePfii??showBoxesP9_IplImagePK7CvPointS3_i8CvScalariiiϖprintBits?????irclesGridClusterFinder??vBlobTrackPredictKalman??
2??8findGridENSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEENS2_5Size_IiEERS7_??1??2hierarchicalClusteringENSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEERKNS2_5Size_IiEERS7_??0rectifyPatternPointsERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEES9_RS7_???Ϯ???1findCornersERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEERS7_??8??6getSortedCornersERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEES9_S9_RS7_?????findOutsideCornersERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEERS7_??parsePatternPointsERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEES9_RS7_?????????ů?ޯGraph??CvSVM??	RFace??C??9addVertexEm??7addEdgeEmm??10removeEdgeEmm??2Em??1Em???????????Graph??CvSVM??
1??9getDegreeEm??5doesVertexExistEm??9areVerticesAdjacentEmm??6getVerticesCountEv??3floydWarshallERN2cv3MatEi??2getNeighborsEm????????Ѐ????????????????C??FaceDetectionList??MouthFaceTemplateD??btv_l1_device_ocl??irclesGridFinder??v??	SMatrixGenerator??
7??C??9find??2??1??8addPointEN2cv6Point_IfEERNSt3__16vectorImNS3_9allocatorImEEEE??SegmentC??findMCSERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEERNS1_I5GraphNS5_ISA_EEEE??2EN2cv6Point_IfEES3_??1EN2cv6Point_IfEES3_????????2Ev??1Ev??Б????2EN2cv5Size_IiEERKNSt3__16vectorINS0_6Point_IfEENS3_9allocatorIS6_EEEERK27CirclesGridFinderParameters??1EN2cv5Size_IiEERKNSt3__16vectorINS0_6Point_IfEENS3_9allocatorIS6_EEEERK27CirclesGridFinderParameters????????HolesEv??BasisERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEERS7_RNS1_I5GraphNS5_ISB_EEEE?????1??9draw??8getHolesERNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEE??0computeRNGER5GraphRNSt3__16vectorIN2cv6Point_IfEENS2_9allocatorIS6_EEEEPNS4_3MatE??3rng2gridGraphER5GraphRNSt3__16vectorIN2cv6Point_IfEENS2_9allocatorIS6_EEEE??4??9??5drawBasisGraphsERKNSt3__16vectorI5GraphNS0_9allocatorIS2_EEEERN2cv3MatEbb??8getAsymmetricHolesERNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEE??7getCornerSegmentsERKNSt3__16vectorINS1_ImNS0_9allocatorImEEEENS2_IS4_EEEERNS1_INS1_INS_7SegmentENS2_IS9_EEEENS2_ISB_EEEERNS1_IN2cv6Point_IiEENS2_ISH_EEEESK_SK_?????3??2computeGraphConfidenceERKNSt3__16vectorI5GraphNS0_9allocatorIS2_EEEEbRKNS1_ImNS3_ImEEEESB_??1doesIntersectionExistERKNSt3__16vectorINS_7SegmentENS0_9allocatorIS2_EEEERKNS1_IS5_NS3_IS5_EEEE??filterOutliersByDensityERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEERS7_??areSegmentsIntersectingENS_7SegmentES0_??????????????eraseUsedGraphERNSt3__16vectorI5GraphNS0_9allocatorIS2_EEEE??getFirstCornerERNSt3__16vectorIN2cv6Point_IiEENS0_9allocatorIS4_EEEES8_S8_S8_?????8??5??1rectifyGridEN2cv5Size_IiEERKNSt3__16vectorINS0_6Point_IfEENS3_9allocatorIS6_EEEESB_RS9_??2??7findCandidateLineERNSt3__16vectorImNS0_9allocatorImEEEEmbN2cv6Point_IfEES5_??3areCentersNewERKNSt3__16vectorImNS0_9allocatorImEEEERKNS1_IS4_NS2_IS4_EEEE??isDetectionCorrectEv??findCandidateHolesERNSt3__16vectorImNS0_9allocatorImEEEES5_bN2cv6Point_IfEES5_S5_?????findLongestPathERNSt3__16vectorI5GraphNS0_9allocatorIS2_EEEER4Path??addHolesByGraphERKNSt3__16vectorI5GraphNS0_9allocatorIS2_EEEEbN2cv6Point_IfEE???????????getDirectionEN2cv6Point_IfEES2_S2_??insertWinnerEfffbRKNSt3__16vectorImNS0_9allocatorImEEEES6_RNS1_IS4_NS2_IS4_EEEE???ݱfindNearestKeypointEN2cv6Point_IfEE??getDetectedGridSizeEv???ޱ?߱??????????????BasisERKNSt3__16vectorIN2cv6Point_IfEENS0_9allocatorIS4_EEEES4_RNS2_3MatE??HolesERKN2cv3MatERS1_??Ѓ????Ќ??????????Ф????????ʲ?Ͳepnp??CvEM??
Face҆perf??half??C??D??2??3??6fill_MEP5CvMatiPKddd??1??5dist2EPKdS1_??8qr_solveEP5CvMatS1_S1_??2ERKN2cv3MatES3_S3_??1ERKN2cv3MatES3_S3_????³2Ev??1Ev???³?ij1choose_control_pointsEv??8compute_A_and_b_gauss_newtonEPKdS1_S1_P5CvMatS3_???ij1compute_barycentric_coordinatesEv??dotEPKdS1_???˳?ѳ1compute_??2??4??9find_betas_approx_??5compute_R_and_tEPKdS1_PA3_dPd??6estimate_R_and_tEPA3_dPd??8reprojection_errorEPA3_KdPS0_??ccsEPKdS1_??pcsEv??rhoEPd???Գ?سco??gauss_newtonEPK5CvMatS2_Pd??mpute_poseERN2cv3MatES2_??py_R_and_tEPA3_KdPS0_PA3_dPd???۳compute_L_6x10EPKdPd??solve_for_signEv??????1EPK5CvMatS2_Pd??2EPK5CvMatS2_Pd??3EPK5CvMatS2_Pd???????????Ё??????????????????????????1??23initUndistortRectifyMapERKNS_11_InputArrayES3_S3_S3_RKNS_5Size_IiEEiRKNS_12_OutputArrayESA_??42estimateNewCameraMatrixForUndistortRectifyERKNS_11_InputArrayES3_RKNS_5Size_IiEES3_RKNS_12_OutputArrayEdS7_d??9calibrateERKNS_11_InputArrayES3_RKNS_5Size_IiEERKNS_12_OutputArrayESA_SA_SA_iNS_12TermCriteriaE??3??5??4undistortImageERKNS_11_InputArrayERKNS_12_OutputArrayES3_S3_S3_RKNS_5Size_IiEE??projectPointsERKNS_11_InputArrayERKNS_12_OutputArrayE??distortPointsERKNS_11_InputArrayERKNS_12_OutputArrayES3_S3_d??stereoRectifyERKNS_11_InputArrayES3_S3_S3_RKNS_5Size_IiEES3_S3_RKNS_12_OutputArrayESA_SA_SA_SA_iS7_dd??RKNS_7Affine3IdEES3_S3_dS6_??S3_S3_S3_S3_dS6_???????????undistortPointsERKNS_11_InputArrayERKNS_12_OutputArrayES3_S3_S3_S3_??stereoCalibrateERKNS_11_InputArrayES3_S3_RKNS_12_OutputArrayES6_S6_S6_NS_5Size_IiEES6_S6_iNS_12TermCriteriaE????????ଶ??ڶС?1??2??8median3dERKNS_11_InputArrayE??3dABERKNS_11_InputArrayES3_RKNS_12_OutputArrayES6_??6medianERKNS_3MatE??5??9CalibrateExtrinsicsERKNS_11_InputArrayES3_RKNS0_15IntrinsicParamsEidRKNS_12_OutputArrayES9_??6??3projectPointsERKNS_11_InputArrayERKNS_12_OutputArrayES3_S3_RKNS0_15IntrinsicParamsES6_??4??7ComputeHomographyENS_3MatES1_??IntrinsicParams??NormalizePixelsERKNS_3MatERKNS0_15IntrinsicParamsE??C??4InitERKNS_3VecIdLi2EEES5_RKNS2_IdLi4EEERKd??plERKNS_3MatE??aSERKNS_3MatE??1E??2E??v??NS_3VecIdLi2EEES3_NS2_IdLi4EEEd???????????ComputeJacobiansERKNS_11_InputArrayES3_RKNS0_15IntrinsicParamsES3_S3_RKiRKdRNS_3MatESC_??JRodriguesMatlabERKNS_3MatERS1_??ಸ???1EstimateUncertaintiesERKNS_11_InputArrayES3_RKNS0_15IntrinsicParamsES3_S3_RS4_RNS_3VecIdLi2EEEdiRd??2ComputeExtrinsicRefineERKNS_3MatES3_RS1_S4_S4_iRKNS0_15IntrinsicParamsEd???ù???????ɾcompose_motionERKNS_11_InputArrayES3_S3_S3_RNS_3MatES5_S5_S5_S5_S5_S5_S5_S5_S5_??InitExtrinsicsERKNS_3MatES3_RKNS0_15IntrinsicParamsERS1_S7_???ξv??NS_3VecIdLi2EEES3_NS2_IdLi4EEEd?????ཿ???????Ŀ??????м??????????Cv??FaceDetectionListElem??DetectionBasedTracker??HomographyEstimator??ANN_MLP_TrainParams??	C??9runKernelEPK5CvMatS2_PS0_??18computeReprojErrorEPK5CvMatS2_S2_PS0_??6refineEPK5CvMatS2_PS0_i??2Ei??1Ei????????????????omography??	andRegion????????MEstimator??	aceElement??C?	9run??	18computeReprojErrorEPK5CvMatS2_S2_PS0_?	2Ei??	1Ei??	??????KernelEPK5CvMatS2_PS0_Ձ	7PointEPK5CvMatS2_PS0_܁	8PointEPK5CvMatS2_PS0_?	???І???????undamentalMat??	eaturesߐace????
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CorrespondEpilines΂	PerspectiveMapƅ???HomographyERKNS_11_InputArrayES2_??	DataMatrixERKNS_11_InputArrayERNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEERKNS_12_OutputArrayESG_??idRKNS_12_OutputArrayEĄ	RKNS_12_OutputArrayEid˄	??????iddRKNS_12_OutputArrayE??	RKNS_12_OutputArrayEidd??	??????omputeCorrespondEpilinesERKNS_11_InputArrayEiS2_RKNS_12_OutputArrayE??	reateEigenFaceRecognizerEid????????onvertPointsToHomogeneousERKNS_11_InputArrayERKNS_12_OutputArrayE??	reateFisherFaceRecognizerEid??????ModelEstimator2҇	ERTreeTrainData??	CamShiftTracker??
FuzzyController??C2Ei6CvSizei??	D??	7setSeedEx׈	11ވ	9??	8runLMeDSEPK5CvMatS2_PS0_S3_di??	???2Evˆ	1EvɈ	0EvЈ	????????????findInliersEPK5CvMatS2_S2_PS0_S3_d??	checkSubsetEPK5CvMati??	??????runRANSACEPK5CvMatS2_PS0_S3_ddi?	getSubsetEPK5CvMatS2_PS0_S3_i??	????????????9runKernelEPK5CvMatS3_PS1_??	1??	???8computeReprojErrorEPK5CvMatS3_S3_PS1_??	1checkSubsetEPK5CvMati??	????????23init_inverse_parametersEvƋ	C͋	5??	1??	???2Eۋ	1E??	N2cv3MatE?	dddd??	???N2cv3MatE??	dddd??	?????????solveEΌ	alignEPA3_ddddddddddS1_Pd??	RN2cv3MatES2_RKS1_S4_?	PA3_??	???dPddddddddddddddddddddd??	A3_dPS0_ddddddddddddddd??	??????7solve_for_lengthsEPA3_dPdS2_??	0jacobi_4x4EPdS0_S0_??	???Ў?К?solve_deg??	icv??cvAddNoiseP9_IplImageidP11CvRandState??fftInversePfS_iiނLSVMparserPKcPPP18CvLSVMFilterObjectPiS5_PS5_PPfS5_S7_·2dddRdS_ʏ	3ddddRdS_S_я	4dddddRdS_S_S_؏	???м??????????????????????Ќ?BMState??	GCState?????BMStateǐ	GCState?????ereoBM?	itcherՌC??	4initEiii?	clERKNS_11_InputArrayES3_RKNS_12_OutputArrayEi??	2E??	1E͑	vƑ	iii?	???vۑ	iii?	?????????????????BM??	??
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????????????alidateDisparity֔	oronoiDiagramFrom???????????Ѓ?П?C??	D?	2E??	1E??	v??	14CvTermCriteriaiddݕ	???v֕	14CvTermCriteriaidd?	??????д?2Ev??	1Ev??	????CҖ	D̗	5??	1??	6createE??	4readEP13CvFileStorageP10CvFileNode??	2E??	1E??	v??	PK5CvMatidd??	RKN2cv3MatEidd??	???v??	PK5CvMatiddŗ	RKN2cv3MatEidd??	?????????2Ev?	1Ev?	0Ev?	?????????clearEv??	trainE??	???4ޘ	2init_weightsEv??	6??	7calc_output_scaleEPK9CvVectorsi??	1??	set_activ_funcEidd??	train_backpropE9CvVectorsS0_PKd??	??????PK5CvMatiddʙ	RKN2cv3MatEidd??	???Cv?	IlmThread??ANN_MLP??	ERTrees7getNameEv??	GBTrees??	7predictE?	1??	21calc_activ_func_derivEP5CvMatS1_PKd??	5writeEP13CvFileStoragePKc??	PK5CvMatPS0_??	RKN2cv3MatERS1_??	???1scale_inputEPK5CvMatPS0_ݛ	2?	5calc_activ_funcEP5CvMatPKd??	???scale_outputEPK5CvMatPS0_??	write_paramsEP13CvFileStorage?	?????????calc_input_scaleEPK9CvVectorsi??	prepare_to_trainEPK5CvMatS2_S2_S2_P9CvVectorsS4_PPdi??	???К???PK5CvMatS2_S2_S2_21CvANN_MLP_TrainParamsi??	RKN2cv3MatES3_S3_S3_21CvANN_MLP_TrainParamsi??	Я????train_rpropE9CvVectorsS0_PKd?	read_paramsEP13CvFileStorageP10CvFileNode??	??????????????????????????????oostParamsC˟	lobTracker??
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1??	20find_split_??	3calc_node_dirEP11CvDTreeNode??	8find_split_??	5split_node_dataEP11CvDTreeNode??	???ord_classEP11CvDTreeNodeifP12CvDTreeSplitPh??	cat_classEP11CvDTreeNodeifP12CvDTreeSplitPh??	??????ord_regEP11CvDTreeNodeifP12CvDTreeSplitPh??	cat_regEP11CvDTreeNodeifP12CvDTreeSplitPh??	?????????C??	D??	5trainE??	11grow_forestE14CvTermCriteria??	2Ev??	1Ev??	??????2Ev??	1Ev??	0Ev??	?????????К?P??	RKN2cv3MatEiS3_S3_S3_S3_S3_10CvRTParams??	K5CvMatiS2_S2_S2_S2_S2_10CvRTParams??	8CvMLData10CvRTParams??	???????????2E??	1E??	v??	iiffib??	???v??	iiffib??	?????????C??	5??	D??	1??	9GetLeavesEPK7CvDTreeRi??	4readEP13CvFileStorageP10CvFileNode??	2E??	1E??	v??	PK5CvMatiS2_S2_S2_S2_S2_15CvGBTreesParams??	RKN2cv3MatEiS3_S3_S3_S3_S3_15CvGBTreesParams??	???v??	PK5CvMatiS2_S2_S2_S2_S2_15CvGBTreesParams??	RKN2cv3MatEiS3_S3_S3_S3_S3_15CvGBTreesParams??	???7??	1??	5writeEP13CvFileStoragePKc??	get_lenEPK5CvMat??	predictE??	???clearEv??	trainE??	???2Ev??	1Ev??	0Ev??	???????????????2??	4predict_serialEPK5CvMatS2_PS0_7CvSlicei??	problem_typeEv??	write_paramsEP13CvFileStorage??	???P??	RKN2cv3MatEiS3_S3_S3_S3_S3_15CvGBTreesParamsb??	8CvMLData15CvGBTreesParamsb??	K5CvMatiS2_S2_S2_S2_S2_15CvGBTreesParamsb??	??????3??	8find_optimal_valueEPK5CvMat??	0??	2do_subsampleEv??	1read_paramsEP13CvFileStorageP10CvFileNode??	find_gradientEi??	change_valuesEP7CvDTreei??	Њ?Р?Ъ????leaves_getEPP11CvDTreeNodeRiS1_??	calc_errorEP8CvMLDataiPNSt3__16vectorIfNS2_9allocatorIfEEEE??	??????е?PK5CvMatS2_PS0_7CvSlicei??	RKN2cv3MatES3_RKNS0_5RangeEi??	м????????????????????????????tatModel??	VM??	C??	D??	5clearEv??	4??	2Ev??	1Ev??	??????2Ev??	1Ev??	0Ev??	????????????StatModel??	ParamGrid5checkEv??	4saveEPKcS1_??	5writeEP13CvFileStoragePKc??	???loadEPKcS1_??	readEP13CvFileStorageP10CvFileNode??	??????І???????icvGenerateRandomClusterCentersiPK5CvMatiPS_??	cvComputeEpipolesFromFundMatrixPfP12CvPoint3D32fS1_؄workcycleObjectDetectorFunctionPv???????PreprocessVarTypePK5CvMatS1_iPi??	ReleaseFGDetectorPP12CvFGDetector?
TestSeqQueryFramePv?????????v??	omputeBorderSizeiiPiS_?GetTrainSamplesPK5CvMatiS1_S1_PiS2_b??	WritebackLabelsPK5CvMatPS_S1_S2_S1_S2_S1_iS1_i??	TestSeq?????C??	ReleaseTestSeqPPv??FindDataMatrixP5CvMat??heckTrainDataPK5CvMatiS1_PiS2_??	reateFVGenPVSv??
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TestSeqGetFGMaskPv????????PreparePredictDataPKviPK5CvMatiS3_PPfi??	ReleaseBlobTrackerPP13CvBlobTracker??
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EMParamsC??
C??	D??	5??	2E??	1E??	v??	PK5CvMatS2_S2_bi??	RKN2cv3MatES3_S3_bi??	???v??	PK5CvMatS2_S2_bi??	RKN2cv3MatES3_S3_bi??	???2Ev??	1Ev??	0Ev??	???????????????clearEv??	trainE??	???9get_max_kEv??	1??	21find_neighbors_directEPK5CvMatiiiPfPPKfS3_??	???3??	6get_sample_countEv??	2find_nearestE??	get_var_countEv??	is_regressionEv??	write_resultsEiiiiPKfS1_P5CvMatS3_S3_P7Cv32suf??	?????????PK5CvMatS2_S2_bib??	RKN2cv3MatES3_S3_bib??	?????????PK5CvMatiPS0_PPKfS3_S3_??	RKN2cv3MatEi??	????????????PS1_PPKfS4_S4_??	RS1_S4_S4_??	????????????NormalBayesClassifier??	FuzzyMeanShiftTracker??C??	5??	D??	4readEP13CvFileStorageP10CvFileNode??	2E??	1E??	v??	PK5CvMatS2_S2_S2_??	RKN2cv3MatES3_S3_S3_??	???v??	PK5CvMatS2_S2_S2_??	RKN2cv3MatES3_S3_S3_??	???clearEv??	trainE??	???2Ev??	1Ev??	0Ev??	Ж????З???????PK5CvMatS2_S2_S2_b??	RKN2cv3MatES3_S3_S3_b??	Л?7predictE??	5writeEP13CvFileStoragePKc??	PK5CvMatPS0_??	RKN2cv3MatEPS1_??	?????????????????????orestTree??	GDetectorC2Evۿ
uzzy??C??	D??	5trainEP??	15find_best_splitEP11CvDTreeNode??	4readEP13CvFileStorageP10CvFileNode??	2Ev??	1Ev??	??????2Ev??	1Ev??	0Ev??	?????????16CvDTreeTrainDataPK5CvMat??	K5CvMatiS2_S2_S2_S2_S2_13CvDTreeParams??	P8CvRTrees??	??	?????????C??	clERKNS_12BlockedRangeE??	2E??	1E??	P12CvForestTreeP11CvDTreeNode??	RKS0_NS_5SplitE??	???P12CvForestTreeP11CvDTreeNode??	RKS0_NS_5SplitE??	Є??????????1??	7get_rngEv??	C??	5??	D??	4readEP13CvFileStorageP10CvFileNode??	9get_active_var_maskEv??	1grow_forestE14CvTermCriteria??	8get_var_importanceEv??	0calc_errorEP8CvMLDataiPNSt3__16vectorIfNS2_9allocatorIfEEEE??	5get_train_errorEv??	6getVarImportanceEv??	???Ќ???P??	??	8CvRTreesP16CvDTreeTrainData??	16CvDTreeTrainData??	?????????2E??	1E??	v??	iifbiPKfbiifi??	???v??	iifbiPKfbiifi??	?????????2Ev??	1Ev??	??????clearEv??	trainE??	???2Ev??	1Ev??	0Ev??	????????7??	1??	5writeEP13CvFileStoragePKc??	8get_treeEi??	getNameEv??	predictE??	???P??	RKN2cv3MatEiS3_S3_S3_S3_S3_10CvRTParams??	K5CvMatiS2_S2_S2_S2_S2_10CvRTParams??	8CvMLData10CvRTParams??	Л??????????3get_proximityEPK5CvMatS2_S2_S2_??	2predict_probE??	4get_tree_countEv??	о???????PK5CvMatS2_??	RKN2cv3MatES3_??	???PK5CvMatS2_??	RKN2cv3MatES3_??	??????????????????????????????1??	C??
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A??Ѓ?????????????А???????к??Ď?ގ?????4??5writeEP13CvFileStoragePKc??loadEPKcS1_i?readEP13CvFileStoragePKc?s?????S3_??i??Ы????aveEPKcS1_PKi??howEPKc????????௏C?4??5writeEP13CvFileStoragePKc??2EiiiP12CvMemStorageb??1EiiiP12CvMemStorageb?????б?loadEPKcS1_i۔readEP13CvFileStoragePKc?s?????S3_?i??????aveEPKcS1_PKi??howEPKc????????о???ÐConstructLCM??NodeMultyplicity??TreatExeptionalCase??ProjectionPointToSegment???ŐComplexNode??SimpleNode??Edge??inearContorModelStorage??SH??atentSvmDetector???ː?̐?͐?ΐ?ܐ????Contour—Imageɗ??????CalcEdgeP16CvVoronoi??WorkEastiiPP8__CvWorkP12CvPoint2D32fS3_ɞSiteInt?NodeIntP16CvVoronoiSiteIntP16CvVoronoiEdgeIntP19CvVoronoiDiagramInt?S0_P16CvVoronoiEdgeIntP19CvVoronoiDiagramInt?P16CvVoronoiNodeIntP16CvVoronoiEdgeIntP19CvVoronoiDiagramInt?????????4DStatusPP5CvMatS1_S1_S0_iS1_??For3ViewP5CvMatS0_S0_S0_S0_S0_S0_???“?ՓЀ???????icv??cvTsZeroP5CvMatPKS_??LMedSPiS_iP9CvMatrix3??CubicdddPd˜MinorPdii?????Choose7PiS_iS_S_??Points8PiS_iPd??GetCoefPdS_S_S_S_?????Point7PiS_PdS_??MedianPiS_iPd??????????????icvDetPd?rot2PIPfiiS_ii???ȕ?ɕ?˕?ѕ?ӕ????????༖???SHSize˝ogL?????Add?Remove??Query??п??ʖ?Ж?ݗ?ޗ??angle12CvPoint2D32fS_žfft2dPfS_ii?????????????C??Dğ8?1??4SaveEPKcߤ5WriteEP13CvFileStoragePKc?2Ev??1Ev????????2Evԟ1Ev۟??????AllocateEi6CvSizei??GetPatchEi?????Б???????5GenerateSamplesEiP9_IplImagei??9??3SetTransformsEPNS_12CvAffinePoseEPP5CvMat??0??4InitializeFastEiP9_IplImagePKcP5CvMatS6_PS0_?П?GenerateSamplesFastEP9_IplImageP5CvMatS4_PS0_??InitializePCACoeffsEP5CvMatS2_??????????в?InitializeEiP9_IplImagePKci٢ReadByNameE?????????1??7GetPoseEi??9GetCenterEv??6ProjectPCASampleEP9_IplImageP5CvMatS4_S4_??5EstimatePosePCAEPvRiRfP5CvMatS5_??2??4GetFeatureNameEv??3GetPCADimHighEv????????EstimatePoseEP9_IplImageRiRfؤGetPCADimLowEv???????ĘRKNS_8FileNodeEPKc??P13CvFileStorageP10CvFileNodePKc???Ƙ?ɘ?ɘRiS5_RfP5CvMatS8_??iRNSt3__16vectorIiNS5_9allocatorIiEEEESA_RNS6_IfNS7_IfEEEEP5CvMatSF_???ɘ?Ϙ?ϘRiS5_RfS6_P5CvMatS8_??iRNSt3__16vectorIiNS5_9allocatorIiEEEESA_RNS6_IfNS7_IfEEEESD_P5CvMatSF_???ؘ?ݘ??????C??1?2??4ReadERKNS_8FileNodeE??5clearEv??Dѯ9SetPCALowEP5CvMatS2_Ѳ8AllocateEi??2E6CvSizei??1E6CvSizei?PKcS3_S3_S3_iii??RKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_SA_fffiii???PKcPP5CvMatS4_S1_?RKNS_8FileNodeEPP5CvMatS5_PKcï??8??1GeneratePCAEPKcS2_i?0??5InitializePosesEv??LoadPCADescriptorsEժSavePCADescriptorsEPKc?PKc?RKNS_8FileNodeEʯ??4InitializePoseTransformsEv??0??9InitializeTransformsFromPosesEv??2AllocatePCADescriptorsEvز1InitializeDescriptorsEP9_IplImageRKNSt3__16vectorINS_8KeyPointENS3_9allocatorIS5_EEEEPKci??6CreateDescriptorsFromImageEP9_IplImageRKNSt3__16vectorINS_8KeyPointENS3_9allocatorIS5_EEEE?????CreatePCADescriptorsEv?InitializeDescriptorEiP9_IplImage????????PKcS3_S3_S3_iiiٮRKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_SA_fffiii??????????Ћ???????LoadPCAallERKNS_8FileNodeE??SetPCAHighEP5CvMatS2_ʲД?????2Ev?1Ev?0Ev??ࣙ??????????RKNS_8KeyPointEPKc??PKc??Ы????1ܰ5WriteERNS_11FileStorageE??4FindDescriptorEP9_IplImageDZ8SavePCADescriptorsEP13CvFileStorage??0SavePCAallERNS_11FileStorageE??NS_6Point_IfEERiS5_Rf??RiS3_RfPfS5_??iRNSt3__16vectorIiNS3_9allocatorIiEEEES8_RNS4_IfNS5_IfEEEESB_PfòЯ?ౙ????????????п??Ù?ՙ?י?ۙ????????8AllocateEii??27InitializeObjectDescriptorsEP9_IplImageRKNSt3__16vectorINS_8KeyPointENS3_9allocatorIS5_EEEEPKcifi??C??D??????6MatchPointToPartE7CvPoint??8IsDescriptorObjectEi??7GetDescriptorPartEi??9_GetLabeledFeaturesEv??????????2E6CvSizei۵1E6CvSizeiŶPKcS3_S3_S3_i??RKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_SA_fffi?????PKcS3_S3_S3_i??RKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_SA_fffi???????????2EvԷ1Ev۷0Ev?????????????6ParamsC??C??1??D??5??4readERKNS_8FileNodeE??2EiNS_5Size_IiEENSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEESA_SA_fff?1EiNS_5Size_IiEENSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEESA_SA_fff???????2ERKNS0_6ParamsE??1ERKNS0_6ParamsE??Ђ?0initializeERKNS0_6ParamsERKNS_3PtrINS_20OneWayDescriptorBaseEEE??5?2knnMatchImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEiRKNS5_IS1_NS7_IS1_EEEEb????????2Ev??1Ev??0Ev??Љ???????clearEvؼtrainEv߼??????isMaskSupportedEv??radiusMatchImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEfRKNS5_IS1_NS7_IS1_EEEEb?????Ж???????writeERNS_11FileStorageE?emptyEv?cloneEb?????????????????????C??14setAffineParamEddd??2E¿1E޿v׿dddbdddddd?????v?dddbdddddd????????Б?23generateRandomTransformENS_6Point_IfEES2_RNS_3MatERNS_3RNGEb??clERKNS_3MatE??14warpWholeImageERKNS_3MatERS1_S4_S4_iRNS_3RNGE?????NS_6Point_IfEERS1_NS_5Size_IiEERNS_3RNGE??S3_RS1_NS_5Size_IiEERNS_3RNGE??????????????C??4readERKNS_8FileNodeE??10setVerboseEb??2E??1E??v??iiiidd?????v??iiiidd???œ?Ü?Ü15getMostStable2DERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEEiRKNS_14PatchGeneratorE??clERKNS??5writeERNS_11FileStorageERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE???Ü_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEEib??t3__16vectorINS_3MatENS1_9allocatorIS3_EEEERNS2_INS_8KeyPointENS4_IS9_EEEEib???????????ĝernClassifier??aceRecognizer??C??D??4readERKNS_8FileNodeE??5??7prepareEiiiiiii??1??8finalizeERNS_3RNGE??2E??1E??v??RKNS???ĝv??RKNS???ǝ_8FileNodeE??t3__16vectorINS2_INS_6Point_IfEENS1_9allocatorIS4_EEEENS5_IS7_EEEERKNS2_INS_3MatENS5_ISC_EEEERKNS2_INS2_IiNS5_IiEEEENS5_ISI_EEEEiiiiiiiRKNS_14PatchGeneratorE???ǝ_8FileNodeE??t3__16vectorINS2_INS_6Point_IfEENS1_9allocatorIS4_EEEENS5_IS7_EEEERKNS2_INS_3MatENS5_ISC_EEEERKNS2_INS2_IiNS5_IiEEEENS5_ISI_EEEEiiiiiiiRKNS_14PatchGeneratorE???ɝ2Ev??1Ev??0Ev???ɝ?˝?̝ernClassifier??aceRecognizer4saveERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??1??20getCompressionMethodEv??5??7getLeafEiRKNS_3MatE??clERKNS_3MatE??3get??4getStructCountEv??6getSignatureSizeEv??2getPatchSizeEv??ClassCountEv??StructSizeEv???͝?Ν?Ν?Ν?Ν?Ν?Ν?ѝwriteERNS_11FileStorageERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE??emptyEv???ѝ?ٝclearEv??trainERKNSt3__16vectorINS2_INS_6Point_IfEENS1_9allocatorIS4_EEEENS5_IS7_EEEERKNS2_INS_3MatENS5_ISC_EEEERKNS2_INS2_IiNS5_IiEEEENS5_ISI_EEEEiiiiiiiRKNS_14PatchGeneratorE?????????????9trainFromSingleViewERKNS_3MatERKNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEEiiiiiiRKNS_14PatchGeneratorE??0setVerboseEb?????NS_6Point_IfEERNSt3__16vectorIfNS6_9allocatorIfEEEE??RNSt3__16vectorIfNS4_9allocatorIfEEEE???ɞ?͞?ٞ??6ParamsC??C??D??5??1??23calcBestProbAndMatchIdxERKNS_3MatERKNS_6Point_IfEERfRiRNSt3__16vectorIfNSA_9allocatorIfEEEE??4readERKNS_8FileNodeE??2E??1E??iiiiiiiRKNS_14PatchGeneratorE??RKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE????iiiiiiiRKNS_14PatchGeneratorE??RKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE????????2ERKNS0_6ParamsE??1ERKNS0_6ParamsE???????2Ev??1Ev??0Ev???????????clearEv??trainEv????????5??2knnMatchImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEiRKNS5_IS1_NS7_IS1_EEEEb??isMaskSupportedEv??radiusMatchImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERNS5_INS5_INS_6DMatchENS7_ISB_EEEENS7_ISD_EEEEfRKNS5_IS1_NS7_IS1_EEEEb???????????Ќ?В?writeERNS_11FileStorageE??emptyEv??cloneEb?????????C??4readERKNS_8FileNodeE??D??5trainERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEE??10setVerboseEb??2E??1E??v??RKNS?????v??RKNS???_8FileNodeE??t3__16vectorINS_3MatENS1_9allocatorIS3_EEEEiiiiiRKNS_9LDetectorERKNS_14PatchGeneratorE??У??_8FileNodeE??t3__16vectorINS_3MatENS1_9allocatorIS3_EEEEiiiiiRKNS_9LDetectorERKNS_14PatchGeneratorE?????????2Ev??1Ev??0Ev???????????14getModelPointsEv??5writeERNS_11FileStorageERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE??clERKNS??г?iiiiiRKNS_9LDetectorERKNS_14PatchGeneratorE??RKNS2_INS_8KeyPointENS4_IS9_EEEEiiiiRKNS_9LDetectorERKNS_14PatchGeneratorE???ൟ???_3MatERS1_RNSt3__16vectorINS_6Point_IfEENS5_9allocatorIS8_EEEE??t3__16vectorINS_3MatENS1_9allocatorIS3_EEEERKNS2_INS_8KeyPointENS4_IS9_EEEERS3_RNS2_INS_6Point_IfEENS4_ISG_EEEEPNS2_IiNS4_IiEEEE?????????ߟ????????????????oefficientStereoP9CvMatrix36CvSizePfS2_PiS3_S3_??rossEpilineFrame6CvSizePfPiS1_S1_S1_??????1P9CvMatrix36CvSizePfS2_??2P9CvMatrix36CvSizePfS2_??3P9CvMatrix36CvSizePfS2_??4P9CvMatrix36CvSizePfS2_??????ǡ?ӡ?ߡ????????????????????????????ಣ???????2DLocate??Delaunay2DInsert??б??Ĥ???????Ѓ???????ImageSizePv??Object???FrameNumPv??SetFramePvi??GetImagePv??AddNoisePvid????????Т????Ф??????NumPv??PosPviP12CvPoint2D32f?????????¥?ť?ť???Descriptors??Image????????ࣦ
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CannyBuf?__Module¨c??filter2DERKNS0_6oclMatERS1_iRKNS_3MatENS_6Point_IiEEdi??integralERKNS0_6oclMatERS1_??SURF_OCL??bgfg_mogE??knearestE??pyr_downE??tvl1flowE??ubtractERKNS0_6oclMatE??
tereo??S3_RS1_S3_??
RKNS_7Scalar_IdEERS1_S3_??
?????RKNS0_6oclMatES3_RS1_d??
dRKNS0_6oclMatERS1_??
??????
d??
absSumERKNS0_6oclMatE??
sqrSumERKNS0_6oclMatE??
minMaxERKNS0_6oclMatEPdS4_S3_??
re??
finishEvƴScharrERKNS0_6oclMatERS1_iiiddi??kmeansERKNS0_6oclMatEiRS1_NS_12TermCriteriaEiiS4_??oclMat??pyr_upE??i??
evice3??videE??
lateERKNS0_6oclMatERS1_RKNS_3MatENS_6Point_IiEEiiRKNS_7Scalar_IdEE??RKNS0_6oclMatES3_RS1_d??
dRKNS0_6oclMatERS1_??
??????inERKNS0_6oclMatES3_RS1_??
axERKNS0_6oclMatES3_RS1_??
?????????absdiffERKNS0_6oclMatE??
compareERKNS0_6oclMatES3_RS1_i??
Context10getContextEv??pyrDownERKNS0_6oclMatERS1_??momentsE??S3_RS1_??
RKNS_7Scalar_IdEERS1_??
?????????umERKNS0_6oclMatE??
vmE???????????
0??
1??
2??
8??9Т6??7??5?4߷3??meanStdDevERKNS0_6oclMatERNS_7Scalar_IdEES6_??
bitwise_orERKNS0_6oclMatE??
DeviceInfoĩopenCLFreeEPv?warpAffineERKNS0_6oclMatERS1_RKNS_3MatENS_5Size_IiEEi??arithm_??stereocsbpE????????normERKNS0_6oclMatE??
flipERKNS0_6oclMatERS1_i??
MOG2΃gemmERKNS0_6oclMatES3_dS3_dRS1_i??surfE??c_TBE??i??
S3_i??
?????????????????m??
t??
s?c??boxFilterERKNS0_6oclMatERS1_iNS_5Size_IiEENS_6Point_IiEEi??LaplacianERKNS0_6oclMatERS1_iiddi??CvSVM_OCL??agnitudeERKNS0_6oclMatES3_RS1_??
inMaxLocERKNS0_6oclMatEPdS4_PNS_6Point_IiEES7_S3_??
e?????p??
CannyERKNS0_6oclMatEޤcanny??er??SobelERKNS0_6oclMatERS1_iiiiddi??remapERKNS0_6oclMatERS1_S4_S4_iiRKNS_7Scalar_IdEE??mergeE??splitERKNS0_6oclMatE??haseERKNS0_6oclMatES3_RS1_b??
yr?????c??
polarToCartERKNS0_6oclMatES3_RS1_S4_b??
b??
a??
se??
ContextImpl1??iʹocl_momentsER??artToPolarERKNS0_6oclMatES3_RS1_S4_b??
lBlasSetupEv??reateCLAHEEdNS_5Size_IiEE??onvertC3C4E??????????
c??
P??CommandQueue6createEPNS0_11ContextImplE??fft_teardownEv??m??GaussianBlurERKNS0_6oclMatERS1_NS_5Size_IiEEddi??equalizeHistERKNS0_6oclMatERS1_??KalmanFilter??g??StereoBM_OCL??arithm_phaseE??blend_linearE??imgproc_gfttE??o??
l_constantsE??untNonZeroERKNS0_6oclMatE??
rnerHarrisERKNS0_6oclMatERS1_iidi??nvolve_32FERKNS0_6oclMatES3_RS1_RNS0_16MatchTemplateBufE?????i??
lendLinearERKNS0_6oclMatES3_S3_S3_RS1_քuild_warpsE??tvWeights_E??twise_??
ndTextureERKNS0_6oclMatE??notERKNS0_6oclMatERS1_??
andERKNS0_6oclMatE??
xorERKNS0_6oclMatE??
???S3_RS1_S3_??
RKNS_7Scalar_IdEERS1_S3_??
??????S3_RS1_S3_??
RKNS_7Scalar_IdEERS1_S3_??
??????S3_RS1_S3_??
RKNS_7Scalar_IdEERS1_S3_??
?????????1??
3MOG18getBackgroundImageERNS0_6oclMatE??4MOG218getBackgroundImageERNS0_6oclMatEȄ26BruteForceMatcher_OCL_base??7Context?6oclMat??9CvSVM_OCL7predictE??8SURF_OCL??0oclMatExpr??
1ContextImpl15supportsFeatureENS0_12FEATURE_TYPEEͬ3HOGDescriptor??cvNS0_6oclMatEEv??
6assignERNS0_6oclMatE??
?????????Њ????iERKNS0_6oclMatES3_??
lERKNS0_6oclMatES3_??
????????????ransposeERKNS0_6oclMatERS1_??
hresholdERKNS0_6oclMatERS1_ddi?????ddWeightedERKNS0_6oclMatEdS3_ddRS1_??
rithm_flipE???????tIdentityERNS0_6oclMatERKNS_7Scalar_IdEE??
pFilter2DERKNS0_6oclMatERS1_iRKNS_3MatES7_NS_6Point_IiEEdi?????peatERKNS0_6oclMatEiiRS1_??
sizeERKNS0_6oclMatERS1_NS_5Size_IiEEddi?????iEN2cv7Scalar_IdEERKNS0_3ocl6oclMatEii??
fEN2cv7Scalar_IdEERKNS0_3ocl6oclMatEii??
dEN2cv7Scalar_IdEERKNS0_3ocl6oclMatEii??
????????hiEvRKN2cv3ocl6oclMatEPdS5_S4_??
ciEvRKN2cv3ocl6oclMatEPdS5_S4_??
tiEvRKN2cv3ocl6oclMatEPdS5_S4_??
siEvRKN2cv3ocl6oclMatEPdS5_S4_??
iiEvRKN2cv3ocl6oclMatEPdS5_S4_??
ffEvRKN2cv3ocl6oclMatEPdS5_S4_??
ddEvRKN2cv3ocl6oclMatEPdS5_S4_??
в???????????????????fEvRKN2cv3ocl6oclMatEPdS5_PNS0_6Point_IiEES8_S4_??
dEvRKN2cv3ocl6oclMatEPdS5_PNS0_6Point_IiEES8_S4_??
??????	0??
6݄4?2??5?3??1??7matchTemplate_SQDIFF_NORMEDERKNS0_6oclMatES3_RS1_RNS0_16MatchTemplateBufE??8filtering_adaptive_bilateralE??BackgroundSubtractor??
openCLExecuteKernel??getOpenCLErrorStringEi??OclCascadeClassifier??matchTemplate_SQDIFFERKNS0_6oclMatES3_RS1_RNS0_16MatchTemplateBufE??FarnebackOpticalFlow??cvPreparePredictDataEPKviPK5CvMatiS5_PPfi?imgproc_integral_sumE??clERKNS0_6oclMatERS2_f??
D??
???2Ev??1Ev??0Ev????????е?C?10initializeENS_5Size_IiEEi??clERKNS0_6oclMatERS2_f??7releaseEv??2Ei??1Ei????????????mog??hog??7mog_oclERKNS0_6oclMatEiRS3_S6_S6_S6_S6_iffff??2??13loadConstantsEfffffffh??8mog2_oclERKNS0_6oclMatEiRS3_S6_S6_S6_S6_ffbi????????2getBackgroundImage_oclEiRKNS0_6oclMatES5_RS3_if??3getBackgroundImage2_oclEiRKNS0_6oclMatES5_S5_RS3_iǃ??????????????C??10initializeENS_5Size_IiEEi??clERKNS0_6oclMatERS2_f??7releaseEvτ2Ei??1Ei???????????????????????BruteForceMatcher_OCL_base??openCLExecuteKernelInteropEPNS0_7ContextERKNS0_13ProgramSourceENSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEPmSD_RNS6_6vectorINS6_4pairImPKvEENSA_ISI_EEEEiiPKc??createMorphologyFilter_GPUEiiRKNS_3MatERKNS_6Point_IiEEi??matchTemplate_CCO??arithm_absdiff_nonsaturateE??C?3addERKNSt3__16vectorINS0_6oclMatENS2_9allocatorIS4_EEEE??5??1??8knnMatchERKNS0_6oclMatEȔ21radiusMatchCollectionERKNS0_6oclMatERS2_S5_S5_S5_fRKNSt3__16vectorIS2_NS6_9allocatorIS2_EEEE?2ENS1_8DistTypeE??1ENS1_8DistTypeE???????????1??5emptyEv??9getTrainDescriptorsEv?5isMaskSupportedEv?????clearEv??matchERKNS0_6oclMatE???????????	1?3matchDownloadERKNS0_6oclMatES4_ƌ2matchConvertERKNS_3MatES4_??7??5??4knnMatchSingleERKNS0_6oclMatES4_RS2_S5_S5_iS4_?6knnMatch??9??8radiusMatchConvertERKNS_3MatES4_S4_??matchSingleERKNS0_6oclMatES4_RS2_S5_S4_??radiusMatchERKNS0_6oclMatE?????RNSt3__16vectorINS_6DMatchENS5_9allocatorIS7_EEEE??S4_RNSt3__16vectorINS_6DMatchENS5_9allocatorIS7_EEEEےЉ?RNSt3__16vectorINS_6DMatchENS5_9allocatorIS7_EEEE??S4_RNSt3__16vectorINS_6DMatchENS5_9allocatorIS7_EEEE????S4_RNSt3__16vectorINS_6DMatchENS5_9allocatorIS7_EEEES4_??RNSt3__16vectorINS_6DMatchENS5_9allocatorIS7_EEEERKNS6_IS2_NS8_IS2_EEEE????makeGpuCollectionERNS0_6oclMatES3_RKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEE??knnMatch2DownloadERKNS0_6oclMatES4_S4_RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEb?radiusMatchSingleERKNS0_6oclMatES4_RS2_S5_S5_fS4_?????matchCollectionERKNS0_6oclMatES4_RS2_S5_S5_S4_ԒknnMatchConvertERKNS_3MatES4_RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEb?????????????????DownloadERKNS0_6oclMatES4_RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEb??2ConvertERKNS_3MatES4_S4_RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEb???????S4_RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEiS4_b??RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEiRKNS6_IS2_NS8_IS2_EEEEb?????knnMatch2CollectionERKNS0_6oclMatES4_RS2_S5_S5_S4_?radiusMatchDownloadERKNS0_6oclMatES4_S4_????????????????RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEb??S4_RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEb?????RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEb??S4_RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEb?????S4_RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEfS4_b?RNSt3__16vectorINS6_INS_6DMatchENS5_9allocatorIS7_EEEENS8_ISA_EEEEfRKNS6_IS2_NS8_IS2_EEEEb????????????????buildWarpPlaneMapsENS_5Size_IiEENS_5Rect_IiEERKNS_3MatES7_S7_fRNS0_6oclMatES9_?getOpenCLPlatformsERNSt3__16vectorIPKNS0_12PlatformInfoENS1_9allocatorIS5_EEEE?queryWaveFrontSizeEP10_cl_kernel??opeõsetBinaryDiskCacheEiNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??createFilter2D_GPUENS_3PtrINS0_14BaseFilter_GPUEEE??meanShiftFilteringERKNS0_6oclMatERS1_iiNS_12TermCriteriaE??arithm_??filtering_filter2DE??i??kernel_sort_by_keyE?????buildWarp??createGaussianFilter_GPUEiNS_5Size_IiEEddiS2_??matchTemplateNaive_CCORRERKNS0_6oclMatES3_RS1_i??OpticalFlowDual_TVL1_OCL?haarobjectdetect_scaled2E??kernel_??CylindricalMapsENS_5Size_IiEENS_5Rect_IiEERKNS_3MatES7_fRNS0_6oclMatES9_??PerspectiveMapsERKNS_3MatEbNS_5Size_IiEERNS0_6oclMatES7_ڣ???buildWarpSphericalMapsENS_5Size_IiEENS_5Rect_IiEERKNS_3MatES7_fRNS0_6oclMatES9_ɢget??c??arithm_add_scalar_maskE??optical_flow_farnebackE?????buildWarpAffineMapsERKNS_3MatEbNS_5Size_IiEERNS0_6oclMatES7_ӣopenCL?g??c??matchTemplate_CCO??filtering_boxFilterE?????М?C??6createERKNS_5Size_IiEEiɤ7releaseEvפ2ERKNS0_6oclMatES4_¤1ERKNS0_6oclMatES4_Ф????????????R??S3_R?S1_ddib??NS0_8CannyBufERS1_ddib????????2??1Ҧ0calcSobelRowPass_gpuERKNS0_6oclMatERS2_S5_ii˦4edgesHysteresisLocal_gpuERNS0_6oclMatES3_S3_ii??5edgesHysteresisGlobal_gpuERNS0_6oclMatES3_S3_S3_ii?????7calcMagnitude_gpuERKNS0_6oclMatES4_RS2_ϧ1calcMap_gpuERNS0_6oclMatES3_S3_S3_iiff??2getEdges_gpuERNS0_6oclMatES3_ii??S5_S5_iib?iib???????S1_ddb??NS0_8CannyBufERS1_ddb???????????е???????CΨD?1Evި2Ev?????1Ev??2Ev?????InitializationMutexEv??LinearRowFilter_GPUEiiRKNS_3MatEii?????CЩD?2Ev?1Ev???????2Ev??1Ev????????latformInfo??rogramCache??C??D֪2EvȪ1EvϪ??????2Ev?1Ev???????????0??4c??7initializeContextEPvS2_S2_?getContextEv۫setContextEPKNS0_10DeviceInfoE????1?2?5supportsFeatureENS0_12FEATURE_TYPEEƬ3getDeviceInfoEvԬ9getOpenCLContextPtrEv۬????????????4getOpenCLCommandQueuePtrEv??0getOpenCLDeviceIDPtrEv???????????leanupContextEvڭurrentContextE??????????????getƮope??PyrLKOpticalFlow??arithm_??haarobjectdetectE??imgproc_??OpenCLDevicesERNSt3__16vectorIPKNS0_10DeviceInfoENS1_9allocatorIS5_EEEEiPKNS0_12PlatformInfoEۯBoxFilter_GPUEiiRKNS_5Size_IiEENS_6Point_IiEEi?????etDeviceEPKNS0_10DeviceInfoE??ortByKeyERNS0_6oclMatES2_??plit_matE?????i??openCLMallocPitchEPNS0_7ContextEPPvPmmm?corner??distanceToCentersERKNS0_6oclMatES3_RNS_3MatES5_i??KNearestNeighbour??arithm_add_scalarE??brute_force_matchE??gDevMemRWValueMapE??n??mgproc_??itializeContextEPvS1_S1_?terpolateFramesERKNS0_6oclMatES3_S3_S3_S3_S3_fRS1_S4_?????supportsFeatureENS0_12FEATURE_TYPEE??warpPerspectiveERKNS0_6oclMatERS1_RKNS_3MatENS_5Size_IiEEi??b??CvSVM??arithm_add_maskE??filtering_morphE??operator_setToME??c_btvRegWeightsE??????????????sCpuDeviceEv?nterpolate1????????nCLReadBufferEPNS0_7ContextEP7_cl_memPvm??rator_copyToME?????nCL?rator_convertToE??C??VerifyKernelEPKNS0_7ContextEP10_cl_kernelPm??reateBufferEPNS0_7ContextEmm?opyBuffer2DEPNS0_7ContextEPvmiPKvmmmi???????MallocPitchExEPNS0_7ContextEPPvPmmmNS0_8DevMemRWENS0_10DevMemTypeEطExecuteKernelEPNS0_7ContextEPϽ???
ope??c??bindTexturePtrERKNS0_6oclMatE??releaseTextureERP7_cl_mem??arithm_??filter_sep_??imgproc_??match_templateE??superres_btvl1E??gDeviceMemTypeE??nCLMemcpy2DEPNS0_7ContextEPvmPKvmmmNS0_16openCLMemcpyKindEi۹rator_setToE???????????openCLGetKernelFromSourceEPػcreateSeparableFilter_GPUERKNS_3PtrINS0_17BaseRowFilter_GPUEEERKNS1_INS0_20BaseColumnFilter_GPUEEE??getLinearColumnFilter_GPUEiiRKNS_3MatEiid??matchTemplateNaive_SQDIFFERKNS0_6oclMatES3_RS1_i??KNS0_7ContextEPKNS0_12ProgramEntryENSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE??NS0_7ContextEPKNS0_12ProgramEntryENSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEiiPKcȽ???
PKc????????Ѕ?10_cl_kernelPmS5_RNSt3__16vectorINS6_4pairImPKvEENS6_9allocatorISB_EEEE??KNS0_12ProgramEntryENSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEPmSD_RNS6_6vectorINS6_4pairImPKvEENSA_ISI_EEEEii?????_EPNS0_7ContextEPKNS0_12ProgramEntryENSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEPmSD_RNS6_6vectorINS6_4pairImPKvEENSA_ISI_EEEEiiPKc??2EPNS0_7ContextEPKNS0_12ProgramEntryENSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEPmSD_RNS6_6vectorINS6_4pairImPKvEENSA_ISI_EEEEii????????
PKc?????И?
load_constantEP11_cl_contextP17_cl_command_queuePKvm??setBinaryPathEPKc??HOGDescriptor??m??arithm_minMaxE??imgproc_??kmeans_kernelE??objdetect_hogE??depth_stringsE??_programCacheE?????1??C??D??5getProgramCacheEv??4releaseProgramEv??0?????1Ev??2Ev?????Ь?2Ev??1Ev?????Я????progLookupERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE??addProgramERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEP11_cl_program??getProgramEPKNS0_7ContextEPKNS0_12ProgramEntryEPKc??mutex????????????????vtColorERKNS0_6oclMatERS1_ii??alcHistERKNS0_6oclMatERS1_??onvolveERKNS0_6oclMatES3_RS1_?????olumnSumERKNS0_6oclMatERS1_??vt_colorE???????rorEPKcS2_iS2_??odeERKNS0_6oclMatERS1_RKNS_3MatENS_6Point_IiEEiiRKNS_7Scalar_IdEE??Ъ???????б?getMorphologyFilter_GPUEiiRKNS_3MatERKNS_5Size_IiEENS_6Point_IiEE??adaptiveBilateralFilterERKNS0_6oclMatERS1_NS_5Size_IiEEddNS_6Point_IiEEi??OclCascadeClassifierBuf??extractFirstChannel_32FERKNS0_6oclMatERS1_??StereoBeliefPropagation??imgproc_???????????Ф?orphologyExERKNS0_6oclMatERS1_iRKNS_3MatENS_6Point_IiEEiiRKNS_7Scalar_IdEE??edianFilterERKNS0_6oclMatERS1_i?????etLinearFilter_GPUEiiRKNS_3MatERKNS_5Size_IiEERKNS_6Point_IiEEi??DevMemTypeValueMapE?????reateLinearFilter_GPUEiiRKNS_3MatERKNS_6Point_IiEEi??ornerMinEigenVal_dxdyERKNS0_6oclMatERS1_S4_S4_iii??????????????reateBoxFilter_GPUEiiRKNS_5Size_IiEERKNS_6Point_IiEEi??ommandQueueTLSDataE??????????????createSeparableLinearFilter_GPUEiiRKNS_3MatES3_RKNS_6Point_IiEEdiNS_5Size_IiEE??GoodFeaturesToTrackDetector_OCL??filtering_sep_filter_singlepassE????????createDerivFilter_GPUEiiiiiiNS_5Size_IiEE??meanShiftSegmentationERKNS0_6oclMatERNS_3MatEiiiNS_12TermCriteriaE??StereoConstantSpaceBP??arithm_minMaxLoc_maskE??imgproc_copymakeboderE???????????????????????hEvRKN2cv3ocl6oclMatES4_S2_iii??cEvRKN2cv3ocl6oclMatES4_S2_iii??tEvRKN2cv3ocl6oclMatES4_S2_iii??sEvRKN2cv3ocl6oclMatES4_S2_iii??iEvRKN2cv3ocl6oclMatES4_S2_iii??fEvRKN2cv3ocl6oclMatES4_S2_iii????????Ы??????????hEvRKN2cv3ocl6oclMatES4_S2_iii??cEvRKN2cv3ocl6oclMatES4_S2_iii??tEvRKN2cv3ocl6oclMatES4_S2_iii??sEvRKN2cv3ocl6oclMatES4_S2_iii??iEvRKN2cv3ocl6oclMatES4_S2_iii??fEvRKN2cv3ocl6oclMatES4_S2_iii????????????????????Ы???lBlasTeardownEv??opyMakeBorderERKNS0_6oclMatERS1_iiiiiRKNS_7Scalar_IdEE????clERKNS0_6oclMatERS2_S4_??14downloadPointsERKNS0_6oclMatERNSt3__16vectorINS_6Point_IfEENS5_9allocatorIS8_EEEE????????20oclHaarDetectObjectsERNS0_6oclMatEP12CvMemStoragedii6CvSizeS6_??16detectMultiScaleERNS0_6oclMatERNSt3__16vectorINS_5Rect_IiEENS4_9allocatorIS7_EEEEdiiNS_5Size_IiEESD_????????1??4InitEiidiiPKm6CvSizeS4_??9GenResultERNSt3__16vectorINS_5Rect_IiEENS2_9allocatorIS5_EEEERKS8_RKNS3_IiNS6_IiEEEE??23CreateFactorRelatedBufsEiiidPKm6CvSizeS4_??7releaseEv??6detectMultiScaleERNS0_6oclMatERNSt3__16vectorINS_5Rect_IiEENS4_9allocatorIS7_EEEEdiiNS_5Size_IiEESD_??4CreateBaseBufsEiiii???????м??????????C??1??2??6detectERKNS0_6oclMatERNSt3__16vectorINS_6Point_IiEENS5_9allocatorIS8_EEEEdNS_5Size_IiEESE_??2ENS_5Size_IiEES3_S3_S3_iddbi??1ENS_5Size_IiEES3_S3_S3_iddbi?????4??1init_bufferERKNS0_6oclMatENS_5Size_IiEE??5computeGradientERKNS0_6oclMatERS2_S5_??6detectMultiScaleERKNS0_6oclMatERNSt3__16vectorINS_5Rect_IiEENS5_9allocatorIS8_EEEEdNS_5Size_IiEESE_di??numPartsWithinE??setSVMDetectorERKNSt3__16vectorIfNS2_9allocatorIfEEEE??getDescriptorsERKNS0_6oclMatENS_5Size_IiEERS2_i??NS_5Size_IiEES3_S3_??iii?????1??22??6cnbinsE??6??3c??5??4c??1??2cdescr_widthE??7qangle_step_shiftE??set_up_constantsEiiiii??cblock_hist_sizeE????????1??21getBlockHistogramSizeEv??7??1getWinSigmaEv??getDescriptorSizeEv??checkDetectorSizeEv???????????????????????compute_gradients_8UC??extract_descrs_by_??1EiiRKNS0_6oclMatEfRS3_S6_b??4EiiRKNS0_6oclMatEfRS3_S6_b???????2??4getDefaultPeopleDetectorEv??3getPeopleDetector64x128Ev??computeBlockHistogramsERKNS0_6oclMatE??getPeopleDetector48x96Ev?????ompute_histsEiiiiiRKNS0_6oclMatES5_S5_RS3_??descr_heightE?????normalize_histsEiiiiiRNS0_6oclMatEf??cblock_stride_????????rowsEiiiiiiiiRKNS0_6oclMatERS3_??colsEiiiiiiiiRKNS0_6oclMatERS3_???????????lassify_histsEiiiiiiiiRKNS0_6oclMatES5_ffRS3_??nblocks_win_????????Щ???????????????????????????Э?S4_?????????????Harris_dxdyERKNS0_6oclMatERS1_S4_S4_iidi??MinEigenValERKNS0_6oclMatERS1_iii??????????????eanShiftProcERKNS0_6oclMatERS1_S4_iiNS_12TermCriteriaE??atchTemplateERKNS0_6oclMatES3_RS1_i??????????????ilateralFilterERKNS0_6oclMatERS1_iddi??tvWeights_sizeE?????????0??1blendFramesERKNS0_6oclMatES4_S4_fRS2_RP7_cl_memS8_??2memsetKernelEfRNS0_6oclMatEii??5normalizeKernelERNS0_6oclMatEiii??7forwardWarpKernelERKNS0_6oclMatERS2_S4_S4_fii??bindImgTexERKNS0_6oclMatERP7_cl_mem??vectorWarpERKNS0_6oclMatES4_S4_RS2_ifi???????????????????C??4initEiiii??7??2E??1E??v??iiii?????v??iiii??????????????predictERKNS0_6oclMatE??correctERKNS0_6oclMatE?????????????C??D??5??12find_nearestERKNS0_6oclMatEiRS2_??2Ev??1Ev???????2Ev??1Ev??0Ev???????????clearEv??trainERKNS_3MatERS2_S5_bib???????????????RRERKNS0_6oclMatES3_RS1_RNS0_16MatchTemplateBufE??FFERKNS0_6oclMatES3_RS1_RNS0_16MatchTemplateBufE??????????????RR_NORMEDERKNS0_6oclMatES3_RS1_RNS0_16MatchTemplateBufE??FF_NORMEDERKNS0_6oclMatES3_RS1_RNS0_16MatchTemplateBufE?????????????????
RNS0_16MatchTemplateBufE?????	6??cvNS_1??aSERKNS_7Scalar_IdEE??5setToERKNS_7Scalar_IdEERKS1_??8createExE??7releaseEv??pLERKS1_??m??dVERKS1_??uploadERKNS_3MatE??createEiii????1_InputArrayEEv??2_OutputArrayEEv????????etOclMatRefERKNS_1??DeviceMemRWE??1_InputArrayE??2_OutputArrayE???????8downloadERNS_3MatE??6copyToERS1_RKS1_??9convertToERS1_idd??7reshapeEii????????????????????NS_5Size_IiEEiNS0_8DevMemRWENS0_10DevMemTypeE??iiiNS0_8DevMemRWENS0_10DevMemTypeE????????????????IERKS1_??LERKS1_????а????NS0_10FLUSH_MODEE??PcNS0_10FLUSH_MODEE?????????????????NS0_6oclMatEb??KNS_11_InputArrayE???????????2??1??C??clERKNS0_6oclMatES4_RS2_S5_??1setGaussianBlurKernelEPKfi??2polynomialExpansionOclERKNS0_6oclMatEiRS2_??8setPolynomialExpansionConstsEid??0updateFlow_boxFilterERKNS0_6oclMatES4_RS2_S5_S5_S5_ib??3updateFlow_gaussianBlurERKNS0_6oclMatES4_RS2_S5_S5_S5_ib?????5??6gaussianBlur5OclERKNS0_6oclMatEiRS2_??3releaseMemoryEv??gaussianBlurOclERKNS0_6oclMatEiRS2_??prepareGaussianEidPfS2_S2_RdS3_S3_S3_??????????2Ev??1Ev?????????????????????????????6sparseERKNS0_6oclMatES4_S4_RS2_S5_PS2_??5denseERKNS0_6oclMatES4_RS2_S5_PS2_????????UpERKNS0_6oclMatERS1_??lkE?????mib??ib??Ђ????PKNS0_6oclMatEmRS1_??RKNSt3__16vectorINS0_6oclMatENS1_9allocatorIS3_EEEERS3_???????PS1_??RNSt3__16vectorIS1_NS4_9allocatorIS1_EEEE????????25estimateRecommendedParamsEiiRiS2_S2_S2_??C??clERKNS0_6oclMatES4_RS2_?????2EiiiiҀ1Eiiii?i?ffffii?????i??ffffii??????????????Cځ24checkIfGpuCallReasonableEv??clERKNS0_6oclMatES4_RS2_??2E?1E??v??iii?????v??iii??????????????25estimateRecommendedParamsEiiRiS2_S2_?C??clERKNS0_6oclMatE˃???2Eiii??1Eiii??i??ffffi?????i??ffffiă?????????S4_RS2_??RS2_??????????i??RKNS_??PK5CvMatPS2_??iRNS_3MatEb??RNS_3MatEb?????Kernel_oclلSolver_oclƆ4calcEiiPfRNS_3MatE??6createEPK11CvSVMParamsMS1_FviiPfRNS_3MatEEM11CvSVMKernelFviiPPKfSC_S5_E??8calc_rbfEiiPfRNS_3MatE??9calc_polyEiiPfRNS_3MatE??1??C?????3MatEb??11_InputArrayERKNS_12_OutputArrayE??І????C?7get_rowEiPfRNS_3MatE??1??2EPK11CvSVMParams??1EPK11CvSVMParams???????????2get_row_baseEiPbRNS_3MatE?3solve_genericER17CvSVMSolutionInfo?????Ћ???????Э???????2calc_sigmoidEiiPfRNS_3MatE??1calc_linearEiiPfRNS_3MatE??7calc_non_rbf_baseEiiPfRNS_3MatE??ж????2EPK11CvSVMParamsMS1_FviiPfRNS_3MatEEM11CvSVMKernelFviiPPKfSC_S5_E??1EPK11CvSVMParamsMS1_FviiPfRNS_3MatEEM11CvSVMKernelFviiPPKfSC_S5_E??????????C̊13create_̋2Eڊ1E??v??RKNS_3MatES4_S4_S4_11CvSVMParams?????v??RKNS_3MatES4_S4_S4_11CvSVMParamsŋ?????????kernelEv?solverEv???????C??clERKNS0_6oclMatES4_RS2_S5_??1Č2Ev??1Ev???????????2procOneScaleERKNS0_6oclMatES4_RS2_S5_??4collectGarbageEv?????1??9estimateUERN2cv3ocl6oclMatES3_S3_S3_S3_S3_S3_S3_S3_S3_S3_ffc??21estimateDualVariablesERN2cv3ocl6oclMatES3_S3_S3_S3_S3_f??6centeredGradientERKN2cv3ocl6oclMatERS2_S5_??2warpBackwardERKN2cv3ocl6oclMatES4_RS2_S5_S5_S5_S5_S5_S5_S5_S5_??Њ?????????????URF??IFTƐ4infoEv??1??clERKNS_11_InputArrayES3_RNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEE?????URF4infoEv??IFT4infoEv?????4infoEv??2Β1??clERKNS_11_InputArrayES3_RNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEE????????4infoEv??1??clERKNS_11_InputArrayES4_RNSt3__16vectorINS_8KeyPointENS5_9allocatorIS7_EEEE????????nonfreeEvǒcontribEv?????0buildGaussianPyramidERKNS_3MatERNSt3__16vectorIS1_NS4_9allocatorIS1_EEEEi??1findScaleSpaceExtremaERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEES8_RNS2_INS_8KeyPointENS4_IS9_EEEE?????5buildDoGPyramidERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEERS6_??4descriptorҖ0detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_??1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_????????IFTC??URFC?2EiidddĖ1Eiiddd˖??????SizeEv?TypeEv?????IFT1??URF1??4descriptor?1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_ĘSizeEv??TypeEv???????????н?
RKNS_12_OutputArrayEb????????Љ????7findMaximaInLayerERKNS_3MatES3_RKNSt3__16vectorIS1_NS4_9allocatorIS1_EEEESA_RKNS5_IiNS6_IiEEEERNS5_INS_8KeyPointENS6_ISF_EEEEiifi?9findMaximaInLayer_mE?????2E??1E??v??diibb????v??diibb??????????4descriptor?0detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEES3_1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_ɝSizeEv??TypeEv?????4descriptor?1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS4_9allocatorIS6_EEEERS1_НSizeEv??TypeEv??д?????????
RKNS_12_OutputArrayEb?????????????C??1ϞclERKNS0_6GpuMatES4_R??2E??1E??v??diibfb??П?v??diibfb????????Ь???5uploadKeypointsERKNSt3__16vectorINS_8KeyPointENS2_9allocatorIS4_EEEERNS0_6GpuMatEߠ7downloadKeypointsERKNS0_6GpuMatERNSt3__16vectorINS_8KeyPointENS5_9allocatorIS7_EEEE?9downloadDescriptorsERKNS0_6GpuMatERNSt3__16vectorIfNS5_9allocatorIfEEEE??3releaseMemoryEv??????????S2_??NSt3__16vectorINS_8KeyPointENS5_9allocatorIS7_EEEEɡ???
S5_b¡??????
RաS2_b??NS6_IfNS8_IfEEEEb??Э?????C??1إclERKNS0_6oclMatES4_R??2E??1EԢv͢diibfb????v?diibfb?????????4descriptor??0detectImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS5_9allocatorIS7_EEEES4_??1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS5_9allocatorIS7_EEEERS2_??SizeEväTypeEvʥ???4descriptor??1computeImplERKNS_3MatERNSt3__16vectorINS_8KeyPointENS5_9allocatorIS7_EEEERS2_??SizeEvåTypeEvѥ?????????5uploadKeypointsERKNSt3__16vectorINS_8KeyPointENS2_9allocatorIS4_EEEERNS0_6oclMatE?7downloadKeypointsERKNS0_6oclMatERNSt3__16vectorINS_8KeyPointENS5_9allocatorIS7_EEEE?9downloadDescriptorsERKNS0_6oclMatERNSt3__16vectorIfNS5_9allocatorIfEEEE??3releaseMemoryEvǩ?????????S2_??NSt3__16vectorINS_8KeyPointENS5_9allocatorIS7_EEEEҨ???
S5_b˨??????
RިS2_b??NS6_IfNS8_IfEEEEb???????????
RKNS_12_OutputArrayEb?????????????????1ک2??0bindImgTexERKN2cv3ocl6oclMatERP7_cl_mem??7icvSetUpright_gpuERKN2cv3ocl6oclMatEi?Ў?7icvCalcLayerDetAndTrace_gpuERN2cv3ocl6oclMatES3_iiiլ4icvFindMaximaInLayer_gpuERKN2cv3ocl6oclMatES4_RS2_S5_iibiiiܬ6icvInterpolateKeypoint_gpuERKN2cv3ocl6oclMatES4_iRS2_S5_iii?2icvCalcOrientation_gpuERKN2cv3ocl6oclMatEi?3compute_descriptors_gpuERKN2cv3ocl6oclMatES4_i???????????????????8initDataEP9_IplImageiiϭC֭9Histogram?DЮ7processEP9_IplImageS1_?????2Eii?1Eiiɮ???C??D??9mergeWithEPS0_d??1??1Ev??2Ev˯??1Ev®2Evү0Evٯ?????2Ev?1Ev?0Ev????????????????9findCurveThresholdsERiS1_dį7findCoverageIndexEdi????в?г??????C?Dٲ5clearEv??3runEiiiiiRNS_3MatES2_S2_NS_12TermCriteriaEPFviiS2_S2_S2_S2_PvEPFviiS2_S2_S2_S4_ES4_??1??8optimizeER5CvMatɵ2E??1E?v?iiiiiRNS_3MatES2_S2_NS_12TermCriteriaEPFviiS2_S2_S2_S2_PvEPFviiS2_S2_S2_S4_ES4_PFbidS4_ES4_?????vҲiiiiiRNS_3MatES2_S2_NS_12TermCriteriaEPFviiS2_S2_S2_S2_PvEPFviiS2_S2_S2_S4_ES4_PFbidS4_ES4_?????2Ev?1Ev??0Ev?????????????????????2ó4ask_for_projacER5CvMatµask_for_projER5CvMatb??bundleAdjustERNSt3__16vectorINS_7Point3_IdEENS1_9allocatorIS4_EEEERKNS2_INS2_INS_6Point_IdEENS5_ISA_EEEENS5_ISC_EEEERKNS2_INS2_IiNS5_IiEEEENS5_ISI_EEEERNS2_INS_3MatENS5_ISN_EEEESQ_SQ_SQ_RKNS_12TermCriteriaEPFbidPvESU_е????????????C??DӶ6resizeEjj?2??4??3??18runFilter_LPfilterERKNSt3__18valarrayIfEE?2EjjjbŶ1Ejjjb̶??????2Ev?1Ev??????????1ɷ5??3_??9runFilter_LPfilter_AutonomousERNSt3__18valarrayIfEEj??7_??setLPfilterParametersEfffj??_verticalCausalFilterEPfjj?????5setProgressiveFilterConstants_CentredAccuracyEfffj?4?1_verticalAnticausalFilter_returnMeanValueEPfjj??0_local_verticalAnticausalFilter_multGainEPfjjPKj??2_horizontalCausalFilter_Irregular_addInputEPKfPfjj?????setProgressiveFilterConstants_CustomAccuracyEfffRKNSt3__18valarrayIfEEj??_verticalAnticausalFilter_Irregular_multGainEPfjj?????runFilter_LocalAdapdationERKNSt3__18valarrayIfEES5_??_Ȼ???
RS3_??localLuminanceAdaptationEP??verticalAnticausalFilterEPfjj??KfS2_Pfb??fPKfľঁ???6??7_˾4_verticalAnticausalFilter_multGainEPfjj??2_horizontalCausalFilter_addInputEPKfPfjj??1_??3_??runFilter_LocalAdapdation_autonomousERKNSt3__18valarrayIfEE?_??Щ?
RS3_??spatiotemporalLPfilterEPKfPfj??horizontalCausalFilterEPfjj???????????local??horizontalAnticausalFilter_IrregularEPfjjPKf??LuminanceAdaptationPosNegValuesEPKfS2_Pf?_squaringHorizontalCausalFilterEPKfPfjjPKj?????j??RS3_j??б??????????horizontalAnticausalFilterEPfjj??local_verticalCausalFilterEPfjjPKj??????????????squaring??verticalCausalFilter_IrregularEPfjjPKf??SpatiotemporalLPfilterEPKfPfj??HorizontalCausalFilterEPKfPfjj??????ā?ƁhorizontalAnticausalFilter_multGainEPfjj??localSquaringSpatioTemporalLPfilterEPKfPfPKjj???Ɂ?ʁ?́?ցlocal_horizontalAnticausalFilterEPfjjPKj??spatiotemporalLPfilter_IrregularEP??horizontalCausalFilter_IrregularEPfjj???ׁ?ف?ہfj??KfPfj???ށ?????????????1??6FabMapȖ7FabMapڠ9FabMapĢ3BOWMSCTrainer??1ChowLiuTree??C??D??2Ed??1Ed????????2Ev??1Ev??0Ev???????????1??6FabMap2??3BOWMSCTrainer7clusterE??1ChowLiuTree17getImgDescriptorsEv??v??RKNS_3MatE????????2??8??9showMatchERNS_3MatE??6??1LocationImageIterator??SlidingWindowImageIterator??LocationScaleImageIterator4nextEv??C??4nextEv??2Eiiiiiff??1Eiiiiiff???˂?̂?͂23SlidingWindowImageRange8iteratorEv??8Template4showEv???΂C??4nextEv??2ERKNSt3__16vectorINS_6Point_IiEENS2_9allocatorIS5_EEEEiff??1ERKNSt3__16vectorINS_6Point_IiEENS2_9allocatorIS5_EEEEiff???ς?ς?Ђ?тMatching??Template??addMatchEfNS_6Point_IiEEPKNS0_8TemplateE??matchingERNS0_8TemplateERNS_3MatE??2??1??8getAngleENSt3__14pairIiiEES4_RiS5_??1findFirstContourPointERNS_3MatERNSt3__14pairIiiEE??3??0??4computeDistanceTransformERNS_3MatES3_S3_fff??6fillNonContourOrientationsERNS_3MatES3_???҂3followContourERNS_3MatERNSt3__16vectorINS4_4pairIiiEENS4_9allocatorIS7_EEEEi??1??4match???ԂfindContourERNS_3MatERNSt3__16vectorINS4_4pairIiiEENS4_9allocatorIS7_EEEE??addTemplateERNS0_8TemplateE???܂?ނfindContourOrientationsERKNSt3__16vectorINS2_4pairIiiEENS2_9allocatorIS5_EEEERNS3_IfNS6_IfEEEE??computeEdgeOrientationsERNS_3MatES3_???ނC??20getTemplateAddressesEi??7rescaleEf??2ERNS_3MatEf??1ERNS_3MatEf???????????????addTemplateFromImageERNS_3MatEf??localChamferDistanceENS_6Point_IiEERNS_3MatES5_PNS0_8TemplateEf???????????Й?ࣃ???TemplatesERNS_3MatES3_RKNS0_10ImageRangeEf??EdgeImageERNS_3MatERKNS0_10ImageRangeEfif?????????˃i??NS0_5MatchE???σ?у?Ӄ?كC??D??3addERKNS??4makeEd??1??2??9buildTreeEiRNSt3__14listINS1_4infoENS2_9allocatorIS4_EEEE??2Ev??1Ev????????2Ev??1Ev??0Ev??С????Ф?_3MatE??t3__16vectorINS_3MatENS2_9allocatorIS4_EEEE?????Я????а?5??PEib??2recAddToTreeERNS_3MatEiiRNSt3__14listINS1_4infoENS4_9allocatorIS6_EEEE??4sortInfoScoresERKNS1_4infoES4_??1calcMutInfoEii??createBaseEdgesERNSt3__14listINS1_4infoENS2_9allocatorIS4_EEEEd??extractChildrenERNSt3__14listINS1_4infoENS2_9allocatorIS4_EEEEi???Ą0reduceEdgesToMinSpanERNSt3__14listINS1_4infoENS2_9allocatorIS4_EEEE??JPEibib??CPEibib???Ƅ?҄?ׄ?ބ?߄????????????????C??D??1??2??2ENS_24CvMeanShiftTrackerParamsE??1ENS_24CvMeanShiftTrackerParamsE???ߊ??2Ev??1Ev???????7??8getTrackingEllipseEv??newTrackingWindowENS_3MatENS_5Rect_IiEE??setTrackingWindowENS_5Rect_IiEE??getTracking?????0updateTrackingWindowENS_3MatE??2getHistogramProjectionEi???????????WindowEv??CenterEv???????????2??1??C??D??7processERKN2cv3MatE??3runEv??4stopEv??1SeparateDetectionWork??0updateTrackedObjectsERKNSt3__16vectorIN2cv5Rect_IiEENS0_9allocatorIS4_EEEE??C??D??3??23workcycleObjectDetectorEv??4stopEv??13resetTrackingEv??2ERS_RKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE??1ERS_RKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE????????2Ev??1Ev??0Ev?????ો?runEv??0communicateWithDetectingThreadERKN2cv3MatERNSt3__16vectorINS1_5Rect_IiEENS5_9allocatorIS8_EEEE????????????ŋ?Nj?ɋ0ParametersC??5InnerParametersC??4detectInRegionERKN2cv3MatERKNS0_5Rect_IiEERNSt3__16vectorIS5_NS8_9allocatorIS5_EEEE??3??2Ev??1Ev???΋?ϋ2Ev??1Ev???ϋ?Ћ2ERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEERKNS_10ParametersE??1ERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEERKNS_10ParametersE???Ћ?ߋ2Ev??1Ev??0Ev???ߋ????????????10getObjectsERNSt3__16vectorIN??31calcTrackedObjectPositionToShowEi??2cv5Rect_IiEENS0_9allocatorIS4_EEEE??S0_4pairIN2cv5Rect_IiEEiEENS0_9allocatorIS6_EEEE?????????????????resetTrackingEv??setParametersERKNS_10ParametersE??getParametersEv???????????6updateERKNS_11_InputArrayES3_??4loadERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??1?????6updateERKNS_11_InputArrayES3_??4loadERKNS_11FileStorageE??5trainERKNS_11_InputArrayES3_?????????Œ3setLabelsInfoERKNSt3__13mapIiNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIiEENS6_INS1_4pairIKiS8_EEEEEE??2getLabelInfoERKi??7getLabelsByStringERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE???Ō?Ȍ?ʌ5trainERKNS_11_InputArrayES3_??4loadERKNS_11FileStorageE???͌7predictERKNS_11_InputArrayE??4??RiRd??????Ս?ՍsaveERNS_11FileStorageE??infoEv????5trainERKNS_11_InputArrayES3_??4loadERKNS_11FileStorageE?????7predictERKNS_11_InputArrayE??4??RiRd?????܎?????saveERNS_11FileStorageE??infoEv????????4??7predictERKNS_11_InputArrayE??saveERNS_11FileStorageE??infoEv????????
b?????RiRd?????א??????????????????C??D??17??2??2ENS_22CvFeatureTrackerParamsE??1ENS_22CvFeatureTrackerParamsE??????2Ev??1Ev??????˒newTrackingWindowENS_3MatENS_5Rect_IiEE??setTrackingWindowENS_5Rect_IiEE??getTracking???̒0updateTrackingWindowENS_3MatE??8updateTrackingWindowWith???͒FlowENS_3MatE??SIFTENS_3MatE???Ւ?????WindowEv??CenterEv????????PointC??Curve??2Edd??1Edd????????7betweenEddd??C??D??9??5clearEv??8?????2Ev??1Ev????????2Ev??1Ev????????setCentreEd??getCentreEv??calcValueEd???????????addPointEdd??getValueEv??setValueEd???????????Џ?C??D??8??11resetValuesEv??9calcValueEv??2Ev??1Ev????????2Ev??1Ev????????addCurveEP12CvFuzzyCurved??newCurveEv??????????????C??D??7setRuleEP12CvFuzzyCurveS1_S1_??9calcValueEdd??14getOutputCurveEv??2Ev??1Ev??Й??2Ev??1Ev?????????????????C??D??7addRuleEP12CvFuzzyCurveS1_S1_??10calcOutputEdd??2Ev??1Ev????????2Ev??1Ev??????????????12??23findOptimumSearchWindowERNS_12SearchWindowEP9_IplImageS3_iib??C??D??5trackEP9_IplImageS1_ibi??FuzzyResizer??SearchWindow??C??10calcOutputEdd??2Ev??1Ev???????C??D??7setSizeEiiii??1??5shiftEv??3??28getResizeAttribsInnerDensityERiS1_S1_S1_??9meanShiftEP9_IplImageS2_ib??2Ev??1Ev????????2Ev??1Ev??????“?Ó5initDepthValuesEP9_IplImageS2_??1extractInfoEP9_IplImageS2_b???Ó?ʓ?˓3getResizeAttribsEdgeDensityLinearERiS1_S1_S1_??2getResizeAttribsEdgeDensityFuzzyERiS1_S1_S1_???ӓ?Փ?ד?ۓ?ݓ2Ev??1Ev??????2Ev??1Ev???????????2EffNS_22CvFeatureTrackerParamsENS_24CvMeanShiftTrackerParamsENS_13CvMotionModelE??1EffNS_22CvFeatureTrackerParamsENS_24CvMeanShiftTrackerParamsENS_13CvMotionModelE???˔1Ei14CvTermCriteria??2Ei14CvTermCriteria???Δ?ϔ?ϔC܀Dҁ21get??1܂30updateTrackerWithLowPassFilterENS_3MatE?2E?1E??v??NS_21CvHybridTrackerParamsEā?Дv??NS_21CvHybridTrackerParamsEˁ?֔?֔?ޔ2Ev?1Ev??ޔ??DistanceProjectionENS_3MatENS_6Point_IfEE΂GaussianProjectionENS_3MatEidNS_6Point_IfEEՂ????0newTrackerENS_3MatENS_5Rect_IiEE׃3updateTrackerENS_3MatEރ9updateTrackerWithEMENS_3MatE?7getTrackingWindowEv??????????Ǖ?ȕC??1?DɅ6resizeEjj??2??2EjjNS0_14PROJECTIONTYPEEb܄1EjjNS0_14PROJECTIONTYPEEb…?ȕ5clearAllBuffersEv??4initProjectionEdd??3runProjectionERKNSt3__18valarrayIfEEbԆ?Ε?Е2Ev??1Ev?0Ev??Е?ҕ?ӕ?Օ?ו2_initLogRetinaSamplingEddƆ7_initLogPolarCortexSamplingEdd͆?ٕ?????2GetListFilesERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_b?4GetListFoldersERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_b?3GetListFilesRERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_b?????????????????KNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE?NS_11FileStorageE?????4loadERKNSΊ3ldaERKNS_11_InputArrayES3_??7??11reconstructERKNS_11_InputArrayE??t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??_11FileStorageE?????????computeERKNS_11_InputArrayES3_??projectERKNS_11_InputArrayE??????Ș?ИC??1?D΍2EiiNS_6Point_IiEEidiiii݌1EiiNS_6Point_IiEEidiiii???љ0create_mapEiiiid??1to_corticalERKNS_3MatE??2to_cartesianERKNS_3MatEǍ?ߙ??????2Evލ1Ev????ചC??1??D??2EiiNS_6Point_IiEEidiii??1EiiNS_6Point_IiEEidiii???0create_mapEiiiid??1to_corticalERKNS_3MatE??2to_cartesianERKNS_3MatE???ƚ??????2Ev??1Ev????????C??1ː21subdivide_recursivelyEddiidd??6get_uvEddRiS1_??Dđ2EiiNS_6Point_IiEEiddiiiĐ1EiiNS_6Point_IiEEiddiii??О?0create_mapEiiiidd??1to_corticalERKNS_3MatE??2to_cartesianERKNS_3MatE??छЩ?ਜ਼???????ě2Evԑ1Evۑ?Λ?ЛC??15clearAllBuffersEvْD?6resizeEjj??2??9runFilterERKNSt3__18valarrayIfEES5_?2EjjҒ1Ejj????????2Ev??1Ev??0Ev?????????????0setCoefficientsTableEffffffߓ3_amacrineCellsComputingEPKfS2_??????????C??D??9build?2E??1E?v۔RKNSt3__16vectorINS_7Point3_IfEENS1_9allocatorIS4_EEEEii?????v??RKNSt3__16vectorINS_7Point3_IfEENS1_9allocatorIS4_EEEEii???????????2Evѕ1Evؕ0Evߕ????????????TreeERKNSt3__16vectorINS_7Point3_IfEENS1_9allocatorIS4_EEEEii??NextEm????????	C??5P??8PzqGzpqLEibbb?D?1?3??7??2??6PzqGeqEbb??2ERKNS_3MatEddii??1ERKNS_3MatEddii????zqGLEibbbڗeqGLEibbӠ?՜?ל?ۜ2Ev??1Ev??0Ev???ۜ?ݜ?ݜ5getTrainingImgDescriptorsEv٘1getTestImgDescriptorsEv???ޜ?ޜ1addTrainingERKNS?4getLikelihoodsERKNS_3MatERKNSt3__16vectorIS2_NS5_9allocatorIS2_EEEERNS6_INS0_6IMatchENS7_ISC_EEEE??_3MatE??t3__16vectorINS_3MatENS2_9allocatorIS4_EEEE???ޜ??addERKNSŚPzqEib??_3MatE??t3__16vectorINS_3MatENS2_9allocatorIS4_EEEE????????compareERKNS??PzqGzpqEibb̠_3MatE?t3__16vectorINS_3MatENS2_9allocatorIS4_EEEE??R??S4_RNSt3__16vectorINS0_6IMatchENS5_9allocatorIS7_EEEES4_??NSt3__16vectorINS0_6IMatchENS5_9allocatorIS7_EEEEbS4_??KNSt3__16vectorIS2_NS5_9allocatorIS2_EEEERNS6_INS0_6IMatchENS7_ISC_EEEES4_?????RNS3_INS0_6IMatchENS5_ISA_EEEEbRKS4_??S9_RNS3_INS0_6IMatchENS5_ISA_EEEERKS4_????????????͝0compareImgDescriptorERKNS_3MatEiRKNSt3__16vectorIS2_NS5_9allocatorIS2_EEEERNS6_INS0_6IMatchENS7_ISC_EEEE??1??pqEiŠ?םnormaliseDistributionERNSt3__16vectorINS0_6IMatchENS2_9allocatorIS4_EEEE??getNewPlaceLikelihoodERKNS_3MatE???ڝ???????????????????1?2??C١D??14getLikelihoodsERKNS_3MatERKNSt3__16vectorIS2_NS5_9allocatorIS2_EEEERNS6_INS0_6IMatchENS7_ISC_EEEE??2ERKNS_3MatEddii??1ERKNS_3MatEddii????????2Ev??1Ev??0Ev??????????????LUTԢFBO??CǣD??14getLikelihoodsERKNS_3MatERKNSt3__16vectorIS2_NS5_9allocatorIS2_EEEERNS6_INS0_6IMatchENS7_ISC_EEEE??2ERKNS_3MatEddiii?1ERKNS_3MatEddiii????????2Ev??1Ev??0Ev??????????????C?D??1ӥ8compInfoERKNS1_9WordStatsES4_??2ERKNS_3MatEddiiddii??1ERKNS_3MatEddiiddii????????2Ev??1Evť0Ev̥?????????4ߥ7?getLikelihoodsERKNS_3MatERKNSt3__16vectorIS2_NS5_9allocatorIS2_EEEERNS6_INS0_6IMatchENS7_ISC_EEEEۦlimitbisectionEdd????setWordStatisticsERKNS_3MatERNSt3__18multisetINS1_9WordStatsENS5_4lessIS7_EENS5_9allocatorIS7_EEEE?bennettInequalityEddd????ࣞ??????C?D??1Щ3addERKNSt3__16vectorINS_3MatENS2_9allocatorIS4_EEEE??21getNewPlaceLikelihoodERKNS_3MatEĭ2ERKNS_3MatEddi??1ERKNS_3MatEddi??ਞ???2Ev??1Ev©0Evɩ??????н?1addTrainingERKNSt3__16vectorINS_3MatENS2_9allocatorIS4_EEEE??0addToIndexERKNS_3MatERNSt3__16vectorIdNS5_9allocatorIdEEEERNS5_3mapIiNS6_IiNS7_IiEEEENS5_4lessIiEENS7_INS5_4pairIKiSD_EEEEEE??4getLikelihoodsERKNS_3MatERKNSt3__16vectorIS2_NS5_9allocatorIS2_EEEERNS6_INS0_6IMatchENS7_ISC_EEEE??9getIndexLikelihoodsERKNS_3MatERNSt3__16vectorIdNS5_9allocatorIdEEEERNS5_3mapIiNS6_IiNS7_IiEEEENS5_4lessIiEENS7_INS5_4pairIKiSD_EEEEEERNS6_INS0_6IMatchENS7_ISM_EEEE??????˞?Ϟ?ݞ????C??15clearAllBuffersEv??D??6resizeEjj??31setOPLandParvoFiltersParametersEffffffǯ9runFilterERKNSt3__18valarrayIfEEbί23_OPL_OnOffWaysComputingEvկ2Ejj??1Ejj???????????2Ev??1Ev??0Ev????????????????????????C??5?D??9inputSizeEv??1??2??3ն8get??2ENS_5Size_IiEEð1ENS_5Size_IiEE???ݟ
bNS_26RETINA_COLORSAMPLINGMETHODEbdd?_initENS_5Size_IiEEbNS_26RETINA_COLORSAMPLINGMETHODEbdd??setupE???????
bNS_26RETINA_COLORSAMPLINGMETHODEbdd??????2Ev??1Ev??0Ev??????????0??8setColorSaturationEbf??3getParametersEv??2clearBuffersEv??outputSizeEv??printSetupEvȵ??????NSγRNS_11FileStorageEb??t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEb??0_16RetinaParametersEϵ?????6?0setupIPLMagnoChannelEbfffffff??8_convert??setupOPLandIPLParvoChannelEbbfffffff??activateContoursProcessingEbȹ????????????5writeE?8get??NSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEǶRNS_11FileStorageEζЬ?Э?runERKNS_3MatE??2activateMovingContoursProcessingEb???àCvMat2ValarrayBufferENS_3MatERNSt3__18valarrayIfEE??ValarrayBuffer2cvMatERKNSt3__18valarrayIfEEjjbRNS_3MatEָ?ɠParvoERNS??MagnoERNSݸ_3MatEϸt3__18valarrayIfEE????????_3MatE??t3__18valarrayIfEE???????????MagnoEv??ParvoEv???????????Џ????C??1ֺDԻ6resizeEjj??2??3??4??2EjjNS_26RETINA_COLORSAMPLINGMETHODEϺ1EjjNS_26RETINA_COLORSAMPLINGMETHODEͻ???8_initColorSamplingEv??5??6_computeGradientEPKf??੡clearAllBuffersEvƻ_applyRIFfilterEPKfPf??????2Ev?1Ev?0Ev??????????????2runColorDemultiplexingERKNSt3__18valarrayIfEEbf??0runColorMultiplexingERKNSt3__18valarrayIfEERS3_??4_adaptiveSpatialLPfilterEPKfPf??9clipRGBOutput_0_maxInputValueEPff??8_interpolateBayerRGBchannelsEPf??7_getNormalizedContoursImageEPKfPf??1applyLMS2LabTransformERNSt3__18valarrayIfEE???ˡ5?3_interpolateSingleChannelImage111EPf??4applyKrauskopfLMS2Acr1cr2TransformERNSt3__18valarrayIfEE??1_applyImageColorSpaceConversionERKNSt3__18valarrayIfEERS3_PKf??_interpolateImageDemultiplexedImageEPf??normalizeRGBOutput_0_maxOutputValueEf???ߡ????????????????????0_adaptiveHorizontalCausalFilter_addInputEPKfPfjj??2_adaptiveVerticalAnticausalFilter_multGainEPfjj?????????????????C??1??D??6resizeEjj??9runFilterERKNSt3__18valarrayIfEEbbbb??31_processRetinaParvoMagnoMappingEv??2??2EjjbNS_26RETINA_COLORSAMPLINGMETHODEbdd??1EjjbNS_26RETINA_COLORSAMPLINGMETHODEbdd?????8??9??5clearAllBuffersEv??0checkInputERKNSt3__18valarrayIfEEb??1getContoursEv??7run??_createHybridTableEv??runGrayToneMappingERKNSt3__18valarrayIfEERS3_ff?????setGlobalParametersEfffffffffbbfff??_?????setInitPeriodCountEv??runGrayToneMappingERKNSt3__18valarrayIfEERS3_ff?????????2Ev??1Ev?????????????????Ģ?Ģ?ŢRGBToneMappingERKNSt3__18valarrayIfEERS3_bff??LMSToneMappingERKNSt3__18valarrayIfEERS3_bff???ʢ?͢1getParvoFoveaResponseERNSt3__18valarrayIfEE??5getMagnoParaFoveaResponseERNSt3__18valarrayIfEE???΢?Т?ҢC??D??aSERKS0_??2E??1E??v??iiiii??RKS0_???ѧv??iiiii??RKS0_???ҧ?ҧ?ӧ?ӧ?ӧ2Ev??1Ev??0Ev???ԧ?ԧ?ԧ?ԧ1??3SSDERKNS_3MatENS_6Point_IiEERS1_??7computeERKNS_3MatERNSt3__16vectorIfNS4_9allocatorIfEEEENS_5Size_IiEERKNS5_INS_6Point_IiEENS6_ISD_EEEE??22computeLogPolarMappingERNS_3MatE??7getDescriptorSizeEv??1getGridSizeENS_5Size_IiEES2_???ԧ?ԧ?է?ݧ???C??D??1??7allzeroE??2E??1E??v??RKNSt3__16vectorINS_7Point3_IfEENS1_9allocatorIS4_EEEE?????v??RKNSt3__16vectorINS_7Point3_IfEENS1_9allocatorIS4_EEEE???????????2Ev??1Ev?????Ћ?1??8estimateResolutionEf??4computeNormalsE??buildOctreeEv??clearOctreeEv???????????fi??RKNSt3__16vectorIiNS1_9allocatorIiEEEEfi?????????1??C??D??9set??7computeEv??5matchERKS0_RNSt3__16vectorINS4_INS_3VecIiLi2EEENS3_9allocatorIS6_EEEENS7_IS9_EEEE??5spinCorrelationERKNS_3MatES3_fRf??3defaultParamsEv??8selectRandomSubsetEf???è2E??1E??RKNS_6Mesh3DE??v???ɨ?ҨRKNS_6Mesh3DE??v???Ө?Ө?ר2Ev??1Ev???ר?ڨLoggerEPNSt3__113basic_ostreamIcNS1_11char_traitsIcEEEE??SubsetERKNSt3__16vectorIiNS1_9allocatorIiEEEE???ڨ21packRandomScaledSpinsEbmm??16???ڨ??????repackSpinImagesERKNSt3__16vectorIhNS1_9allocatorIhEEEERNS_3MatEb??matchSpinToModelERKNS_3MatERNSt3__16vectorIiNS4_9allocatorIiEEEERNS5_IfNS6_IfEEEEb?????????ܩ??C??5??4stopEv??1Ev??2Ev??࿪startEv??resetEv?????????????????2getTime??0get??TicksEv??Mi???ªTimeSecEv??CounterEv???ªlliEv??croEv???ª?ê?êC??D??1??3FMGERNS_3MatES2_S2_S2_i??clERKNS_3MatES3_RS1_??2E??1E??v??idiiiidffiii?????v??idiiiidffiii???????????2Ev??1Ev??0Ev???????????7VariationalSolverERNS_3MatES2_S2_S2_i??2VCycle_MyFASERNS_3MatES2_S2_S2_i??0autoParamsEv??????ӫ???????0??3??9restoreImageFromLaplacePyrGpuERNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEE??6restoreImageFromLaplacePyrERNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEE??1??4HomographyBasedEstimator8estimateERKNSt3__16vectorINS0_13ImageFeaturesENS2_9allocatorIS4_EEEERKNS3_INS0_11MatchesInfoENS5_ISA_EEEERNS3_INS0_12CameraParamsENS5_ISF_EEEE??focalsFromHomographyERKNS_3MatERdS4_RbS5_??BundleAdjusterReproj??matchesGraphAsStringERNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEERNS2_INS0_11MatchesInfoENS6_ISC_EEEEf??CylindricalWarperGpuƖ?߬estimateFocalERKNSt3__16vectorINS0_13ImageFeaturesENS1_9allocatorIS3_EEEERKNS2_INS0_11MatchesInfoENS4_IS9_EEEERNS2_IdNS4_IdEEEE??ProjectorBase15setCameraParamsERKNS_3MatES4_S4_????calibrateRotatingCameraERKNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEERS3_??normalizeUsingWeightMapERKNS_3MatERS1_??SphericalPortraitWarper15detectResultRoiENS_5Size_IiEERNS_6Point_IiEES6_????13createDefaultEib??7prepareE??4feedERKNS_3MatES4_NS_6Point_IiEE??5blendERNS_3MatES3_?????C??7prepa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RKNSt3__16vectorINS_5Rect_IiEENS7_9allocatorISA_EEEE?????S??BundleAdjusterBase8estimateERKNSt3__16vectorINS0_13ImageFeaturesENS2_9allocatorIS4_EEEERKNS3_INS0_11MatchesInfoENS5_ISA_EEEERNS3_INS0_12CameraParamsENS5_ISF_EEEE??PairwiseSeamFinder??GraphCutSeamFinder??selectRandomSubsetEiiRNSt3__16vectorIiNS1_9allocatorIiEEEE?urfFeaturesFinder??phericalWarperGpu̕C??4findERKNS_3MatERNS0_13ImageFeaturesE??2Ediiii??1Ediiii???ð?װ?װOrbFeaturesFinder??BundleAdjusterRay??VoronoiSeamFinder??stitchingLogLevelEv?C??4findERKNS_3MatERNS0_13ImageFeaturesE??2ENS_5Size_IiEEifi??1ENS_5Size_IiEEifi??????????C??4findERKNS_3MatERNS0_13ImageFeaturesE??14collectGarbageEv??2Ediiii??1Ediiii???????????঱MatchesInfo??waveCorrectERNSt3__16vectorINS_3MatENS1_9allocatorIS3_EEEENS0_15WaveCorrectKindE??PlaneWarper??C??aSERKS1_??2E??1E??v??RKS1_?????v??RKS1_?????Ъ??????????C??5matchERKNS0_13ImageFeaturesES4_RNS0_11MatchesInfoE??14collectGarbageEv??2Ebfii??1Ebfii???α?ֱ?ױ???????Ћ?24setUpInitialCameraParamsERKNSt3__16vectorINS0_12CameraParamsENS2_9allocatorIS4_EEEE??9calcErrorERNS_3MatE??12calcJacobianERNS_3MatE??৳????Ƴ??24setUpInitialCameraParamsERKNSt3__16vectorINS0_12CameraParamsENS2_9allocatorIS4_EEEE??9calcErrorERNS_3MatE??12calcJacobianERNS_3MatE???????????????ƴ??????4findERKNSt3__16vectorINS_3MatENS2_9allocatorIS4_EEEERKNS3_INS_6Point_IiEENS5_ISB_EEEERS7_??3runEv???ʶ?Ͷ4findERKNSt3__16vectorINS_5Size_IiEENS2_9allocatorIS5_EEEERKNS3_INS_6Point_IiEENS6_ISC_EEEERNS3_INS_3MatENS6_ISH_EEEE??10findInPairEmmNS_5Rect_IiEE???϶?жpSeamFinder??isjointSets??C??4findERKNSt3__16vectorINS_3MatENS2_9allocatorIS4_EEEERKNS3_INS_6Point_IiEENS5_ISB_EEEERS7_ق7processERKNS_3MatES4_NS_6Point_IiEES6_RS2_S7_??1?9findEdgesEv??21updateLabelsUsingSeamEiiRKNSt3__16vectorINS_6Point_IiEENS2_9allocatorIS5_EEEEb??2ENS1_12CostFunctionE˂1ENS1_12CostFunctionE҂Ї???????ष4??6??8hasOnlyOneNeighborEi?1getSeamTipsEiiRNS_6Point_IiEES4_??2??findComponentsEv?closeToContourEiiRKNS_4Mat_IhEE??????ͷresolveConflictsERKNS_3MatES4_NS_6Point_IiEES6_RS2_S7_ۄcomputeGradientsERKNS_3MatES4_??????Ў????estimateSeamERKNS_3MatES4_NS_6Point_IiEES6_iS6_S6_RNSt3__16vectorIS6_NS7_9allocatorIS6_EEEERb??computeCostsERKNS_3MatES4_NS_6Point_IiEES6_iRNS_4Mat_IfEES9_??????ٸ??????4džCӉD??Impl??findERKNSt3__16vectorINS_3MatENS2_9allocatorIS4_EEEERKNS3_INS_6Point_IiEENS5_ISB_EEEERS7_??4findERKNSt3__16vectorINS_3MatENS3_9allocatorIS5_EEEERKNS4_INS_6Point_IiEENS6_ISC_EEEERS8_??2??10findInPairEmmNS_5Rect_IiEẺࢹ0setGraphWeightsColorERKNS_3MatES5_S5_S5_R7GCGraphIfE??4setGraphWeightsColorGradERKNS_3MatES5_S5_S5_S5_S5_S5_S5_R7GCGraphIfEʼn???????ȹ2Eiff?1Eiff???????2Ev??1Ev??0Ev????????࠺С?4findERKNSt3__16vectorINS_3MatENS2_9allocatorIS4_EEEERKNS3_INS_6Point_IiEENS5_ISB_EEEERS7_??10findInPairEmmNS_5Rect_IiEE??2??????0setGraphWeightsColorERKNS_3MatES4_S4_S4_RS2_S5_S5_S5_S5_nj4setGraphWeightsColorGradERKNS_3MatES4_S4_S4_S4_S4_S4_S4_RS2_S5_S5_S5_S5_Ό???࿻1??20estimateCameraParamsEv?6stitchERKNS_11_InputArrayERKNS??3createDefaultEb??7estimateTransformERKNS_11_InputArrayE??1matchImagesEv?5composePanoramaERKNS_1??ල
RKNSt3__16vectorINS5_INS_5Rect_IiEENS4_9allocatorIS7_EEEENS8_ISA_EEEEߎ????????2_OutputArrayE??1_InputArrayERKNS_12_OutputArrayE????????_12_OutputArrayE??t3__16vectorINS5_INS_5Rect_IiEENS4_9allocatorIS7_EEEENS8_ISA_EEEERKNS_12_OutputArrayE?????Џ?1А9mergeSetsEii??7createOneElemSetsEi??3findSetByElemEi?????????????3MatENS5_ISA_EEEEґ5Size_IiEENS5_ISB_EEEEّ????????????????9??4warpERKNS_3MatES4_S4_S4_iiRS2_??7warpRoiENS_5Size_IiEERKNS_3MatES6_S6_??15detectResultRoiENS_5Size_IiEERNS_6Point_IiEES6_??warpPointERKNS_6Point_IfEERKNS_3MatES8_S8_?buildMapsENS_5Size_IiEERKNS_3MatES6_S6_RS4_S7_?Д???????????????9buildMapsENS_5Size_IiEERKNS_3MatES6_?4warpERKNS_3gpu6GpuMatERKNS_3MatES8_??RNS_3gpu6GpuMatES9_??S6_RNS_3gpu6GpuMatES9_????????iiRS3_??S8_iiRS3_ŕ?????9buildMapsENS_5Size_IiEERKNS_3MatES6_RNS_3gpu6GpuMatES9_??4warpERKNS_3gpu6GpuMatERKNS_3MatES8_iiRS3_????????9buildMapsENS_5Size_IiEERKNS_3MatES6_RNS_3gpu6GpuMatES9_??4warpERKNS_3gpu6GpuMatERKNS_3MatES8_iiRS3_???????????2Ԙ31createSuperResolution_BTVL1_?1?9arrGetMatERKNS_11_InputArrayERNS_3MatE??7arrCopyERKNS_11_InputArrayERKNS_12_OutputArrayEß7createՙ3create??4createFrameSource_CameraEi??0createOptFlow_SimpleEv??2createOptFlow_Ƞ6createOptFlow_DualTVL1_??SuperResolution_BTVL1Ev?FrameSource_Video_GPUERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??OptFlow_Farneback_?????GPUEv??OCLEv?????15buildMotionMapsERKN2cv3ocl6oclMatES4_S4_S4_RS2_S5_S5_S5_Ĝ7upscaleERKN2cv3ocl6oclMatERS2_i˜8diffSignERKN2cv3ocl6oclMatES4_RS2_Ҝ21calcBtvRegularizationERKN2cv3ocl6oclMatERS2_iٜ??????????????1FrameSourceD??2arrGetGpuMatERKNS_11_InputArrayERNS_3gpu6GpuMatE??3convertToTypeERKNS_3ʟ9initModule_superresEvڡ5SuperResolution?2Ev??1Ev??0Ev???????????FrameSource_??OptFlow_??EmptyEv??VideoERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE????????????????????О?MatEiRS1_S4_??gpu6GpuMatEiRS2_S5_??ocl6oclMatEiRS2_S5_???????????FarnebackEv??PyrLK_????????DualTVL1Ev?Brox_GPUEv????????GPUEv??OCLEvš???GPUEv??OCLEvӡ???GPUEv??OCLEv̡??????????????C2Evܢ8setInputERKNS_3PtrINS0_11FrameSourceEEE?9nextFrameERKNS_12_OutputArrayE?5resetEv??14collectGarbageEv???????????9nextFrameERKNS_12_OutputArrayE??5resetEv????????????9readImageERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEi??7??1??8TestBase??4sortERNSt3__16vectorIN2cv8KeyPointENS0_9allocatorIS3_EEEERKNS2_12_OutputArrayE?????PrintToERKNS_??GpuPerf12targetDeviceEv??12CvtColorInfoEPNSt3__113basic_ostreamIcNS3_11char_traitsIcEEEE??7MatTypeEPNSt3__113basic_ostreamIcNS3_11char_traitsIcEEEE????3?0Regression??9performance_metrics??printCudaInfoEvاsortKeyPointsERNSt3__16vectorIN2cv8KeyPointENS0_9allocatorIS3_EEEERKNS2_12_OutputArrayEߧ??????
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B44Compressor??newCompressorENS_11CompressionEmRKNS_6HeaderE??h??pixelTypeSizeENS_9PixelTypeE?defaultFormatEPNS_10CompressorE??PizCompressor?RleCompressor??worldToCameraER??addWorldToNDCERNS_6HeaderERKN5Imath8Matrix44IfEE??ZipCompressor??C??D??8compressEPKci??1??2ERKNS_6HeaderEmmb??1ERKNS_6HeaderEmmb????????2Ev??1Ev??0Ev????????࿡1??7??6Header??9InputFile??8TimeCode??3??4T??1??0??5??7ScanLineInputFile??B44Compressor??PizCompressor?RleCompressor12numScanLinesEv??ZipCompressor12numScanLinesEv??12numScanLinesEv??6formatEv????????iRS2_??N5Imath3BoxINS3_4Vec2IiEEEERS2_????????2compressTileEPKciN5Imath3BoxINS3_4Vec2IiEEEERS2_??0uncompressEPKci??4uncompressTileEPKciN5Imath3BoxINS3_4Vec2IiEEEERS2_????iRS2_??N5Imath3BoxINS3_4Vec2IiEEEERS2_?????????T??ChromaticitiesC??lineBufferM??c??whiteLuminanceER??adoptedNeutralER??ownerAttributeER??focusAttributeER??ypedAttributeI??iledInputFile??N??dE1??fE1??iE1??5Imath??S??3BoxINS1_4Vec2I??8Matrix?4VecȕiEEEEE1??fEEEEE1??4staticTypeNameEv??3readValueFromERNS_7IStreamEii??6makeNewAttributeEvފ???ypedAttributeIN??iledInputFile??5Imath??S??3BoxINS1_4Vec2I??8Matrix??4Vec?iEEEEE12writeValueToERNS_7OStreamEi??fEEEEE12writeValueToERNS_7OStreamEi????????4staticTypeNameEv??3readValueFromERNS_7IStreamEii??6makeNewAttributeEv׊???Ђ????ChannelC??IStream?OStream??KeyCode?capDateER??expTimeER??keyCodeER??2ENS_9PixelTypeEiib??1ENS_9PixelTypeEiib????????ChanneleqERKS0_??IStreamڠOStream8fileNameEv?KeyCode??І?ChannelList??floatTo??FrameBuffer??h??skipChannelERPKcNS_9PixelTypeEm??add??Std??TileOffsets??6insertE??ixE??1??5beginEv??3endEv??4findE??PKcRKNS_7ChannelE??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_7ChannelE????????PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??Ѝ?ChannelList??FrameBuffer??TileOffsets??ixE??1??5beginEv??3endEv??4findE??6layersERNSt3__13setINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIS8_EENS6_IS8_EEEE??eqERKS0_??PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE????????М?1findChannelE??5channelsInLayerERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS0_8IteratorESB_??8channelsWithPrefixE??PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE???1findChannelE??5channelsInLayerERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS0_13ConstIteratorESB_??8channelsWithPrefixE??PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??О???????????????????PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??Ф?PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE??????Щ?????RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS0_8IteratorESB_??PKcRNS0_8IteratorES4_????????RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS0_13ConstIteratorESB_??PKcRNS0_13ConstIteratorES4_??????г????_??t3__1ԑ1??6EnvmapEE1??7KeyCodeEE1ċ9LineOrderEE1??8ݐ1C??4ChromaticitiesEE1??2PreviewImageEE1??5TileDescriptionEE1??hannelListEE1??ompressionEE1??4staticTypeNameEv??3readValueFromERNS_7IStreamEii??6makeNewAttributeEv????_??t3__1??1??6EnvmapEE12writeValueToERNS_7OStreamEi??7KeyCodeEE12writeValueToERNS_7OStreamEiå9LineOrderEE12writeValueToERNS_7OStreamEiإ8??1C??4ChromaticitiesEE12writeValueToERNS_7OStreamEi??2PreviewImageEE12writeValueToERNS_7OStreamEi??5TileDescriptionEE12writeValueToERNS_7OStreamEi??hannelListEE12writeValueToERNS_7OStreamEi??ompressionEE12writeValueToERNS_7OStreamEi??????2ERKN5Imath4Vec2IfEES5_S5_S5_??1ERKN5Imath4Vec2IfEES5_S5_S5_???Ȣ?ɢ
RGBtoXYZENS_14ChromaticitiesEf??XYZtoRGBENS_14ChromaticitiesEf??xDensityER??a??has??commentsER??latitudeER??isoSpeedER??timeCodeER??TimeCode???ɢ?΢4staticTypeNameEv??3readValueFromERNS_7IStreamEii??6makeNewAttributeEv???????4staticTypeNameEv??3readValueFromERNS_7IStreamEii??6makeNewAttributeEv?????????Compressor??ha??uintToHalfEj??numSamplesEiii?OutputFile?add??w??C2ERKNS_6HeaderE??D??1????2Ev??1Ev??0Ev????????Compressor6formatEv??OutputFileݶ??2compressTileEPKciN5Imath3BoxINS3_4Vec2IiEEEERS2_??4uncompressTileEPKciN5Imath3BoxINS3_4Vec2IiEEEERS2_??????	isValidCompressionENS_11CompressionE??renderingTransformER??utcOffsetAttributeER??longitudeAttributeER??wrapmodesAttributeER??addFramesPerSecondERNS_6HeaderERKNS_8RationalE??hasFramesPerSecondERKNS_6HeaderE??multiViewAttributeER??dataWindowForLevelERKNS_15TileDescriptionEiiiiii??????newTileCompressorENS_11CompressionEmmRKNS_6HeaderE??bytesPerLineTableERKNS_6HeaderERNSt3__16vectorImNS3_9allocatorImEEEE?ScanLineInputFile??a??has??xDensityAttributeER??commentsAttributeER??latitudeAttributeER??isoSpeedAttributeER??timeCodeAttributeER??globalThreadCountEv??dataWindowForTileERKNS_15TileDescriptionEiiiiiiii????lfToUintE4half??s?????UintEf??HalfEf???????????4staticTypeNameEv??6makeNewAttributeEv?????4staticTypeNameEv??3readValueFromERNS_7IStreamEii??6makeNewAttributeEv???????????4staticTypeNameEv??6makeNewAttributeEv?????SliceC??ownerER??focusER??2ENS_9PixelTypeEPcmmiidbb??1ENS_9PixelTypeEPcmmiidbb????????6insertE??ixE??9findSliceE??5beginEv??3endEv??4findE??PKcRKNS_5SliceE??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_5SliceE????????PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE?????ixE??9findSliceE??5beginEv??3endEv??4findE??PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE???????????PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE?????PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE????????????????????И?PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE?????PKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE???????????Header??envmapER??C??6insertE??D??aSERKS0_??ixE??5beginEvւ3endEv?4findE?1??8??9lineOrderEv??2E??1E??ii??RK??fRKN5Imath4Vec2IfEEfNS_9LineOrderENS_11CompressionE??RKN5Imath3BoxINS1_4Vec2IiEEEEfRKNS3_IfEEfNS_9LineOrderENS_11CompressionE?????staticInitializeEv??numLinesInBufferEPNS_10CompressorEªlookModTransformER??capDateAttributeER??expTimeAttributeER??keyCodeAttributeER??addWorldToCameraERNS_6HeaderERKN5Imath8Matrix44IfEE??hasWorldToCameraERKNS_6HeaderE???ii??RK??fRKN5Imath4Vec2IfEEfNS_9LineOrderENS_11CompressionE??RKN5Imath3BoxINS1_4Vec2IiEEEEfRKNS3_IfEEfNS_9LineOrderENS_11CompressionE?????ౣ???N5Imath3BoxINS1_4Vec2IiEEEES7_fRKNS3_IfEEfNS_9LineOrderENS_11CompressionE??S0_?????N5Imath3BoxINS1_4Vec2IiEEEES7_fRKNS3_IfEEfNS_9LineOrderENS_11CompressionE??S0_??ൣ???PKcRKNS_9AttributeE??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_9AttributeE??????ȣ2Ev??1Ev???ɣ?ʣ?̣?ϣPKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEȂ?ϣixE?5beginEv݂3endEv?4findEȃ1υ8channelsEv??9lineOrderEv??7writeToERNS_7OStreamEbɊPKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEς?֣?ݣ?ޣ?ޣ?ޣ?ޣ?ޣPKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE???ޣPKc??RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE????????	3displayWindowEvȅ0dataWindowEvΆ6pixelAspectRatioEv܆8s?7screenWindowWidthEv??1compressionEvƈ5??2previewImageEv??4setMaxTileSizeEiiŠ??3displayWindowEvdž0dataWindowEvՆ6pixelAspectRatioEv?8??7screenWindowWidthEv??1͈5??2previewImageEv????????????creenWindowCenterEv??etTileDescriptionERKNS_15TileDescriptionE????screenWindowCenterEv?hasTileDescriptionEv????????channelsEv??readFromERNS_7IStreamERiЊ??????????compressionEv?sanityCheckEb????????tileDescriptionEv??setމ??tileDescriptionEv׉hasPreviewImageEv?????PreviewImageERKNS_12PreviewImageE??MaxImageSizeEii??????????????̤?̤?̤?ڤ???????????????????૥6makeNewAttributeEv??4staticTypeNameEv????6makeNewAttributeEv??4staticTypeNameEv??3readValueFromERNS_7IStreamEiiʥ???6makeNewAttributeEv?4staticTypeNameEvѥ3readValueFromERNS_7IStreamEiiߥ???33I??44IĎdEEE1??fEEE1?6makeNewAttributeEv?4staticTypeNameEvצ3readValueFromERNS_7IStreamEii????6makeNewAttributeEv??4staticTypeNameEv?3readValueFromERNS_7IStreamEiiЦ???dEEE1؎fEEE1??6makeNewAttributeEv??4staticTypeNameEvͧ3readValueFromERNS_7IStreamEiiۧ???6makeNewAttributeEv??4staticTypeNameEv?3readValueFromERNS_7IStreamEiiƧ?ť6makeNewAttributeEv֐4staticTypeNameEv??3readValueFromERNS_7IStreamEii???ƥRationalEE1??TimeCodeEE1?6makeNewAttributeEv͑4staticTypeNameEv??3readValueFromERNS_7IStreamEii???ǥ12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE1?6vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEEE1Ó6makeNewAttributeEv??4staticTypeNameEv??3readValueFromERNS_7IStreamEii???ȥ6makeNewAttributeEv??4staticTypeNameEv??3readValueFromERNS_7IStreamEii???ȥ6makeNewAttributeEv?4staticTypeNameEv??3readValueFromERNS_7IStreamEii???ɥ6makeNewAttributeEv??4staticTypeNameEvׄ3readValueFromERNS_7IStreamEii??ʥ2I֕3I??dEEE1?fEEE1ʖiEEE1??6makeNewAttributeEvÖ4staticTypeNameEv??3readValueFromERNS_7IStreamEii???˥6makeNewAttributeEv??4staticTypeNameEv??3readValueFromERNS_7IStreamEii???ϥ6makeNewAttributeEv?4staticTypeNameEv?3readValueFromERNS_7IStreamEii???ϥdEEE1??fEEE1?iEEE1Ù6makeNewAttributeEv?4staticTypeNameEvŇ3readValueFromERNS_7IStreamEiiӇ?ӥ6makeNewAttributeEv??4staticTypeNameEv??3readValueFromERNS_7IStreamEii???ץ6makeNewAttributeEv??4staticTypeNameEv??3readValueFromERNS_7IStreamEii???ۥufCompressEPKtiPc??as???ޥufUncompressEPKciPti??asWorldToNDCERKNS_6HeaderE?????4Data??C??1͜D??5tFileEv?CɛD?18deleteCachedBufferEv??2Ebiۛ1Ebi???????2Ev??1Ev??0Ev???????????ж?2E??1E??PKciƜRNS_7IStreamEi?????0??4setFrameBufferERKNS_11FrameBufferE??2rawPixelDataEiRPKcRi֟1rawTileDataERiS1_S1_S1_RPKcS1_ݟinitializeEvٝreadPixelsEi?????PKci??RNS_7IStreamEi???æ?æ?ͦ2Ev??1Ev??0Ev???ͦ?ͦ?Φ8fileNameEv?6headerEv??7versionEv??1???Φ?Ϧ?Ϧ?Ϧ1frameBufferEv??0isCompleteEv???ݦ?ަiȟϟ?ަ?????????????C2EPKc??D??16readMemoryMappedEi??5clearEv?????2EvŠ1Ev̠0EvӠ?????????14isMemoryMappedEv??8fileNameEv??????????????C2EPKc??D?????2Ev̡1Evӡ0EvڡЃ???????Є?C??1ʢ9setPrefixEi??8setCountEi??aSERKS0_??2E??1E?iiiiiiiâRKS0_?????4setFilmMfcCodeEi??1setFilmTypeEi??3setPerfOffsetEi??6setPerfsPer???????????Љ????FrameEiңCountEi٣??????iiiiiii??RKS0_??Ќ??????????1??8filmTypeEv??6prefixEv??5countEv??1filmMfcCodeEv?0perfOffsetEv??3perfsPer??Ѝ?????????????FrameEv??CountEv???????????Ў????????????????33I??44I?fEEE12writeValueToERNS_7OStreamEiɦdEEE12writeValueToERNS_7OStreamEiަ???????????????૧fEEE12writeValueToERNS_7OStreamEi??dEEE12writeValueToERNS_7OStreamEiԧ???????????ç?Ч?ѧ?ҧ3?1ޭ7renderingTransformAttributeER??5lookModTransformAttributeER??4framesPerSecondAttributeER??2??0??offsetInLineBufferTableERKNSt3__16vectorImNS0_9allocatorImEEEEiRS4_??chromaticitiesAttributeER??whiteLuminanceAttributeER??adoptedNeutralAttributeER???ۧinYEiii??axYEiii???ݧ?ݧ?ݧ?ݧcopy??addLookModTransformERNS_6HeaderERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE??hasLookModTransformERKNS_6HeaderE??worldToNDCAttributeER??IntoFrameBufferERPKcPcS3_mbdNS_10Compressor6FormatENS_9PixelTypeES6_??FromFrameBufferERPcRPKcS3_mNS_10Compressor6FormatENS_9PixelTypeE׭?ާ???onvertInPlaceERPcRPKcNS_9PixelTypeEmЭhromaticitiesER????????fillChannelWithZeroesERPcNS_10Compressor6FormatENS_9PixelTypeEm??addRenderingTransformERNS_6HeaderERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE??hasRenderingTransformERKNS_6HeaderE?????OpaqueAttribute??Pxr24Compressor??envmapAttributeER??framesPerSecondER??C??D?13??2E??1EİPKc??RKS0_ݰ???PKcְRKS0_??????????2Ev??1Ev??0Ev?????Ѕ????OpaqueAttribute??Pxr24Compressor??8typeNameEv??4copyEv??12writeValueToERNS_7OStreamEi???????????readValueFromERNS_7IStreamEiiֲcopyValueFromERKNS_9AttributeEݲ??????4Data??Cֳ1??D??C??D??2Ebi??1Ebi??????2Ev??1Evȳ0Evϳ?????????2E?1E?PKcRKNS_6HeaderEi??RNS_7OStreamERKNS_6HeaderEi??П?0??4setFrameBufferERKNS_11FrameBufferE??1writePixelsEiƷ8updatePreviewImageEPKNS_11PreviewRgbaE۷3breakScanLineEiiic?initializeERKNS_6HeaderEߵcopyPixelsERNS_9InputFileEԷਨPKcRKNS_6HeaderEi??RNS_7OStreamERKNS_6HeaderEi??Я?௨и?2Evȶ1Ev϶0Evֶ?????????8fileNameEv??6headerEv??1????????о?1frameBufferEv??5currentScanLineEvͷ??????????৩???C??DƸ8compressEPKci??1߹2ERKNS_6HeaderEmm??1ERKNS_6HeaderEmm???ө?ש2Evݸ1Ev?0Ev??ة?ة?٩12numScanLinesEv??6formatEv???ک?کiRS2_ѹN5Imath3BoxINS3_4Vec2IiEEEERS2_ع?ک?۩2compressTileEPKciN5Imath3BoxINS3_4Vec2IiEEEERS2_?0uncompressEPKci?4uncompressTileEPKciN5Imath3BoxINS3_4Vec2IiEEEERS2_????iRS2_??N5Imath3BoxINS3_4Vec2IiEEEERS2_?????????PreviewImage˻add??has??C?DռaSERKS0_?2E?1E??jjPKNS_11PreviewRgbaE??RKS0_Ǽ???jjPKNS_11PreviewRgbaE??RKS0_μ?????????2Ev?1Ev????І?????????????C??D?8compressEPKciɾ1??2ERKNS_6HeaderEmm޽1ERKNS_6HeaderEmm???????2Ev??1Ev??0Ev???????????12numScanLinesEv??6formatEv¾??????iRS2_??N5Imath3BoxINS3_4Vec2IiEEEERS2_????????2compressTileEPKciN5Imath3BoxINS3_4Vec2IiEEEERS2_??0uncompressEPKci??4uncompressTileEPKciN5Imath3BoxINS3_4Vec2IiEEEERS2_??Н?iRS2_??N5Imath3BoxINS3_4Vec2IiEEEERS2_????????????RationalEE12writeValueToERNS_7OStreamEi??TimeCodeEE12writeValueToERNS_7OStreamEiބ??????C??D??8compressEPKciiRS2_??10uncompressEPKciiRS2_??2ERKNS_6HeaderEm??1ERKNS_6HeaderEm??????2Ev??1Ev??0Ev???????????୪?б?4Data??C??D??1??C??D??2EPNS_7IStreamEi??1EPNS_7IStreamEi???п?2Ev??1Ev??0Ev??࿪?ƪ?ƪ2ERKNS_6HeaderEPNS_7IStreamEi??1ERKNS_6HeaderEPNS_7IStreamEi???ƪ?ժ2Ev??1Ev??0Ev???ժ?֪?ת8fileNameEv??6headerEv??7versionEv??1???٪?٪?٪4setFrameBufferERKNS_11FrameBufferE??0readPixelsEi??2rawPixelDataEiRPKcRi???٪1frameBufferEv??0isCompleteEv????????i?????????????dd??ltitudeAttributeER??pertureAttributeER??ChromaticitiesERNS_6HeaderERKNS_14ChromaticitiesE??WhiteLuminanceERNS_6HeaderERKf??AdoptedNeutralERNS_6HeaderERKN5Imath4Vec2IfEE?????ChromaticitiesERKNS_6HeaderE??WhiteLuminanceERKNS_6HeaderE??AdoptedNeutralERKNS_6HeaderE??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stExIihEENS_19SymmColumnVec_32s8uEEE??4CastI????????isEENS_25SymmColumnSmallVec_32s16sEEE??ffEENS_22SymmColumnSmallVec_32fEEE?????isEENS_??f??d??25SymmColumnSmallVec_32s16sEEE??11ColumnNoVecEEE??В?25SymmColumnSmallVec_32s16sEEE??11ColumnNoVecEEE???????fEENS_??hEENS_11ColumnNoVecEEE??tEENS_11ColumnNoVecEEE??sEENS_20SymmColumnVec_32f16sEEE??22SymmColumnSmallVec_32fEEE??17SymmColumnVec_32fEEE????????Е?hEENS_11ColumnNoVecEEE??tEENS_11ColumnNoVecEEE??sEENS_11ColumnNoVecEEE??dEENS_11ColumnNoVecEEE?????Ж????З????И????????????????Filter2DI??HOGCacheEͥcolormap±superres1??hNS_4CastI??tNS_4CastI??sNS_4CastI??fNS_4CastIffEENS_13FilterVec_32fEEE??dNS_4CastIddEENS_11FilterNoVecEEE??f??ddEENS_11FilterNoVecEEE??hEENS_12FilterVec_8uEEE??tEENS_11FilterNoVecEEE??sEENS_15FilterVec_8u16sEEE??fEENS_11FilterNoVecEEE????????М???????f??ddEENS_11FilterNoVecEEE??tEENS_11FilterNoVecEEE??fEENS_11FilterNoVecEEE???????О?f??ddEENS_11FilterNoVecEEE??sEENS_11FilterNoVecEEE??fEENS_11FilterNoVecEEE??????????????Р????EqualizeHistCalcHist_Invoker??CvBlobTrackAnalysisTrackDist????EqualizeHistLut_Invoker??Cvٗ??????emapInvokerE??LByteStreamE??MByteStreamE?????Ч???3HResizeLinearI??2HResizeCubicI??5HResizeLanczos4I??hisLi2048ENS_12HResizeNoVecEEENS_13VResizeLinearIhisNS_11FixedPtCastIihLi22EEENS_22VResizeLinearVec_32s8uEEEEE??tffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearItffNS_4CastIftEENS_22VResizeLinearVec_32f16ILin32768EEEEEEE??sffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIsffNS_4CastIfsEENS_22VResizeLinearVec_32f16ILi0EEEEEEE??fffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIfffNS_4CastIffEENS_20VResizeLinearVec_32fEEEEE??ddfLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIddfNS_4CastIddEENS_12VResizeNoVecEEEEE????????Ъ??????hisEENS_12VResizeCubicIhisNS_11FixedPtCastIihLi22EEENS_21VResizeCubicVec_32s8uEEEEE??tffEENS_12VResizeCubicItffNS_4CastIftEENS_21VResizeCubicVec_32f16ILin32768EEEEEEE??sffEENS_12VResizeCubicIsffNS_4CastIfsEENS_21VResizeCubicVec_32f16ILi0EEEEEEE??fffEENS_12VResizeCubicIfffNS_4CastIffEENS_19VResizeCubicVec_32fEEEEE??ddfEENS_12VResizeCubicIddfNS_4CastIddEENS_12VResizeNoVecEEEEE?????????????????hisEENS_15VResizeLanczos4IhisNS_11FixedPtCastIihLi22EEENS_12VResizeNoVecEEEEE??tffEENS_15VResizeLanczos4ItffNS_4CastIftEENS_12VResizeNoVecEEEEE??sffEENS_15VResizeLanczos4IsffNS_4CastIfsEENS_12VResizeNoVecEEEEE??fffEENS_15VResizeLanczos4IfffNS_4CastIffEENS_12VResizeNoVecEEEEE??ddfEENS_15VResizeLanczos4IddfNS_4CastIddEENS_12VResizeNoVecEEEEE?????????????????hiNS_17ResizeAreaFastVecIhEEEE??tfNS_17ResizeAreaFastVecItEEEE??sfNS_17ResizeAreaFastVecIsEEEE??ffNS_19ResizeAreaFastNoVecIffEEEE??ddNS_19ResizeAreaFastNoVecIddEEEE?????и????й????hfEE??tfEE??sfEE??ffEE??ddEE??к?????????л????inOpI??axOpI??hEENS_12MorphRowIVecINS_6VMin8uEEEEE??tEENS_12MorphRowIVecINS_7VMin16uEEEEE??sEENS_12MorphRowIVecINS_7VMin16sEEEEE??fEENS_12MorphRowFVecINS_7VMin32fEEEEE??dEENS_13MorphRowNoVecEEE?????????????????hEENS_12MorphRowIVecINS_6VMax8uEEEEE??tEENS_12MorphRowIVecINS_7VMax16uEEEEE??sEENS_12MorphRowIVecINS_7VMax16sEEEEE??fEENS_12MorphRowFVecINS_7VMax32fEEEEE??dEENS_13MorphRowNoVecEEE?????????????????inOpI??axOpI??hEENS_15MorphColumnIVecINS_6VMin8uEEEEE??tEENS_15MorphColumnIVecINS_7VMin16uEEEEE??sEENS_15MorphColumnIVecINS_7VMin16sEEEEE??fEENS_15MorphColumnFVecINS_7VMin32fEEEEE??dEENS_16MorphColumnNoVecEEE?????????????????hEENS_15MorphColumnIVecINS_6VMax8uEEEEE??tEENS_15MorphColumnIVecINS_7VMax16uEEEEE??sEENS_15MorphColumnIVecINS_7VMax16sEEEEE??fEENS_15MorphColumnFVecINS_7VMax32fEEEEE??dEENS_16MorphColumnNoVecEEE?????????????????inOpI??axOpI??hEENS_9MorphIVecINS_6VMin8uEEEEE??tEENS_9MorphIVecINS_7VMin16uEEEEE??sEENS_9MorphIVecINS_7VMin16sEEEEE??fEENS_9MorphFVecINS_7VMin32fEEEEE??dEENS_10MorphNoVecEEE?????????????????hEENS_9MorphIVecINS_6VMax8uEEEEE??tEENS_9MorphIVecINS_7VMax16uEEEEE??sEENS_9MorphIVecINS_7VMax16sEEEEE??fEENS_9MorphFVecINS_7VMax32fEEEEE??dEENS_10MorphNoVecEEE?????????????????i??d??hEE?tEE?sEE??iEE˅fEE҅dEE???????????BilateralFilter_8u_InvokerEGridAdaptedFeatureDetectorE????????4adaptiveBilateralFilter_8u_InvokerE??2OpponentColorDescriptorExtractorEُORBE??6HaarDetectObjects_ScaleImage_InvokerE??8HaarDetectObjects_ScaleCascade_InvokerE??gpu20GeneralizedHough_GPUE??o?????R?detailЙOctreeE??Mesh3D18EmptyMeshExceptionE?owSumI??etinaEµh??t??sބiiEE??fdEE??ddEE??iEE??dEE????????iEEЄdEEׄ??????iEE?dEE?????????????????hEE??tEE??sEEąfEEمdEE?????????????????????????Tab_cv??SqrTab????????????????????CvކSample_predictor??predict_body_svm??Capture_Images??VideoWriter_QT։DTreeTrainData??FGDetectorBase??Blobޜ???CvׇBoostingFaceTemplateƣVideoWriter_Images??Blob?????Capture??ANN_MLP??ERTreesҖGBTrees??BlobSeq?????Cv׈FaceDetection??VideoWriter??Ca??F??BlobTrackerܛ???CaptureCAM??F??KDTreeWrap????ptureFileωlibFilter¡?????DecoderE??EncoderE????????DecoderE??EncoderE????????xrÊigenfacesE߳DecoderE݊EncoderE???????DecoderE??EncoderE????????DecoderE??EncoderE????????ngɋxM?DecoderE?EncoderE???????DecoderE??EncoderE????????DecoderE??EncoderE????????DecoderEیEncoderE?Ў????BaseStreamE??etinaColor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ain_10_Java_org_opencv_ml_CvBoost_train_11_Java_org_opencv_ml_CvDTreeParams_CvDTreeParams_10_Java_org_opencv_ml_CvDTreeParams_delete_Java_org_opencv_ml_CvDTreeParams_get_1cv_1folds_10_Java_org_opencv_ml_CvDTreeParams_get_1max_1categories_10_Java_org_opencv_ml_CvDTreeParams_get_1max_1depth_10_Java_org_opencv_ml_CvDTreeParams_get_1min_1sample_1count_10_Java_org_opencv_ml_CvDTreeParams_get_1regression_1accuracy_10_Java_org_opencv_ml_CvDTreeParams_get_1truncate_1pruned_1tree_10_Java_org_opencv_ml_CvDTreeParams_get_1use_11se_1rule_10_Java_org_opencv_ml_CvDTreeParams_get_1use_1surrogates_10_Java_org_opencv_ml_CvDTreeParams_set_1cv_1folds_10_Java_org_opencv_ml_CvDTreeParams_set_1max_1categories_10_Java_org_opencv_ml_CvDTreeParams_set_1max_1depth_10_Java_org_opencv_ml_CvDTreeParams_set_1min_1sample_1count_10_Java_org_opencv_ml_CvDTreeParams_set_1regression_1accuracy_10_Java_org_opencv_ml_CvDTreeParams_set_1truncate_1pruned_1tree_10_Java_org_opencv_ml_CvDTreeParams_set_1use_11se_1rule_10_Java_org_opencv_ml_CvDTreeParams_set_1use_1surrogates_10_Java_org_opencv_ml_CvDTree_CvDTree_10_Java_org_opencv_ml_CvDTree_clear_10_Java_org_opencv_ml_CvDTree_delete_Java_org_opencv_ml_CvDTree_getVarImportance_10_Java_org_opencv_ml_CvDTree_train_10_Java_org_opencv_ml_CvDTree_train_11_Java_org_opencv_ml_CvERTrees_CvERTrees_10_Java_org_opencv_ml_CvERTrees_delete_Java_org_opencv_ml_CvERTrees_train_10_Java_org_opencv_ml_CvERTrees_train_11_Java_org_opencv_ml_CvGBTreesParams_CvGBTreesParams_10_Java_org_opencv_ml_CvGBTreesParams_delete_Java_org_opencv_ml_CvGBTreesParams_get_1loss_1function_1type_10_Java_org_opencv_ml_CvGBTreesParams_get_1shrinkage_10_Java_org_opencv_ml_CvGBTreesParams_get_1subsample_1portion_10_Java_org_opencv_ml_CvGBTreesParams_get_1weak_1count_10_Java_org_opencv_ml_CvGBTreesParams_set_1loss_1function_1type_10_Java_org_opencv_ml_CvGBTreesParams_set_1shrinkage_10_Java_org_opencv_ml_CvGBTreesParams_set_1subsample_1portion_10_Java_org_opencv_ml_CvGBTreesParams_set_1weak_1count_10_Java_org_opencv_ml_CvGBTrees_CvGBTrees_10_Java_org_opencv_ml_CvGBTrees_CvGBTrees_11_Java_org_opencv_ml_CvGBTrees_CvGBTrees_12_Java_org_opencv_ml_CvGBTrees_clear_10_Java_org_opencv_ml_CvGBTrees_delete_Java_org_opencv_ml_CvGBTrees_predict_10_Java_org_opencv_ml_CvGBTrees_predict_11_Java_org_opencv_ml_CvGBTrees_train_10_Java_org_opencv_ml_CvGBTrees_train_11_Java_org_opencv_ml_CvKNearest_CvKNearest_10_Java_org_opencv_ml_CvKNearest_CvKNearest_11_Java_org_opencv_ml_CvKNearest_CvKNearest_12_Java_org_opencv_ml_CvKNearest_delete_Java_org_opencv_ml_CvKNearest_find_1nearest_10_Java_org_opencv_ml_CvKNearest_train_10_Java_org_opencv_ml_CvKNearest_train_11_Java_org_opencv_ml_CvNormalBayesClassifier_CvNormalBayesClassifier_10_Java_org_opencv_ml_CvNormalBayesClassifier_CvNormalBayesClassifier_11_Java_org_opencv_ml_CvNormalBayesClassifier_CvNormalBayesClassifier_12_Java_org_opencv_ml_CvNormalBayesClassifier_clear_10_Java_org_opencv_ml_CvNormalBayesClassifier_delete_Java_org_opencv_ml_CvNormalBayesClassifier_predict_10_Java_org_opencv_ml_CvNormalBayesClassifier_predict_11_Java_org_opencv_ml_CvNormalBayesClassifier_train_10_Java_org_opencv_ml_CvNormalBayesClassifier_train_11_Java_org_opencv_ml_CvParamGrid_CvParamGrid_10_Java_org_opencv_ml_CvParamGrid_delete_Java_org_opencv_ml_CvParamGrid_get_1max_1val_10_Java_org_opencv_ml_CvParamGrid_get_1min_1val_10_Java_org_opencv_ml_CvParamGrid_get_1step_10_Java_org_opencv_ml_CvParamGrid_set_1max_1val_10_Java_org_opencv_ml_CvParamGrid_set_1min_1val_10_Java_org_opencv_ml_CvParamGrid_set_1step_10_Java_org_opencv_ml_CvRTParams_CvRTParams_10_Java_org_opencv_ml_CvRTParams_delete_Java_org_opencv_ml_CvRTParams_get_1calc_1var_1importance_10_Java_org_opencv_ml_CvRTParams_get_1nactive_1vars_10_Java_org_opencv_ml_CvRTParams_get_1term_1crit_10_Java_org_opencv_ml_CvRTParams_set_1calc_1var_1importance_10_Java_org_opencv_ml_CvRTParams_set_1nactive_1vars_10_Java_org_opencv_ml_CvRTParams_set_1term_1crit_10_Java_org_opencv_ml_CvRTrees_CvRTrees_10_Java_org_opencv_ml_CvRTrees_clear_10_Java_org_opencv_ml_CvRTrees_delete_Java_org_opencv_ml_CvRTrees_getVarImportance_10_Java_org_opencv_ml_CvRTrees_predict_10_Java_org_opencv_ml_CvRTrees_predict_11_Java_org_opencv_ml_CvRTrees_predict_1prob_10_Java_org_opencv_ml_CvRTrees_predict_1prob_11_Java_org_opencv_ml_CvRTrees_train_10_Java_org_opencv_ml_CvRTrees_train_11_Java_org_opencv_ml_CvSVMParams_CvSVMParams_10_Java_org_opencv_ml_CvSVMParams_delete_Java_org_opencv_ml_CvSVMParams_get_1C_10_Java_org_opencv_ml_CvSVMParams_get_1coef0_10_Java_org_opencv_ml_CvSVMParams_get_1degree_10_Java_org_opencv_ml_CvSVMParams_get_1gamma_10_Java_org_opencv_ml_CvSVMParams_get_1kernel_1type_10_Java_org_opencv_ml_CvSVMParams_get_1nu_10_Java_org_opencv_ml_CvSVMParams_get_1p_10_Java_org_opencv_ml_CvSVMParams_get_1svm_1type_10_Java_org_opencv_ml_CvSVMParams_get_1term_1crit_10_Java_org_opencv_ml_CvSVMParams_set_1C_10_Java_org_opencv_ml_CvSVMParams_set_1coef0_10_Java_org_opencv_ml_CvSVMParams_set_1degree_10_Java_org_opencv_ml_CvSVMParams_set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