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Use fromarray() to convert numpy arrays to CvMatCvMat argument '%s' has no dataCannot delete the state_post attributeThe state_post attribute value must be a list of CvMatCannot delete the transition_matrix attributeThe transition_matrix attribute value must be a list of CvMatCannot delete the process_noise_cov attributeThe process_noise_cov attribute value must be a list of CvMatCannot delete the control_matrix attributeThe control_matrix attribute value must be a list of CvMatCannot delete the error_cov_pre attributeThe error_cov_pre attribute value must be a list of CvMatCannot delete the measurement_noise_cov attributeThe measurement_noise_cov attribute value must be a list of CvMatCannot delete the gain attributeThe gain attribute value must be a list of CvMatCannot delete the state_pre attributeThe state_pre attribute value must be a list of CvMatCannot delete the error_cov_post attributeThe error_cov_post attribute value must be a list of CvMatCannot delete the measurement_matrix attributeThe measurement_matrix attribute value must be a list of CvMatArgument '%s' must be IplImageIplImage argument '%s' has no dataArgument 'mat' must be either IplImage or CvMatCvPoint2D32f argument '%s' expects two floatsCvSize argument '%s' expects two integersCvPoint argument '%s' expects two integersCvRect argument '%s' expects four integersExpected CvSeq for argument '%s'CvScalar value for argument '%s' is longer than 4CvScalar value for argument '%s' is not numericCannot delete the size attributeCannot delete the response attributeCannot delete the angle attributeCannot delete the thresholdStep attributeCannot delete the minThreshold attributeCannot delete the minInertiaRatio attributeCannot delete the minDistBetweenBlobs attributeCannot delete the minConvexity attributeCannot delete the minCircularity attributeCannot delete the minArea attributeCannot delete the maxThreshold attributeCannot delete the maxInertiaRatio attributeCannot delete the maxConvexity attributeCannot delete the maxCircularity attributeCannot delete the maxArea attributeCannot delete the distance attributeCannot delete the blobColor attributeCannot delete the octave attributeCannot delete the class_id attributeCannot delete the trainIdx attributeCannot delete the queryIdx attributeCannot delete the imgIdx attributeCannot delete the nOctaves attributeCannot delete the nOctaveLayers attributeCannot delete the hessianThreshold attributecvsubdiv2d has no such attributecvsubdiv2dpoint has no such attributeCannot delete the filterByInertia attributeCannot delete the filterByConvexity attributeCannot delete the filterByColor attributeCannot delete the filterByCircularity attributeCannot delete the filterByArea attributeCannot delete the upright attributeCannot delete the extended attributeCannot delete the minRepeatability attributeExpected list of floats, or float for argument '%s'Cannot delete the anchorY attributeThe anchorY attribute value must be a integerCannot delete the nCols attributeThe nCols attribute value must be a integerCannot delete the anchorX attributeThe anchorX attribute value must be a integerCannot delete the nRows attributeThe nRows attribute value must be a integerCannot delete the speckleWindowSize attributeThe speckleWindowSize attribute value must be a integerCannot delete the SADWindowSize attributeThe SADWindowSize attribute value must be a integerCannot delete the preFilterType attributeThe preFilterType attribute value must be a integerCannot delete the minDisparity attributeThe minDisparity attribute value must be a integerCannot delete the numberOfDisparities attributeThe numberOfDisparities attribute value must be a integerCannot delete the preFilterCap attributeThe preFilterCap attribute value must be a integerCannot delete the uniquenessRatio attributeThe uniquenessRatio attribute value must be a integerCannot delete the speckleRange attributeThe speckleRange attribute value must be a integerCannot delete the preFilterSize attributeThe preFilterSize attribute value must be a integerCannot delete the textureThreshold attributeThe textureThreshold attribute value must be a integerCannot delete the lambda attributeThe lambda attribute value must be a floatCannot delete the lambda2 attributeThe lambda2 attribute value must be a floatCannot delete the lambda1 attributeThe lambda1 attribute value must be a floatCannot delete the interactionRadius attributeThe interactionRadius attribute value must be a integerThe K attribute value must be a floatCannot delete the occlusionCost attributeThe occlusionCost attribute value must be a integerCannot delete the Ithreshold attributeThe Ithreshold attribute value must be a integerCannot delete the maxIters attributeThe maxIters attribute value must be a integerCannot delete the MP attributeThe MP attribute value must be a integerCannot delete the DP attributeThe DP attribute value must be a integerCannot delete the CP attributeThe CP attribute value must be a integerCannot delete the mu30 attributeThe mu30 attribute value must be a floatCannot delete the mu21 attributeThe mu21 attribute value must be a floatCannot delete the mu20 attributeThe mu20 attribute value must be a floatCannot delete the m01 attributeThe m01 attribute value must be a floatCannot delete the m00 attributeThe m00 attribute value must be a floatCannot delete the m03 attributeThe m03 attribute value must be a floatCannot delete the m02 attributeThe m02 attribute value must be a floatCannot delete the m12 attributeThe m12 attribute value must be a floatCannot delete the m10 attributeThe m10 attribute value must be a floatCannot delete the m11 attributeThe m11 attribute value must be a floatCannot delete the m30 attributeThe m30 attribute value must be a floatCannot delete the m21 attributeThe m21 attribute value must be a floatCannot delete the m20 attributeThe m20 attribute value must be a floatCannot delete the inv_sqrt_m00 attributeThe inv_sqrt_m00 attribute value must be a floatCannot delete the mu03 attributeThe mu03 attribute value must be a floatCannot delete the mu02 attributeThe mu02 attribute value must be a floatCannot delete the mu12 attributeThe mu12 attribute value must be a floatCannot delete the mu11 attributeThe mu11 attribute value must be a floatExpected single character string for argument '%s'CvSeq indices must be integersCannot delete the normType attributeCannot delete the fullDP attributeCannot delete the disp12MaxDiff attributeCannot delete the P2 attributeCannot delete the P1 attributeCannot delete the pyrScale attributeCannot delete the poly_sigma attributeCannot delete the poly_n attributeCannot delete the penalization attributeCannot delete the nIt attributeCannot delete the minDisp attributeCannot delete the maxDisp attributeCannot delete the levels attributeCannot delete the flags attributeCannot delete the fi attributeCannot delete the cycle attributeCvPoint3D32f argument '%s' expects three floatsExpected sequence for point list argument '%s'Expected tuple for element in point list argument '%s'Expected CvVideoWriter for argument '%s'Expected CvPOSITObject for argument '%s'Access specifies %d dimensions, but array only has %dExpected CvMoments for argument '%s'Expected CvKalman for argument '%s'Expected IplConvKernel for argument '%s'Expected CvRNG for argument '%s'Expected CvMemStorage for argument '%s'CvSlice argument '%s' expects two integersExpected CvCapture for argument '%s'Expected CvSubdiv2DEdge for argument '%s'Expected number for CvNextEdgeType argument '%s'Expected CvSubdiv2D for argument '%s'Unexpected loc from cvSubdiv2DLocateCvMatND argument '%s' has no dataTotal number of elements must be unchangedCvArr argument '%s' must be IplImage, CvMat or CvMatND. Use fromarray() to convert numpy arrays to CvMat or cvMatNDExpected CvHistogram for argument '%s'Expected CvFont for argument '%s'Expected integer for CvPoint2D32f countExpected CvStereoGCState for argument '%s'Expected CvStereoBMState for argument '%s'CreateData argument must be either IplImage, CvMat or CvMatNDflag CV_LKFLOW_INITIAL_GUESSES is determined automatically from function arguments - it is not requiredcurr_features must have same length as prev_featureskey length does not match array dimensionsequence size must be same as channel countscalar supplied but channel count does not equal 1Zero sized dimension is illegalExpected CvHaarClassifierCascade for argument '%s'Incorrect type of self (must be 'CvSVM' or its derivative)Incorrect type of self (must be 'FileNode' or its derivative)Sequence '%s' must contain sequencesAll elements of sequence '%s' must be same sizeArgument '%s' must be CvSeq, CvArr, or a sequence of numbersSetData argument must be either IplImage, CvMat or CvMatNDCannot delete the pt attribute/home/fit/opencv-2.4.7/modules/python/src2/cv2.cppThe numpy array of typenum=%d, ndims=%d can not be createdlabels.isContinuous() && labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1) && labels.cols + labels.rows - 1 == data.rows/home/fit/opencv-2.4.7/modules/python/src2/cv2.cv.hpp%s data type = %d is not supported%s dimensionality (=%d) is too highLayout of the output array %s is incompatible with cv::Mat (step[ndims-1] != elemsize or step[1] != elemsize*nchannels)%s is not a numpy array, neither a scalarIncorrect type of self (must be 'CLAHE' or its derivative)Incorrect type of self (must be 'EM' or its derivative)Incorrect type of self (must be 'FeatureDetector' or its derivative)Incorrect type of self (must be 'DescriptorExtractor' or its derivative)Incorrect type of self (must be 'DescriptorMatcher' or its derivative)Incorrect type of self (must be 'SIFT' or its derivative)Incorrect type of self (must be 'SURF' or its derivative)OOOO|OOd:stereoRectifyUncalibrated{s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d}OOOii|OOid:initWideAngleProjMapOOOOOi|OO:initUndistortRectifyMapOidd|OOibd:goodFeaturesToTrackO|Ob:getDefaultNewCameraMatrixO|Offii:fastNlMeansDenoisingColoredOii|OOi:distanceTransformWithLabelsO|OOOOOOO:decomposeProjectionMatrixO|O:convertPointsToHomogeneousO|O:convertPointsFromHomogeneousOiO|O:computeCorrespondEpilinesOOOiiiddidddiddd:calcOpticalFlowSFOOO|OOOOiOid:calcOpticalFlowPyrLKOOdiiiidi|O:calcOpticalFlowFarnebackOOd|OdOi:adaptiveBilateralFilterO|Od:BackgroundSubtractor.applyIncorrect type of self (must be 'BackgroundSubtractor' or its derivative)Incorrect type of self (must be 'FaceRecognizer' or its derivative)Incorrect type of self (must be 'KDTree' or its derivative)Incorrect type of self (must be 'FileStorage' or its derivative)Incorrect type of self (must be 'flann_Index' or its derivative)Incorrect type of self (must be 'Subdiv2D' or its derivative)OOOOOOO|OOOOOidO:stereoRectifyOOOd|Ob:getOptimalNewCameraMatrixIncorrect type of self (must be 'KalmanFilter' or its derivative)Incorrect type of self (must be 'VideoCapture' or its derivative)Incorrect type of self (must be 'VideoWriter' or its derivative)Incorrect type of self (must be 'CvStatModel' or its derivative)OO|OOb:NormalBayesClassifier.trainIncorrect type of self (must be 'CvNormalBayesClassifier' or its derivative)O:NormalBayesClassifier.predictIncorrect type of self (must be 'CvKNearest' or its derivative)Incorrect type of self (must be 'CvDTree' or its derivative)Incorrect type of self (must be 'CvRTrees' or its derivative)Incorrect type of self (must be 'CvERTrees' or its derivative)Incorrect type of self (must be 'CvBoost' or its derivative)Incorrect type of self (must be 'CvGBTrees' or its derivative)Incorrect type of self (must be 'CvANN_MLP' or its derivative)Incorrect type of self (must be 'StereoBM' or its derivative)Incorrect type of self (must be 'StereoSGBM' or its derivative)Incorrect type of self (must be 'CascadeClassifier' or its derivative)O:HOGDescriptor.setSVMDetectorIncorrect type of self (must be 'HOGDescriptor' or its derivative)O|OOOO:HOGDescriptor.computeGradientIncorrect type of self (must be 'StereoVar' or its derivative)Odi|OOO:flann_Index.radiusSearchIncorrect type of self (must be 'Algorithm' or its derivative)numpy.core.multiarray failed to import_ARRAY_API is not PyCObject objectmodule compiled against ABI version %x but this version of numpy is %xmodule compiled against API version %x but this version of numpy is %xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimeCV_GRAPH_SEARCH_TREE_NODE_FLAGCV_FONT_HERSHEY_SCRIPT_SIMPLEXCV_FONT_HERSHEY_SCRIPT_COMPLEXCV_SUBDIV2D_VIRTUAL_POINT_FLAGCV_CAP_OPENNI_DISPARITY_MAP_32FCV_CAP_OPENNI_VALID_DEPTH_MASKCV_CAP_PROP_OPENNI_OUTPUT_MODECV_CAP_PROP_OPENNI_REGISTRATIONCV_CAP_PROP_OPENNI_FRAME_MAX_DEPTHCV_CAP_PROP_OPENNI_FOCAL_LENGTHCV_CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODECV_CAP_OPENNI_DEPTH_GENERATOR_BASELINECV_CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTHCV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATIONADAPTIVE_SKIN_DETECTOR_MORPHING_METHOD_ERODEADAPTIVE_SKIN_DETECTOR_MORPHING_METHOD_ERODE_DILATEADAPTIVE_SKIN_DETECTOR_MORPHING_METHOD_ERODE_ERODEADAPTIVE_SKIN_DETECTOR_MORPHING_METHOD_NONEDRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMGDRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTSDRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTSFAST_FEATURE_DETECTOR_TYPE_5_8FAST_FEATURE_DETECTOR_TYPE_7_12FAST_FEATURE_DETECTOR_TYPE_9_16FUZZY_MEAN_SHIFT_TRACKER_MIN_KERNEL_MASSFUZZY_MEAN_SHIFT_TRACKER_RM_EDGE_DENSITY_FUZZYFUZZY_MEAN_SHIFT_TRACKER_RM_EDGE_DENSITY_LINEARFUZZY_MEAN_SHIFT_TRACKER_RM_INNER_DENSITYFUZZY_MEAN_SHIFT_TRACKER_TS_DISABLEDFUZZY_MEAN_SHIFT_TRACKER_TS_NONEFUZZY_MEAN_SHIFT_TRACKER_TS_SEARCHINGFUZZY_MEAN_SHIFT_TRACKER_TS_SET_WINDOWFUZZY_MEAN_SHIFT_TRACKER_TS_TRACKINGIMWRITE_PNG_STRATEGY_HUFFMAN_ONLYSELF_SIM_DESCRIPTOR_DEFAULT_LARGE_SIZESELF_SIM_DESCRIPTOR_DEFAULT_NUM_ANGLESSELF_SIM_DESCRIPTOR_DEFAULT_NUM_DISTANCE_BUCKETSSELF_SIM_DESCRIPTOR_DEFAULT_SMALL_SIZESELF_SIM_DESCRIPTOR_DEFAULT_START_DISTANCE_BUCKETSTEREO_BM_PREFILTER_NORMALIZED_RESPONSESTEREO_VAR_PENALIZATION_CHARBONNIERSTEREO_VAR_PENALIZATION_PERONA_MALIKSTEREO_VAR_PENALIZATION_TICHONOVSTEREO_VAR_USE_INITIAL_DISPARITYSTEREO_VAR_USE_MEDIAN_FILTERING_INPUT_ARRAY_STD_VECTOR_VECTORExpected cv::SimpleBlobDetector::Params for argument '%s'Expected cv::Algorithm for argument '%s'OO|Ob:Feature2D.detectAndComputeIncorrect type of self (must be 'Feature2D' or its derivative)Incorrect type of self (must be 'StarDetector' or its derivative)Incorrect type of self (must be 'MSER' or its derivative)O|dOOddb:HOGDescriptor.detectMultiScaleCV_IS_MAT_CONT(m->type) && (m->cols == 1 || m->rows == 1) && CV_MAT_TYPE(m->type) == CV_32FC1Expected cv::FeatureDetector for argument '%s'O|i:PyramidAdaptedFeatureDetector|Oiii:GridAdaptedFeatureDetector|iiiid:createLBPHFaceRecognizer|id:createFisherFaceRecognizerOOOOOOOOOOOOOdOi|OOOOOOO:rectify3CollinearOOi|Obiib:buildOpticalFlowPyramidOii|Ofii:fastNlMeansDenoisingMultiOOi|Ob:DescriptorMatcher.knnMatchOi|Ob:DescriptorMatcher.knnMatchOii|Offii:fastNlMeansDenoisingColoredMultiOOOO|OOOOOOOOOi:stereoCalibrateExpected cv::KeyPoint for argument '%s'OO|O:DescriptorExtractor.computeO:Subdiv2D.getVoronoiFacetListIncorrect type of self (must be 'BRISK' or its derivative)OOO|diiOOb:CascadeClassifier.detectMultiScaleO|diiOO:CascadeClassifier.detectMultiScaledeque::_M_new_elements_at_frontdeque::_M_new_elements_at_backbuild(points[, copyAndReorderPoints]) -> None  or  build(points, labels[, copyAndReorderPoints]) -> NonefindNearest(vec, K, Emax[, neighborsIdx[, neighbors[, dist[, labels]]]]) -> retval, neighborsIdx, neighbors, dist, labelsfindOrthoRange(minBounds, maxBounds[, neighborsIdx[, neighbors[, labels]]]) -> neighborsIdx, neighbors, labelsgetPoints(idx[, pts[, labels]]) -> pts, labelsgetFirstTopLevelNode() -> retvalopen(filename, flags[, encoding]) -> retvaloperator[](nodename) -> retvalreleaseAndGetString() -> retvaloperator[](nodename) -> retval  or  operator[](i) -> retvalsetAlgorithm(name, value) -> NonesetDouble(name, value) -> NonesetMatVector(name, value) -> NonesetString(name, value) -> Nonebuild(features, params[, distType]) -> NoneknnSearch(query, knn[, indices[, dists[, params]]]) -> indices, distsload(features, filename) -> retvalradiusSearch(query, radius, maxResults[, indices[, dists[, params]]]) -> retval, indices, distssetClipLimit(clipLimit) -> NonesetTilesGridSize(tileGridSize) -> NoneedgeDst(edge) -> retval, dstptedgeOrg(edge) -> retval, orgptfindNearest(pt) -> retval, nearestPtgetEdge(edge, nextEdgeType) -> retvalgetTriangleList() -> triangleListgetVertex(vertex) -> retval, firstEdgegetVoronoiFacetList(idx) -> facetList, facetCentersinsert(pt) -> retval  or  insert(ptvec) -> Nonelocate(pt) -> retval, edge, vertexrotateEdge(edge, rotate) -> retvalapply(image[, fgmask[, learningRate]]) -> fgmaskcorrect(measurement) -> retvalpredict([, control]) -> retvalopen(filename) -> retval  or  open(device) -> retvalread([, image]) -> retval, imageretrieve([, image[, channel]]) -> retval, imageopen(filename, fourcc, fps, frameSize[, isColor]) -> retvalload(filename[, name]) -> Nonesave(filename[, name]) -> Nonepredict(samples) -> retval, resultstrain(trainData, responses[, varIdx[, sampleIdx[, update]]]) -> retvalfind_nearest(samples, k[, results[, neighborResponses[, dists]]]) -> retval, results, neighborResponses, diststrain(trainData, responses[, sampleIdx[, isRegression[, maxK[, updateBase]]]]) -> retvalget_support_vector_count() -> retvalpredict(sample[, returnDFVal]) -> retvalpredict_all(samples[, results]) -> resultstrain(trainData, responses[, varIdx[, sampleIdx[, params]]]) -> retvaltrain_auto(trainData, responses, varIdx, sampleIdx, params[, k_fold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retvalpredict(sample[, probs]) -> retval, probstrain(samples[, logLikelihoods[, labels[, probs]]]) -> retval, logLikelihoods, labels, probstrainE(samples, means0[, covs0[, weights0[, logLikelihoods[, labels[, probs]]]]]) -> retval, logLikelihoods, labels, probstrainM(samples, probs0[, logLikelihoods[, labels[, probs]]]) -> retval, logLikelihoods, labels, probspredict(sample[, missingDataMask[, preprocessedInput]]) -> retvaltrain(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]) -> retvalpredict(sample[, missing]) -> retvalpredict_prob(sample[, missing]) -> retvalpredict(sample[, missing[, slice[, rawMode[, returnSum]]]]) -> retvaltrain(trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params[, update]]]]]]) -> retvalpredict(sample[, missing[, slice[, k]]]) -> retvalcreate(layerSizes[, activateFunc[, fparam1[, fparam2]]]) -> Nonepredict(inputs[, outputs]) -> retval, outputstrain(inputs, outputs, sampleWeights[, sampleIdx[, params[, flags]]]) -> retvaldetect(image[, mask]) -> keypointscompute(image, keypoints[, descriptors]) -> keypoints, descriptorsdetectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptorsgenerateKernel(radiusList, numberList[, dMax[, dMin[, indexChange]]]) -> Nonedetect(image[, mask]) -> mserscv2.GridAdaptedFeatureDetectorcv2.PyramidAdaptedFeatureDetectorgetTrainDescriptors() -> retvalknnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) -> matches  or  knnMatch(queryDescriptors, k[, masks[, compactResult]]) -> matchesmatch(queryDescriptors, trainDescriptors[, mask]) -> matches  or  match(queryDescriptors[, masks]) -> matchescompute(left, right[, disparity[, disptype]]) -> disparitycompute(left, right[, disp]) -> dispdetectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) -> objects  or  detectMultiScale(image, rejectLevels, levelWeights[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]) -> objectscompute(img[, winStride[, padding[, locations]]]) -> descriptorscomputeGradient(img[, grad[, angleOfs[, paddingTL[, paddingBR]]]]) -> grad, angleOfsdetect(img[, hitThreshold[, winStride[, padding[, searchLocations]]]]) -> foundLocations, weightsdetectMultiScale(img[, hitThreshold[, winStride[, padding[, scale[, finalThreshold[, useMeanshiftGrouping]]]]]]) -> foundLocations, foundWeightsload(filename[, objname]) -> retvalsave(filename[, objname]) -> NonesetSVMDetector(_svmdetector) -> Nonepredict(src) -> label, confidenceAlgorithm__create(name) -> retvalAlgorithm_getList() -> algorithmsBFMatcher([, normType[, crossCheck]]) -> BRISK([, thresh[, octaves[, patternScale]]]) ->   or  BRISK(radiusList, numberList[, dMax[, dMin[, indexChange]]]) -> BackgroundSubtractorMOG([history, nmixtures, backgroundRatio[, noiseSigma]]) -> CamShift(probImage, window, criteria) -> retval, windowCanny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient]]]) -> edgesCascadeClassifier([filename]) -> ANN_MLP([layerSizes[, activateFunc[, fparam1[, fparam2]]]]) -> Boost([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]]) -> GBTrees([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]]) -> KNearest([trainData, responses[, sampleIdx[, isRegression[, max_k]]]]) -> NormalBayesClassifier([trainData, responses[, varIdx[, sampleIdx]]]) -> SVM([trainData, responses[, varIdx[, sampleIdx[, params]]]]) -> DMatch() ->   or  DMatch(_queryIdx, _trainIdx, _distance) ->   or  DMatch(_queryIdx, _trainIdx, _imgIdx, _distance) -> DescriptorExtractor_create(descriptorExtractorType) -> retvalDescriptorMatcher_create(descriptorMatcherType) -> retvalEM([, nclusters[, covMatType[, termCrit]]]) -> FastFeatureDetector([, threshold[, nonmaxSuppression]]) -> Feature2D_create(name) -> retvalFeatureDetector_create(detectorType) -> retvalFileNode() -> FileStorage([source, flags[, encoding]]) -> FlannBasedMatcher([, indexParams[, searchParams]]) -> GFTTDetector([, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) -> GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]) -> dstGridAdaptedFeatureDetector([, detector[, maxTotalKeypoints[, gridRows[, gridCols]]]]) -> HOGDescriptor() ->   or  HOGDescriptor(_winSize, _blockSize, _blockStride, _cellSize, _nbins[, _derivAperture[, _winSigma[, _histogramNormType[, _L2HysThreshold[, _gammaCorrection[, _nlevels]]]]]]) ->   or  HOGDescriptor(filename) -> HOGDescriptor_getDaimlerPeopleDetectorHOGDescriptor_getDaimlerPeopleDetector() -> retvalHOGDescriptor_getDefaultPeopleDetectorHOGDescriptor_getDefaultPeopleDetector() -> retvalHoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) -> circlesHoughLines(image, rho, theta, threshold[, lines[, srn[, stn]]]) -> linesHoughLinesP(image, rho, theta, threshold[, lines[, minLineLength[, maxLineGap]]]) -> linesflann_Index([features, params[, distType]]) -> KDTree() ->   or  KDTree(points[, copyAndReorderPoints]) ->   or  KDTree(points, _labels[, copyAndReorderPoints]) -> KalmanFilter([dynamParams, measureParams[, controlParams[, type]]]) -> KeyPoint([x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]]) -> LUT(src, lut[, dst[, interpolation]]) -> dstLaplacian(src, ddepth[, dst[, ksize[, scale[, delta[, borderType]]]]]) -> dstMSER([, _delta[, _min_area[, _max_area[, _max_variation[, _min_diversity[, _max_evolution[, _area_threshold[, _min_margin[, _edge_blur_size]]]]]]]]]) -> Mahalanobis(v1, v2, icovar) -> retvalORB([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize]]]]]]]]) -> PCABackProject(data, mean, eigenvectors[, result]) -> resultPCACompute(data[, mean[, eigenvectors[, maxComponents]]]) -> mean, eigenvectorsPCAComputeVar(data, retainedVariance[, mean[, eigenvectors]]) -> mean, eigenvectorsPCAProject(data, mean, eigenvectors[, result]) -> resultSimpleBlobDetector_Params() -> PyramidAdaptedFeatureDetector(detector[, maxLevel]) -> RQDecomp3x3(src[, mtxR[, mtxQ[, Qx[, Qy[, Qz]]]]]) -> retval, mtxR, mtxQ, Qx, Qy, QzRodrigues(src[, dst[, jacobian]]) -> dst, jacobianSIFT([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> SURF([hessianThreshold[, nOctaves[, nOctaveLayers[, extended[, upright]]]]]) -> SVBackSubst(w, u, vt, rhs[, dst]) -> dstSVDecomp(src[, w[, u[, vt[, flags]]]]) -> w, u, vtScharr(src, ddepth, dx, dy[, dst[, scale[, delta[, borderType]]]]) -> dstSimpleBlobDetector([, parameters]) -> Sobel(src, ddepth, dx, dy[, dst[, ksize[, scale[, delta[, borderType]]]]]) -> dstStarDetector([, _maxSize[, _responseThreshold[, _lineThresholdProjected[, _lineThresholdBinarized[, _suppressNonmaxSize]]]]]) -> StereoBM([preset[, ndisparities[, SADWindowSize]]]) -> StereoSGBM([minDisparity, numDisparities, SADWindowSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, fullDP]]]]]]]]]) -> StereoVar([levels, pyrScale, nIt, minDisp, maxDisp, poly_n, poly_sigma, fi, lambda, penalization, cycle, flags]) -> Subdiv2D([rect]) -> VideoCapture() ->   or  VideoCapture(filename) ->   or  VideoCapture(device) -> VideoWriter([filename, fourcc, fps, frameSize[, isColor]]) -> absdiff(src1, src2[, dst]) -> dstaccumulate(src, dst[, mask]) -> NoneaccumulateProduct(src1, src2, dst[, mask]) -> NoneaccumulateSquare(src, dst[, mask]) -> NoneaccumulateWeighted(src, dst, alpha[, mask]) -> NoneadaptiveBilateralFilter(src, ksize, sigmaSpace[, dst[, maxSigmaColor[, anchor[, borderType]]]]) -> dstadaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]) -> dstadd(src1, src2[, dst[, mask[, dtype]]]) -> dstaddWeighted(src1, alpha, src2, beta, gamma[, dst[, dtype]]) -> dstapplyColorMap(src, colormap[, dst]) -> dstapproxPolyDP(curve, epsilon, closed[, approxCurve]) -> approxCurvearcLength(curve, closed) -> retvalbatchDistance(src1, src2, dtype[, dist[, nidx[, normType[, K[, mask[, update[, crosscheck]]]]]]]) -> dist, nidxbilateralFilter(src, d, sigmaColor, sigmaSpace[, dst[, borderType]]) -> dstbitwise_and(src1, src2[, dst[, mask]]) -> dstbitwise_not(src[, dst[, mask]]) -> dstbitwise_or(src1, src2[, dst[, mask]]) -> dstbitwise_xor(src1, src2[, dst[, mask]]) -> dstblur(src, ksize[, dst[, anchor[, borderType]]]) -> dstborderInterpolate(p, len, borderType) -> retvalboundingRect(points) -> retvalboxFilter(src, ddepth, ksize[, dst[, anchor[, normalize[, borderType]]]]) -> dstbuildOpticalFlowPyramid(img, winSize, maxLevel[, pyramid[, withDerivatives[, pyrBorder[, derivBorder[, tryReuseInputImage]]]]]) -> retval, pyramidcalcBackProject(images, channels, hist, ranges, scale[, dst]) -> dstcalcCovarMatrix(samples, flags[, covar[, mean[, ctype]]]) -> covar, meancalcGlobalOrientation(orientation, mask, mhi, timestamp, duration) -> retvalcalcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]]) -> histcalcMotionGradient(mhi, delta1, delta2[, mask[, orientation[, apertureSize]]]) -> mask, orientationcalcOpticalFlowFarneback(prev, next, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags[, flow]) -> flowcalcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, flags[, minEigThreshold]]]]]]]]) -> nextPts, status, errcalcOpticalFlowSF(from, to, flow, layers, averaging_block_size, max_flow) -> None  or  calcOpticalFlowSF(from, to, flow, layers, averaging_block_size, max_flow, sigma_dist, sigma_color, postprocess_window, sigma_dist_fix, sigma_color_fix, occ_thr, upscale_averaging_radius, upscale_sigma_dist, upscale_sigma_color, speed_up_thr) -> NonecalibrateCamera(objectPoints, imagePoints, imageSize[, cameraMatrix[, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]]]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecscalibrationMatrixValues(cameraMatrix, imageSize, apertureWidth, apertureHeight) -> fovx, fovy, focalLength, principalPoint, aspectRatiocartToPolar(x, y[, magnitude[, angle[, angleInDegrees]]]) -> magnitude, anglechamerMatching(img, templ[, templScale[, maxMatches[, minMatchDistance[, padX[, padY[, scales[, minScale[, maxScale[, orientationWeight[, truncate]]]]]]]]]]) -> retval, results, costcheckHardwareSupport(feature) -> retvalcheckRange(a[, quiet[, minVal[, maxVal]]]) -> retval, poscircle(img, center, radius, color[, thickness[, lineType[, shift]]]) -> NoneclipLine(imgRect, pt1, pt2) -> retval, pt1, pt2compare(src1, src2, cmpop[, dst]) -> dstcompareHist(H1, H2, method) -> retvalcompleteSymm(mtx[, lowerToUpper]) -> NonecomposeRT(rvec1, tvec1, rvec2, tvec2[, rvec3[, tvec3[, dr3dr1[, dr3dt1[, dr3dr2[, dr3dt2[, dt3dr1[, dt3dt1[, dt3dr2[, dt3dt2]]]]]]]]]]) -> rvec3, tvec3, dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2computeCorrespondEpilines(points, whichImage, F[, lines]) -> linescontourArea(contour[, oriented]) -> retvalconvertMaps(map1, map2, dstmap1type[, dstmap1[, dstmap2[, nninterpolation]]]) -> dstmap1, dstmap2convertPointsFromHomogeneous(src[, dst]) -> dstconvertPointsToHomogeneous(src[, dst]) -> dstconvertScaleAbs(src[, dst[, alpha[, beta]]]) -> dstconvexHull(points[, hull[, clockwise[, returnPoints]]]) -> hullconvexityDefects(contour, convexhull[, convexityDefects]) -> convexityDefectscopyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value]]) -> dstcornerEigenValsAndVecs(src, blockSize, ksize[, dst[, borderType]]) -> dstcornerHarris(src, blockSize, ksize, k[, dst[, borderType]]) -> dstcornerMinEigenVal(src, blockSize[, dst[, ksize[, borderType]]]) -> dstcornerSubPix(image, corners, winSize, zeroZone, criteria) -> NonecorrectMatches(F, points1, points2[, newPoints1[, newPoints2]]) -> newPoints1, newPoints2createCLAHE([, clipLimit[, tileGridSize]]) -> retvalcreateEigenFaceRecognizer([, num_components[, threshold]]) -> retvalcreateFisherFaceRecognizer([, num_components[, threshold]]) -> retvalcreateHanningWindow(winSize, type[, dst]) -> dstcreateLBPHFaceRecognizer([, radius[, neighbors[, grid_x[, grid_y[, threshold]]]]]) -> retvalcvtColor(src, code[, dst[, dstCn]]) -> dstdct(src[, dst[, flags]]) -> dstdecomposeProjectionMatrix(projMatrix[, cameraMatrix[, rotMatrix[, transVect[, rotMatrixX[, rotMatrixY[, rotMatrixZ[, eulerAngles]]]]]]]) -> cameraMatrix, rotMatrix, transVect, rotMatrixX, rotMatrixY, rotMatrixZ, eulerAnglesdestroyWindow(winname) -> Nonedft(src[, dst[, flags[, nonzeroRows]]]) -> dstdilate(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dstdistanceTransform(src, distanceType, maskSize[, dst]) -> dstdistanceTransformWithLabels(src, distanceType, maskSize[, dst[, labels[, labelType]]]) -> dst, labelsdivide(src1, src2[, dst[, scale[, dtype]]]) -> dst  or  divide(scale, src2[, dst[, dtype]]) -> dstdrawChessboardCorners(image, patternSize, corners, patternWasFound) -> NonedrawContours(image, contours, contourIdx, color[, thickness[, lineType[, hierarchy[, maxLevel[, offset]]]]]) -> NonedrawDataMatrixCodes(image, codes, corners) -> NonedrawKeypoints(image, keypoints[, outImage[, color[, flags]]]) -> outImageeigen(src, computeEigenvectors[, eigenvalues[, eigenvectors]]) -> retval, eigenvalues, eigenvectorsellipse(img, center, axes, angle, startAngle, endAngle, color[, thickness[, lineType[, shift]]]) -> None  or  ellipse(img, box, color[, thickness[, lineType]]) -> Noneellipse2Poly(center, axes, angle, arcStart, arcEnd, delta) -> ptsequalizeHist(src[, dst]) -> dsterode(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dstestimateAffine3D(src, dst[, out[, inliers[, ransacThreshold[, confidence]]]]) -> retval, out, inliersestimateRigidTransform(src, dst, fullAffine) -> retvalextractChannel(src, coi[, dst]) -> dstfastNlMeansDenoising(src[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) -> dstfastNlMeansDenoisingColored(src[, dst[, h[, hColor[, templateWindowSize[, searchWindowSize]]]]]) -> dstfastNlMeansDenoisingColoredMultifastNlMeansDenoisingColoredMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize[, dst[, h[, hColor[, templateWindowSize[, searchWindowSize]]]]]) -> dstfastNlMeansDenoisingMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) -> dstfillConvexPoly(img, points, color[, lineType[, shift]]) -> NonefillPoly(img, pts, color[, lineType[, shift[, offset]]]) -> Nonefilter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]]) -> dstfilterSpeckles(img, newVal, maxSpeckleSize, maxDiff[, buf]) -> NonefindChessboardCorners(image, patternSize[, corners[, flags]]) -> retval, cornersfindCirclesGrid(image, patternSize[, centers[, flags[, blobDetector]]]) -> retval, centersfindCirclesGridDefault(image, patternSize[, centers[, flags]]) -> retval, centersfindContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> contours, hierarchyfindDataMatrix(image[, corners[, dmtx]]) -> codes, corners, dmtxfindFundamentalMat(points1, points2[, method[, param1[, param2[, mask]]]]) -> retval, maskfindHomography(srcPoints, dstPoints[, method[, ransacReprojThreshold[, mask]]]) -> retval, maskfindNonZero(src[, idx]) -> idxfitLine(points, distType, param, reps, aeps[, line]) -> lineflip(src, flipCode[, dst]) -> dstfloodFill(image, mask, seedPoint, newVal[, loDiff[, upDiff[, flags]]]) -> retval, rectgemm(src1, src2, alpha, src3, gamma[, dst[, flags]]) -> dstgetAffineTransform(src, dst) -> retvalgetBuildInformation() -> retvalgetDefaultNewCameraMatrix(cameraMatrix[, imgsize[, centerPrincipalPoint]]) -> retvalgetDerivKernels(dx, dy, ksize[, kx[, ky[, normalize[, ktype]]]]) -> kx, kygetGaborKernel(ksize, sigma, theta, lambd, gamma[, psi[, ktype]]) -> retvalgetGaussianKernel(ksize, sigma[, ktype]) -> retvalgetOptimalDFTSize(vecsize) -> retvalgetOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]) -> retval, validPixROIgetPerspectiveTransform(src, dst) -> retvalgetRectSubPix(image, patchSize, center[, patch[, patchType]]) -> patchgetRotationMatrix2D(center, angle, scale) -> retvalgetStructuringElement(shape, ksize[, anchor]) -> retvalgetTextSize(text, fontFace, fontScale, thickness) -> retval, baseLinegetTrackbarPos(trackbarname, winname) -> retvalgetValidDisparityROI(roi1, roi2, minDisparity, numberOfDisparities, SADWindowSize) -> retvalgetWindowProperty(winname, prop_id) -> retvalgoodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> cornersgrabCut(img, mask, rect, bgdModel, fgdModel, iterCount[, mode]) -> NonegroupRectangles(rectList, groupThreshold[, eps]) -> rectList, weightsidct(src[, dst[, flags]]) -> dstidft(src[, dst[, flags[, nonzeroRows]]]) -> dstimdecode(buf, flags) -> retvalimencode(ext, img[, params]) -> retval, bufimread(filename[, flags]) -> retvalimwrite(filename, img[, params]) -> retvalinRange(src, lowerb, upperb[, dst]) -> dstinitCameraMatrix2D(objectPoints, imagePoints, imageSize[, aspectRatio]) -> retvalinitModule_nonfree() -> retvalinitUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]) -> map1, map2initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize, destImageWidth, m1type[, map1[, map2[, projType[, alpha]]]]) -> retval, map1, map2inpaint(src, inpaintMask, inpaintRadius, flags[, dst]) -> dstinsertChannel(src, dst, coi) -> Noneintegral(src[, sum[, sdepth]]) -> sumintegral2(src[, sum[, sqsum[, sdepth]]]) -> sum, sqsumintegral3(src[, sum[, sqsum[, tilted[, sdepth]]]]) -> sum, sqsum, tiltedintersectConvexConvex(_p1, _p2[, _p12[, handleNested]]) -> retval, _p12invert(src[, dst[, flags]]) -> retval, dstinvertAffineTransform(M[, iM]) -> iMisContourConvex(contour) -> retvalkmeans(data, K, criteria, attempts, flags[, bestLabels[, centers]]) -> retval, bestLabels, centersline(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> Nonemagnitude(x, y[, magnitude]) -> magnitudematMulDeriv(A, B[, dABdA[, dABdB]]) -> dABdA, dABdBmatchShapes(contour1, contour2, method, parameter) -> retvalmatchTemplate(image, templ, method[, result]) -> resultmeanShift(probImage, window, criteria) -> retval, windowmeanStdDev(src[, mean[, stddev[, mask]]]) -> mean, stddevmedianBlur(src, ksize[, dst]) -> dstminEnclosingCircle(points) -> center, radiusminMaxLoc(src[, mask]) -> minVal, maxVal, minLoc, maxLocmixChannels(src, dst, fromTo) -> Nonemoments(array[, binaryImage]) -> retvalmorphologyEx(src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dstmoveWindow(winname, x, y) -> NonemulSpectrums(a, b, flags[, c[, conjB]]) -> cmulTransposed(src, aTa[, dst[, delta[, scale[, dtype]]]]) -> dstmultiply(src1, src2[, dst[, scale[, dtype]]]) -> dstnamedWindow(winname[, flags]) -> Nonenorm(src1[, normType[, mask]]) -> retval  or  norm(src1, src2[, normType[, mask]]) -> retvalnormalize(src[, dst[, alpha[, beta[, norm_type[, dtype[, mask]]]]]]) -> dstperspectiveTransform(src, m[, dst]) -> dstphase(x, y[, angle[, angleInDegrees]]) -> anglephaseCorrelate(src1, src2[, window]) -> retvalphaseCorrelateRes(src1, src2, window) -> retval, responsepointPolygonTest(contour, pt, measureDist) -> retvalpolarToCart(magnitude, angle[, x[, y[, angleInDegrees]]]) -> x, ypolylines(img, pts, isClosed, color[, thickness[, lineType[, shift]]]) -> NonepreCornerDetect(src, ksize[, dst[, borderType]]) -> dstprojectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs[, imagePoints[, jacobian[, aspectRatio]]]) -> imagePoints, jacobianputText(img, text, org, fontFace, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) -> NonepyrDown(src[, dst[, dstsize[, borderType]]]) -> dstpyrMeanShiftFiltering(src, sp, sr[, dst[, maxLevel[, termcrit]]]) -> dstpyrUp(src[, dst[, dstsize[, borderType]]]) -> dstrandShuffle(dst[, iterFactor]) -> Nonerandn(dst, mean, stddev) -> Nonerectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> Nonerectify3Collinear(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, cameraMatrix3, distCoeffs3, imgpt1, imgpt3, imageSize, R12, T12, R13, T13, alpha, newImgSize, flags[, R1[, R2[, R3[, P1[, P2[, P3[, Q]]]]]]]) -> retval, R1, R2, R3, P1, P2, P3, Q, roi1, roi2reduce(src, dim, rtype[, dst[, dtype]]) -> dstremap(src, map1, map2, interpolation[, dst[, borderMode[, borderValue]]]) -> dstrepeat(src, ny, nx[, dst]) -> dstreprojectImageTo3D(disparity, Q[, _3dImage[, handleMissingValues[, ddepth]]]) -> _3dImageresize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) -> dstresizeWindow(winname, width, height) -> NonescaleAdd(src1, alpha, src2[, dst]) -> dstsegmentMotion(mhi, timestamp, segThresh[, segmask]) -> segmask, boundingRectssepFilter2D(src, ddepth, kernelX, kernelY[, dst[, anchor[, delta[, borderType]]]]) -> dstsetTrackbarPos(trackbarname, winname, pos) -> NonesetUseOptimized(onoff) -> NonesetWindowProperty(winname, prop_id, prop_value) -> Nonesolve(src1, src2[, dst[, flags]]) -> retval, dstsolveCubic(coeffs[, roots]) -> retval, rootssolvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]]) -> retval, rvec, tvecsolvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, iterationsCount[, reprojectionError[, minInliersCount[, inliers[, flags]]]]]]]]) -> rvec, tvec, inlierssolvePoly(coeffs[, roots[, maxIters]]) -> retval, rootssort(src, flags[, dst]) -> dstsortIdx(src, flags[, dst]) -> dststereoCalibrate(objectPoints, imagePoints1, imagePoints2, imageSize[, cameraMatrix1[, distCoeffs1[, cameraMatrix2[, distCoeffs2[, R[, T[, E[, F[, criteria[, flags]]]]]]]]]]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, FstereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T[, R1[, R2[, P1[, P2[, Q[, flags[, alpha[, newImageSize]]]]]]]]) -> R1, R2, P1, P2, Q, validPixROI1, validPixROI2stereoRectifyUncalibrated(points1, points2, F, imgSize[, H1[, H2[, threshold]]]) -> retval, H1, H2subtract(src1, src2[, dst[, mask[, dtype]]]) -> dstthreshold(src, thresh, maxval, type[, dst]) -> retval, dsttransform(src, m[, dst]) -> dsttriangulatePoints(projMatr1, projMatr2, projPoints1, projPoints2[, points4D]) -> points4Dundistort(src, cameraMatrix, distCoeffs[, dst[, newCameraMatrix]]) -> dstundistortPoints(src, cameraMatrix, distCoeffs[, dst[, R[, P]]]) -> dstupdateMotionHistory(silhouette, mhi, timestamp, duration) -> NonevalidateDisparity(disparity, cost, minDisparity, numberOfDisparities[, disp12MaxDisp]) -> NonewarpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) -> dstwarpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) -> dstwatershed(image, markers) -> NonecreateTrackbar(trackbarName, windowName, value, count, onChange) -> NonesetMouseCallback(windowName, onMouse [, param]) -> Nonefromarray(array [, allowND]) -> CvMatAbsDiff(src1, src2, dst) -> NoneAbsDiffS(src, dst, value) -> NoneAcc(image, sum [, mask]) -> NoneAdaptiveThreshold(src, dst, maxValue [, adaptive_method [, thresholdType [, blockSize [, param1]]]]) -> NoneAdd(src1, src2, dst [, mask]) -> NoneAddS(src, value, dst [, mask]) -> NoneAddWeighted(src1, alpha, src2, beta, gamma, dst) -> NoneAnd(src1, src2, dst [, mask]) -> NoneAndS(src, value, dst [, mask]) -> NoneApproxChains(src_seq, storage [, method [, parameter [, minimal_perimeter [, recursive]]]]) -> CvSeq*ApproxPoly(src_seq, storage, method [, parameter [, parameter2]]) -> NoneArcLength(curve [, slice [, isClosed]]) -> doubleAvgSdv(arr [, mask]) -> mean,stdDevBackProjectPCA(proj, avg, eigenvects, result) -> NoneBoundingRect(points [, update]) -> CvRectCV_FOURCC(c1, c2, c3, c4) -> intCV_RGB(red, grn, blu) -> CvScalarCalcArrBackProject(image, back_project, hist) -> NoneCalcArrHist(image, hist [, accumulate [, mask]]) -> NoneCalcBackProject(image, back_project, hist) -> NoneCalcBackProjectPatch(images, dst, patch_size, hist, method, factor) -> NoneCalcCovarMatrix(vects, covMat, avg, flags) -> NoneCalcEMD2(signature1, signature2, distance_type [, distance_func [, cost_matrix [, flow [, lower_bound [, userdata]]]]]) -> floatCalcGlobalOrientation(orientation, mask, mhi, timestamp, duration) -> doubleCalcHist(image, hist [, accumulate [, mask]]) -> NoneCalcMotionGradient(mhi, mask, orientation, delta1, delta2 [, apertureSize]) -> NoneCalcOpticalFlowBM(prev, curr, blockSize, shiftSize, max_range, usePrevious, velx, vely) -> NoneCalcOpticalFlowFarneback(prev, curr, flow [, pyr_scale [, levels [, winsize [, iterations [, poly_n [, poly_sigma [, flags]]]]]]]) -> NoneCalcOpticalFlowHS(prev, curr, usePrevious, velx, vely, lambda, criteria) -> NoneCalcOpticalFlowLK(prev, curr, winSize, velx, vely) -> NoneCalcOpticalFlowPyrLK(prev, curr, prevPyr, currPyr, prevFeatures, winSize, level, criteria, flags [, guesses]) -> currFeatures,status,track_errorCalcPCA(data, avg, eigenvalues, eigenvectors, flags) -> NoneCalcProbDensity(hist1, hist2, dst_hist [, scale]) -> NoneCalcSubdivVoronoi2D(subdiv) -> NoneCalibrateCamera2(objectPoints, imagePoints, pointCounts, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs [, flags]) -> NoneCalibrationMatrixValues(calibMatr, image_size [, apertureWidth [, apertureHeight]]) -> fovx,fovy,focalLength,principalPoint,pixelAspectRatioCamShift(prob_image, window, criteria) -> int,comp,boxCanny(image, edges, threshold1, threshold2 [, aperture_size]) -> NoneCaptureFromCAM(index) -> CvCapture*CaptureFromFile(filename) -> CvCapture*CartToPolar(x, y, magnitude [, angle [, angleInDegrees]]) -> NoneCheckArr(arr [, flags [, min_val [, max_val]]]) -> intCheckContourConvexity(contour) -> intCircle(img, center, radius, color [, thickness [, lineType [, shift]]]) -> NoneClearSubdivVoronoi2D(subdiv) -> NoneClipLine(imgSize, pt1, pt2) -> point1,point2CloneImage(image) -> IplImage*CloneSeq(seq, storage) -> NoneCmp(src1, src2, dst, cmpOp) -> NoneCmpS(src, value, dst, cmpOp) -> NoneCompareHist(hist1, hist2, method) -> doubleComputeCorrespondEpilines(points, whichImage, F, lines) -> NoneContourArea(contour [, slice]) -> doubleConvertImage(src, dst [, flags]) -> NoneConvertMaps(mapx, mapy, mapxy, mapalpha) -> NoneConvertPointsHomogeneous(src, dst) -> NoneConvertScale(src, dst [, scale [, shift]]) -> NoneConvertScaleAbs(src, dst [, scale [, shift]]) -> NoneConvexHull2(points, storage [, orientation [, return_points]]) -> CvSeq*ConvexityDefects(contour, convexhull, storage) -> CvSeqOfCvConvexityDefect*Copy(src, dst [, mask]) -> NoneCopyMakeBorder(src, dst, offset, bordertype [, value]) -> NoneCornerEigenValsAndVecs(image, eigenvv, blockSize [, aperture_size]) -> NoneCornerHarris(image, harris_dst, blockSize [, aperture_size [, k]]) -> NoneCornerMinEigenVal(image, eigenval, blockSize [, aperture_size]) -> NoneCreateCameraCapture(index) -> CvCapture*CreateFileCapture(filename) -> CvCapture*CreateHist(dims, type [, ranges [, uniform]]) -> CvHistogramCreateImage(size, depth, channels) -> IplImage*CreateImageHeader(size, depth, channels) -> IplImage*CreateKalman(dynam_params, measure_params [, control_params]) -> CvKalman*CreateMat(rows, cols, type) -> CvMatCreateMatHeader(rows, cols, type) -> CvMatCreateMatND(dims, type) -> CvMatNDCreateMatNDHeader(dims, type) -> CvMatNDCreateMemStorage( [, blockSize]) -> CvMemStorageCreatePOSITObject(points) -> CvPOSITObject*CreateStereoBMState( [, preset [, numberOfDisparities]]) -> CvStereoBMState*CreateStereoGCState(numberOfDisparities, maxIters) -> CvStereoGCState*CreateStructuringElementEx(cols, rows, anchorX, anchorY, shape [, values]) -> IplConvKernel*CreateSubdivDelaunay2D(rect, storage) -> CvSubdiv2D*CreateTrackbar(trackbarName, windowName, value, count, onChange) -> NoneCreateVideoWriter(filename, fourcc, fps, frame_size [, is_color]) -> CvVideoWriter*CrossProduct(src1, src2, dst) -> NoneCvtColor(src, dst, code) -> NoneCvtPixToPlane(src, dst0, dst1, dst2, dst3) -> NoneCvtScale(src, dst [, scale [, shift]]) -> NoneDFT(src, dst, flags [, nonzeroRows]) -> NoneDecodeImage(buf [, iscolor]) -> IplImage*DecodeImageM(buf [, iscolor]) -> CvMat*DecomposeProjectionMatrix(projMatrix, cameraMatrix, rotMatrix, transVect [, rotMatrX [, rotMatrY [, rotMatrZ]]]) -> eulerAnglesDilate(src, dst [, element [, iterations]]) -> NoneDistTransform(src, dst [, distance_type [, mask_size [, mask [, labels]]]]) -> NoneDiv(src1, src2, dst [, scale]) -> NoneDotProduct(src1, src2) -> doubleDrawChessboardCorners(image, patternSize, corners, patternWasFound) -> NoneDrawContours(img, contour, external_color, hole_color, max_level [, thickness [, lineType [, offset]]]) -> NoneEigenVV(mat, evects, evals, eps [, lowindex [, highindex]]) -> NoneEllipse(img, center, axes, angle, start_angle, end_angle, color [, thickness [, lineType [, shift]]]) -> NoneEllipseBox(img, box, color [, thickness [, lineType [, shift]]]) -> NoneEncodeImage(ext, image [, params]) -> CvMat*EqualizeHist(src, dst) -> NoneErode(src, dst [, element [, iterations]]) -> NoneEstimateRigidTransform(A, B, M, full_affine) -> NoneExtractSURF(image, mask, storage, params) -> keypoints,descriptorsFillConvexPoly(img, pn, color [, lineType [, shift]]) -> NoneFillPoly(img, polys, color [, lineType [, shift]]) -> NoneFilter2D(src, dst, kernel [, anchor]) -> NoneFindChessboardCorners(image, patternSize [, flags]) -> cornersFindContours(image, storage [, mode [, method [, offset]]]) -> CvSeqFindCornerSubPix(image, corners, win, zero_zone, criteria) -> cornersFindExtrinsicCameraParams2(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec [, useExtrinsicGuess]) -> NoneFindFundamentalMat(points1, points2, fundamentalMatrix [, method [, param1 [, param2 [, status]]]]) -> intFindHomography(srcPoints, dstPoints, H [, method [, ransacReprojThreshold [, status]]]) -> NoneFindNearestPoint2D(subdiv, pt) -> CvSubdiv2DPoint*FindStereoCorrespondenceBM(left, right, disparity, state) -> NoneFindStereoCorrespondenceGC(left, right, dispLeft, dispRight, state [, useDisparityGuess]) -> NoneFitEllipse2(points) -> CvBox2DFitLine(points, dist_type, param, reps, aeps) -> lineFlip(src [, dst [, flipMode]]) -> NoneFloodFill(image, seed_point, new_val [, lo_diff [, up_diff [, flags [, mask]]]]) -> compGEMM(src1, src2, alpha, src3, beta, dst [, tABC]) -> NoneGet2D(arr, idx0, idx1) -> CvScalarGet3D(arr, idx0, idx1, idx2) -> CvScalarGetAffineTransform(src, dst, mapMatrix) -> NoneGetCaptureProperty(capture, property_id) -> doubleGetCentralMoment(moments, x_order, y_order) -> doubleGetCols(arr, startCol, endCol) -> submatGetDiag(arr [, diag]) -> submatGetMat(arr [, allowND]) -> CvMatGetMinMaxHistValue(hist) -> min_value,max_value,min_idx,max_idxGetND(arr, indices) -> CvScalarGetNormalizedCentralMoment(moments, x_order, y_order) -> doubleGetOptimalDFTSize(size0) -> intGetOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha, newCameraMatrix [, newImageSize [, validPixROI [, centerPrincipalPoint]]]) -> NoneGetPerspectiveTransform(src, dst, mapMatrix) -> NoneGetQuadrangleSubPix(src, dst, mapMatrix) -> NoneGetReal1D(arr, idx0) -> doubleGetReal2D(arr, idx0, idx1) -> doubleGetReal3D(arr, idx0, idx1, idx2) -> doubleGetRectSubPix(src, dst, center) -> NoneGetRotationMatrix2D(center, angle, scale, mapMatrix) -> NoneGetRows(arr, startRow, endRow [, deltaRow]) -> submatGetSpatialMoment(moments, x_order, y_order) -> doubleGetStarKeypoints(image, storage [, params]) -> CvSeqOfCvStarKeypoint*GetSubRect(arr, rect) -> submatGetTextSize(textString, font) -> textSize,baselineGetTrackbarPos(trackbarName, windowName) -> intGetWindowProperty(name, prop_id) -> doubleGoodFeaturesToTrack(image, eigImage, tempImage, cornerCount, qualityLevel, minDistance [, mask [, blockSize [, useHarris [, k]]]]) -> cornerCountGrabCut(image, mask, rect, bgdModel, fgdModel, iterCount, mode) -> NoneHOGDetectMultiScale(image, storage [, svm_classifier [, win_stride [, hit_threshold [, scale [, group_threshold [, padding [, win_size [, block_size [, block_stride [, cell_size [, nbins [, gammaCorrection]]]]]]]]]]]]) -> CvSeq*HaarDetectObjects(image, cascade, storage [, scale_factor [, min_neighbors [, flags [, min_size]]]]) -> CvSeqOfCvAvgComp*HoughCircles(image, circle_storage, method, dp, min_dist [, param1 [, param2 [, min_radius [, max_radius]]]]) -> NoneHoughLines2(image, storage, method, rho, theta, threshold [, param1 [, param2]]) -> CvSeq*InRange(src, lower, upper, dst) -> NoneInRangeS(src, lower, upper, dst) -> NoneInitFont(fontFace, hscale, vscale [, shear [, thickness [, lineType]]]) -> fontInitIntrinsicParams2D(objectPoints, imagePoints, npoints, imageSize, cameraMatrix [, aspectRatio]) -> NoneInitLineIterator(image, pt1, pt2 [, connectivity [, left_to_right]]) -> line_iteratorInitUndistortMap(cameraMatrix, distCoeffs, map1, map2) -> NoneInitUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, map1, map2) -> NoneInpaint(src, mask, dst, inpaintRadius, flags) -> NoneIntegral(image, sum [, sqsum [, tiltedSum]]) -> NoneInvert(src, dst [, method]) -> doubleKMeans2(samples, nclusters, labels, termcrit [, attempts [, flags [, centers]]]) -> doubleKalmanCorrect(kalman, measurement) -> ROCvMat*KalmanPredict(kalman [, control]) -> ROCvMat*Laplace(src, dst [, apertureSize]) -> NoneLine(img, pt1, pt2, color [, thickness [, lineType [, shift]]]) -> NoneLoad(filename [, storage [, name]]) -> genericLoadImage(filename [, iscolor]) -> NoneLoadImageM(filename [, iscolor]) -> NoneLogPolar(src, dst, center, M [, flags]) -> NoneMahalonobis(vec1, vec2, mat) -> NoneMatMul(src1, src2, dst) -> NoneMatMulAdd(src1, src2, src3, dst) -> NoneMatchShapes(object1, object2, method [, parameter]) -> doubleMatchTemplate(image, templ, result, method) -> NoneMaxRect(rect1, rect2) -> CvRectMeanShift(prob_image, window, criteria) -> compMerge(src0, src1, src2, src3, dst) -> NoneMinAreaRect2(points [, storage]) -> CvBox2DMinEnclosingCircle(points) -> int,center,radiusMinMaxLoc(arr [, mask]) -> minVal,maxVal,minLoc,maxLocMixChannels(src, dst, fromTo) -> NoneMoments(arr [, binary]) -> momentsMorphologyEx(src, dst, temp, element, operation [, iterations]) -> NoneMoveWindow(name, x, y) -> NoneMul(src1, src2, dst [, scale]) -> NoneMulSpectrums(src1, src2, dst, flags) -> NoneMulTransposed(src, dst, order [, delta [, scale]]) -> NoneMultiplyAcc(image1, image2, acc [, mask]) -> NoneNamedWindow(name [, flags]) -> NoneNorm(arr1, arr2 [, normType [, mask]]) -> doubleNormalize(src, dst [, a [, b [, norm_type [, mask]]]]) -> NoneNormalizeHist(hist, factor) -> NoneOr(src1, src2, dst [, mask]) -> NoneOrS(src, value, dst [, mask]) -> NonePOSIT(posit_object, imagePoints, focal_length, criteria) -> rotationMatrix,translation_vectorPerspectiveTransform(src, dst, mat) -> NonePointPolygonTest(contour, pt, measure_dist) -> doublePolarToCart(magnitude, angle, x, y [, angleInDegrees]) -> NonePolyLine(img, polys, is_closed, color [, thickness [, lineType [, shift]]]) -> NonePreCornerDetect(image, corners [, apertureSize]) -> NoneProjectPCA(data, avg, eigenvectors, result) -> NoneProjectPoints2(objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints [, dpdrot [, dpdt [, dpdf [, dpdc [, dpddist]]]]]) -> NonePutText(img, text, org, font, color) -> NonePyrDown(src, dst [, filter]) -> NonePyrMeanShiftFiltering(src, dst, sp, sr [, max_level [, termcrit]]) -> NonePyrSegmentation(src, dst, storage, level, threshold1, threshold2) -> compPyrUp(src, dst [, filter]) -> NoneQueryFrame(capture) -> ROIplImage*QueryHistValue_1D(hist, idx0) -> doubleQueryHistValue_2D(hist, idx0, idx1) -> doubleQueryHistValue_3D(hist, idx0, idx1, idx2) -> doubleQueryHistValue_nD(hist, idx) -> doubleRQDecomp3x3(M, R, Q [, Qx [, Qy [, Qz]]]) -> eulerAnglesRandArr(rng, arr, distType, param1, param2) -> NoneRandShuffle(mat, rng [, iter_factor]) -> NoneRange(mat, start, end) -> NoneRectangle(img, pt1, pt2, color [, thickness [, lineType [, shift]]]) -> NoneReduce(src, dst [, dim [, op]]) -> NoneRemap(src, dst, mapx, mapy [, flags [, fillval]]) -> NoneReprojectImageTo3D(disparity, _3dImage, Q [, handleMissingValues]) -> NoneReshape(arr, newCn [, newRows]) -> CvMatReshapeMatND(arr, newCn, newDims) -> CvMatResize(src, dst [, interpolation]) -> NoneResizeWindow(name, width, height) -> NoneRetrieveFrame(capture [, index]) -> ROIplImage*Rodrigues2(src, dst [, jacobian]) -> NoneRunningAvg(image, acc, alpha [, mask]) -> NoneSVBkSb(W, U, V, B, X, flags) -> NoneSVD(A, W [, U [, V [, flags]]]) -> NoneSave(filename, structPtr [, name [, comment]]) -> NoneSaveImage(filename, image) -> NoneScalar(val0 [, val1 [, val2 [, val3]]]) -> CvScalarScalarAll(val0123) -> CvScalarScale(src, dst [, scale [, shift]]) -> NoneScaleAdd(src1, scale, src2, dst) -> NoneSegmentMotion(mhi, seg_mask, storage, timestamp, seg_thresh) -> CvSeq*SeqRemoveSlice(seq, slice) -> NoneSet(arr, value [, mask]) -> NoneSet1D(arr, idx, value) -> NoneSet2D(arr, idx0, idx1, value) -> NoneSet3D(arr, idx0, idx1, idx2, value) -> NoneSetCaptureProperty(capture, property_id, value) -> intSetData(arr, data, step) -> NoneSetIdentity(mat [, value]) -> NoneSetImageCOI(image, coi) -> NoneSetImageROI(image, rect) -> NoneSetMouseCallback(windowName, onMouse [, param]) -> NoneSetND(arr, indices, value) -> NoneSetReal1D(arr, idx, value) -> NoneSetReal2D(arr, idx0, idx1, value) -> NoneSetReal3D(arr, idx0, idx1, idx2, value) -> NoneSetRealND(arr, indices, value) -> NoneSetTrackbarPos(trackbarName, windowName, pos) -> NoneSetWindowProperty(name, prop_id, prop_value) -> NoneShowImage(name, image) -> NoneSmooth(src, dst [, smoothtype [, param1 [, param2 [, param3 [, param4]]]]]) -> NoneSnakeImage(image, points, alpha, beta, gamma, win, criteria [, calc_gradient]) -> pointsSobel(src, dst, xorder, yorder [, apertureSize]) -> NoneSolve(A, B, X [, method]) -> NoneSolveCubic(coeffs, roots) -> NoneSolvePoly(coeffs, roots [, maxiter [, fig]]) -> NoneSort(src, dst, idxmat [, flags]) -> NoneSplit(src, dst0, dst1, dst2, dst3) -> NoneSquareAcc(image, sqsum [, mask]) -> NoneStereoCalibrate(objectPoints, imagePoints1, imagePoints2, pointCounts, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T [, E [, F [, term_crit [, flags]]]]) -> NoneStereoRectify(cameraMatrix1, cameraMatrix2, distCoeffs1, distCoeffs2, imageSize, R, T, R1, R2, P1, P2 [, Q [, flags [, alpha [, newImageSize]]]]) -> roi1,roi2StereoRectifyUncalibrated(points1, points2, F, imageSize, H1, H2 [, threshold]) -> NoneSub(src1, src2, dst [, mask]) -> NoneSubRS(src, value, dst [, mask]) -> NoneSubS(src, value, dst [, mask]) -> NoneSubdiv2DEdgeDst(edge) -> CvSubdiv2DPoint*Subdiv2DEdgeOrg(edge) -> CvSubdiv2DPoint*Subdiv2DGetEdge(edge, type) -> CvSubdiv2DEdgeSubdiv2DLocate(subdiv, pt) -> loc,whereSubdiv2DNextEdge(edge) -> CvSubdiv2DEdgeSubdiv2DRotateEdge(edge, rotate) -> CvSubdiv2DEdgeSubdivDelaunay2DInsert(subdiv, pt) -> CvSubdiv2DPoint*ThreshHist(hist, threshold) -> NoneThreshold(src, dst, threshold, maxValue, thresholdType) -> NoneTransform(src, dst, transmat [, shiftvec]) -> NoneUndistort2(src, dst, cameraMatrix, distCoeffs) -> NoneUndistortPoints(src, dst, cameraMatrix, distCoeffs [, R [, P]]) -> NoneUpdateMotionHistory(silhouette, mhi, timestamp, duration) -> NoneWarpAffine(src, dst, mapMatrix [, flags [, fillval]]) -> NoneWarpPerspective(src, dst, mapMatrix [, flags [, fillval]]) -> NoneWatershed(image, markers) -> NoneWriteFrame(writer, image) -> intXor(src1, src2, dst [, mask]) -> NoneXorS(src, value, dst [, mask]) -> NonemSet(mat, row, col, value) -> NoneiiiiO(ff)(ff)Oseed|Lval0val2val3d|ddd(ffff)delaysidssi(ii)IiCreateImage failedsiiCreateMat failedallowND__array_struct__flagss|iconnectivityleft_to_rightO&O&O&|iifontFacehscalevscaleshearthicknesslineTypeidd|diianchorXanchorYiiiii|OpresetnumberOfDisparitiesdynam_paramsmeasure_paramscontrol_params(ii)Ii:CreateImage(iiii)(fNN)seq elem size is %d
KIND %d
(ii),(ii)Unknown element type %08x
type=%08x C%d rows=%d cols=%d step=%d %lux.%dnChannels=%d width=%d height=%d widthStep=%d (ff)(ff)f(ff)(ff)(ff)(ff)cameraMatrixdistCoeffsOOOO|OOcoeffsrootsmaxiterfigjacobianQxQyQzobjectPointsrvecimagePointsdpdrotdpdtdpdfdpdcdpddistOOOOOO|OOOOOmapMatrixsrcPointsdstPointsransacReprojThresholdstatusOOO|idOpoints1points2fundamentalMatrixOOO|iddOuseExtrinsicGuessprojMatrixrotMatrixtransVectrotMatrXrotMatrYrotMatrZOOOO|OOOiscolor<%c%d(ss)selfUnrecognized depth 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OOOOi|ididbi:HOGDescriptorO:HOGDescriptorOO|b:KDTreendisparitiesi|ii:StereoBMnumDisparitiesdisp12MaxDiffpreFilterCapuniquenessRatiospeckleWindowSizespeckleRangefullDPiii|iiiiiiib:StereoSGBMpyrScalenItminDispmaxDispfilambdapenalizationcycleidiiiidffiii:StereoVarO:Subdiv2Di:VideoCaptureO:VideoCaptureOidO|b:VideoWriterOO|O:IndexO|O:FeatureDetector.detectdescriptorsuseProvidedKeypointsO:StarDetector.detecttemplScalemaxMatchesminMatchDistancepadXpadYscalesminScalemaxScaleorientationWeighttruncateOO|didiiidddd:chamerMatchingO|O:MSER.detectsegThreshsegmaskOdd|O:segmentMotionhitThresholdwinStridepaddingfinalThresholduseMeanshiftGroupingsvm_classifierwin_stridehit_thresholdgroup_thresholdwin_sizeblock_stridecell_sizeOO|OOddiOOOOOiiblobDetectorOO|OiO:findCirclesGridmaxTotalKeypointsgridRowsgridColsdetectorTypeO:FeatureDetector_create|dO:createCLAHEdescriptorExtractorTypeO:DescriptorExtractor_createdescriptorMatcherTypeO:DescriptorMatcher_creategrid_xgrid_ynum_components|id:createEigenFaceRecognizerO:Feature2D_createdynamParamsmeasureParamscontrolParamsii|ii:KalmanFilterindexParamssearchParams|OO:FlannBasedMatcherO:Algorithm.getMatVectorvector::_M_fill_insertcameraMatrix3distCoeffs3imgpt1imgpt3R12T12R13T13R3P3OO:FaceRecognizer.traincontourshierarchyOii|OOO:findContoursO|O:mergeO|O:hconcatpyramidwithDerivativespyrBorderderivBordertryReuseInputImageOO:FaceRecognizer.updatecontourIdxOOiO|iiOiO:drawContourssrcImgsimgToDenoiseIndextemporalWindowSizeOOO|iiO:fillPolyqueryDescriptorstrainDescriptorsOO|O:DescriptorMatcher.matchmaskscompactResultO:DescriptorMatcher.adddmtxO|OO:findDataMatrixO|O:splitOOO|OOOOiO:calibrateCameraOOO|d:initCameraMatrix2DO|O:vconcatOO:Algorithm.setMatVector(NNNNNNNNN)OObO|iii:polylinesOO|O:Feature2D.computeoutImageOO|OOi:drawKeypointscodesOOO:drawDataMatrixCodesOOO:mixChannelsOO|O:imwriteOO|O:imencodeptvecO:Subdiv2D.insertOOOOO|Ob:calcHistradiusListnumberListdMaxdMinindexChangeOO|ffO:BRISK.generateKerneloctavespatternScale|iif:BRISKOO|ffO:BRISKOOOOd|O:calcBackProjectrejectLevelslevelWeightsminNeighborsminSizeoutputRejectLevelssearchLocationsO|dOOO:HOGDescriptor.detectlocationsO|OOO:HOGDescriptor.computerectListgroupThresholdOi|d:groupRectangles(sOO)cv2.KDTreemaxDepthdims() -> retvalcv2.FileStoragegetFirstTopLevelNodeisOpenedisOpened() -> retvalreleaserelease() -> NonereleaseAndGetStringroot([, streamidx]) -> retvalcv2.FileNodeempty() -> retvalisIntisInt() -> retvalisMapisMap() -> retvalisNamedisNamed() -> retvalisNoneisNone() -> retvalisRealisReal() -> retvalisSeqisSeq() -> retvalisStringisString() -> retvalname() -> retvalsize() -> retvaltype() -> retvalcv2.AlgorithmgetAlgorithm(name) -> retvalgetBool(name) -> retvalgetDouble(name) -> retvalgetInt(name) -> retvalgetMat(name) -> retvalgetMatVector(name) -> retvalgetParamsgetParams() -> namesgetString(name) -> retvalparamHelp(name) -> retvalparamType(name) -> retvalsetBool(name, value) -> NonesetInt(name, value) -> NonesetMat(name, value) -> Nonecv2.flann_IndexgetAlgorithm() -> retvalgetDistancegetDistance() -> retvalradiusSearchsave(filename) -> Nonecv2.CLAHEapply(src[, dst]) -> dstcv2.Subdiv2DgetEdgeListgetEdgeList() -> edgeListgetTriangleListgetVoronoiFacetListinitDelaunay(rect) -> NonenextEdge(edge) -> retvalsymEdge(edge) -> retvalcv2.BackgroundSubtractorcv2.BackgroundSubtractorMOGcv2.KalmanFiltercv2.VideoCaptureget(propId) -> retvalgrabgrab() -> retvalset(propId, value) -> retvalcv2.VideoWriterwrite(image) -> Nonecv2.StatModelcv2.NormalBayesClassifierclearclear() -> Nonecv2.KNearestcv2.SVMget_support_vector_countget_var_countget_var_count() -> retvalcv2.EMisTrainedisTrained() -> retvalcv2.DTreegetVarImportancegetVarImportance() -> retvalcv2.RTreescv2.ERTreescv2.Boostprune(slice) -> Nonecv2.GBTreescv2.ANN_MLPcv2.KeyPointcv2.FeatureDetectorcv2.DescriptorExtractorcv2.Feature2DdetectAndComputecv2.BRISKcv2.ORBcv2.MSERcv2.StarDetectordetect(image) -> keypointscv2.FastFeatureDetectorcv2.GFTTDetectorcv2.SimpleBlobDetectorcv2.SimpleBlobDetector_ParamsblobColorfilterByAreafilterByCircularityfilterByColorfilterByConvexityfilterByInertiamaxAreamaxCircularitymaxConvexitymaxInertiaRatiomaxThresholdminAreaminCircularityminConvexityminDistBetweenBlobsminInertiaRatiominRepeatabilityminThresholdthresholdStepcv2.DMatchcv2.DescriptorMatcheradd(descriptors) -> NonegetTrainDescriptorsknnMatchtrain() -> Nonecv2.BFMatchercv2.FlannBasedMatchercv2.StereoBMcv2.StereoSGBMcv2.CascadeClassifierdetectMultiScaleload(filename) -> retvalcv2.HOGDescriptorsvmDetectorcheckDetectorSizecheckDetectorSize() -> retvalcomputeGradientgetDescriptorSizegetDescriptorSize() -> retvalgetWinSigmagetWinSigma() -> retvalsetSVMDetectorcv2.StereoVarcv2.FaceRecognizerload(filename) -> Nonetrain(src, labels) -> Noneupdate(src, labels) -> Nonecv2.SIFTdescriptorSizedescriptorSize() -> retvaldescriptorTypedescriptorType() -> retvalcv2.SURFAlgorithm_getListDTree() -> ERTrees() -> RTrees() -> GridAdaptedFeatureDetectorHuMoments(m[, hu]) -> huPSNR(src1, src2) -> retvalPyramidAdaptedFeatureDetectoradaptiveBilateralFilterbuildOpticalFlowPyramidcalcOpticalFlowFarnebackcalcOpticalFlowPyrLKcomputeCorrespondEpilinesconvertPointsFromHomogeneousconvertPointsToHomogeneouscountNonZero(src) -> retvalcreateFisherFaceRecognizercreateLBPHFaceRecognizercubeRoot(val) -> retvaldecomposeProjectionMatrixdestroyAllWindowsdestroyAllWindows() -> Nonedeterminant(mtx) -> retvaldistanceTransformWithLabelsexp(src[, dst]) -> dstfastAtan2(y, x) -> retvalfastNlMeansDenoisingColoredfastNlMeansDenoisingMultifitEllipse(points) -> retvalgetBuildInformationgetCPUTickCountgetCPUTickCount() -> retvalgetDefaultNewCameraMatrixgetNumberOfCPUsgetNumberOfCPUs() -> retvalgetOptimalNewCameraMatrixgetTickCountgetTickCount() -> retvalgetTickFrequencygetTickFrequency() -> retvalgoodFeaturesToTrackhconcat(src[, dst]) -> dstimshow(winname, mat) -> NoneinitModule_nonfreeinitUndistortRectifyMapinitWideAngleProjMaplog(src[, dst]) -> dstmax(src1, src2[, dst]) -> dstmean(src[, mask]) -> retvalmerge(mv[, dst]) -> dstmin(src1, src2[, dst]) -> dstminAreaRect(points) -> retvalpatchNaNs(a[, val]) -> Nonepow(src, power[, dst]) -> dstrandu(dst, low, high) -> Nonerectify3CollinearsetIdentity(mtx[, s]) -> Nonesplit(m[, mv]) -> mvsqrt(src[, dst]) -> dststartWindowThreadstartWindowThread() -> retvalstereoCalibratestereoRectifystereoRectifyUncalibratedsumElems(src) -> retvaltrace(mtx) -> retvaltranspose(src[, dst]) -> dstuseOptimizeduseOptimized() -> retvalvconcat(src[, dst]) -> dstwaitKey([, delay]) -> retvalcreateTrackbarsetMouseCallbackcv2.cv.cvcontourtreecv2.cv.cvfontcv2.cv.cvhistogramcv2.cv.cvlineiteratorcv2.cv.cvmattostringcv2.cv.cvmatndcv2.cv.cvmemstoragecv2.cv.cvsubdiv2dedgecv2.cv.cvrngcv2.cv.cvseqh_nexth_prevv_nextv_prevcv2.cv.cvsetcv2.cv.cvsubdiv2dcv2.cv.cvsubdiv2dpointcv2.cv.iplimagenChannelscv2.cv.memtrackcv2.IplConvKernelnRowsnColscv2.Capturecv2.HaarClassifierCascadecv2.POSITObjectcv2.StereoBMStatetextureThresholdpreFilterSizepreFilterTypecv2.StereoGCStateIthresholdocclusionCostinteractionRadiuslambda1lambda2cv2.Kalmanmeasurement_matrixerror_cov_poststate_pregainCPmeasurement_noise_coverror_cov_precontrol_matrixprocess_noise_covtransition_matrixstate_postcv2.Momentsinv_sqrt_m00fromarrayFindDataMatrixtemp_testAbs(src, dst) -> NoneAbsDiffAbsDiffSAdaptiveThresholdAddSAddWeightedAndAndSApproxChainsApproxPolyArcLengthAvg(arr [, mask]) -> CvScalarAvgSdvBackProjectPCABoundingRectBoxPointsBoxPoints(box) -> pointsCV_16SCCV_16SC(n) -> intCV_16UCCV_16UC(n) -> intCV_32FCCV_32FC(n) -> intCV_32SCCV_32SC(n) -> intCV_64FCCV_64FC(n) -> intCV_8SCCV_8SC(n) -> intCV_8UCCV_8UC(n) -> intCV_CMPCV_CMP(a, b) -> intCV_FOURCCCV_IABSCV_IABS(a) -> intCV_IS_SEQ_CLOSEDCV_IS_SEQ_CLOSED(s) -> intCV_IS_SEQ_CONVEXCV_IS_SEQ_CONVEX(s) -> intCV_IS_SEQ_CURVECV_IS_SEQ_CURVE(s) -> intCV_IS_SEQ_HOLECV_IS_SEQ_HOLE(s) -> intCV_IS_SEQ_INDEXCV_IS_SEQ_INDEX(s) -> intCV_IS_SEQ_SIMPLECV_IS_SEQ_SIMPLE(s) -> intCV_MAKETYPECV_MAKETYPE(depth, cn) -> intCV_MAT_CNCV_MAT_CN(i) -> intCV_MAT_DEPTHCV_MAT_DEPTH(i) -> intCV_RGBCV_SIGNCV_SIGN(a) -> intCalcArrBackProjectCalcArrHistCalcBackProjectCalcBackProjectPatchCalcCovarMatrixCalcEMD2CalcGlobalOrientationCalcHistCalcMotionGradientCalcOpticalFlowBMCalcOpticalFlowFarnebackCalcOpticalFlowHSCalcOpticalFlowLKCalcOpticalFlowPyrLKCalcPCACalcProbDensityCalcSubdivVoronoi2DCalibrateCamera2CalibrationMatrixValuesCaptureFromCAMCaptureFromFileCartToPolarCbrtCbrt(value) -> floatCeilCeil(value) -> intCheckArrCheckContourConvexityClearHistClearHist(hist) -> NoneClearNDClearND(arr, idx) -> NoneClearSeqClearSeq(seq) -> NoneClearSubdivVoronoi2DClipLineCloneImageCloneMatCloneMat(mat) -> CvMat*CloneMatNDCloneMatND(mat) -> CvMatND*CloneSeqCmpCmpSCompareHistComputeCorrespondEpilinesContourAreaConvertConvert(src, dst) -> NoneConvertImageConvertMapsConvertPointsHomogeneousConvertScaleConvertScaleAbsConvexHull2ConvexityDefectsCopyCopyMakeBorderCornerEigenValsAndVecsCornerHarrisCornerMinEigenValCountNonZeroCountNonZero(arr) -> intCreateCameraCaptureCreateDataCreateData(arr) -> NoneCreateFileCaptureCreateHistCreateImageHeaderCreateKalmanCreateMatCreateMatHeaderCreateMatNDCreateMatNDHeaderCreateMemStorageCreatePOSITObjectCreateStereoBMStateCreateStereoGCStateCreateStructuringElementExCreateSubdivDelaunay2DCreateTrackbarCreateVideoWriterCrossProductCvtColorCvtPixToPlaneCvtScaleDCTDCT(src, dst, flags) -> NoneDFTDecodeImageDecodeImageMDecomposeProjectionMatrixDestroyAllWindowsDestroyAllWindows() -> NoneDestroyWindowDestroyWindow(name) -> NoneDetDet(mat) -> doubleDilateDistTransformDivDotProductDrawChessboardCornersDrawContoursEigenVVEllipseBoxEncodeImageEqualizeHistErodeEstimateRigidTransformExpExp(src, dst) -> NoneExtractSURFFastArctanFastArctan(y, x) -> floatFillConvexPolyFillPolyFindChessboardCornersFindContoursFindCornerSubPixFindExtrinsicCameraParams2FindFundamentalMatFindHomographyFindNearestPoint2DFindStereoCorrespondenceBMFindStereoCorrespondenceGCFitEllipse2FitLineFlipFloodFillFloorFloor(value) -> intGEMMGet1DGet1D(arr, idx) -> CvScalarGet2DGet3DGetAffineTransformGetCapturePropertyGetCentralMomentGetColGetCol(arr, col) -> submatGetColsGetDiagGetDimsGetDims(arr) -> dim1,dim2,...GetElemTypeGetElemType(arr) -> intGetHuMomentsGetHuMoments(moments) -> huGetImageGetImage(arr) -> iplimageGetImageCOIGetImageCOI(image) -> intGetImageROIGetImageROI(image) -> CvRectGetMatGetMinMaxHistValueGetNDGetNormalizedCentralMomentGetOptimalDFTSizeGetOptimalNewCameraMatrixGetPerspectiveTransformGetQuadrangleSubPixGetReal1DGetReal2DGetReal3DGetRealNDGetRealND(arr, idx) -> doubleGetRectSubPixGetRotationMatrix2DGetRowGetRow(arr, row) -> submatGetRowsGetSizeGetSize(arr) -> CvSizeGetSpatialMomentGetStarKeypointsGetSubRectGetTextSizeGetTickCountGetTickCount() -> int64GetTickFrequencyGetTickFrequency() -> int64GetTrackbarPosGetWindowPropertyGoodFeaturesToTrackGrabCutGrabFrameGrabFrame(capture) -> intHOGDetectMultiScaleHaarDetectObjectsHoughLines2InRangeInRangeSInitFontInitIntrinsicParams2DInitLineIteratorInitUndistortMapInitUndistortRectifyMapInpaintIntegralInvSqrtInvSqrt(value) -> floatIsInfIsInf(value) -> intIsNaNIsNaN(value) -> intKMeans2KalmanCorrectKalmanPredictLUT(src, dst, lut) -> NoneLaplaceLoadLoadImageLoadImageMLogLog(src, dst) -> NoneLogPolarMahalonobisMatMulMatMulAddMatchShapesMatchTemplateMax(src1, src2, dst) -> NoneMaxRectMaxSMaxS(src, value, dst) -> NoneMeanShiftMergeMin(src1, src2, dst) -> NoneMinAreaRect2MinEnclosingCircleMinMaxLocMinSMinS(src, value, dst) -> NoneMixChannelsMorphologyExMoveWindowMulSpectrumsMulTransposedMultiplyAccNamedWindowNormNormalizeNormalizeHistNotNot(src, dst) -> NoneOrOrSPOSITPointPolygonTestPolarToCartPolyLinePowPow(src, dst, power) -> NonePreCornerDetectProjectPoints2PutTextPyrDownPyrMeanShiftFilteringPyrSegmentationPyrUpQueryFrameQueryHistValue_1DQueryHistValue_2DQueryHistValue_3DQueryHistValue_nDRNG( [, seed]) -> CvRNGRandArrRandIntRandInt(rng) -> unsignedRandRealRandReal(rng) -> doubleRandShuffleRealScalarRealScalar(val0) -> CvScalarRectangleReduceRemapRepeatRepeat(src, dst) -> NoneReprojectImageTo3DResetImageROIResetImageROI(image) -> NoneReshapeReshapeMatNDResizeResizeWindowRetrieveFrameRodrigues2RoundRound(value) -> intRunningAvgSVBkSbSaveSaveImageScalarAllScaleAddSegmentMotionSeqInvertSeqInvert(seq) -> NoneSeqRemoveSeqRemove(seq, index) -> NoneSeqRemoveSliceSet1DSet2DSet3DSetCapturePropertySetDataSetIdentitySetImageCOISetImageROISetMouseCallbackSetNDSetReal1DSetReal2DSetReal3DSetRealNDSetTrackbarPosSetWindowPropertySetZeroSetZero(arr) -> NoneShowImageSmoothSnakeImageSolveSolveCubicSolvePolySortSplitSquareAccStartWindowThreadStartWindowThread() -> NoneStereoCalibrateStereoRectifyStereoRectifyUncalibratedSubSubRSSubSSubdiv2DEdgeDstSubdiv2DEdgeOrgSubdiv2DGetEdgeSubdiv2DLocateSubdiv2DNextEdgeSubdiv2DRotateEdgeSubdivDelaunay2DInsertSum(arr) -> CvScalarThreshHistTraceTrace(mat) -> CvScalarTransposeTranspose(src, dst) -> NoneUndistort2UndistortPointsUpdateMotionHistoryWaitKeyWaitKey( [, delay]) -> intWarpAffineWarpPerspectiveWatershedWriteFrameXorXorSmGetmGet(mat, row, col) -> doublemSetN2cv12MatAllocatorE14NumpyAllocatorN2cv9Feature2DEN2cv5CLAHEE??@???G?z???@??ư>?<??A?=?o@Y@{?G?z???????????????v@-DT?!?????????????????{?G?z??-C??6?4@-DT?!	@$@??????????????????)\???(???~j?t?h??>333333??????????@D@?????ư>?/home/fit/opencv-2.4.7/modules/core/src/alloc.cppCustom memory allocator is not supportedFailed to allocate %lu bytesOutOfMemoryErrorcvSetMemoryManagerX.size == Y.size && type == Y.type() && (depth == CV_32F || depth == CV_64F)/home/fit/opencv-2.4.7/modules/core/src/mathfuncs.cppthe value at (%d, %d)=%g is out of rangesrc.type() == dst.type() && src.size == dst.sizectype == CV_32F || ctype == CV_64F(coeffs.size() == Size(n0, 1) || coeffs.size() == Size(n0+1, 1) || coeffs.size() == Size(1, n0) || coeffs.size() == Size(1, n0+1))CV_MAT_DEPTH(ctype) >= CV_32F && CV_MAT_CN(ctype) <= 2coeffs0.rows == 1 || coeffs0.cols == 1Mag.empty() || (Angle.size == Mag.size && type == Mag.type() && (depth == CV_32F || depth == CV_64F))Mag.size() == Angle.size() && Mag.type() == Angle.type()X.size() == Angle.size() && X.type() == Angle.type()Y.size() == Angle.size() && Y.type() == Angle.type()Mag.size() == X.size() && Mag.type() == X.type()Angle.size() == X.size() && Angle.type() == X.type()a.depth() == CV_32F_roots.data == _roots0.data_r.data == _r0.datamagnitudephasecartToPolarpolarToCart,?)????
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&s????T?[???y??j??????k??????Mb????6?W|???ur??Y???y͵????4???Q??}??%?????d?II??,?5L@???AA??s?0}????G???8??dlܱ???^??0??#?W?????)w?d(??u$?????@ ????????7zQ6$??؈G?2?????9??B.?????`?powcheckRangepatchNaNscvCartToPolarcvPolarToCartcvExpcvLogcvPowsolveCubicsolvePolycvSolveCubiccvSolvePoly???m0_$@l?l???'??@??$?-DT?!??~m?Cf?s??9??B.??`UU????UUUUUU???$I?$I??UUUUUU??pAp???+eGW@???G?@?l?8@?Z?Q@@'?Y@?i??Q????	??????H5?Z?@?8?ll?8@p^??Z?Q@~\?G'?Y@p?Sf??F@?al?h@??C??]@u??p?*@-]??_???!?f ?-@X(??b@??j?e@u?????@@???????A?????????G@??q?q??"@;@h/???????es-8R?@es-8R?@?=UUUU??0??.?++5??<?%ي"???A?V?Ao"eB?B4C?C???>???????????????????%?%?%?%ي"?ي"?ي"?ي"???A??A??A??A?V???V???V???V??o"eBo"eBo"eBo"eB?B?B?B?B4C4C4C4C?C?C?C?C????????????????9??B.???9??B.?????????????????>???>???>???>????????????????????`?`????????????????$I?$I???$I?$I??UUUUUUſUUUUUUſ????????????????ппUUUUUU??UUUUUU??????????????+eGW@??+eGW@?<?<?<?<?E?E?E?E????????=??@=??@=??@=??@d??Ad??Ad??Ad??A?Z?B?Z?B?Z?B?Z?B:??B:??B:??B:??B????????????????????pApAp?p??i??Q????i??Q????	??????	??????H5?Z?@?H5?Z?@?8?ll?8@?8?ll?8@p^??Z?Q@p^??Z?Q@~\?G'?Y@~\?G'?Y@???????????????????/home/fit/opencv-2.4.7/modules/core/src/rand.cpp_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))_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))errcode == 0dst.elemSize() <= 32scaleFunc != 0Unknown distribution typefillmakeRNGKeyrandShuffle߰??+mG|?@??r?	M??>͠?S*????A?<`????@p@`@?@?;?A???A?A??G?/??S\@?S\?/home/fit/opencv-2.4.7/modules/core/src/matmul.cppmat.type() == type() && mat.size == size && func != 0src1.size == dst.size && src1.type() == dst.type()type == B.type() && (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2)C.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))(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()mean.data && eigenvectors.data && ((mean.rows == 1 && eigenvectors.rows == data.cols) || (mean.cols == 1 && eigenvectors.rows == data.rows))data.cols <= evects.rows && dst.rows == data.rowsdata.rows <= evects.rows && dst.cols == data.colsmean.data && eigenvectors.data && ((mean.rows == 1 && mean.cols == data.cols) || (mean.cols == 1 && mean.rows == data.rows))dst.cols <= evects.rows && dst.rows == data.rowsdst.rows <= evects.rows && dst.cols == data.colsdelta.channels() == 1 && (delta.rows == src.rows || delta.rows == 1) && (delta.cols == src.cols || delta.cols == 1)(*each).size() == size && (*each).type() == type((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0)data[i].size() == size && data[i].type() == typeretainedVariance > 0 && retainedVariance <= 1(evals0.cols == 1 || evals0.rows == 1) && ecount0 <= ecount && evects0.cols == evects.cols && evects0.rows == ecount0type == v2.type() && type == icovar.type() && sz == v2.size() && len == icovar.rows && len == icovar.colsscn + 1 == m.cols && (depth == CV_32F || depth == CV_64F)dst.type() == src.type() && dst.channels() == m.rows-1scn == m.cols || scn + 1 == m.colsdst.depth() == src.depth() && dst.channels() == m.rowssrc1.type() == src2.type()a_size.width == lena_size.height == lentype == CV_64FC2dst0.data == dst.datasrc.channels() == 1src.size() > 0_mean.size() == sizedata && nsamples > 0data.channels() == 1_mean.size() == mean_szmean0.data == mean.datavecarr != 0 && count >= 1gemmtransformperspectiveTransformscaleAddcalcCovarMatrixcalcCovarMatrixMahalanobismulTransposeddotoperator()computeVarprojectbackProjectcvGEMMcvTransformcvPerspectiveTransformcvScaleAddcvCalcCovarMatrixcvCalcPCAcvProjectPCAcvBackProjectPCA??????GGGBF?D/home/fit/opencv-2.4.7/modules/core/src/lapack.cpptype == CV_32F || type == CV_64Fw.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))w.type() == u.type() && u.type() == vt.type() && u.data && vt.data && w.datau.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)type == _src2.type() && (type == CV_32F || type == CV_64F)(method != DECOMP_LU && method != DECOMP_CHOLESKY) || is_normal || src.rows == src.colsThe function can not solve under-determined linear systemsA.type() == x.type() && A.cols == x.rows && x.cols == b.colsmethod == DECOMP_LU || method == DECOMP_CHOLESKYsrc.type() == dst.type() && src.rows == dst.cols && src.cols == dst.rowsmat.rows == mat.cols && (type == CV_32F || type == CV_64F)u.type() == typev.type() == typeu.size() == svd.u.size()v.size() == svd.vt.size()dst.data == dst0.datasrc.rows == src.colsp == evects0.datap == evals0.datam == nrows == mat->colsdeterminantinvert???solveeigen_SVDcomputebackSubstcvDetcvInvertcvSolvecvEigenVVcvSVDcvSVBkSb?<8?<4??/home/fit/opencv-2.4.7/modules/core/src/matop.cppInvalid matrix initializer typeCV_MAT_CN(_type) == e.a.channels()Unknown operationN2cv5MatOpEN2cv14MatOp_IdentityEN2cv11MatOp_AddExEN2cv9MatOp_BinEN2cv9MatOp_CmpEN2cv10MatOp_GEMMEN2cv12MatOp_InvertEN2cv7MatOp_TEN2cv11MatOp_SolveEN2cv17MatOp_InitializerEassignassignassignintegershortbooleancv::Matstd::vectorunsigned intunsigned int64unsigned charWrong argument typeNo parameter '%s' is foundArgument error: the getter' of the algorithm '', the parameter has  type, Argument error: the setterParameter '%s' is readonly!nestedAlgo.empty()/home/fit/opencv-2.4.7/modules/core/src/algorithm.cpp method was called for the parameter 'so 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 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 i == 0 || vec[i].first != vec[i-1].firstUnknown/unsupported parameter typeunknown/unsupported type of '%s' parameter == %dWrong argument type in the setterWrong parameter type in the setterargType == 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::UCHAR8@??X@??x@???@???@???@???@??A??8A??XA??xA??@??addwritereadsetgetNameOfTypegetparamTypeparamHelpaddParam_addN2cv9AlgorithmE?C?u?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)0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rowsr == Range::all() || (0 <= r.start && r.start < r.end && r.end <= m.size[i])0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.colsThe matrix is not continuous, thus its number of rows can not be changedThe total number of matrix elements is not divisible by the new number of rowsThe total width is not divisible by the new number of channelsimg->dataOrder == IPL_DATA_ORDER_PIXELimg->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0COI is not supported by the functionseq->total > 0 && CV_ELEM_SIZE(seq->flags) == seq->elem_sizech.size == mat.size && ch.depth() == mat.depth() && 0 <= coi && coi < mat.channels()!src[i].empty() && src[i].dims <= 2 && src[i].rows == src[0].rows && src[i].type() == src[0].type()!src[i].empty() && src[i].dims <= 2 && src[i].cols == src[0].cols && src[i].type() == src[0].type()src.dims <= 2 && esz <= (size_t)32src.size() == dst.size() && (src.cols == 1 || src.rows == 1)dims <= 2 && m.dims <= 2 && size() == m.size() && tp == m.type() && ((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1))op == 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 != 0data.dims <= 2 && type == CV_32F && K > 0(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)Ksrc.rows == dst.cols && src.cols == dst.rows && src.type() == dst.type()srcA.size() == dst.size() && srcA.type() == dst.type()The reduced dimensionality index is out of rangeThe output array size is incorrectInput and output arrays must have the same number of channelsThe function only supports 32sC1 and 32fC1 datatypessrc.size() == idx.size() && idx.type() == CV_32S && src.data != idx.datasrc.size() == dst.size() && src.type() == dst.type()centers.depth() == data.depth()normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2Only 32f and 64f are supported_sizes && 0 < d && d <= CV_MAX_DIMCV_MAT_TYPE(mtype) == m.type()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!fixedType() || (CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0)k == GPU_MATi < 0k == MATk == STD_VECTOR_MATm.dims <= 2m.dims >= 20 <= i && i < (int)vv.size()0 <= i && i < (int)v.size()nelems <= (size_t)size.p[0]dims <= 2 && step[0] > 0Bad new number of rowsk == OPENGL_BUFFERk == OPENGL_TEXTUREUnknown array typeCV_IS_IMAGE(arr)(int)nelems >= 0m.rows == m.colssrc.dims <= 2N >= Kcounters[k] != 0idx0.data == idx.data!centers.empty()centers.rows == cluster_countcenters.cols == data.cols_arrays && (_ptrs || _planes)narrays <= 1000arrays[i] != 0A.size == arrays[i0]->sizeA.step[d-1] == A.elemSize()m != 0 && _idxhdrhdr && hdr->dims == 3hdr && hdr->dims == 2hdr && hdr->dims == 1_sizes[i] > 0hdr != 0Unknown/unsupported norm type!fixedType() && !fixedSize()m.dims == dimsm.size[j] == sizes[j]!fixedSize() || len == len0v[j].empty()src.data != dst.data???? ??????????????????8???createsetSizeMatMatMatdiagMatoperator IplImagepop_backreserveresizepush_backcvarrToMatlocateROIadjustROIextractImageCOIinsertImageCOIreshapescalarToRawDatagetMatgetMatVectorgetGlBuffergetGlTexturegetGpuMatgetOGlBuffergetOGlTexture2DsizetotaltypeemptycreatecreatecreatereleasecleargetMatRefgetGpuMatRefgetOGlBufferRefgetOGlTexture2DRefhconcatvconcatsetIdentitytracetransposecompleteSymmcrossreducesortsortIdxsortIdx_sortIdx_sortIdx_sortIdx_sortIdx_sortIdx_sortIdx_kmeanscvTransposecvCrossProductcvReducecvRangecvSortcvKMeans2reshapeinitposgetConvertElemgetConvertScaleElemSparseMatcreatecopyToconvertToconvertToptrptrptrptreraseeraseerasenormminMaxLocnormalizeN2cv11_InputArrayEN2cv12_OutputArrayEN2cv22KMeansDistanceComputerEN2cv24KMeansPPDistanceComputerE=?f@%d%cThe storage is not opened-.Inf%d.%.16e%s(%d): %sTabs are prohibited in YAML!Invalid characterIncorrect indentationComments are not allowed hereUnknown type of file nodeNULL double pointer%ui%uuNull key elementDuplicated keyKey may not start with '-'Missing ':'An empty keyNull element namelayoutinterleavedvertex_dtedge_dtvertex_countedge_countheader_dtheader_user_data%df%sverticesNo edges dataNo vertices dataDuplicated edge has occuredInvalid type infoEmpty tags are not supportedtype_idMismatched closing tagClosing " is expectedgtaposquotToo long string literalPreliminary end of the streamTag should start with '<'Unknown tag typeEmpty type nameThe wrong closing bracketUnknown object typeNULL structure pointersequencesUnknown objectelem_size > 0.gzInvalid filenamevector::_M_insert_auxExtra closing '%c'Incorrect element name %sInvalid fs.stateCV_IS_SPARSE_MAT(m)An extra closing tagNull string pointer#x%02xThe key is an emptyThe key is too longNegative number of elementsNull data pointer%.8etop-leftbottom-leftplanaropencv-sparse-matrixopencv-graph2if%sopencv-sequence hole curve untypedfalseFalseFALSEopencv-sequence-treelen % elemSize == 0!!%s %c!!%sNull comment{}
NULL or empty filenameNULL or empty buffera+twt.xml.XML.XmlUTF-16utf-16Utf-16strlen(encoding) < 1000

r+t %YAML:1.0
...
---
%YAML:
/home/fit/opencv-2.4.7/modules/core/src/persistence.cppToo long string or a last string w/o newlineInvalid character in the streamBad format of floating-point constantSequence element should not have name (use <_>)Invalid data type specificationToo long data type specificationToo complex format for the matrixThe size of element calculated from "dt" and the elem_size do not matchSize of sequence element (elem_size) is inconsistent with seq->flagsInvalid pointer to file storageThe node is neither a map nor an empty collectionThe file storage is opened for readingNull pointer to source file node or readerThe file node should be a numerical scalar or a sequenceNull 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 recordsSome of essential image attributes are absentOnly interleaved images can be readThe image data is not found in file storageThe matrix size does not match to the number of stored elementsNull pointers to source file node or destination arraySome of essential matrix attributes are absentCould not determine the matrix dimensionalityThe matrix data is not found in file storageCould not determine sparse matrix dimensionalitySparse matrix data is corruptedSome of essential graph attributes are absentThe sequence flags are invalidOne of "header_dt" and "header_user_data" is there, while the other is notGraph edges should start with 2 integers and a floatSome of stored vertex indices are out of rangeSome of essential sequence attributes are absentOnly one of "header_user_data", "rect" and "origin" tags may occurThe number of stored elements does not match to "count"Some 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 _Map element should have a nameDirective tags are not allowed hereThere should be space between literalsInvalid numeric value (inconsistent explicit type specification?)Literal " is not allowed within a string. Use "Literal ' or > are not allowed. Use ' or >Invalid numeric value in the stringInvalid character in the symbol entity nameThe actual type is different from the specified typeClosing tag should not contain any attributesAttribute name should be followed by '='Attribute value should be put into single or double quotesInvalid closing tag for 
Could not find  in the end of file.
Valid XML should start with '' tag is missing tag is missingUnsupported YAML version (it must be 1.x)The YAML streams must start with '---', except the first oneOnly collections as YAML streams are supported by this parserCould not find the/an object in file storageNULL memory storage is passed - the loaded dynamic structure can not be storedCould not open the file storage. Check the path and permissions?4?? 5??@5??`5???3???4???4??Pt???r??(t???t???q??t???t???q???s???r???s???s?? s??@s??hs?? s??ph??h???g??xg???f??Hf???e???h??getDefaultObjectNamecvReleaseFileStorageicvCloseicvPutscvGetFileNodecvGetFileNodeByNamecvGetRootFileNodecvOpenFileStorageicvYMLWriteCommenticvYMLWriteStringicvYMLWriteicvYMLEndWriteStructicvYMLStartWriteStructicvXMLWriteCommenticvXMLWriteStringicvXMLWriteScalaricvXMLWriteTagicvXMLEndWriteStructicvXMLStartWriteStructicvGetsicvXMLParseicvXMLParseValueicvProcessSpecialDoubleicvFSCreateCollectionicvXMLParseTagicvXMLSkipSpacesicvYMLParseicvYMLParseValueicvYMLParseKeyicvYMLSkipSpacescvStartWriteStructcvEndWriteStructcvWriteIntcvWriteRealcvWriteStringcvWriteCommentcvStartNextStreamcvWriteRawDataicvDecodeFormatucwsifdrcvStartReadRawDatacvReadRawDataSlicecvReadRawDatacvWriteFileNodeicvWriteFileNodecvRegisterTypecvReleasecvClonecvReadcvWritecvSavecvLoadoperator<<writeRawreadRawreadreadicvReadMatNDicvDecodeSimpleFormaticvReadMaticvWriteImageicvReadImageicvReadSparseMaticvGetFormaticvWriteHeaderDataicvReadGraphicvReleaseGraphicvReadSeqTreeicvReadSeqicvReleaseSeqN2cv11FileStorageE????????/home/fit/opencv-2.4.7/modules/core/src/dxt.cpptype == srcB.type() && srcA.size() == srcB.size()type == CV_32FC1 || type == CV_32FC2 || type == CV_64FC1 || type == CV_64FC2srcA.size == dst.size && srcA.type() == dst.type()type == CV_32FC1 || type == CV_64FC1Odd-size DCT's are not implementedsrc.size == dst.size && src.type() == dst.type()This 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 insteadfactors[0] == factors[nf-1]src.size == dst.sizedftDFTDFT?@? ?`??P?0?p??H?(?h??X?8?x??D?$?d??T?4?t??L?,?l??\??~??A?!?a??Q?1?q?	?I?)?i??Y?9?y??E?%?e??U?5?u?
?M?-?m??]?=?}??C?#?c??S?3?s??K?+?k??[?;?{??G?'?g??W?7?w??O?/?o??_??????????;f?????;f????F?2?k???c????}???\?ϗb??
?ib!??????N?AT?!?>X????????|CT?!?>b????????CT?!?>????????5DT?!?>!????????|CT?!?>?????????|CT?!?>b???????g^FT?!?>??????????@T?!y>????????$"LT?!i>????????$"LT?!Y>????????:T?!I>??@zT?!9>??@zT?!)>mulSpectrumsdct?;f???????;f???????;f???????;f???????;f???????;f???????;f???????;f??v?p??;f??f?`??;f??V?P??;f??F?@??;f??6?0??;f??&? ??;f?????;f????	
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General configuration for OpenCV 2.4.7 =====================================
  Version control:               unknown

  Platform:
    Host:                        Linux 3.5.0-21-generic i686
    CMake:                       2.8.9
    CMake generator:             Unix Makefiles
    CMake build tool:            /usr/bin/make
    Configuration:               Release

  C/C++:
    Built as dynamic libs?:      NO
    C++ Compiler:                /usr/bin/c++  (ver 4.7.2)
    C++ flags (Release):         -fPIC   -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -fdiagnostics-show-option -pthread -march=i686 -fomit-frame-pointer -msse -msse2 -msse3 -mfpmath=sse -ffunction-sections -O2 -DNDEBUG  -DNDEBUG
    C++ flags (Debug):           -fPIC   -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -fdiagnostics-show-option -pthread -march=i686 -fomit-frame-pointer -msse -msse2 -msse3 -mfpmath=sse -ffunction-sections -g  -O0 -DDEBUG -D_DEBUG -ggdb3
    C Compiler:                  /usr/bin/gcc
    C flags (Release):           -fPIC   -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -fdiagnostics-show-option -pthread -march=i686 -fomit-frame-pointer -msse -msse2 -msse3 -mfpmath=sse -ffunction-sections -O2 -DNDEBUG  -DNDEBUG
    C flags (Debug):             -fPIC   -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -fdiagnostics-show-option -pthread -march=i686 -fomit-frame-pointer -msse -msse2 -msse3 -mfpmath=sse -ffunction-sections -g  -O0 -DDEBUG -D_DEBUG -ggdb3
    Linker flags (Release):      
    Linker flags (Debug):        
    Precompiled headers:         YES

  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

  GUI: 
    QT:                          NO
    GTK+ 2.x:                    YES (ver 2.24.13)
    GThread :                    YES (ver 2.34.1)
    GtkGlExt:                    NO
    OpenGL support:              NO

  Media I/O: 
    ZLib:                        /usr/lib/i386-linux-gnu/libz.so (ver 1.2.7)
    JPEG:                        /usr/lib/i386-linux-gnu/libjpeg.so (ver )
    PNG:                         /usr/lib/i386-linux-gnu/libpng.so (ver 1.2.49)
    TIFF:                        /usr/lib/i386-linux-gnu/libtiff.so (ver 42 - 3.9.6)
    JPEG 2000:                   /usr/lib/i386-linux-gnu/libjasper.so (ver 1.900.1)
    OpenEXR:                     /usr/lib/libImath.so /usr/lib/libIlmImf.so /usr/lib/libIex.so /usr/lib/libHalf.so /usr/lib/libIlmThread.so (ver 1.6.1)

  Video I/O:
    DC1394 1.x:                  NO
    DC1394 2.x:                  YES (ver 2.2.0)
    FFMPEG:                      YES
      codec:                     YES (ver 53.35.0)
      format:                    YES (ver 53.21.0)
      util:                      YES (ver 51.22.1)
      swscale:                   YES (ver 2.1.0)
      gentoo-style:              YES
    GStreamer:                   
      base:                      YES (ver 0.10.36)
      app:                       YES (ver 0.10.36)
      video:                     YES (ver 0.10.36)
    OpenNI:                      NO
    OpenNI PrimeSensor Modules:  NO
    PvAPI:                       NO
    GigEVisionSDK:               NO
    UniCap:                      NO
    UniCap ucil:                 NO
    V4L/V4L2:                    Using libv4l (ver 0.8.8)
    XIMEA:                       NO
    Xine:                        NO

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

  OpenCL:
    Version:                     dynamic
    Include path:                /home/fit/opencv-2.4.7/3rdparty/include/opencl/1.2
    Use AMD FFT:                 NO
    Use AMD BLAS:                NO

  Python:
    Interpreter:                 /usr/bin/python2 (ver 2.7.3)
    Libraries:                   /usr/lib/libpython2.7.so (ver 2.7.3)
    numpy:                       /usr/lib/python2.7/dist-packages/numpy/core/include (ver 1.6.2)
    packages path:               lib/python2.7/dist-packages

  Java:
    ant:                         /usr/bin/ant (ver 1.8.2)
    JNI:                         /usr/lib/jvm/java-6-oracle/include /usr/lib/jvm/java-6-oracle/include/linux /usr/lib/jvm/java-6-oracle/include
    Java tests:                  YES

  Documentation:
    Build Documentation:         YES (only HTML and without math expressions)
    Sphinx:                      /usr/bin/sphinx-build (ver 1.1.3)
    PdfLaTeX compiler:           NO

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

  Install path:                  /usr/local

  cvconfig.h is in:              /home/fit/opencv-2.4.7
-----------------------------------------------------------------

%s:%d: error: (%d) %s in function %s
Inplace 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 formatsBad flag (parameter or structure field)The function/feature is not implementedMemory block has been corruptedOpenCV Error: %s (%s) in %s, file %s, line %d/home/fit/opencv-2.4.7/modules/core/src/system.cppmodule != 0 && module->name != 0 && module->version != 0%s:%d: error: (%d) %s
OPENCV_TEMP_PATH__opencv_temp.XXXXXX/No ErrorBacktraceUnspecified errorInternal errorInsufficient memoryBad argumentIterations do not convergeAutotrace callIncorrect size of input arrayNull pointerDivision by zero occuredImage step is wrongInput COI is not supportedBad number of channelsBad parameter of type CvPointBad type of mask argumentParsing errorAssertion failedNo GPU supportGpu API callNo OpenGL supportOpenGL API callUnknown %s code %dunknown functionThe module is not found%s: %s%scxcoreО??????????????????????p???`???P???@???0??? ???????????????Н??????????ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??ל??????ל??ל??????ל??ל??ל??ל??ל??ל??????ל??????ל??????ל??ל??ל??ל??x???p???ל??h???`???X???P???H???@???*?5???4???????????3???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????2?6?????????1?????????7?cvRegisterModulecvGetModuleInfoN2cv9ExceptionEe??A/home/fit/opencv-2.4.7/modules/core/src/stat.cppbad cell size (not 1, 2 or 4) in normHammingsrc.channels() == 1 && func != 0mask.empty() || mask.type() == CV_8Udst.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)normType == 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)(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 supportedimg.dims <= 2_nidx.needed() == (K > 0)src.type() == CV_8UC1idx.isContinuous()0 < coi && coi <= 4cn <= 4 && func != 0sumcountNonZeromeanmeanStdDevminMaxIdxminMaxLoc????????????normHammingnormnormHammingnormbatchDistancefindNonZerocvSumcvAvgcvAvgSdvN2cv16BatchDistInvokerE/home/fit/opencv-2.4.7/modules/core/src/drawing.cppconnectivity == 8 || connectivity == 40 <= shift && shift <= XY_SHIFTpoints.checkVector(2, CV_32S) >= 0font != 0 && hscale > 0 && vscale > 0 && thickness >= 00 <= shift && shift <= XY_SHIFT && thickness >= 0pts && npts && ncontours >= 0 && 0 <= thickness && thickness <= 255 && 0 <= shift && shift <= XY_SHIFT0 <= thickness && thickness <= 255box.size.width >= 0 && box.size.height >= 0 && thickness <= 255axes.width >= 0 && axes.height >= 0 && thickness <= 255 && 0 <= shift && shift <= XY_SHIFTradius >= 0 && thickness <= 255 && 0 <= shift && shift <= XY_SHIFTpts && npts && ncontours >= 0 && 0 <= shift && shift <= XY_SHIFTUnknown font typept1 && pt2iterator != 0text != 0 && _font != 0vector::reservep.checkVector(2, CV_32S) >= 0thickness <= 255elem_type == CV_32SC2????0??8??P??h???????LineIteratorV??^0 >??1>ocC>??T>?Yf>`?w>탄>X ?>???>z7?>ద>B?>?{?>p̿>?
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????fillConvexPolyfillPolypolylinescvDrawContours????????????????????????cvClipLinecvInitLineIteratorcvPutTextcvInitFontcvGetTextSize?_@?>G/home/fit/opencv-2.4.7/modules/core/src/arithm.cppThe lower bounary is neither an array of the same size and same type as src, nor a scalarThe upper bounary is neither an array of the same size and same type as src, nor a scalar((int)lbScalar ^ (int)ubScalar) == 0src1.size == dst.size && dst.type() == CV_8Uop == CMP_LT || op == CMP_LE || op == CMP_EQ || op == CMP_NE || op == CMP_GE || op == CMP_GTThe 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 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'src2.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 specified(mask.type() == CV_8UC1 || mask.type() == CV_8SC1)src1.size == dst.size && src1.channels() == dst.channels()src2.size == dst.size && src2.channels() == dst.channels()The operation is neither 'array op array' (where arrays have the same size and type), nor 'array op scalar', nor 'scalar op array'scn == 1lb.type() == ub.type()mask.size == src1.sizeconvertAndUnrollScalarbinary_oparithm_op??????????????????????????compare`???????????????????o@?_@???@???@?????A????G???????inRangecvNotcvAndcvOrcvXorcvAndScvOrScvXorScvAddcvSubcvAddScvSubRScvMulcvDivcvAddWeightedcvAbsDiffcvAbsDiffScvInRangecvInRangeScvCmpcvCmpScvMincvMaxcvMinScvMaxS????????????????/home/fit/opencv-2.4.7/modules/core/src/convert.cpp(lutcn == cn || lutcn == 1) && lut.total() == 256 && lut.isContinuous() && (src.depth() == CV_8U || src.depth() == CV_8S)src.size == dst.size && dst.type() == CV_8UC(src.channels())src.size == dst.size && src.channels() == dst.channels()dst.size() == src.size() && dst.type() == CV_MAKETYPE(lut.depth(), src.channels())dst.size() == src.size() && src.channels() == dst.channels()src && nsrcs > 0 && dst && ndsts > 0 && fromTo && npairs > 0j < nsrcs && src[j].depth() == depthi1 >= 0 && j < ndsts && dst[j].depth() == depthsrc.size == dst.size && src.depth() == dst.depth()0 <= coi && coi < dst.channels() && src.channels() == 10 <= coi && coi < src.channels()fromTo.size()%2 == 0 && nsrc > 0 && ndst > 0mv[i].size == mv[0].size && mv[i].depth() == depth!_mv.fixedType() || CV_MAT_TYPE(_mv.flags) == m.depth()dvec[j].depth() == src.depth()svec[j].size == dst.size && svec[j].depth() == dst.depth() && svec[j].channels() == 1 && i < dst.channels()interpolation == 0mv && n > 00 < cn && cn <= CV_CN_MAXnz > 0dvec[j].size() == src.size()dvec[j].channels() == 1i < src.channels()splitsplitmergemixChannelsmixChannelsextractChannelinsertChannelconvertScaleAbsconvertToLUTnormalizecvSplitcvMergecvConvertScaleAbscvConvertScalecvLUTcvNormalize/home/fit/opencv-2.4.7/modules/core/src/gpumat.cppThe library is compiled without CUDA supportmask.empty() || mask.type() == CV_8UC1OpenCV Error: () in , file , line setDevicegetDeviceresetDevicedeviceSupportsbuiltWithhashasPtxhasBinhasEqualOrLessPtxhasEqualOrGreaterhasEqualOrGreaterPtxhasEqualOrGreaterBinsharedMemPerBlockqueryMemoryfreeMemorytotalMemorysupportsisCompatiblequeryprintCudaDeviceInfoprintShortCudaDeviceInfoGpuMatGpuMatreshapemallocPitchsetToconvertconvertcopyWithMaskcopycopycopysetTo*N12_GLOBAL__N_112GpuFuncTableE*N12_GLOBAL__N_114EmptyFuncTableE/home/fit/opencv-2.4.7/modules/core/src/datastructs.cppToo large memory block is requestedrequested size is negative or too bigStorage block size is too small to fit the sequence elementsThe sequence has NULL storage pointerSpecified element size doesn't match to the size of the specified element type (try to use 0 for element type)Element size doesn't match to the size of predefined element type (try to use 0 for sequence element type)number of removed elements is negativestart slice index is out of rangeInvalid destination sequence headerSource is not a sequence nor matrixThe source array must be 1d coninuous vectorSource and destination sequence element sizes are different.vertex pointers coinside (or set to NULL)The vertex does not belong to the graphNull double pointer to graph scannerframe node could not be deletedidxmat.isContinuous() && idxmat.type() == CV_32S && (idxmat.cols == 1 || idxmat.rows == 1)(unsigned)k < (unsigned)points.rows(unsigned)ptidx < (unsigned)points.rowsvecmat.isContinuous() && vecmat.type() == CV_32F && vecmat.total() == (size_t)points.colsK > 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)ptdims_points.type() == CV_32F && !_points.empty()NULL storage pointerInvalid indexNULL sequence pointerInvalid sequence headerBad sequence sliceBad input sequenceNull compare functionNull element pointergraph pointer is NULLThe vertex is not foundNull graph pointergraph->storage != 0Null graph scannerInvalid graph pointerNULL iterator pointerlabelsmat.isContinuous()nlabels == nvals[ofs[k]] <= pivotvals[ofs[k]] >= pivotstd::abs(more - less) <= 1icvInitMemStoragecvCreateChildMemStoragecvReleaseMemStorageicvDestroyMemStoragecvClearMemStoragecvSaveMemStoragePoscvRestoreMemStoragePoscvMemStorageAllocicvGoNextMemBlockcvCreateSeqcvSetSeqBlockSizecvSeqElemIdx??????????????????????????cvCvtSeqToArraycvMakeSeqHeaderForArraycvStartAppendToSeqcvStartWriteSeqcvFlushSeqWritercvEndWriteSeqcvCreateSeqBlockicvGrowSeqcvStartReadSeqcvChangeSeqBlockcvGetSeqReaderPoscvSetSeqReaderPoscvSeqPushcvSeqPopcvSeqPushFrontcvSeqPopFrontcvSeqInsertcvSeqRemovecvSeqPushMulticvSeqPopMulticvClearSeqcvSeqSlicecvSeqRemoveSlicecvSeqInsertSlicecvSeqSortcvSeqSearchcvSeqPartitioncvCreateSetcvSetAddcvSetRemovecvCreateGraphcvClearGraphcvGraphAddVtxcvGraphRemoveVtxByPtrcvGraphRemoveVtxcvFindGraphEdgeByPtrcvFindGraphEdgecvGraphAddEdgeByPtrcvGraphAddEdgecvGraphRemoveEdgeByPtrcvGraphRemoveEdgecvGraphVtxDegreeByPtrcvGraphVtxDegreecvCreateGraphScannericvSeqElemsClearFlagscvReleaseGraphScannercvNextGraphItemicvSeqFindNextElemcvCloneGraphcvTreeToNodeSeqcvInsertNodeIntoTreecvRemoveNodeFromTreecvInitTreeNodeIteratorcvNextTreeNodecvPrevTreeNodebuildmedianPartitionfindNearestfindOrthoRangegetPointsgetPointN2cv16ParallelLoopBodyEMWRMNV RMVV PSTSMXVNTMRMPNOPOSPURVTVVUMWOMOV OMRMTNUPUSTURVOVMWOMOV OMUM OQSQ OVUVMVOMOV OMUM OQSQMXVNTMRMPNOPOSPURVTVVUVR SRVRMWOMOV UMUV OQUQPTRMRVNUSMSTRVPVOTOSMWOMOV UMOS QQUVMVOMOV OVUVLXNMNV NMRV VMRV VMVVMWOMOV OMUV UMUVMXRMPNOPOSPURVSVUUVSVPUNSMRMMWOMOV OMSMUNUQSRORMWOMOV OMSMUNUQSROR RRUVMWUNSMQMONOOPPTRUSUUSVQVOUMWRMRV NMVMMXOMOSPURVSVUUVSVMMWNMRV VMRVLXNMPV RMPV RMTV VMTVMWOMUV UMOVMWNMRQRV VMRQMWUMOV OMUM OVUVMVOMOV OMUMMWRMNV RMVV NVVVMWRMNV RMVVMWOMUM PQTR TQPR OVUVMWOMOV UMUV OMUMMWOMRQOV OMUM OVUVMWNONNOMPMQNRPRV VOVNUMTMSNRPLXRMRV PONPNSPTTTVSVPTOPOLXRMRV NOOPOSQTSTUSUPVOMXOVQVOROPPNRMSMUNVPVRTVVVMWSMMV SMUV OSTSLXVPUNTMRMPNOONQNSOUPVRVTUUTMXQMNV QMUMVOVQUTTURVNVMVQMNV QMVM PQSQ NVSVMVQMNV QMVM PQSQMXQMNV WMTV PQUQPUTMQVOVUMSSRUQVPVOUOTMVQMNV VMOS RQTVNVRMOV OVTVLYPMMV PMQV XMQV XMUVMXQMNV QMTV WMTVMWQMNV QMUMVNVPUQSRPRMWVNTMRMPNPPQQTRUSUUSVPVNUMVSMPV PMVMLXPMNSNUOVRVTUUSWMMWOMQV WMQVKXNMNV SMNV SMSV XMSVNWQMTV WMNVNWQMSQQV WMSQMWQMWMNVTVLXNMRV VMRV NMVMMWNLVXLXRONU ROVUMWNVVVPVRMUQMWMMOKQKTMVMWKNWQPTPUQUV URQSPTPUQVSVUUMWOMOV OSPURVTUUSTQRPPQOSMWUQSPRPPQOSPURVSVUUMWUMUV USTQRPPQOSPURVTUUSMWOSUSTQRPPQOSPURVTVNVUNTMSMRNRV PPTPMWOMOV OSPQRPTQUSUVPTRLQMRNSMRL RPRVPUSLRMSNTMSL SPSXRYQYPXNWPMPV UPPT RSUVMWOPOV OSPQRPTQUSUVMWRPPQOSPURVTUUSTQRPMWOPOY OSPURVTUUSTQRPPQOSMWUPUY USTQRPPQOSPURVTUUSNVPPPV PSQQSPTPNWUQTPQPPQPRQSTSUTUUTVQVPUNVRMRUSVTVUU PPTPMWUPUV OPOSPURVTUUSNVOPRV UPRVLXNPPV RPPV RPTV VPTVMWOPUV UPOVMWOPRV UPRVQXPYOYMWOPUPOVUVMXVPUSTURVPUOSPQRPTQUUVVMXNQOPQPRQRSQW VPURSTQWPYNWUQSPQPPQPRQS SSQSPTPUQVSVUUOVRPQSQURVSVTUMWQPOV UPTPRQPS PSQUSVTVMWOMPMQNRPUV RPOVMWNPOPOV UPTSRUOVMXOQQPVP QPQRPV TPTRUVMWOSPURVTUUSTQRPPQOSNYMXVPRPPQOSPURVTUUSTQRPMXOQQPVP SPRVKXMQNPOPPQPUQVSVTUUSVPMWOPPPQQSXTYUY UPTRPWOYKYTMRY MQNPOPPQPUQVTVUUVSWPLXOPNRNTOVQVRTRR UPVRVTUVSVRTMWQMOSPURVTUUSTQRPPQOSMWUQSPRPPQOSPURVTVMWOSTSURUQSPRPPQOSPURVTVOVVMUMTNSPQVPXOYNY QPUPMVQMNV OSPQQPSPTQTRSTSUTVUVPUSMSNTNTMSM QPRPSQSRRTRUSVTVNVRMOV UPTPRQPS 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?????$?@?d??)?a?D??9??@??Y !?!?"A#$?$?%I&'?'?(q)@*+?+?,?-i.D/!01?1?2?3?4y5d6Q7@819$:;<	=>?@AB	CDE$F1G@HQIdJyK?L?M?N?OQ!RDSiT?U?V?WY@Zq[?\?]_I`?a?bdAe?f?giYj?k?l@n?o?p9r?s?tDv?wyaz?{)}?~?d?т@???$??????????????????$???@?ѝd?????)?ĥa????D?魐?9?䲑?@?񷤹Y??ɾ??A???ń?I???̤?q?@???չא?i?D?!????????y?d?Q?@?1?$???	????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????|?x?t?p?l?h?d?`?\?X?T?P?L?H?D?@?C?C@CACBCCCDCECFCGCHCICJCKCLCMCNCOCPCQCRCSCTCUCVCWCXCYCZC[C\C]C^C_C`CaCbCcCdCeCfCgChCiCjCkClCmCnCoCpCqCrCsCtCuCvCwCxCyCzC{C|C}C~CC/home/fit/opencv-2.4.7/modules/core/src/copy.cppchannels() == CV_MAT_CN(dtype)mask.depth() == CV_8U && (mcn == 1 || mcn == cn)checkScalar(value, type(), _value.kind(), _InputArray::MAT )src.depth() == dst.depth() && src.size == dst.size(coi1 != 0 || src.channels() == 1) && (coi2 != 0 || dst.channels() == 1)src.type() == dst.type() && src.size() == dst.size()src.type() == dst.type() && dst.rows % src.rows == 0 && dst.cols % src.cols == 0ny > 0 && nx > 0maskarr == 0copyTocopyTosetTofliprepeatcvCopycvFlipcvRepeatx??????????????h??p??H?????????????^Q??)Q???P???P???P??`P??HP???V???V??uV??JV??%V???U??W??cvSetIPLAllocatorscvCreateMatHeadercvInitMatHeadercvReleaseMatcvCloneMatcvInitMatNDHeadercvCreateMatNDHeadercvCloneMatNDcvInitNArrayIteratorcvGetMatNDcvCreateSparseMatcvReleaseSparseMatcvCloneSparseMatcvInitSparseMatIteratorcvCreateDatacvSetDatacvReleaseDatacvGetRawDatacvGetElemTypecvGetDimscvGetDimSizecvGetSizecvGetSubRectcvGetRowscvGetColscvGetDiagcvScalarToRawDatacvRawDataToScalarcvPtr1DicvGetNodePtrcvPtr2DcvPtr3DcvPtrNDcvGet1DcvGet2DcvGetReal1DcvGetReal2DcvGetReal3DcvGetRealNDcvSet1DcvSet2DcvSetReal1DcvSetReal2DcvSetReal3DcvSetRealNDicvDeleteNodecvGetMatcvReshapeMatNDcvReshapecvGetImagecvInitImageHeadercvReleaseImageHeadercvReleaseImagecvSetImageROIcvResetImageROIcvGetImageROIcvSetImageCOIcvGetImageCOIcvCloneImagecvCheckTermCriteria/home/fit/opencv-2.4.7/modules/core/src/array.cppEither all the pointers should be null or they all should be non-nullnon-positive or too large number of dimensionsone of dimesion sizes is non-positiveOne of indices is out of rangeunrecognized or unsupported array typeFor multidimensional array only CV_AUTOSTEP is allowed hereArray should be CvMat or IplImageThe number of channels must be 1, 2, 3 or 4COI must be non-null in case of planar imagesOnly continuous nD arrays are supported herecvGetReal* support only single-channel arrayscvSetReal* support only single-channel arraysThe 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 typeSome of required array pointers is NULLNumber 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 arraysNULL pointer to array or destination headerNone 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 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 operationrect.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)Iterations 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 typeNon-positive width or heightInvalid matrix typeNon-positive cols or rowsNULL matrix header pointerinvalid array data typeNULL  pointerThe array is too bigbad number of dimensionsInvalid sparse array headerInvalid sparse matrix headerData is already allocatedToo big buffer is allocatedBad CvMatND headersrc->dims <= CV_MAX_DIM_dst.data == data0Bad CvMat headerbad dimension indexindex is out of rangeNULL pointer to indicesNULL array pointer is passedIncorrect number of arraysIterator pointer is NULLCOI set is not allowed herenull pointer to headerBad input roiUnsupported formatBad input originBad input alignNull pointer to imageBad image headerUnknown type of term criteria/home/fit/opencv-2.4.7/modules/core/src/opengl_interop_deprecated.cpp15CvOpenGlFuncTabicvSetOpenGlFuncTabGlBufferGlBufferGlBufferGlBuffercreatecopyFrombindunbindmapHostunmapHostmapDeviceunmapDeviceGlTextureGlTextureGlTextureGlTexturecreatecopyFrombindunbindsetVertexArraysetColorArraysetNormalArraysetTexCoordArraybindunbindGlFontdrawgetrenderrenderrenderGlCameralookAtsetCameraPossetScalesetProjectionMatrixsetPerspectiveProjectionsetOrthoProjectionsetupProjectionMatrixsetupModelViewMatrixicvCheckGlError??F@/home/fit/opencv-2.4.7/modules/core/src/opengl_interop.cppThe library is compiled without OpenGL supportdepth == CV_16S || depth == CV_32S || depth == CV_32F || depth == CV_64Fdepth == CV_8S || depth == CV_16S || depth == CV_32S || depth == CV_32F || depth == CV_64Fcn == 2 || cn == 3 || cn == 4cn >= 1 && cn <= 4throw_noglsetVertexArraysetColorArraysetNormalArraysetTexCoordArray/home/fit/opencv-2.4.7/modules/flann/src/miniflann.cppUnknown/unsupported distance typeInvalid index file, cannot readInvalid index file, wrong signature). Valid values for your system are /home/fit/opencv-2.4.7/modules/flann/include/opencv2/flann/lsh_table.hquery.type() == type && indices.type() == CV_32S && dists.type() == dtypequery.isContinuous() && indices.isContinuous() && dists.isContinuous()LSH index does not support radiusSearch operationCan not open file %s for writing FLANN index
I can only search one feature at a time for range search
Unknown algorithm for choosing initial centers.Datatype of saved index is different than of the one to be created.The index saved belongs to a different datasetDataType::type == data.type() && data.isContinuous()Only continuous arrays are supportedReading FLANN index error: unsupported feature type %d for the index type %d
Reading FLANN index error: unsupported distance type %d
Reading FLANN index error: the saved data size (%d, %d) or type (%d) is different from the passed one (%d, %d), %d
It doesn't make any sense to use more than one tree for exact searchGround truth is not computed for as many neighbors as requested%8d %10.4g %10.5g %10.5g %10.5g
  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist
---------------------------------------------------------
No need for linear estimation
KMeans algorithm, estimating cluster border factor
Required number of checks: %d 
KDTree using params: trees=%d
KDTree buildTime=%g, searchTime=%g
LSH is not implemented for that typeBranching factor must be at least 2KMeansTree using params: max_iterations=%d, branching=%d
KMeansTree buildTime=%g, searchTime=%g, build_weight=%g
KMEANS, Step 1: Exploring parameter space
KD-TREE, Step 1: Exploring parameter space
Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g
Choosing linear, dataset too small
----------------------------------------------------
Failed to allocate memory.
[empty_any] : Building kmeans tree...
Building kdtree tree...
FLANN_INDEXInvalid key_size (= <= key_size < Cannot read from filemaxResults > 0wbcheckssortedtable_numberkey_sizemulti_probe_levelbranchingcenters_initleaf_sizecb_indextarget_precisionbuild_weightmemory_weightsample_fraction' in the parameters givenMissing parameter 'leaf_max_sizereorderUnknown index typetype=%d
matches.cols=%d, nn=%d
Got as close as I can
Start linear estimation
Computing ground truth
Estimating number of checks
Optimum cb_index: %g
Computing ground truth... 
Autotuning parameters...
Autotuned parameters:
Search parameters:
N2cv5flann5IndexESsN7cvflann7anyimpl12bad_any_castEN7cvflann7anyimpl14big_any_policyINS0_9empty_anyEEEN7cvflann7anyimpl21typed_base_any_policyINS0_9empty_anyEEEN7cvflann7anyimpl15base_any_policyEN7cvflann7anyimpl14big_any_policyISsEEN7cvflann7anyimpl21typed_base_any_policyISsEEN7cvflann7anyimpl16small_any_policyIiEEN7cvflann7anyimpl21typed_base_any_policyIiEEN7cvflann7anyimpl14big_any_policyIdEEN7cvflann7anyimpl21typed_base_any_policyIdEEN7cvflann7anyimpl16small_any_policyIfEEN7cvflann7anyimpl21typed_base_any_policyIfEEN7cvflann7anyimpl16small_any_policyIbEEN7cvflann7anyimpl21typed_base_any_policyIbEEN7cvflann7anyimpl14big_any_policyINS_17flann_algorithm_tEEEN7cvflann7anyimpl21typed_base_any_policyINS_17flann_algorithm_tEEEN7cvflann17flann_algorithm_tEN7cvflann7anyimpl14big_any_policyINS_20flann_centers_init_tEEEN7cvflann7anyimpl21typed_base_any_policyINS_20flann_centers_init_tEEEbuildbuildIndex_buildIndex_buildIndex_releaseknnSearchrunKnnSearch_runKnnSearch_runKnnSearch_radiusSearchrunRadiusSearch_runRadiusSearch_runRadiusSearch_saveloadIndex_loadIndex_loadIndex_initializeN7cvflann5IndexINS_11HammingLUT2EEEN7cvflann7NNIndexINS_11HammingLUT2EEEN7cvflann14FLANNExceptionEN7cvflann11LinearIndexINS_11HammingLUT2EEEN7cvflann27HierarchicalClusteringIndexINS_11HammingLUT2EEEN7cvflann20flann_centers_init_tEN7cvflann8LshIndexINS_11HammingLUT2EEEN7cvflann5IndexINS_2L2IfEEEEN7cvflann7NNIndexINS_2L2IfEEEEN7cvflann11LinearIndexINS_2L2IfEEEEN7cvflann17KDTreeSingleIndexINS_2L2IfEEEEN7cvflann11KDTreeIndexINS_2L2IfEEEEN7cvflann11KMeansIndexINS_2L2IfEEEEN7cvflann14CompositeIndexINS_2L2IfEEEEN7cvflann14AutotunedIndexINS_2L2IfEEEEN7cvflann27HierarchicalClusteringIndexINS_2L2IfEEEEN7cvflann8LshIndexINS_2L2IfEEEEN7cvflann5IndexINS_2L1IfEEEEN7cvflann7NNIndexINS_2L1IfEEEEN7cvflann11LinearIndexINS_2L1IfEEEEN7cvflann17KDTreeSingleIndexINS_2L1IfEEEEN7cvflann11KDTreeIndexINS_2L1IfEEEEN7cvflann11KMeansIndexINS_2L1IfEEEEN7cvflann14CompositeIndexINS_2L1IfEEEEN7cvflann14AutotunedIndexINS_2L1IfEEEEN7cvflann27HierarchicalClusteringIndexINS_2L1IfEEEEN7cvflann8LshIndexINS_2L1IfEEEEN7cvflann9ResultSetIiEEN7cvflann15UniqueResultSetIiEEN7cvflann18KNNUniqueResultSetIiEEN7cvflann9ResultSetIfEEN7cvflann15UniqueResultSetIfEEN7cvflann18KNNUniqueResultSetIfEEN7cvflann21RadiusUniqueResultSetIiEEN7cvflann21RadiusUniqueResultSetIfEEN7cvflann7anyimpl16small_any_policyIjEEN7cvflann7anyimpl21typed_base_any_policyIjEEN7cvflann12KNNResultSetIfEEN7cvflann7anyimpl16small_any_policyIPPNS_11KDTreeIndexINS_2L1IfEEE4NodeEEEN7cvflann7anyimpl21typed_base_any_policyIPPNS_11KDTreeIndexINS_2L1IfEEE4NodeEEEN7cvflann18KNNSimpleResultSetIfEEN7cvflann7anyimpl16small_any_policyIPPNS_11KDTreeIndexINS_2L2IfEEE4NodeEEEN7cvflann7anyimpl21typed_base_any_policyIPPNS_11KDTreeIndexINS_2L2IfEEE4NodeEEEN7cvflann7anyimpl9empty_anyEPPN7cvflann11KDTreeIndexINS_2L2IfEEE4NodeEPN7cvflann11KDTreeIndexINS_2L2IfEEE4NodeEN7cvflann11KDTreeIndexINS_2L2IfEEE4NodeEPPN7cvflann11KDTreeIndexINS_2L1IfEEE4NodeEPN7cvflann11KDTreeIndexINS_2L1IfEEE4NodeEN7cvflann11KDTreeIndexINS_2L1IfEEE4NodeE?@??L>?̌?X????.A??ؗ?Ҝ<@?@?MbP?/home/fit/opencv-2.4.7/modules/imgproc/src/emd.cppThe arrays must have equal number of columns (which is number of dimensions but 1)The array must have equal typesThe flow matrix size does not match to the signatures' sizesOnly 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' sizesIn 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 typeThe signatures must be 32fC1The flow matrix must be 32fC1The cost matrix must be 32fC1cvCalcEMD2icvInitEMD?x???x?`??'7mask is emptymask must have CV_8UC1 typei>=0 && i<(int)vtcs.size()j>=0 && j<(int)vtcs.size()w>=0 && revw>=0i != jimage is emptyimage mush have CV_8UC3 type/home/fit/opencv-2.4.7/modules/imgproc/src/grabcut.cppmask 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_FGDcovDeterms[ci] > std::numeric_limits::epsilon()dtrm > std::numeric_limits::epsilon()_model must have CV_64FC1 type, rows == 1 and cols == 13*componentsCount/home/fit/opencv-2.4.7/modules/imgproc/src/gcgraph.hpp!bgdSamples.empty() && !fgdSamples.empty()GMMoperator()calcInverseCovAndDetermgrabCutinitGMMscheckMaskaddEdgesaddTermWeightsinSourceSegment |@I@q?&?{?A@/home/fit/opencv-2.4.7/modules/imgproc/src/gabor.cppktype == CV_32F || ktype == CV_64FgetGaborKernel-DT?!@/home/fit/opencv-2.4.7/modules/imgproc/src/utils.cpparr != 0 && contour_header != 0 && block != 0Input 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 >= 0value[0] == value[1] && value[0] == value[2] && value[0] == value[3]src1.depth() == CV_8Udst.type() == src.type()cvPointSeqFromMatcopyMakeBorderPSNRcvCopyMakeBorder/home/fit/opencv-2.4.7/modules/imgproc/src/morph.cppop == MORPH_ERODE || op == MORPH_DILATEshape == MORPH_RECT || shape == MORPH_CROSS || shape == MORPH_ELLIPSEanchor.inside(Rect(0, 0, ksize.width, ksize.height))/home/fit/opencv-2.4.7/modules/imgproc/precomp.hppunknown morphological operationcols > 0 && rows > 0 && anchor.inside(cv::Rect(0,0,cols,rows)) && (shape != CV_SHAPE_CUSTOM || values != 0)depth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F || depth == CV_64F((size_t)_src[i] & 15) == 0((size_t)src[i] & 15) == 0Unsupported data type (=%d)_kernel.type() == CV_8UH???G???????@???A??XC???;??getMorphologyRowFiltergetMorphologyColumnFiltergetMorphologyFilternormalizeAnchorMorphFilterMorphFilterMorphFilterMorphFilterMorphFilterMorphFilterMorphFilterMorphFilterMorphFilterMorphFiltercreateMorphologyFiltergetStructuringElementmorphOpmorphologyExcvCreateStructuringElementExcvReleaseStructuringElementcvErodecvDilatecvMorphologyExoperator()operator()operator()operator()operator()operator()operator()operator()N2cv14MorphRowFilterINS_5MinOpIhEENS_12MorphRowIVecINS_6VMin8uEEEEEN2cv14MorphRowFilterINS_5MinOpItEENS_12MorphRowIVecINS_7VMin16uEEEEEN2cv14MorphRowFilterINS_5MinOpIsEENS_12MorphRowIVecINS_7VMin16sEEEEEN2cv14MorphRowFilterINS_5MinOpIfEENS_12MorphRowFVecINS_7VMin32fEEEEEN2cv14MorphRowFilterINS_5MinOpIdEENS_13MorphRowNoVecEEEN2cv14MorphRowFilterINS_5MaxOpIhEENS_12MorphRowIVecINS_6VMax8uEEEEEN2cv14MorphRowFilterINS_5MaxOpItEENS_12MorphRowIVecINS_7VMax16uEEEEEN2cv14MorphRowFilterINS_5MaxOpIsEENS_12MorphRowIVecINS_7VMax16sEEEEEN2cv14MorphRowFilterINS_5MaxOpIfEENS_12MorphRowFVecINS_7VMax32fEEEEEN2cv14MorphRowFilterINS_5MaxOpIdEENS_13MorphRowNoVecEEEN2cv17MorphColumnFilterINS_5MinOpIhEENS_15MorphColumnIVecINS_6VMin8uEEEEEN2cv17MorphColumnFilterINS_5MinOpItEENS_15MorphColumnIVecINS_7VMin16uEEEEEN2cv17MorphColumnFilterINS_5MinOpIsEENS_15MorphColumnIVecINS_7VMin16sEEEEEN2cv17MorphColumnFilterINS_5MinOpIfEENS_15MorphColumnFVecINS_7VMin32fEEEEEN2cv17MorphColumnFilterINS_5MinOpIdEENS_16MorphColumnNoVecEEEN2cv17MorphColumnFilterINS_5MaxOpIhEENS_15MorphColumnIVecINS_6VMax8uEEEEEN2cv17MorphColumnFilterINS_5MaxOpItEENS_15MorphColumnIVecINS_7VMax16uEEEEEN2cv17MorphColumnFilterINS_5MaxOpIsEENS_15MorphColumnIVecINS_7VMax16sEEEEEN2cv17MorphColumnFilterINS_5MaxOpIfEENS_15MorphColumnFVecINS_7VMax32fEEEEEN2cv17MorphColumnFilterINS_5MaxOpIdEENS_16MorphColumnNoVecEEEN2cv11MorphFilterINS_5MinOpIhEENS_9MorphIVecINS_6VMin8uEEEEEN2cv11MorphFilterINS_5MinOpItEENS_9MorphIVecINS_7VMin16uEEEEEN2cv11MorphFilterINS_5MinOpIsEENS_9MorphIVecINS_7VMin16sEEEEEN2cv11MorphFilterINS_5MinOpIfEENS_9MorphFVecINS_7VMin32fEEEEEN2cv11MorphFilterINS_5MinOpIdEENS_10MorphNoVecEEEN2cv11MorphFilterINS_5MaxOpIhEENS_9MorphIVecINS_6VMax8uEEEEEN2cv11MorphFilterINS_5MaxOpItEENS_9MorphIVecINS_7VMax16uEEEEEN2cv11MorphFilterINS_5MaxOpIsEENS_9MorphIVecINS_7VMax16sEEEEEN2cv11MorphFilterINS_5MaxOpIfEENS_9MorphFVecINS_7VMax32fEEEEEN2cv11MorphFilterINS_5MaxOpIdEENS_10MorphNoVecEEEN2cv16MorphologyRunnerE???@???@/home/fit/opencv-2.4.7/modules/imgproc/src/cornersubpix.cppThe source image must be 8-bit single-channel (CV_8UC1)(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_32FcvFindCornerSubPixcornerSubPixp9/home/fit/opencv-2.4.7/modules/imgproc/src/matchcontours.cppUnknown comparison methodcvMatchShapes?h㈵??>/home/fit/opencv-2.4.7/modules/imgproc/src/deriv.cppdx >= 0 && dy >= 0 && dx+dy == 1The kernel size must be odd and not larger than 31dx >= 0 && dy >= 0 && dx+dy > 0src.size() == dst.size() && src.channels() == dst.channels()ksize > order??????????@@?@@getScharrKernelsgetSobelKernelscvSobelcvLaplace??/home/fit/opencv-2.4.7/modules/imgproc/src/subdivision2d.cppedgeDst(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) == eSubdiv2D::locate returned invalid location = %dSubdivision is emptyedge > 0edgeOrg(e) == edgeOrg(o_next)edgeOrg(e) == edgeOrg(o_prev)edgeDst(e) == edgeDst(d_next)edgeDst(e) == edgeDst(d_prev)edgeDst(edge, &t) > 0edgeOrg( edge, &t ) > 0locateinsertfindNearestcheckSubdiv????GP>P?@@/home/fit/opencv-2.4.7/modules/imgproc/src/filter.cppsrcRoi.x >= 0 && srcRoi.y >= 0 && srcRoi.width >= 0 && srcRoi.height >= 0 && srcRoi.x + srcRoi.width <= src.cols && srcRoi.y + srcRoi.height <= src.rowssrc.type() == srcType && dst.type() == dstTypedstOfs.x >= 0 && dstOfs.y >= 0 && dstOfs.x + srcRoi.width <= dst.cols && dstOfs.y + srcRoi.height <= dst.rowsUnknown/unsupported border typewholeSize.width > 0 && wholeSize.height > 0(symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0kernel.type() == DataType
::type && (kernel.rows == 1 || kernel.cols == 1)kernel.type() == DataType::type && (kernel.rows == 1 || kernel.cols == 1)(symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0 && this->ksize <= 5cn == CV_MAT_CN(bufType) && ddepth >= std::max(sdepth, CV_32S) && kernel.type() == ddepthUnsupported combination of source format (=%d), and buffer format (=%d)columnBorderType != BORDER_WRAP!rowFilter.empty() && !columnFilter.empty()0 <= 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.heightcn == CV_MAT_CN(bufType) && sdepth >= std::max(ddepth, CV_32S) && kernel.type() == sdepthUnsupported combination of buffer format (=%d), and destination format (=%d)ktype == CV_8U || ktype == CV_32S || ktype == CV_32F || ktype == CV_64F_kernel.type() == DataType::typecn == CV_MAT_CN(dstType) && ddepth >= sdepthUnsupported combination of source format (=%d), and destination format (=%d)src && dst && count > 0srcY >= startYdstY <= roi.height_kernel.channels() == 1this->ksize == 3bufType == srcTypecn == CV_MAT_CN(_dstType)N2cv13BaseRowFilterEN2cv16BaseColumnFilterEN2cv10BaseFilterEN2cv12FilterEngineEborderInterpolateinitstartstartproceedapplygetKernelTypegetLinearRowFilterSymmRowSmallFilterRowFilterSymmRowSmallFilterRowFilterRowFilterRowFilterRowFilterRowFilterRowFilterRowFilterRowFilterRowFilterRowFilterRowFiltergetLinearColumnFilterColumnFilterColumnFilterColumnFilterColumnFilterColumnFilterColumnFilterColumnFilterColumnFilterColumnFilterSymmColumnSmallFilterSymmColumnFilterColumnFilterSymmColumnSmallVec_32s16sSymmColumnSmallFilterSymmColumnFilterColumnFilterSymmColumnSmallVec_32fSymmColumnSmallFilterSymmColumnFilterColumnFilterSymmColumnVec_32s8uSymmColumnFilterSymmColumnFilterSymmColumnFilterSymmColumnFilterSymmColumnFilterColumnFilterSymmColumnVec_32f16sSymmColumnFilterColumnFilterSymmColumnFilterSymmColumnVec_32fSymmColumnFilterColumnFilterSymmColumnFiltercreateSeparableLinearFilterpreprocess2DKernelgetLinearFilternormalizeAnchorFilter2DFilter2DFilter2DFilter2DFilter2DFilter2DFilter2DFilter2DFilter2DFilter2DFilter2DFilter2DFilter2DcreateLinearFiltercvFilter2DN2cv9RowFilterIhiNS_21SymmRowSmallVec_8u32sEEEN2cv18SymmRowSmallFilterIhiNS_21SymmRowSmallVec_8u32sEEEN2cv9RowFilterIffNS_19SymmRowSmallVec_32fEEEN2cv18SymmRowSmallFilterIffNS_19SymmRowSmallVec_32fEEEN2cv9RowFilterIhiNS_12RowVec_8u32sEEEN2cv9RowFilterIhfNS_8RowNoVecEEEN2cv9RowFilterIhdNS_8RowNoVecEEEN2cv9RowFilterItfNS_8RowNoVecEEEN2cv9RowFilterItdNS_8RowNoVecEEEN2cv9RowFilterIsfNS_13RowVec_16s32fEEEN2cv9RowFilterIsdNS_8RowNoVecEEEN2cv9RowFilterIffNS_10RowVec_32fEEEN2cv9RowFilterIfdNS_8RowNoVecEEEN2cv9RowFilterIddNS_8RowNoVecEEEN2cv12ColumnFilterINS_13FixedPtCastExIihEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIfhEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIdhEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIftEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIdtEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIfsEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIdsEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIffEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIddEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_13FixedPtCastExIihEENS_19SymmColumnVec_32s8uEEEN2cv16SymmColumnFilterINS_13FixedPtCastExIihEENS_19SymmColumnVec_32s8uEEEN2cv21SymmColumnSmallFilterINS_13FixedPtCastExIihEENS_19SymmColumnVec_32s8uEEEN2cv16SymmColumnFilterINS_4CastIisEENS_25SymmColumnSmallVec_32s16sEEEN2cv12ColumnFilterINS_4CastIisEENS_25SymmColumnSmallVec_32s16sEEEN2cv21SymmColumnSmallFilterINS_4CastIisEENS_25SymmColumnSmallVec_32s16sEEEN2cv16SymmColumnFilterINS_4CastIffEENS_22SymmColumnSmallVec_32fEEEN2cv12ColumnFilterINS_4CastIffEENS_22SymmColumnSmallVec_32fEEEN2cv21SymmColumnSmallFilterINS_4CastIffEENS_22SymmColumnSmallVec_32fEEEN2cv16SymmColumnFilterINS_4CastIfhEENS_11ColumnNoVecEEEN2cv16SymmColumnFilterINS_4CastIdhEENS_11ColumnNoVecEEEN2cv16SymmColumnFilterINS_4CastIftEENS_11ColumnNoVecEEEN2cv16SymmColumnFilterINS_4CastIdtEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIisEENS_11ColumnNoVecEEEN2cv16SymmColumnFilterINS_4CastIisEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIfsEENS_20SymmColumnVec_32f16sEEEN2cv16SymmColumnFilterINS_4CastIfsEENS_20SymmColumnVec_32f16sEEEN2cv16SymmColumnFilterINS_4CastIdsEENS_11ColumnNoVecEEEN2cv12ColumnFilterINS_4CastIffEENS_17SymmColumnVec_32fEEEN2cv16SymmColumnFilterINS_4CastIffEENS_17SymmColumnVec_32fEEEN2cv16SymmColumnFilterINS_4CastIddEENS_11ColumnNoVecEEEN2cv8Filter2DIhNS_4CastIfhEENS_12FilterVec_8uEEEN2cv8Filter2DIhNS_4CastIftEENS_11FilterNoVecEEEN2cv8Filter2DIhNS_4CastIfsEENS_15FilterVec_8u16sEEEN2cv8Filter2DIhNS_4CastIffEENS_11FilterNoVecEEEN2cv8Filter2DIhNS_4CastIddEENS_11FilterNoVecEEEN2cv8Filter2DItNS_4CastIftEENS_11FilterNoVecEEEN2cv8Filter2DItNS_4CastIffEENS_11FilterNoVecEEEN2cv8Filter2DItNS_4CastIddEENS_11FilterNoVecEEEN2cv8Filter2DIsNS_4CastIfsEENS_11FilterNoVecEEEN2cv8Filter2DIsNS_4CastIffEENS_11FilterNoVecEEEN2cv8Filter2DIsNS_4CastIddEENS_11FilterNoVecEEEN2cv8Filter2DIfNS_4CastIffEENS_13FilterVec_32fEEEN2cv8Filter2DIdNS_4CastIddEENS_11FilterNoVecEEE@??8???:???:??9:??:???8???9???8??|9???8???8???8???9??cn <= 4 && ssize.area() > 0Unknown interpolation methodmap1.size().area() > 0nnfunc != 0ifunc != 0src.cols > 0 && src.rows > 0src.type() == dst.type()M.size() == M0.size()dsize.area()func != 0 && cn <= 4M should be >0/home/fit/opencv-2.4.7/modules/imgproc/src/imgwarp.cppUnknown/unsupported interpolation type!map2.data || (map2.size() == map1.size())((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)dstm1type == CV_16SC2 || dstm1type == CV_32FC1 || dstm1type == CV_32FC2Unsupported combination of input/output matrices(M0.type() == CV_32F || M0.type() == CV_64F) && M0.rows == 3 && M0.cols == 3matM.rows == 2 && matM.cols == 3src.checkVector(2, CV_32F) == 4 && dst.checkVector(2, CV_32F) == 4src.checkVector(2, CV_32F) == 3 && dst.checkVector(2, CV_32F) == 3src.type() == dst.type() && dst.size() == mapx.size()(M0.type() == CV_32F || M0.type() == CV_64F) && M0.rows == 2 && M0.cols == 3dsize.area() || (inv_scale_x > 0 && inv_scale_y > 0)resizeremapremapBilinearremapBilinearremapBilinearremapBilinearremapBilinearinitInterTab2DinitInterTab1DconvertMapswarpAffinewarpPerspectiveinvertAffineTransformgetPerspectiveTransformgetAffineTransformcvResizecvWarpAffinecvWarpPerspectivecvRemapcv2DRotationMatrixcvGetPerspectiveTransformcvGetAffineTransformcvLogPolarcvLinearPolarN2cv18ResizeArea_InvokerIddEEN2cv18ResizeArea_InvokerIffEEN2cv18ResizeArea_InvokerIsfEEN2cv22resizeAreaFast_InvokerIddNS_19ResizeAreaFastNoVecIddEEEEN2cv22resizeAreaFast_InvokerIffNS_19ResizeAreaFastNoVecIffEEEEN2cv22resizeAreaFast_InvokerIsfNS_17ResizeAreaFastVecIsEEEEN2cv22resizeAreaFast_InvokerItfNS_17ResizeAreaFastVecItEEEEN2cv22resizeAreaFast_InvokerIhiNS_17ResizeAreaFastVecIhEEEEN2cv18ResizeArea_InvokerItfEEN2cv18ResizeArea_InvokerIhfEEN2cv21resizeGeneric_InvokerINS_12HResizeCubicIddfEENS_12VResizeCubicIddfNS_4CastIddEENS_12VResizeNoVecEEEEEN2cv21resizeGeneric_InvokerINS_12HResizeCubicIfffEENS_12VResizeCubicIfffNS_4CastIffEENS_19VResizeCubicVec_32fEEEEEN2cv21resizeGeneric_InvokerINS_12HResizeCubicIsffEENS_12VResizeCubicIsffNS_4CastIfsEENS_21VResizeCubicVec_32f16ILi0EEEEEEEN2cv21resizeGeneric_InvokerINS_12HResizeCubicItffEENS_12VResizeCubicItffNS_4CastIftEENS_21VResizeCubicVec_32f16ILin32768EEEEEEEN2cv21resizeGeneric_InvokerINS_12HResizeCubicIhisEENS_12VResizeCubicIhisNS_11FixedPtCastIihLi22EEENS_21VResizeCubicVec_32s8uEEEEEN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4IddfEENS_15VResizeLanczos4IddfNS_4CastIddEENS_12VResizeNoVecEEEEEN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4IfffEENS_15VResizeLanczos4IfffNS_4CastIffEENS_12VResizeNoVecEEEEEN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4IsffEENS_15VResizeLanczos4IsffNS_4CastIfsEENS_12VResizeNoVecEEEEEN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4ItffEENS_15VResizeLanczos4ItffNS_4CastIftEENS_12VResizeNoVecEEEEEN2cv21resizeGeneric_InvokerINS_15HResizeLanczos4IhisEENS_15VResizeLanczos4IhisNS_11FixedPtCastIihLi22EEENS_12VResizeNoVecEEEEEN2cv21resizeGeneric_InvokerINS_13HResizeLinearIddfLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIddfNS_4CastIddEENS_12VResizeNoVecEEEEEN2cv21resizeGeneric_InvokerINS_13HResizeLinearIfffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIfffNS_4CastIffEENS_20VResizeLinearVec_32fEEEEEN2cv21resizeGeneric_InvokerINS_13HResizeLinearIsffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearIsffNS_4CastIfsEENS_22VResizeLinearVec_32f16ILi0EEEEEEEN2cv21resizeGeneric_InvokerINS_13HResizeLinearItffLi1ENS_12HResizeNoVecEEENS_13VResizeLinearItffNS_4CastIftEENS_22VResizeLinearVec_32f16ILin32768EEEEEEEN2cv21resizeGeneric_InvokerINS_13HResizeLinearIhisLi2048ENS_12HResizeNoVecEEENS_13VResizeLinearIhisNS_11FixedPtCastIihLi22EEENS_22VResizeLinearVec_32s8uEEEEEN2cv15resizeNNInvokerEN2cv12RemapInvokerEN2cv17warpAffineInvokerEN2cv22warpPerspectiveInvokerE????MbP??;f???@@?@9?R?Fߑ??=@?p@?@??@E?4BBBB@@@@????????????????????/home/fit/opencv-2.4.7/modules/imgproc/src/approx.cppCV_IS_SEQ_CHAIN_CONTOUR( chain )header_size >= (int)sizeof(CvContour)header_size is negative. Pass 0 to make the destination header_size == input header_sizeInput curves are not polygonal. Use cvApproxChains firstNew header size must be non-less than sizeof(CvContour)Unsupported sequence typeNULL storage pointer Input curves have uknown typeUnknown approximation methodAccuracy must be non-negativeInvalid approximation methodicvApproximateChainTC89cvApproxChainscvApproxPolysrc.type() == CV_8UC1 || src.type() == CV_32FC1/home/fit/opencv-2.4.7/modules/imgproc/src/corner.cppsrc.size() == dst.size() && dst.type() == CV_32FC1src.rows == dst.rows && src.cols*6 == dst.cols*dst.channels() && dst.depth() == CV_32FcornerEigenValsVecspreCornerDetectcvCornerMinEigenValcvCornerHarriscvCornerEigenValsAndVecscvPreCornerDetect/home/fit/opencv-2.4.7/modules/imgproc/src/shapedescr.cppNull center or radius pointersThe passed sequence is not a valid contourOnly curves with integer coordinates are supported in case of contour slice(icvContourSecArea( contour, slice, &area )) >= 0The image/matrix format is not supported by the functionNumber of points should be >= 5cvArcLengthcvMinEnclosingCirclecvContourAreacvFitEllipse2cvBoundingRect{?G?z??@???h㈵??wJ????:?0?yE>?V@ ׃?4?src.depth() == CV_8U/home/fit/opencv-2.4.7/modules/imgproc/src/canny.cppsrc.size == dst.size && src.depth() == CV_8U && dst.type() == CV_8UCannycvCanny/home/fit/opencv-2.4.7/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 pointsFloating-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)CV_SEQ_ELTYPE(contour) == CV_32FC2cvConvexHull2icvCalcAndWritePtIndicescvConvexityDefectscvCheckContourConvexity!_src.empty()/home/fit/opencv-2.4.7/modules/imgproc/src/pyramids.cppstd::abs(dsize.width*2 - ssize.width) <= 2 && std::abs(dsize.height*2 - ssize.height) <= 2_filter == CV_GAUSSIAN_5x5 && src.type() == dst.type()The number of extra layers must be non negativeThe buffer is too small to fit the pyramidstd::abs(dsize.width - ssize.width*2) == dsize.width % 2 && std::abs(dsize.height - ssize.height*2) == dsize.height % 2pyrDownpyrDown_pyrDown_pyrDown_pyrDown_pyrDown_pyrUppyrUp_pyrUp_pyrUp_pyrUp_pyrUp_cvPyrDowncvPyrUpcvReleasePyramidcvCreatePyramid?;A?@?<?@?@?@?@?;?;?;?;????????????? @@@p?/home/fit/opencv-2.4.7/modules/imgproc/src/thresh.cppblockSize % 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)Unknown threshold type??"??n???????????V???????????!??? ?????????"???&???%???$???#???'??thresh_8uthresh_16sthresh_32fthresholdadaptiveThresholdcvThresholdcvAdaptiveThresholdN2cv15ThresholdRunnerE?????/home/fit/opencv-2.4.7/modules/imgproc/src/rotcalipers.cppInput sequence must consist of 2d points or pointers to 2d pointscvMinAreaRect2opencv-histis_uniformhave_rangesExpected CvMatNDUnknown Histogram type'thresh' node is missingInvalid histogram headerH1.size(i) == H2.size(i)Invalid histogram header[s]NULL ranges pointerNull histogram header pointerNull pointerInvalid histogram typeNULL histogram array pointerscale must be positiveInvalid histogram pointer[s]images[j].channels() == 1c >= 0j < nimagesranges[i][k] < ranges[i][k+1]dims > 0 && hist.dataH0.isContinuous()nimages > 0dims > 0 && histSizenimages > 0 && dims > 0csz == 0 || csz == dimsBad histogram pointerNull double array pointer/home/fit/opencv-2.4.7/modules/imgproc/src/histogram.cppH1.type() == H2.type() && H1.type() == CV_32Fit.planes[0].isContinuous() && it.planes[1].isContinuous()dims > 0 && dims == H2.dims() && H1.type() == H2.type() && H1.type() == CV_32FOne of histograms is sparse and other is notThe histograms have different numbers of dimensionsThe histograms have different sizesOne of elements is NULLBin ranges should go in ascenting orderOnly uniform bin ranges can be used here (to avoid memory allocation)Number of dimensions is out of rangeDestination double pointer is NULLToo small number of histogramsThe function supports dense histograms onlyAll histograms must have 32fC1 typechannels != 0 || nimages == dimsimages[j].size() == imsize && images[j].depth() == depthmask.size() == imsize && mask.channels() == 1ranges[i] && ranges[i][0] < ranges[i][1]!mask.data || mask.type() == CV_8UC1rsz == dims*2 || (rsz == 2 && _1d) || (rsz == 0 && images.depth(0) == CV_8U)csz == 0 || csz == dims || (csz == 1 && _1d)rsz == dims*2 || (rsz == 0 && images.depth(0) == CV_8U)Bad 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)_dst.size() == images[0].size() && _dst.depth() == images[0].depth()calcHisthistPrepareImagescalcHistcalcHistcalcBackProjectcalcBackProjectcalcBackProjectcompareHistcompareHistcvCreateHistcvMakeHistHeaderForArraycvReleaseHistcvClearHistcvThreshHistcvNormalizeHistcvGetMinMaxHistValuecvCompareHistcvCopyHistcvSetHistBinRangescvCalcArrHistcvCalcArrBackProjectcvCalcArrBackProjectPatchcvCalcBayesianProbcvCalcProbDensityequalizeHisticvReadHist23EqualizeHistLut_Invoker28EqualizeHistCalcHist_InvokerC/home/fit/opencv-2.4.7/modules/imgproc/src/segmentation.cppOnly 8-bit, 3-channel input images are supportedOnly 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 negativeOnly 8-bit, 3-channel images are supportedThe input and output images must have the same typecvWatershedcvPyrMeanShiftFiltering/home/fit/opencv-2.4.7/modules/imgproc/src/contours.cpp[Start]FindContours support only 8uC1 and 32sC1 imagescurve.checkVector(2) >= 0 && (curve.depth() == CV_32F || curve.depth() == CV_32S)points.checkVector(2) >= 0 && (points.depth() == CV_32F || points.depth() == CV_32S)contour.checkVector(2) >= 0 && (contour.depth() == CV_32F || contour.depth() == CV_32S)contour1.checkVector(2) >= 0 && contour2.checkVector(2) >= 0 && (contour1.depth() == CV_32F || contour1.depth() == CV_32S) && contour1.depth() == contour2.depth()nelems >= 0 && (depth == CV_32F || depth == CV_32S)(is2d || is3d) && (points.depth() == CV_32F || points.depth() == CV_32S)hull.checkVector(1, CV_32S) > 2/home/fit/opencv-2.4.7/modules/core/include/opencv2/core/operations.hpp!_seq || _seq->elem_size == sizeof(_Tp)npoints >= 0 && (depth == CV_32S || depth == CV_32F)Nonzero offset is not supported in CV_LINK_RUNS yetContour header size must be >= sizeof(CvContour)Input array must be 8uC1 or 8sC10 <= contourIdx && contourIdx < (int)lasthierarchy.total() == ncontours && hierarchy.type() == CV_32SC4l.isContinuous()ptnum > 30 <= idx0 && idx0 < ptnum0 <= idx1 && idx1 < ptnum0 <= idx2 && idx2 < ptnumd.depth >= 0NULL double CvSeq pointerci.isContinuous()npoints > 0cvStartReadChainPoints????????????????????????cvReadChainPointcvStartFindContourscvSubstituteContourcvFindNextContourcvEndFindContourscvFindContoursicvFindContoursInIntervalfindContoursSeqdrawContoursapproxPolyDParcLengthboundingRectcontourAreaminAreaRectminEnclosingCirclematchShapesconvexHullconvexityDefectsSeqisContourConvexfitEllipsefitLinepointPolygonTest?C/home/fit/opencv-2.4.7/modules/imgproc/src/samplers.cpp(func( src->data.ptr, src_step, src_size, dst->data.ptr, dst_step, dst_size, center )) >= 0Transformation 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 )) >= 0map matrix is not validcvSampleLinecvGetRectSubPixcvGetQuadrangleSubPix$G???@??8ccn == 1 || delta == 0the input arrays are too big/home/fit/opencv-2.4.7/modules/imgproc/src/templmatch.cppimg.dims <= 2 && templ.dims <= 2 && corr.dims <= 2depth == tdepth || tdepth == CV_32Fcorrsize.height <= img.rows + templ.rows - 1 && corrsize.width <= img.cols + templ.cols - 1CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED(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_32FcrossCorrmatchTemplatecvMatchTemplate@??/home/fit/opencv-2.4.7/modules/imgproc/src/distransform.cppsource image must be 8uC1 and the distance map must be 32fC1 (or 8uC1 in case of simple L1 distance transform)the source and the destination images must be of the same sizeMask size should be 3 or 5 or 0 (presize)The input image must have 8uC1 type and the output one must have 32fC1 typethe 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 maskCV_IS_MASK_ARR( src ) && CV_MAT_TYPE( dst->type ) == CV_8UC1CV_ARE_SIZES_EQ( src, dst )????N???3?????????????????????????????????????????????????????? ???????????cvDistTransformicvTrueDistTransicvDistanceATS_L1_8uN2cv12DTRowInvokerEN2cv15DTColumnInvokerE?7/home/fit/opencv-2.4.7/modules/imgproc/src/phasecorr.cppsrc1.type() == CV_32FC1 || src1.type() == CV_64FC1src1.size == src2.sizesrc1.type() == window.type()src1.size == window.sizephaseCorrelateResmagSpectrumsdivSpectrumsweightedCentroidcreateHanningWindow/home/fit/opencv-2.4.7/modules/imgproc/src/featureselect.cppqualityLevel > 0 && minDistance >= 0 && maxCorners >= 0mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size())_corners && _corner_countgoodFeaturesToTrackcvGoodFeaturesToTrack/home/fit/opencv-2.4.7/modules/imgproc/src/color.cpphrange == 180 || hrange == 256coeffs[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)coeffs[j] >= 0 && coeffs[j + 1] >= 0 && coeffs[j + 2] >= 0 && coeffs[j] + coeffs[j + 1] + coeffs[j + 2] < 1.5f*LabCbrtTabScalecoeffs[i*3] >= 0 && coeffs[i*3+1] >= 0 && coeffs[i*3+2] >= 0 && coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 1.5fdepth == CV_8U || depth == CV_16U || depth == CV_32F(scn == 3 || scn == 4) && depth == CV_8U(dcn == 3 || dcn == 4) && scn == 2 && depth == CV_8Uscn == 1 && (dcn == 3 || dcn == 4)scn == 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)Bayer->Gray demosaicing only supports 8u and 16u typesBayer->RGB demosaicing only supports 8u and 16u typessz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8UUnknown/unsupported color conversion codesz.width % 2 == 0 && sz.height % 2 == 0whitept[1] == 1.fscn == 3 || scn == 4scn == 2 && depth == CV_8Uscn == 1 && depth == CV_8Uscn == 1 && dcn == 1scn == 1 && dcn == 3dcn == 3 || dcn == 4scn == 4 && dcn == 4Unsupported image depthsrc.depth() == dst.depth()??????????????????X.??X.???)???)??X.??X.???+???+?????????+???+?????????%???#???+???+?????????+???+?????????%???#??H???H??????????? ?? ????????x ??x ??????????`???`???????????????????`???`???x ??x ??????????????????????????????????????????x ??x ??x ??x ??????????????`???`???`???`??????????????? ?? ???????? ??? ??? ??? ?????????????????????????????????????????????? ??? ??????????? ??? ??????????? ??? ??? ??? ??? ??? ??? ??? ??((??((??(???(???@???@???@???@???@???@???@???@???cvtColor?x?=?E???>m??>?`?L?%#}!8J @q=ʾj????? ??????????H -?>??>??8>m?Y>?7?W͓=Vb???*>>???=???=(I?=?=cvCvtColorN2cv20CvtColorLoop_InvokerINS_7RGB2RGBIhEEEEN2cv20CvtColorLoop_InvokerINS_7RGB2RGBItEEEEN2cv20CvtColorLoop_InvokerINS_7RGB2RGBIfEEEEN2cv20CvtColorLoop_InvokerINS_10RGB2RGB5x5EEEN2cv20CvtColorLoop_InvokerINS_10RGB5x52RGBEEEN2cv20CvtColorLoop_InvokerINS_8RGB2GrayIhEEEE#?%LN2cv20CvtColorLoop_InvokerINS_8RGB2GrayItEEEE??>?E??x?=N2cv20CvtColorLoop_InvokerINS_8RGB2GrayIfEEEEN2cv20CvtColorLoop_InvokerINS_11RGB5x52GrayEEEN2cv20CvtColorLoop_InvokerINS_8Gray2RGBIhEEEEN2cv20CvtColorLoop_InvokerINS_8Gray2RGBItEEEEN2cv20CvtColorLoop_InvokerINS_8Gray2RGBIfEEEEN2cv20CvtColorLoop_InvokerINS_11Gray2RGB5x5EEE#?%L?-$N2cv20CvtColorLoop_InvokerINS_11RGB2YCrCb_iIhEEEE#?%L?-$N2cv20CvtColorLoop_InvokerINS_11RGB2YCrCb_iItEEEE??>?E??x?=+?6?Nb?N2cv20CvtColorLoop_InvokerINS_11RGB2YCrCb_fIfEEEE?YN???????yqN2cv20CvtColorLoop_InvokerINS_11YCrCb2RGB_iIhEEEE?YN???????yqN2cv20CvtColorLoop_InvokerINS_11YCrCb2RGB_iItEEEE??????6?? ??????N2cv20CvtColorLoop_InvokerINS_11YCrCb2RGB_fIfEEEE???gq (O?4N2cv20CvtColorLoop_InvokerINS_9RGB2XYZ_iIhEEEE???gq (O?4N2cv20CvtColorLoop_InvokerINS_9RGB2XYZ_iItEEEEN2cv20CvtColorLoop_InvokerINS_9RGB2XYZ_fIfEEEE?3h??????~??????????N2cv20CvtColorLoop_InvokerINS_9XYZ2RGB_iIhEEEE?3h??????~??????????N2cv20CvtColorLoop_InvokerINS_9XYZ2RGB_iItEEEEN2cv20CvtColorLoop_InvokerINS_9XYZ2RGB_fIfEEEEN2cv20CvtColorLoop_InvokerINS_9RGB2HSV_bEEEN2cv20CvtColorLoop_InvokerINS_9RGB2HSV_fEEEN2cv20CvtColorLoop_InvokerINS_9RGB2HLS_bEEEN2cv20CvtColorLoop_InvokerINS_9RGB2HLS_fEEEN2cv20CvtColorLoop_InvokerINS_9HSV2RGB_bEEEN2cv20CvtColorLoop_InvokerINS_9HSV2RGB_fEEEN2cv20CvtColorLoop_InvokerINS_9HLS2RGB_bEEEN2cv20CvtColorLoop_InvokerINS_9HLS2RGB_fEEEN2cv20CvtColorLoop_InvokerINS_9RGB2Lab_bEEEN2cv20CvtColorLoop_InvokerINS_9RGB2Lab_fEEEN2cv20CvtColorLoop_InvokerINS_9RGB2Luv_bEEEN2cv20CvtColorLoop_InvokerINS_9RGB2Luv_fEEEN2cv20CvtColorLoop_InvokerINS_9Lab2RGB_bEEEN2cv20CvtColorLoop_InvokerINS_9Lab2RGB_fEEEN2cv20CvtColorLoop_InvokerINS_9Luv2RGB_bEEEN2cv20CvtColorLoop_InvokerINS_9Luv2RGB_fEEEN2cv22YUV420sp2RGB888InvokerILi0ELi0EEEN2cv22YUV420sp2RGB888InvokerILi0ELi1EEEN2cv22YUV420sp2RGB888InvokerILi2ELi0EEEN2cv22YUV420sp2RGB888InvokerILi2ELi1EEEN2cv24YUV420sp2RGBA8888InvokerILi0ELi0EEEN2cv24YUV420sp2RGBA8888InvokerILi0ELi1EEEN2cv24YUV420sp2RGBA8888InvokerILi2ELi0EEEN2cv24YUV420sp2RGBA8888InvokerILi2ELi1EEEN2cv21YUV420p2RGB888InvokerILi0EEEN2cv21YUV420p2RGB888InvokerILi2EEEN2cv23YUV420p2RGBA8888InvokerILi0EEEN2cv23YUV420p2RGBA8888InvokerILi2EEEN2cv22RGB888toYUV420pInvokerILi0EEEN2cv22RGB888toYUV420pInvokerILi2EEEN2cv21YUV422toRGB888InvokerILi0ELi0ELi0EEEN2cv21YUV422toRGB888InvokerILi0ELi0ELi1EEEN2cv21YUV422toRGB888InvokerILi0ELi1ELi0EEEN2cv21YUV422toRGB888InvokerILi0ELi1ELi1EEEN2cv21YUV422toRGB888InvokerILi2ELi0ELi0EEEN2cv21YUV422toRGB888InvokerILi2ELi0ELi1EEEN2cv21YUV422toRGB888InvokerILi2ELi1ELi0EEEN2cv21YUV422toRGB888InvokerILi2ELi1ELi1EEEN2cv23YUV422toRGBA8888InvokerILi0ELi0ELi0EEEN2cv23YUV422toRGBA8888InvokerILi0ELi0ELi1EEEN2cv23YUV422toRGBA8888InvokerILi0ELi1ELi0EEEN2cv23YUV422toRGBA8888InvokerILi0ELi1ELi1EEEN2cv23YUV422toRGBA8888InvokerILi2ELi0ELi0EEEN2cv23YUV422toRGBA8888InvokerILi2ELi0ELi1EEEN2cv23YUV422toRGBA8888InvokerILi2ELi1ELi0EEEN2cv23YUV422toRGBA8888InvokerILi2ELi1ELi1EEEN2cv20CvtColorLoop_InvokerINS_10RGBA2mRGBAIhEEEEN2cv20CvtColorLoop_InvokerINS_10mRGBA2RGBAIhEEEEa 6;pB?BpC?:?</?@?= >?:?%=???=R?NA???;?D??:?B?A3?aD?CHC?= <ى?=@A?A???@??S>?MPA??*DpAPB?E???>A??33#@#h8? ?B?t C????C???? C????????????????"K"K"K"K"K"K"K"K?/A?&A0A)\???(??`? ??T????såi??????????z?G???/home/fit/opencv-2.4.7/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 imagecvFloodFill/home/fit/opencv-2.4.7/modules/imgproc/src/moments.cppInvalid image typehu.isContinuous()cvMomentscvGetHuMomentscvGetSpatialMomentcvGetCentralMomentHuMoments??????????UUUUUU??UUUUUU????????????UUUUUU??UUUUUU??UUUUUUſp?/home/fit/opencv-2.4.7/modules/imgproc/src/undistort.cppCV_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)CV_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)m1type == CV_16SC2 || m1type == CV_32FC1 || m1type == CV_32FC2A.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)mapx0.data == mapx.data && mapy0.data == mapy.data(distCoeffs0.cols == 1 || distCoeffs0.rows == 1) && (ndcoeffs == 4 || ndcoeffs == 5 || ndcoeffs == 8)cameraMatrix0.size() == Size(3,3)Unknown projection typedst.data != src.dataavg iters = %g initUndistortRectifyMapundistortcvUndistort2cvInitUndistortMapcvInitUndistortRectifyMapcvUndistortPointsundistortPointsinitWideAngleProjMapmapPointSpherical?-???q=p?@w??>  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????F|FxF$tF@pFdlF?hF?dFaFD]F?YF?UF@RF?NFKF?GFDF?@F=F?9F@6F?2F?/FD,F)F?%F?"FdF@F$FFFF F F$F@FdF ?E??E?E??E ?E??E??EH?E ?E?E?E?E ?EH?E??EȯE ?E??E?E??E ?EȔE??EH?E ?E?E?ExE@pE?hEaE?YE@REKEDE=E@6E?/E)E?"E@EEE E@E ?D?D ?D??D ?D?D ?D??D ?D?D ?D??D ?D?D@pDaD@RDDD@6D)D@DD@D?C??C?C??C?C??C?CaCDC)CC?B?B?B?BDBB?A?AA?@?????@A?A?ABDB?B?B?B?BC)CDCaC?C??C?C??C?C??C?C@DD@D)D@6DDD@RDaD@pD?D ?D??D ?D?D ?D??D ?D?D ?D??D ?D?D ?D@E EEE@E?"E)E?/E@6E=EDEKE@RE?YEaE?hE@pExE?E?E ?EH?E??EȔE ?E??E?E??E ?EȯE??EH?E ?E?E?E?E ?EH?E??E??E ?E??E?E??E ?EdF@F$F F FFFF$F@FdF?"F?%F)FD,F?/F?2F@6F?9F=F?@FDF?GFKF?NF@RF?UF?YFD]FaF?dF?hFdlF@pF$tFxF|F?F?F?F?F ?F2?FH?Fb?F??F??FȔF?F ?FR?F??FŸF?FB?F??FҨF ?Fr?FȯF"?F??F?FH?F??F ?F??F?F??F?F??F?F??F ?F??FH?F??F??F"?F??Fr?F ?F??F??FB?F?F??F??FR?F ?F??FdG?G@G?G$G?G G? G G?GG?GG?GG?G$G?G@G?GdG? G?"G)$G?%Ga'G)G?*GD,G?-G?/G91G?2G?4G@6G?7G?9GY;G=G?>G?@GABGDG?EG?GGIIGKG?LG?NGqPG@RGTG?UG?WG?YGi[GD]G!_GaG?bG?dG?fG?hGyjGdlGQnG@pG1rG$tGvGxG zG|G~G???~?}?|?{?z?y?x?w?v?u?t?s?r?q?p?o?n?m?l?k?j?i?h?g?f?e?d?c?b?a?`?_?^?]?\?[?Z?Y?X?W?V?U?T?S?R?Q?P?O?N?M?L?K?J?I?H?G?F?E?D?C?B?A?@???>?=?C?C@CACBCCCDCECFCGCHCICJCKCLCMCNCOCPCQCRCSCTCUCVCWCXCYCZC[C\C]C^C_C`CaCbCcCdCeCfCgChCiCjCkClCmCnCoCpCqCrCsCtCuCvCwCxCyCzC{C|C}C~CC?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??C?C??Cdst.size == src.size && dst.channels() == cnmask.empty() || (mask.size == src.size && mask.type() == CV_8U)/home/fit/opencv-2.4.7/modules/imgproc/src/accum.cppsrc2.size && src1.size && src2.type() == src1.type()dst.size == src1.size && dst.channels() == cnmask.empty() || (mask.size == src1.size && mask.type() == CV_8U)accumulateaccumulateSquareaccumulateProductaccumulateWeightedtilesXtilesY/home/fit/opencv-2.4.7/modules/imgproc/src/clahe.cppapply*N12_GLOBAL__N_110CLAHE_ImplE*N12_GLOBAL__N_118CLAHE_CalcLut_BodyE*N12_GLOBAL__N_124CLAHE_Interpolation_BodyE/home/fit/opencv-2.4.7/modules/imgproc/src/sumpixels.cppsum.data == sum0.data && sqsum.data == sqsum0.data && tilted.data == tilted0.dataintegralcvIntegral/home/fit/opencv-2.4.7/modules/imgproc/src/hough.cppCV_IS_MAT(img) && CV_MAT_TYPE(img->type) == CV_8UC1The source image must be 8-bit, single-channeldp, min_dist, canny_threshold and acc_threshold must be all positive numbersThe destination matrix should be continuous and have a single row or a single columnDestination is not CvMemStorage* nor CvMat*rho, theta and threshold must be positiveThe destination matrix data type is inappropriate, see the manuallinesMax > 0 && rho > 0 && theta > 0CV_IS_MAT(image) && CV_MAT_TYPE(image->type) == CV_8UC1NULL destinationUnrecognized method idcvHoughLines2icvHoughLinesStandardicvHoughLinesSDivicvHoughLinesProbabilisticcvHoughCircles??@????I@?${?(??9???/home/fit/opencv-2.4.7/modules/imgproc/src/smooth.cppCV_MAT_CN(sumType) == CV_MAT_CN(srcType)CV_MAT_CN(sumType) == CV_MAT_CN(dstType)Unsupported combination of sum format (=%d), and destination format (=%d)ksize.width > 0 && ksize.width % 2 == 1 && ksize.height > 0 && ksize.height % 2 == 1src.depth() == CV_8U && (cn == 1 || cn == 3 || cn == 4)(src.type() == CV_8UC1 || src.type() == CV_8UC3) && src.type() == dst.type() && src.size() == dst.size() && src.data != dst.data(src.type() == CV_32FC1 || src.type() == CV_32FC3) && src.type() == dst.type() && src.size() == dst.size() && src.data != dst.dataBilateral filtering is only implemented for 8u and 32f imagesdst.size() == src.size() && (smooth_type == CV_BLUR_NO_SCALE || dst.type() == src.type())The destination image does not have the proper typesrc.type() == CV_8UC1 || src.type() == CV_8UC3(ksize.width & 1) && (ksize.height & 1)Adaptive Bilateral filtering is only implemented for 8u imagesksize % 2 == 1sumCount == ksize-1operator()operator()operator()getRowSumFiltergetColumnSumFilter???>??>?=?>?>?>?==?=`>?>`>?==getGaussianKernelcreateGaussianFiltermedianBlurbilateralFilterbilateralFilter_8ubilateralFilter_32fadaptiveBilateralFilternormalizeAnchoradaptiveBilateralFilter_8uadaptiveBilateralFilter_8u_InvokercvSmoothoperator()operator()operator()operator()operator()operator()operator()operator()N2cv6RowSumIhiEEN2cv6RowSumIhdEEN2cv6RowSumItiEEN2cv6RowSumItdEEN2cv6RowSumIsiEEN2cv6RowSumIiiEEN2cv6RowSumIsdEEN2cv6RowSumIfdEEN2cv6RowSumIddEEN2cv9ColumnSumIihEEN2cv9ColumnSumIdhEEN2cv9ColumnSumIitEEN2cv9ColumnSumIdtEEN2cv9ColumnSumIisEEN2cv9ColumnSumIdsEEN2cv9ColumnSumIiiEEN2cv9ColumnSumIifEEN2cv9ColumnSumIdfEEN2cv9ColumnSumIidEEN2cv9ColumnSumIddEE????????N2cv26BilateralFilter_8u_InvokerEN2cv27BilateralFilter_32f_InvokerEN2cv34adaptiveBilateralFilter_8u_InvokerE333333???????????> ?#<?????/home/fit/opencv-2.4.7/modules/imgproc/src/geometry.cppChains are not supported. Convert them to polygonal representation using cvApproxChains()Input contour is neither a valid sequence nor a matrixp1.depth() == CV_32S || p1.depth() == CV_32Fp2.depth() == CV_32S || p2.depth() == CV_32FNULL vertex array pointern >= 0 && m >= 0temp.ptr() == dstcvBoxPointscvPointPolygonTestintersectConvexConvex????????/home/fit/opencv-2.4.7/modules/imgproc/src/linefit.cppNULL pointer to line parametersInput 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 )) >= 0The sequence has no points??"??????????????????j????????j?????k?????`??cvFitLine$tI?(???6???>?7?5N2cv12VideoCaptureEN2cv11VideoWriterE%ud%u%dd7??X7???7???7??07??P7??16CvCapture_Images20CvVideoWriter_Images9CvCapture13CvVideoWriter/home/fit/opencv-2.4.7/modules/highgui/src/cap_ffmpeg.cppImmediate exit was requested; the called function should not be restartedInvalid data found when processing inputNot yet implemented in FFmpeg, patches welcomeCould not find encoder for codec id %d: %simage->depth == 8Bitstream filter not foundDecoder not foundDemuxer not foundEncoder not foundEnd of fileFilter not foundMuxer not foundOption not foundProtocol not foundStream not foundCould not open codec '%s': %s8S??(S??S???R???R???R???R???S??3f???b??3f???b??f???b???e???b???b???b???b???b???b???b???b???b???b???b???e??H264h264X264x264avc1VSSHH263H263I263H261U263viv1 FMP4 DIVX DX50 XVID MP4S M4S2  DIV1 BLZ0 mp4v UMP4 WV1F SEDG RMP4DIV3MP43MPG3DIV5DIV6DIV4AP41COL1COL0MP42DIV2MPG4WMV1WMV2dvsddvhddvsldv25mpg1mpg2mpg2MPEGPIM1VCR2DVR MMESMJPGLJPG LJPGJPGLMJLSjpegIJPGAVRnHFYUEFFVHCYUVI420YUY2Y422YV12UYVYIYUVY800HDYCIV31IV32VP31VP30 ASV1!ASV2$VCR1"FFV1*Xxan.mrle./MSVC/msvc/CRAM/cram/WHAM/wham,cvid4DUCK6MSZH7ZLIB9SNOW#4XMVFLV1svq1:tscc;ULTI=VIXL>QPEG>Q1.0>Q1.1IWMV3JLOCOtheoKWNV1LAASCMRT21NFPS1OTM20XFSV1 MJLSHWVC1HWMVAQCSCDSZMBVWKMVC\VP50]VP60]VP61]VP62^VP6FZMJ2C[VMnc_tga writeFrame22CvCapture_FFMPEG_proxy26CvVideoWriter_FFMPEG_proxy-C??6?>?????Af != 0buf.data && buf.isContinuous()/home/fit/opencv-2.4.7/modules/highgui/src/loadsave.cpp_buf && CV_IS_MAT_CONT(_buf->type)image.channels() == 1 || image.channels() == 3 || image.channels() == 4could not find a writer for the specified extensionencoder->isFormatSupported(CV_8U)channels == 1 || channels == 3 || channels == 4could not find encoder for the specified extensionimwrite_imdecode_imencodecvDecodeImagecvDecodeImageM/home/fit/opencv-2.4.7/modules/highgui/src/utils.cppSource image must have 1, 3 or 4 channelsDestination image must have 1 or 3 channelsUnsupported combination of input/output formatsInner function failed.Destination image must be 8u????????????????????????????????????????????????????????????????????????????????p???????????????????????????????8???????????/home/fit/opencv-2.4.7/modules/highgui/src/window.cppThe library is compiled without QT supportsize.width>0 && size.height>0imshowpointCloudShowpointCloudShowcvSetOpenGlDrawCallbackcvSetOpenGlContextcvUpdateWindowfontQtaddTextdisplayStatusBardisplayOverlaystartLoopstopLoopsaveWindowParametersloadWindowParameterscreateButton/home/fit/opencv-2.4.7/modules/highgui/src/window_gtk.cppLibrary was built without OpenGL supportNULL window or trackbar nameBad trackbar maximal valuevalue-changedwidget != NULLCV_IS_IMAGE_WIDGET (widget)event != NULLallocation != NULLobject != NULLCV_IS_IMAGE_WIDGET (object)NULL name stringNULL windowkey-press-eventbutton-press-eventbutton-release-eventmotion-notify-eventdelete-eventexpose-eventNULL nameNULL window nameNULL trackbar or window nameCvImageWidgetvoid cvImageWidget_size_allocate(GtkWidget*, GtkAllocation*)void cvImageWidget_realize(GtkWidget*)void cvImageWidget_destroy(GtkObject*)gboolean cvImageWidget_expose(GtkWidget*, GdkEventExpose*, gpointer)[left_camera][right_camera]f %fCxCykappa1kappa2tau1tau2kappa3 L??@L??`L??J??J??J??xL??J??J??J??J??J??J??J??J???K??J??J??J??J??J??J??J??J??J??J??J???L??`O???O??xP???N???N???N??`P???N???N???N???N???N???N???N???P???O???N???N???N???N???N???N???N???O???N???N??0P??HP??26CvCaptureCAM_DC1394_v2_CPP??????????.@appsrcfilesinkavimuxffmpegcolorspacelocationbppframeratevideo/x-raw-grayuridecodebinuriblue_maskgreen_maskred_maskvideo/x-raw-rgbappsinkpad-addedready, relinkingfiltering with %s relinked, pausingstate now pauseddiracencffenc_huffyuvjpegencmpeg2enctheoraencvp8encx264encxvidency4menc/home/fit/opencv-2.4.7/modules/highgui/src/cap_gstreamer.cppcvWriteFrame() needs images with depth = IPL_DEPTH_8U and nChannels = 3.cvWriteFrame() needs images with depth = IPL_DEPTH_8U and nChannels = 1.Gstreamer Opencv backend doesn't support this codec acutally.Your version of Gstreamer doesn't support this codec acutally or needed plugin missing.GStreamer: cannot link elements GStreamer: cannot put pipeline to play GStreamer Plugin: Embedded video playback halted; module %s reported: %s GStreamer: cannot find appsink in manual pipeline GStreamer: cannot link color -> sink restarting pipeline, going to readyGStreamer: unable to start pipeline 19CvCapture_GStreamer23CvVideoWriter_GStreamerVIDIOC_QUERYMENUUnable to stop the stream.munmapVIDIOC_G_FMTVIDIOC_QBUFVIDIOC_STREAMONselectselect timeout VIDIOC_DQBUFVIDIOC_REQBUFSVIDIOC_QUERYBUFmmap/dev/video%1dHIGHGUI ERROR: V4L: Unable to determine size of incoming image HIGHGUI ERROR: V4L2: Unable to get property %s(%u) - %s HIGHGUI ERROR: V4L2: Property %s(%u) not supported by device HIGHGUI ERROR: V4L: Gain control in V4L is not supported HIGHGUI ERROR: V4L: Exposure control in V4L is not supported HIGHGUI ERROR: V4L: Initial Capture Error: Unable to load initial memory buffers. HIGHGUI ERROR: V4L2: device %s is unable to capture video memory. HIGHGUI ERROR: V4L2: Aren't able to set channel number HIGHGUI ERROR: V4L2: Could not obtain specifics of capture window. HIGHGUI ERROR: libv4l unable to ioctl S_FMT HIGHGUI ERROR: libv4l unable convert to requested pixfmt %s does not support memory mapping Insufficient buffer memory on %s Insufficient buffer memory on %s -- decreaseing buffers HIGHGUI ERROR: V4L: index %d is not correct! HIGHGUI ERROR: V4L: Could not allocate memory for capture process. HIGHGUI ERROR: V4L: device %s: Unable to open for READ ONLY HIGHGUI ERROR: V4L: device %s: Unable to query number of channels HIGHGUI ERROR: V4L: device %s is unable to capture video memory. HIGHGUI ERROR: V4L: Could not obtain specifics of capture window. HIGHGUI ERROR: libv4l unable to ioctl VIDIOCSPICT HIGHGUI ERROR: libv4l unable to ioctl VIDIOCGPICT HIGHGUI ERROR: libv4l unable convert to requested pixfmt HIGHGUI ERROR: V4L: Mapping Memmory from video source error: %s HIGHGUI ERROR: V4L: Could not memory map video frames. HIGHGUI ERROR: V4L/V4L2: Could not obtain specifics of capture window. HIGHGUI ERROR: V4L: Unable to set camera FPS HIGHGUI ERROR: V4L: Property %s(%u) not supported by device HIGHGUI ERROR: V4L2: Failed to set control "%d": %s (value %d) HIGHGUI WARNING: Setting property %u through v4l2 failed. Trying with v4l1. HIGHGUI ERROR: V4L: property #%d is not supported HIGHGUI ERROR: V4L: Unable to set video informations HIGHGUI ERROR: V4L: Could not SYNC to video stream. %s HIGHGUI ERROR: V4L: Cannot convert from palette %d to RGB ȑ??ȑ??????????????????????????p???@???(??????????A???;???5?????????G???????\???,?????????????N???H??????????????T???20CvCaptureCAM_V4L_CPP?GWindows bitmap (*.bmp;*.dib)??????????????????????????????? ??????????????????????????????????????????????????????????????x???????????????????????????????????N2cv10BmpDecoderEN2cv10BmpEncoderE/home/fit/opencv-2.4.7/modules/highgui/src/grfmt_png.cppPNG input buffer is incompletePortable Network Graphics files (*.png)decoder?PNG  1.2.49encoder && encoder->m_bufreadDataFromBufwriteDataToBufN2cv10PngDecoderEN2cv10PngEncoderEbX9?????A`??"??P%c %d %d %d % 4d% 6dPortable image format (*.pbm;*.pgm;*.ppm;*.pxm;*.pnm)N2cv10PxMDecoderEN2cv10PxMEncoderEN2cv5._212ERaw image encoder error: /home/fit/opencv-2.4.7/modules/highgui/src/grfmt_base.cppN2cv16BaseImageDecoderEN2cv16BaseImageEncoderEthrowOnErorJPEG-2000 files (*.jp2)jp2 jP ? JPEG 2000 LOADER ERROR: cannot convert colorspace JPEG 2000 LOADER ERROR: unable to create colorspace JPEG2000 LOADER ERROR: colorspace conversion failed N2cv13Jpeg2KDecoderEN2cv13Jpeg2KEncoderETIFF Files (*.tiff;*.tif)r+bN2cv11TiffDecoderEN2cv11TiffEncoderEII*MM*???JPEG files (*.jpeg;*.jpg;*.jpe)N2cv11JpegDecoderEN2cv11JpegEncoderEv/1BYOpenEXR Image files (*.exr)N2cv10ExrDecoderEN2cv10ExrEncoderEp>?@Y?j?Sun raster files (*.sr;*.ras)N2cv16SunRasterDecoderEN2cv16SunRasterEncoderEbuf.isContinuous()open/home/fit/opencv-2.4.7/modules/highgui/src/bitstrm.cppN2cv11RBaseStreamEN2cv12RLByteStreamEN2cv12RMByteStreamEN2cv11WBaseStreamEN2cv12WLByteStreamEN2cv12WMByteStreamE/home/fit/opencv-2.4.7/modules/features2d/src/draw.cppIncorrect type of input image. outImg has size less than need to draw img1 and img2 togethermatchesMask must have the same size as matches1to2i1 >= 0 && i1 < static_cast(keypoints1.size())i2 >= 0 && i2 < static_cast(keypoints2.size())!outImage.empty()!img.empty()drawKeypoints_drawKeypointdrawMatches_prepareImgAndDrawKeypointsdrawMatches/home/fit/opencv-2.4.7/modules/features2d/src/descriptors.cppimageCollection.size() == pointCollection.size()input image must be an BGR image of type CV_8UC3!descriptorExtractor.empty()Opponentbasic_string::substrFeature2D.vector::_M_range_insertN2cv19DescriptorExtractorEN2cv32OpponentColorDescriptorExtractorEcomputeOpponentColorDescriptorExtractorconvertBGRImageToOpponentColorSpace/home/fit/opencv-2.4.7/modules/features2d/src/stardetector.cppimg.type() == CV_8UC1matI.type() == CV_8UStarDetectorComputeResponses     ????????  -.@Z?????computeIntegralImages/home/fit/opencv-2.4.7/modules/features2d/src/detectors.cppDynamicN2cv15FeatureDetectorEN2cv26GridAdaptedFeatureDetectorEN2cv29PyramidAdaptedFeatureDetectorEdetect*N2cv12_GLOBAL__N_133GridAdaptedFeatureDetectorInvokerEFeature2D.GridFeature2D.SimpleBlobFeature2D.MSERmaxVariationminDiversitymaxEvolutionareaThresholdminMarginedgeBlurSizeFeature2D.FREAKDescriptorMatcher.BFMatcherFeature2D.BRIEFFeature2D.BRISKFeature2D.DenseinitFeatureScalefeatureScaleLevelsfeatureScaleMulinitXyStepinitImgBoundvaryXyStepWithScalevaryImgBoundWithScaleFeature2D.FASTFeature2D.HARRISFeature2D.FASTXFeature2D.ORBFeature2D.STARFeature2D.GFTTthresorientationNormalizedscaleNormalizednbOctavenFeaturesDescriptorMatcher.FlannBasedMatcherN2cv4MSEREN2cv12StarDetectorEN2cv19FastFeatureDetectorEN2cv12GFTTDetectorEN2cv20DenseFeatureDetectorEN2cv20FastFeatureDetector2E14HarrisDetectorNo element name has been given/home/fit/opencv-2.4.7/modules/features2d/src/matchers.cppimgIdx < (int)startIdxs.size()(globalDescIdx>=0) && (globalDescIdx < size())masks[i].rows == queryDescriptorsCount && masks[i].cols == trainDescCollection[i].rows && masks[i].type() == CV_8UC1localPointIdx < (int)keypoints[imgIdx].size()globalPointIdx < (int)keypointCount()deep clone functionality is not implemented, because Flann::Index has not copy constructor or clone method descriptors[i].cols == dim && descriptors[i].type() == typemaxDistance > std::numeric_limits::epsilon()!extractor.empty() && !matcher.empty()queryDescriptors.type() == trainDescCollection[0].type()(int64)imgCount*IMGIDX_ONE < INT_MAXtrainDescCollection[iIdx].rows < IMGIDX_ONE_images.size() == _points.size()images.size() == keypoints.size()globalDescIdx < size()globalIdx < (int)size()masks.size() == imageCountimgIdx < (int)imageCount()imgIdx < (int)images.size()ip.type() == FileNode::SEQip[i].type() == FileNode::MAPsp.type() == FileNode::SEQsp[i].type() == FileNode::MAPknnMatches[i].size() <= 1DescriptorMatcher.!_indexParams.empty()!_searchParams.empty()FlannBasedBruteForceBruteForce-SL2BruteForce-L1BruteForce-HammingBruteForce-HammingLUTUnknown matcher nameBruteForce-Hamming(2)imageCount > 0knn > 0{typename!_images.empty()!images.empty()!images[i].empty()Pg??Pg??Pg??Pg??Pg???f??@f??He???_???_???_???_???_???_???_???d???_???_???_???_???_???_???_??@d???k???k???k???k???k??Xl??Hk??Pj???b???b???b???b???b???b???b???i???b???b???b???b???b???b???b??Hi??0??????Ч??????p???@?????????H???H???H???H???H???H???H???p???H???H???H???H???H???H???H???p???0??????Ю??????p???@?????????H???H???H???H???H???H???H???p???H???H???H???H???H???H???H???p???setgetDescriptorgetDescriptorgetLocalIdxconvertMatchescheckMasksknnMatchradiusMatchknnMatchImplradiusMatchImplcreateFlannBasedMatcherreadoperator<<operator<<operator<<operator<<operator<<operator<<operator<<cloneaddgetKeypointsgetKeyPointgetLocalIdxgetImageaddVectorDescriptorMatcherN2cv17DescriptorMatcher20DescriptorCollectionEN2cv17DescriptorMatcherEN2cv9BFMatcherEN2cv17FlannBasedMatcherEN2cv24GenericDescriptorMatcherEN2cv23VectorDescriptorMatcherEvector::_M_range_checkN2cv5BRISKE@A???A@@A@?@E??*?????:333??A?Jff???Bgf?@r1???P??@??333333??333333@??????@??????@??????%@ffffff @ffffff@PAbitset::setNumber of images: number of keypoints: /home/fit/opencv-2.4.7/modules/features2d/src/freak.cppInput vector does not match the required sizecorrelation threshold too small (restrictive)N2cv5FREAKEbuildPattern??2??47j???? 0? '2?Y?=7?,?P????q?%?????M$??????QS??:?zG?HS;???D}?]|Gk?(????&U_~8|????KO?=I?d?eN2??F^?????n??MY ?8+??{??AHq?l9???_j?1?-T8s.?h&c??rBBt??g??5 C?T{ /?Z???>1 &???? +5?????????"?? ? >!?*?<???v?? X1??k Z?????Y?Gwe3f????L????(?l?CmL?????2R?0 ? ???A?>+??Oa?5>?-??,?c,d??6?m?????=J?? 4???#3`pob???????????A'yRy??6?T???S?UJ3 F?XD;`? ?}??\%??pPWG?[H?!??{LIsN?????XJ???K? ??8)?CQ?B?????[.-??????@x????.???a???jfV??&??zDfh:i??FR?ME??0??n~??#F???$E?]K?6selectPairsUUUUUU???q?q????XUUUUU?? ?q?q???q?q????XUUUUU?? ?q?q???@?9??B.??P@??z?????cܥL@l?l?f?N2cv18SimpleBlobDetectorEoperator<<operator<< A-DT?!)@/home/fit/opencv-2.4.7/modules/features2d/src/keypoint.cppkeypointIndexes has element < 0. TODO: process this caseminSize >= 0maxSize >= 0minSize <= maxSizeconvertrunByKeypointSizesrc != 0/home/fit/opencv-2.4.7/modules/features2d/src/mser.cppCV_MAT_TYPE(src->type) == CV_8UC1 || CV_MAT_TYPE(src->type) == CV_8UC3mask == 0 || (CV_ARE_SIZES_EQ(src, mask) && CV_MAT_TYPE(mask->type) == CV_8UC1)extractMSER?X?N???˝>?Ņ????)K?#??W????????ص?ݦ??^?"??????t?Ь?{40Ml???ؙ?-?$???$??Xr??????붳?8h?????aL?{)???+?=*Y???9?7W?????$Ί???q:ɺ?s? ???????B????I?p???E??(&??=??S????0????:"ߥ?%????"ڎ???V?p;4,????D-ͭ????b.???G?z?????d?,??d=???????(??Xᖏ????!?Ky ????S㥛????+H3????2???wH1@? ??E?^Ӄ???????????ei??r??ץF?g???Q?|a??8??+????Z??vN??Z??m???Ie?9:??M? O?????#bJ$???"i7?????T?^ ???L?*????h??n???? ?ui???_???????hW!?'??????oa??Ho??ܚ????4???8j??{ ??q?::?F???):????? Uܸ???f??????*????A%?c??9 ?/????/i??Q????:?????)??F????????? ?'???NCT?????"?T3k)???>U?b???ԗ???????0????/??? ?? ?%qVD?????_?|???Oqx???H?? ????8+?&???({K9_??j?{?ԗ??k :!t????Z?? ??%y???A???d:tz???nض(???/??????ʥ? ?$???ٲ|]?????V?????9???z?m?(??7?[ A??|DL?$z??J'L5??????R?????À%??A?9w?^??^??????1??Z??????v? ??˃?9D????=z?}??=??tZ???? ?g?????S?*??ۢ??d???ީ?{???L??$w?????B?????@??L??Z?Pۆ?????*????7?W????G 6??CUL??p??z?rK????N?j???? S[? ??W$&??[??Ҭl????????????gx????R??c$??$a?N"B??P@??_??????}????K??????Y?$????M??f??????SV??????'????]??X32???ܵ?|P???ypw?n????C????/?혺???q!??F???~5????+??ݓ????ޫV&??*A*E???0?d?? ??# ????Y?X???1?Tm7????]?zk??????g????Ü?M??? 0,>????t^??&????}????@?M???>"?D????;????????p?????9?Վ??????}=????? Y]?????????????????5#???????a????????& ???R?{/??m????R??f?ʉv????$xC????- ?????? ~???????;??ܜJ?*???ן??N???G?)ss?????5"???-?2????????????ؘ????$??W,????q??Q??2"QhYw??qǛ????J??P?????v?????:u?<??hyܝ5??E???&\?????ۂ????l亩???;1????????????Q?v0b??`?n?F??cD?вn??[}uU????]?P????k?3 ???m??3?????2?68????-Ya??Z_&??????k???DkE?????(??я??C?up0???ݓ??Z??????T???????p?q???? ??????72??A?????Ni???8 ????ĬC???n??????иp $ ??? %?4???????^?????????i?????8??d?????@e?? ?? Y??9????C?lg??f????????g?R???2????????p= ?#??1?*?T????y????GZ*oG???A??4F???;?f????.?o?S??E ?a??????p>???pUj????lV}.??d???g???wb֋????;Nё???Է????????U??N???P???R~R????0 ?G????L?V?? .V?`?????E???P?s?@I?f??6@?n??[@?uT5?@????@W?/?'?@?N#-??@pB!!@닄??K@?Q?Qw@????@Q?y?@n??@P?}:3@?Z_$?e@??????@r?t???@?n??@nLOX?A@?(? ~@"O????@YiR ??@?\??A@?K????@???? ?@i??!@OjMs@C?8?@?W?L?%@? ???@rm???@??m??_@?F???@^?/? [@`vO?@??@?? @??&N?7 @?? @?.4?i? @P6? ?? @>"?D=@?%P6?@xz?,C@?7?0@-??離`@Seq?????|?=?x@ _?BN2cv29DynamicAdaptedFeatureDetectorEN2cv12FastAdjusterEN2cv12StarAdjusterEN2cv12SurfAdjusterEN2cv15AdjusterAdapterE????????i@>@[email protected]() == CV_8UC1 && blockSize*blockSize <= 2048/home/fit/opencv-2.4.7/modules/features2d/src/orb.cppWrong WTA_K. It can be only 2, 3 or 4.WTA_K == 2 || WTA_K == 3 || WTA_K == 4patchSize >= 2image.type() == CV_8UC1N2cv3ORBEoperator()HarrisResponsescomputeDescriptorscomputeOrbDescriptor ?#=???%????o@bytes must be 16, 32, or 64/home/fit/opencv-2.4.7/modules/features2d/src/brief.cppdescriptorSize must be 16, 32, or 64N2cv24BriefDescriptorExtractorEBriefDescriptorExtractorreadoperator<</home/fit/opencv-2.4.7/modules/features2d/src/fast_score.cpppixel && offsets????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????makeOffsets????????????????nimages > 0 && nimages == (int)imagePoints1.total() && (!imgPtMat2 || nimages == (int)imagePoints2.total())/home/fit/opencv-2.4.7/modules/calib3d/src/calibration.cppCV_ARE_TYPES_EQ(A, B) && (CV_MAT_TYPE(A->type) == CV_32F || CV_MAT_TYPE(A->type) == CV_64F)CV_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->colsInput argument is not a valid matrixThe 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 datatypeInput matrix must be 1x3, 3x1 or 3x3Output matrix must be 3x3, single-channel floating point matrixOutput matrix must be 1x3 or 3x1CV_IS_MAT(_rvec1) && CV_IS_MAT(_rvec2)CV_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)Some of parameters is a NULL pointer!Input parameters must be a matrices!CV_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)Size of projection matrix must be 3x4!Size of calibration and rotation matrices must be 3x3!Size of position vector must be 4x1!Both object points and image points must be 2Dobject points and image points must be a single-row matricesCV_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)One of required arguments is not a valid matrixHomogeneous 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 matrixDistortion coefficients must be 1x4, 4x1, 1x5, 5x1, 1x8 or 8x1 floating-point vectordp/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 notnpoints >= 0 && (depth == CV_32F || depth == CV_64F)CV_IS_MAT(objectPoints) && CV_IS_MAT(imagePoints) && CV_IS_MAT(A) && CV_IS_MAT(rvec) && CV_IS_MAT(tvec)(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) == 3One of required vector arguments is not a valid matriximage 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 specifiedCV_IS_MAT(_imagePoints1) && CV_IS_MAT(_imagePoints2) && CV_IS_MAT(_objectPoints) && CV_IS_MAT(_npoints) && CV_IS_MAT(matR) && CV_IS_MAT(matT)CV_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))stype == CV_8UC1 || stype == CV_16SC1 || stype == CV_32SC1 || stype == CV_32FC1dtype == CV_16SC3 || dtype == CV_32SC3 || dtype == CV_32FC3ni >= 0ni > 0 && ni == ni1ni == ni2err.empty()CV_IS_MAT(A) && CV_IS_MAT(B)A->cols == B->rowsJacobian must be 3x9 or 9x3Size of matrices must be 3x3!cvDet(&tmpMatrixM) != 0.0Q.size() == Size(4,4)cols >= 0disp.size() == _3dimg.size()ni1 > 0 && ni1 == ni3???????????????updateAltcvCalcMatMulDerivcvComposeRTcvRodrigues2cvProjectPoints2cvFindExtrinsicCameraParams2cvInitIntrinsicParams2DcvCalibrateCamera2cvCalibrationMatrixValuescvStereoCalibrate????cvStereoRectifyUncalibratedreprojectImageTo3DcvReprojectImageTo3DcvRQDecomp3x3cvDecomposeProjectionMatrixprojectPointscollectCalibrationDatacalibrateCameraadjust3rdMatrixU???k@Y?0C@Cimg.type() == CV_8UC1 || img.type() == CV_16SC1/home/fit/opencv-2.4.7/modules/calib3d/src/stereosgbm.cppleft.size() == right.size() && left.type() == right.type() && left.depth() == DataType::depthnumberOfDisparities > 0 && disp.type() == CV_16S && (costType == CV_16S || costType == CV_32S) && disp.size() == cost.size()D % 16 == 0N2cv10StereoSGBMEoperator()computeDisparitySGBMfilterSpecklesvalidateDisparity????????/home/fit/opencv-2.4.7/modules/calib3d/src/solvepnp.cppnpoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F))The flags argument must be one of CV_ITERATIVE or CV_EPNP(opoints.rows == 1 && opoints.channels() == 3) || opoints.cols*opoints.channels() == 3(ipoints.rows == 1 && ipoints.channels() == 2) || ipoints.cols*ipoints.channels() == 2npoints == 4opoints.isContinuous()opoints.depth() == CV_32Fipoints.isContinuous()ipoints.depth() == CV_32FsolvePnPsolvePnPRansac/home/fit/opencv-2.4.7/modules/calib3d/src/fundam.cpp(_m1->cols == 1 || _m1->rows == 1) && CV_ARE_SIZES_EQ(_m1, _m2)points parameter is not a valid matrixThe 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 3fundamental 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 differentnpoints >= 0 && (points.depth() == CV_32F || points.depth() == CV_32S)The input parameter is not a valid matrixThe output parameter is not a valid matrixEither 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 && (src.depth() == CV_32F || src.depth() == CV_32S)CV_IS_MAT(points1) && CV_IS_MAT(points2) && CV_ARE_SIZES_EQ(points1, points2)CV_IS_MAT(fmatrix) && fmatrix->cols == 3 && (fmatrix->rows == 3 || (fmatrix->rows == 9 && method == CV_FM_7POINT))CV_IS_MASK_ARR(mask) && CV_IS_MAT_CONT(mask->type) && (mask->rows == 1 || mask->cols == 1) && mask->rows*mask->cols == countnpoints >= 0 && points2.checkVector(2) == npoints && points1.type() == points2.type()CV_IS_MAT(imagePoints) && CV_IS_MAT(objectPoints)fmatrix is not a valid matrixInvalid inplace operation_dst.fixedType()count >= 421CvHomographyEstimator13CvFMEstimatorcvFindHomographyrun8PointcvFindFundamentalMatcvComputeCorrespondEpilinescvConvertPointsHomogeneousfindHomographyfindFundamentalMatcomputeCorrespondEpilinesconvertPointsFromHomogeneousconvertPointsToHomogeneousconvertPointsHomogeneous?;f????????????ףp= ???/home/fit/opencv-2.4.7/modules/calib3d/src/triangulate.cppSome of parameters is a NULL pointerInput 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 3x4The 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 yThe 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 yThe 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 yCV_IS_MAT(new_points1)CV_IS_MAT(new_points2)cvTriangulatePointscvCorrectMatches/home/fit/opencv-2.4.7/modules/calib3d/src/modelest.cppCV_MAT_TYPE(ms1->type) == CV_64FC3the number of model points should be positiveCV_ARE_SIZES_EQ(m1, m2) && CV_ARE_SIZES_EQ(m1, mask)count >= 0 && to.checkVector(3) == count17CvModelEstimator2N2cv17Affine3DEstimatorEcvRANSACUpdateNumItersrunRANSACrunLMeDScheckSubsetestimateAffine3D?Zd;??????????????x? @samples is emptyfilteredSamples is emptysortedIndices.cols == 1Unknown pattern typeCenters are not newh >= 2 && w >= 2Basis size is not 2degenerate basisUnkown pattern type/home/fit/opencv-2.4.7/modules/calib3d/src/circlesgrid.cpp_patternSize.height >= 0 && _patternSize.width >= 0sortedIndices.type() == CV_32SC1maxLoc.x >= 0 && maxLoc.y >= 0largeSegments.size() == cornersCountlargeHoles != 0 && smallHoles != 0Number of basis graphs is not 2findCornersCirclesGridFinderfindHolesisDetectionCorrectinsertWinnerfilterOutliersByDensityfindBasisfindLongestPathgetAsymmetricHolesgetCornerSegmentsgetFirstCorner???AC??????????K@?dy???=??/home/fit/opencv-2.4.7/modules/calib3d/src/calibinit.cppNumber of channels must be 1, 3 or 4Only 8-bit, 16-bit or floating-point 32-bit images are supportedisAsymmetricGrid ^ isSymmetricGridOnly 8-bit grayscale or color images are supportedBoth width and height of the pattern should have bigger than 2out_corners && out_quadsNull pointer to cornerscvFindChessboardCornersicvGenerateQuadscvDrawChessboardCorners?o@`@?o@i@i@?o@i@i@?o@?o@?o@drawChessboardCornersfindCirclesGrid9@333333??All the images must have the same sizeBoth input images must have CV_8UC1Disparity image must have CV_16SC1 or CV_32FC1 format/home/fit/opencv-2.4.7/modules/calib3d/src/stereobm.cpppreFilterType 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-negativedisptype == CV_16S || disptype == CV_32FStereo BM state is NULL.cvReleaseStereoBMStateoperator()findStereoCorrespondenceBMN2cv24FindStereoCorrespInvokerE??^A??>A(icvCreatePOSITObject( points, numPoints, &pObject )) >= 0/home/fit/opencv-2.4.7/modules/calib3d/src/posit.cpp(icvPOSIT( pObject, imagePoints,(float) focalLength, criteria, rotation, translation )) >= 0cvCreatePOSITObjectcvPOSIT/home/fit/opencv-2.4.7/modules/calib3d/src/checkchessboard.cppsupports single-channel images onlycvCheckChessboardsupports depth=8 images only???>33???Bopencv-ml-bayesianvar_allvar_idxcls_labelsproductsuminv_eigen_valuescov_rotate_matsNumber of classes is less 1my_nb/home/fit/opencv-2.4.7/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 dataNo "cls_labels" in NBayes classifierThe field "var_count" of NBayes classifier is missingThe 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 matrix23CvNormalBayesClassifierpredict12predict_body???opencv-ml-random-treesInvalid CvRTrees objectnclassesoob_errorntrees tag is missingmy_random_treesvar_count == data->var_count/home/fit/opencv-2.4.7/modules/ml/src/rtrees.cppThis method is not supported for regression problems must be non-negative(active_var_mask->cols >= 1) && (params.nactive_vars > 0) && (params.nactive_vars <= active_var_mask->cols)Some , , , , , of tags are missing is not equal to the number of trees saved in file0 <= class_idx && class_idx < nclassesThis function works for binary classification problems only...12CvForestTree8CvRTreesN2cv25ForestTreeBestSplitFinderEfind_best_splittrainget_train_errorpredictpredict_probwriteread?B/home/fit/opencv-2.4.7/modules/ml/src/ann_mlp.cppSome of new output training vector components run exceed the original range too muchinput_scale tag is not found or is invalidinv_output_scale tag is not found or is invalidweights tag is not found or is invalidThe 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 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 negativeThe network has not been initializedBoth 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 layerUnknown activation functionlayer_sizesinput_scaleinv_output_scaleopencv-ml-ann-mlpf_param1f_param2min_val1max_val1training_paramsterm_criteriaactivation_functionlayer_sizes != 0my_nn9CvANN_MLPpredictpredict10rprop_loopjM?St??ffffff??ffffff?\???(\??\???(\?{?G?z?????????ffffff??@?z??333333??opencv-ml-extremely-randomized-trees/home/fit/opencv-2.4.7/modules/ml/src/ertrees.cppCvERTrees do not support surrogate splitsCvERTrees 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 limitThe 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 positive_subsample_idx must be null for extra-treesNo training data has been set17CvERTreeTrainData14CvForestERTree9CvERTreesset_datatrain4opencv-ml-svmclass_countclass_labelsclass_weightssv_totalsupport_vectorsdecision_functionssv_countToo small storage block sizeCvSVM::trainUnknown/unsupported SVM typeGrid step must greater then 1CvSVM::train_autoCvSVM::get_default_gridsvm_type tag is not foundSVM kernel tag is not foundMissing of invalid SVM typemy_svmrho is missingoptimize_linearsv_count != 0/home/fit/opencv-2.4.7/modules/ml/src/svm.cppAssertion: sv_count == sv_total failedThe SVM should be trained firstUnknown/unsupported kernel typegamma parameter of the kernel must be positiveThe kernel parameter must be positive or zeroThe kernel parameter must be positiveThe parameter C must be positiveThe parameter nu must be between 0 and 1The parameter p must be positiveINTERNAL ERROR: Unknown SVM type, the SVM structure is probably corruptedLower bound of the grid must be less then the upper oneLower bound of the grid must be positiveParameter must be > 1Invalid type of parameter (use one of CvSVM::C, CvSVM::GAMMA et al.)SVM kernel type tag is not foundMissing of invalid SVM kernel typeThe requested element is not foundSVM model data is invalid, check sv_count, var_* and class_count tagsArray 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-positivealpha 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) failedparams.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 11CvSVMKernel11CvSVMSolver5CvSVMdo_trainpredict16predict_body_svmZb???ti ??@@?r@,@n?z/home/fit/opencv-2.4.7/modules/ml/src/data.cppdelimited, miss_character and flt_separator must be differentit`s impossible to assign CV_VAR_ORDERED type to categorical variabletrain samples count is not correctdata is emptyvar_idx is not correcttype is not correctordtypes string is not correcttest_sample_count > 0variable index is not correctidx value is not correct 8CvMLDataset_train_test_split/home/fit/opencv-2.4.7/modules/ml/src/em.cppstartStep == 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)/home/fit/opencv-2.4.7/modules/core/include/opencv2/core/mat.hpp!covsEigenValues.empty()!means.empty()sample.type() == CV_64FC1sample.rows == 1sample.cols == means.colsisTrained()!sample.empty()!samples.empty()samples.channels() == 1nclusters > 0nclusters <= nsamples!covs.empty()!covs[clusterIndex].empty()meansFlt.type() == CV_32FC1!clusterSamples.empty()weights.empty()invCovsEigenValues.empty()d.cols == 1 || d.rows == 1N2cv2EMEpredictcheckTrainDatadecomposeCovsclusterTrainSamplescomputeLogWeightDivDetdoTraindiagcomputeProbabilities??d??g????,C??6?/home/fit/opencv-2.4.7/modules/ml/src/tree.cppparams.max_categories should be >= 2params.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 >= 0priors 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 varssome of the tree elements are not stored properlysplits tag must stored as a sequencesome of the splits are not stored properlySplit variable index is out of rangesome of in/not_in elements are out of rangeEither 'in' or 'not_in' tags should be inside a categorical split dataThe new training data must have the same types and the input and output variables and the same categories for categorical variablesThe 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 trainingthe missing data mask must be 8-bit vector of the same size as input sampleone of input categorical variable is not an integerbest_tree_idxnodesopencv-ml-treenorm_class_idxTncomplexitynode_risktree_risktree_errorsplitsis_classifierord_var_countcat_var_countcross_validation_foldspriorsvar_typecat_countcat_mapincorrect node depthroot->leftroot->rightnodes tag is missingnot_inqualitymy_tree16CvDTreeTrainData7CvDTreeN2cv20DTreeBestSplitFinderEset_datado_trainpredict????~StatModel.EMcovsDevianceLossHuberLossSquaredLossAbsoluteLossensemble_lengthloss_functionUnknown loss functionmy_boost_treebase_valuetrees_ tag is missing/home/fit/opencv-2.4.7/modules/ml/src/gbt.cppclass_labels must stored as a matrixResponse should be a 32fC1 or 32sC1 vector._sample_idx should be a 32sC1, 8sC1 or 8uC1 vector.basic_string::_S_construct null not validThe number of trees stored does not match tag valueopencv-ml-gradient-boosting-treesThe model has not been trained yet9CvGBTreestrain14Tree_predictor16Sample_predictor/home/fit/opencv-2.4.7/modules/ml/src/boost.cppUnknown/unsupported boosting typeThe classifier has not been trained yetThe 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 output matrix of weak classifier responses must be valid floating-point vector of the same number of components as the length of input sliceIn raw mode the input vectors must be continuousBoosted trees can only be used for 2-class classification.opencv-ml-boost-treeboosting_typeDiscreteAdaboostRealAdaboostLogitBoostGentleAdaboostUnknown boosting typesplitting_criteriaGiniMisclassificationSquaredErrweight_trimming_rate11CvBoostTree7CvBoostpredict ?CvKNearest::train/home/fit/opencv-2.4.7/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 methodInput 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 size10CvKNearestfind_nearest2P1/home/fit/opencv-2.4.7/modules/ml/src/inner_functions.cppThe function supports only single-channel arraysUnsupported combination of input and output vectorsthe 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 vectorResponse array must be 1-dimensionalResponse array must contain as many elements as the total number of samplessample index array should be continuous 1-dimensional integer vectorsample index array is too largeout_response_map pointer is NULLInvalid 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 failedAssertion: 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 failedInvalid 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 failedInvalid 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 failedCvStatModel::readCvStatModel::writemy_stat_modelInvalid index arrayvar index array is too largeInvalid response arrayUnsupported response typeresponse #%d is not integralThere is only a single classNULL labelsNULL centersNULL probsAssertion: prob_vector failed11CvStatModelBackgroundSubtractor.MOGBackgroundSubtractor.MOG2BackgroundSubtractor.GMGvarThresholddetectShadowsvarThresholdGenfVarInitfVarMinfVarMaxfCTnShadowDetectionfTaumaxFeaturesinitializationFramesquantizationLevelsbackgroundPriorsmoothingRadiusdecisionThresholdupdateBackgroundModelMaximum number of features to store in histogram. Harsh enforcement of sparsity constraint.Adaptation rate of histogram. Close to 1, slow adaptation. Close to 0, fast adaptation, features forgotten quickly.Number of frames to use to initialize histograms of pixels.Number of discrete colors to be used in histograms. Up-front quantization.Prior probability that each individual pixel is a background pixel.Radius of smoothing kernel to filter noise from FG mask image.Threshold for FG decision rule. Pixel is FG if posterior probability exceeds threshold.Perform background model update./home/fit/opencv-2.4.7/modules/video/src/simpleflow.cppconfidence.at(r0, c0) >= 0(int)pyr_from_images.size() == layers && (int)pyr_to_images.size() == layerscalcOpticalFlowSFcalcConfidence??&? .>?9@?K@ffffff??ffffff@prev0.size() == next0.size() && prev0.channels() == next0.channels() && prev0.channels() == 1 && pyr_scale < 1/home/fit/opencv-2.4.7/modules/video/src/optflowgf.cppflow.size() == prev.size() && flow.type() == CV_32FC2calcOpticalFlowFarneback)\>)\>f??>f??>f??>cvCalcOpticalFlowFarneback/home/fit/opencv-2.4.7/modules/video/src/lkpyramid.cppSome of the precomputed pyramids are missingThe number of pyramid levels is negativeimgA and imgB must have equal stepspyramid A has insufficient sizepyramid B has insufficient sizethe search window is too smallimg.depth() == CV_8U && winSize.width > 2 && winSize.height > 2maxLevel >= 0 && winSize.width > 2 && winSize.height > 2(npoints = prevPtsMat.checkVector(2, CV_32F, true)) >= 0nextPtsMat.checkVector(2, CV_32F, true) == npointsofs.x >= winSize.width && ofs.y >= winSize.height && ofs.x + prevPyr[lvlStep1].cols + winSize.width <= fullSize.width && ofs.y + prevPyr[lvlStep1].rows + winSize.height <= fullSize.heightofs.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()Output parameter M is not a valid matrixBoth input images must have the same sizeBoth input images must have the same data typeBoth input images must have either 8uC1 or 8uC3 typeInvalid termination criteriastatusMat.isContinuous()errMat.isContinuous()levels1 >= 0levels2 >= 0featuresA && featuresBN2cv6detail16LKTrackerInvokerEbuildOpticalFlowPyramidcalcSharrDerivcalcOpticalFlowPyrLKcvCalcOpticalFlowPyrLKcvCalcAffineFlowPyrLKicvInitPyramidalAlgorithmcvEstimateRigidTransformd@^@ ?=?>?F?5/home/fit/opencv-2.4.7/modules/video/src/motempl.cppBoth MHI and the destination maskaperture_size must be 3, 5 or 7MHI and orientation must be single-channel floating-point imagesorientation image must be different from MHINULL memory storageboth delta's must be positiveMHI duration must be positivecvUpdateMotionHistorycvCalcMotionGradientcvCalcGlobalOrientationcvSegmentMotionSeq??????????/home/fit/opencv-2.4.7/modules/video/src/bgfg_gmg.cpplearningRate >= 0.0 && learningRate <= 1.0quantizationLevels >= 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 <= 1.0min < maxmaxFeatures > 0numInitializationFrames >= 1N2cv23BackgroundSubtractorGMGEinitializeoperator()operator()*N12_GLOBAL__N_112GMG_LoopBodyE????????/home/fit/opencv-2.4.7/modules/video/src/camshift.cppInput window has non-positive sizescvMeanShift/home/fit/opencv-2.4.7/modules/video/src/bgfg_gaussmix.cppCV_MAT_DEPTH(frameType) == CV_8UOnly 1- and 3-channel 8-bit images are supported in BackgroundSubtractorMOGimage.depth() == CV_8UlearningRate >= 0N2cv20BackgroundSubtractorEN2cv23BackgroundSubtractorMOGEinitializeoperator()@??L=/home/fit/opencv-2.4.7/modules/video/src/bgfg_gaussmix2.cppnchannels == 3N2cv24BackgroundSubtractorMOG2Eoperator()getBackgroundImageN2cv11MOG2InvokerE0@/home/fit/opencv-2.4.7/modules/video/src/kalman.cppstate and measurement vectors must have positive number of dimensionsDP > 0 && MP > 0cvCreateKalmancvReleaseKalmancvKalmanPredictcvKalmanCorrectinit/home/fit/opencv-2.4.7/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)state != 0dispLeft && dispRightcvFindStereoCorrespondenceGC/home/fit/opencv-2.4.7/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 )) >= 0cvPyrSegmentation??????>?>??L>??*>$I>>9??=???=?.?=???=ى?=$I?=???=?=??p=:?c=6?W=??L=1 C=?.:=C2=??*= ?#=ى='?=&I=?= =??=!==/home/fit/opencv-2.4.7/modules/legacy/src/snakes.cppinput image has more than one channel(icvSnake8uC1R( data, step, size, points, length, alpha, beta, gamma, coeffUsage, win, criteria, calcGradient ? 2 : 1 )) >= 0cvSnakeImage?v?q6?/home/fit/opencv-2.4.7/modules/legacy/src/optflowbm.cppSource images have different formatsDestination images have different formatsSource images must have 8uC1 type and destination images must have 32fC1 typetwo source or two destination images have different stepscvCalcOpticalFlowBM/home/fit/opencv-2.4.7/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 )) >= 0cvCalcOpticalFlowHS/home/fit/opencv-2.4.7/modules/legacy/src/subdiv2.cppcvSubdiv2DLocate returned invalid location = %dedge != 0&???'??!??b ??? ??cvCreateSubdiv2DcvSubdiv2DLocatecvSubdivDelaunay2DInsertcvSubdiv2DMakeEdgecvSubdiv2DSetEdgePointscvInitSubdivDelaunay2DcvClearSubdivVoronoi2DcvCalcSubdivVoronoi2DcvFindNearestPoint2DFeature2D.SURF/home/fit/opencv-2.4.7/modules/legacy/src/features2d.cppOpenCV was built without SURF supportcvExtractSURF/home/fit/opencv-2.4.7/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 )) >= 0cvCalcOpticalFlowLKstagesstage_thresholdparenttree %dnode %drectsleft_nodeleft_valright_noderight_valInvalid classifier pointerInvalid stages nodeInvalid stage %dInvalid cascade pointerScale must be positiveNull path is passedInvalid path%d%d%d%d%d%f%n%s%n%f%d%d%nInvalid classifier cascadeNull storage pointerscale factor must be > 1/home/fit/opencv-2.4.7/modules/objdetect/src/haar.cpphid_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 windowNumber of stages should be positivesize node is not a valid sequence.Invalid size node: width must be positive integerInvalid size node: height must be positive integerTrees 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)Feature 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 node must be valid node number. (stage %d, tree %d, node %d)left 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 node must be valid node number. (stage %d, tree %d, node %d)right 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 threshold must be real number. (stage %d)parent must be integer number. (stage %d)next must be integer number. (stage %d)Hidden cascade has not been created. Use cvSetImagesForHaarClassifierCascadeAll 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)%s%s%d/AdaBoostCARTHaarClassifier.txtCould not allocate memory for input_cascade%s/%d/AdaBoostCARTHaarClassifier.txtelements_read == (size_t)(size)Only 8-bit images are supportedcvSetImagesForHaarClassifierCascadeicvCreateHidHaarClassifierCascadecvRunHaarClassifierCascadeSumcvHaarDetectObjectsForROCcvLoadHaarClassifierCascadeicvCreateHaarClassifierCascadeicvReadHaarClassifierN2cv38HaarDetectObjects_ScaleCascade_InvokerEN2cv36HaarDetectObjects_ScaleImage_InvokerE????????corners.depth() == CV_32S && corners.cols*corners.channels() == 8 && n == (int)codes.size()/home/fit/opencv-2.4.7/modules/objdetect/src/datamatrix.cpp????????????????5&??+6??'r?j??,7v????`(?sg?Nk}??????-c18 w????[L??? a?)$??t?h??VO?l?~??" J ?T???̎Q??.Xd??2?9?Cx???է???n\??M|?f?_?Z ?b0??*?%??4??u?i?q??W?P???m??>??B??#? ?K?S!I??U??A?=??͝??RH?׿?/?Y?e^?{p?3?Ѓ:E?Dy???;?F???Gɜ@|?ݗ 0`??w??ϳK?drawDataMatrixCodes????yD=????A???=?A?A?A?A?̌??̌??̌??̌??>?>?>?>img.type() == CV_8U || img.type() == CV_8UC3/home/fit/opencv-2.4.7/modules/objdetect/src/cascadedetect.cpporigWinSize.height > 0 && origWinSize.width > 0scaleFactor > 1 && image.depth() == CV_8UoldCascade.empty()stageTypeBOOSTfeatureTypestageParamsfeatureParamsmaxCatCountweakClassifiersleafValuesinternalNodesstageThresholdN2cv16FeatureEvaluatorEN2cv17CascadeClassifierEN2cv13HaarEvaluatorEN2cv12LBPEvaluatorEN2cv12HOGEvaluatorEintegralHistogramrunAtdetectMultiScaleSeqreadN2cv24CascadeClassifierInvokerEܹ?{????o?:??'?/home/fit/opencv-2.4.7/modules/objdetect/src/hog.cppblockSize.width % cellSize.width == 0 && blockSize.height % cellSize.height == 0(winSize.width - blockSize.width) % blockStride.width == 0 && (winSize.height - blockSize.height) % blockStride.height == 0(unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) && (unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height)pt.x % cacheStride.width == 0 && pt.y % cacheStride.height == 0rectList.size() == weights.size()pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0{opencv-object-detector-hogcheckDetectorSize()blockHist != 0file not existversion?V6.01version doesnot matchN2cv13HOGDescriptorEN2cv8HOGCacheEgetDescriptorSizesetSVMDetectorreadoperator<<operator<<operator<<operator<<operator<<computeGradientgetBlockcomputedetect?==9?_?ԗ?=#|?=??????5?=O?O<??=?6???>&?=dU???0??O?=Q??=???L?a??6?r?DT?=?[=G%????5?;??=G?f=&XV??Nͼ[?o(_?ok??hik=S????=e??:?;?ҡ???????|??*?7=?)?<I;??=?\;Y޽tN???׽?혼?n$>?K߀=ܖ??~5?=?eZ?H= ?j??߼??*<Ѫ???=?2z=?X?=떐=W)9=?P5?מ>\??=?R?nx=?N)=gɼ:=NĽ8e"??X-=?e?<?7????P??<{n?=???=5)??b???P??ނ?X?Z???1??Nn??\???i=?y>?@L?.??=8?M=?(>??=?,?=ǗysX?=??/=?? ?Yu??Os8؅ ?p??‡???*?=??z??!;;?}?=m??0?????%?z?^????P?? >?>??\?0??F??z_{??Y?s]????~???e7?Bz??C?=?U>'绽?::u?=?Ó=O?ܙ??6??5????\??r<0??=??C??n??????DZ[9q?????= ?Y?_KA?&;Ҽ??X?9?4?Ch??3 ? 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Q>?/??s??R????<?=?y? >ͼj???;??W?n?pCL??\ ?s????兽C?readALTModelgroupRectanglesN2cv10HOGInvokerEN2cv14HOGConfInvokerE/home/fit/opencv-2.4.7/modules/photo/src/inpaint.cppAll the input and output images must have the same sizeOnly 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_NScvInpaint???;?<?/home/fit/opencv-2.4.7/modules/photo/src/denoising.cppInput images vector should not be empty!All windows sizes should be odd!imgToDenoiseIndex and temporalWindowSize should be choosen corresponding srcImgs size!Input images should have the same size and type!/home/fit/opencv-2.4.7/modules/photo/src/fast_nlmeans_denoising_invoker.hpptemplate_window_size_ <= 46340almost_dist2weight_[0] == fixed_point_mult_Unsupported image format! Only CV_8UC1, CV_8UC2 and CV_8UC3 are supportedType of input image should be CV_8UC3!/home/fit/opencv-2.4.7/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hppsrcImgs[0].channels() == sizeof(T)almost_dist2weight[0] == fixed_point_mult_Unsupported matrix format! Only uchar, Vec2b, Vec3b are supportedType of input images should be CV_8UC3!src.channels() == sizeof(T)srcImgs.size() > 0fastNlMeansDenoisingFastNlMeansDenoisingInvokerFastNlMeansDenoisingInvokerFastNlMeansDenoisingInvokerfastNlMeansDenoisingColoredfastNlMeansDenoisingMultifastNlMeansDenoisingMultiCheckPreconditionsFastNlMeansMultiDenoisingInvokerFastNlMeansMultiDenoisingInvokerFastNlMeansMultiDenoisingInvokerfastNlMeansDenoisingColoredMulti27FastNlMeansDenoisingInvokerIhE27FastNlMeansDenoisingInvokerIN2cv3VecIhLi2EEEE27FastNlMeansDenoisingInvokerIN2cv3VecIhLi3EEEE32FastNlMeansMultiDenoisingInvokerIhE32FastNlMeansMultiDenoisingInvokerIN2cv3VecIhLi2EEEE32FastNlMeansMultiDenoisingInvokerIN2cv3VecIhLi3EEEE ??@ ??@?A????????operator()operator()SURFInvoker/home/fit/opencv-2.4.7/modules/nonfree/src/surf.cppwinbuf->cols >= win_size*win_size!img.empty() && img.depth() == CV_8Umask.empty() || (mask.type() == CV_8U && mask.size() == img.size())nOriSamples <= nOriSampleBoundhessianThreshold >= 0nOctaves > 0nOctaveLayers > 0N2cv4SURFEN2cv15SURFFindInvokerEN2cv16SURFBuildInvokerEN2cv11SURFInvokerE@`ff @)\O??A????image is empty or has incorrect depth (!=CV_8U)mask has incorrect type (!=CV_8UC1)/home/fit/opencv-2.4.7/modules/nonfree/src/sift.cppfirstOctave >= -1 && actualNLayers <= nOctaveLayersoctave >= firstOctave && layer <= nOctaveLayers+2N2cv4SIFTEoperator()calcDescriptors?O?a ?<D?=?>??;???:??*N?@??L?BFeature2D.SIFTleft.size() == right.size() && left.type() == right.type()disp.size() == left.size() && disp.type() == CV_8UC1/home/fit/opencv-2.4.7/modules/contrib/src/stereovar.cppN2cv9StereoVarEoperator()??????y?zD/home/fit/opencv-2.4.7/modules/contrib/src/colormap.cppcv::argsort only sorts 1D matrices.cv::LUT only supports tables of size 256.cv::sortRowsByIndices only works on integer indices!Unknown colormap id; use one of COLORMAP_*?1???6??"7??i=???C???H??{+??9?c<9???8>?q>??*>??8>rG>UUU>9?c>?q>?>r?>?8?>UU?>?q?>9??>???>DZ>??>?>r?>?8?>UU?>?q?>9??>???>??>???>?9??r??? ??8???UU?????q? ?9?#?r'??0,??1?v]7????8>?q>??*>??8>rG>UUU>9?c>?q>?>r?>?8?>UU?>?q?>9??>???>???>?R?>?$?>???>w??>j??>\l?>O>?>!???? ? ????????}??f$??O)??8.??!3?? 8???'> ?0>?fD>? X>??k>?R>B{?>5M?>'?>?> ð>???>?f?>?8?>? ?>???>???>??>???>?9??r??? ??8???UU?????q? ?9?#?r'???*??8.??1?UU5???8??q?,K>=?s>?8?>)??>n۶>?,?>?}?>=??>A??8??a?)?"?˲,?n?6?A??,K?UUU??}_???i?=?s???}?????????????????????????????????????????????????????????????????????????????!=%I?=9??=?i>1 C>??k>?(?>?y?>-˲>r?>?m?>???>!??0 ?fY?? ???*?M?4???>??$I?5MS??u]?z?g??q???{???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????!?%I>)?">-?2>1 C>5MS>9?c>=?s>!?>?(?>%I?>?i?>)??>???>-˲>??>1 ?>?,?>5M?>?m?>9??>???>=??>???>!?b??( ??8?%I?fY??i??y?)?"?j?&???*??.?-?2?n?6???:???>?1 C?rG??,K????>??&>??6>??F>??V>??f>??v>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>?? ??????"""?&&&?***?...?222?666?:::?>>>?BBB?FFF?JJJ?NNN?RRR?VVV?ZZZ?^^^?bbb?fff?jjj?nnn?rrr?vvv?zzz?~~~?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????|||?xxx?ttt?ppp?lll?hhh?ddd?```?\\\?XXX?TTT?PPP?LLL?HHH?DDD?@@@?<<??>??">??2>??B>??R>??b>??r>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>?? ? ?????!!!?%%%?)))?---?111?555?999?===?AAA?EEE?III?MMM?QQQ?UUU?YYY?]]]?aaa?eee?iii?mmm?qqq?uuu?yyy?}}}?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????}}}?yyy?uuu?qqq?mmm?iii?eee?aaa?]]]?YYY?UUU?QQQ?MMM?III?EEE?AAA?===?999?555?111?---?)))?%%%?!!!????? ? ??????>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>??r>??b>??R>??B>??2>??">??>??>???=???=???=???=??H=??=???>>?:::?666?222?...?***?&&&?"""?????? ??????>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>??v>??f>??V>??F>??6>??&>??>??>???=???=???=???=??X=??=???1 ?>?i?>9?c>%I>!?=!=+`?=#? >1 C>>:y>&??>-˲>3??>:??>!????'???*?)?"=)??==??=)?">?,K>=?s>?8?>)??>n۶>?,?>?}?>=??>A??8??a?)?"?˲,?n?6?A??,K?UUU??}_???i?=?s???}???????????????????????????{?z?g?5MS???>???*?fY?!??m?>-˲>?(?>1 C>9??=!=!?<1 ?=-?2>!?>???>5M?>???>%I?j?&???:?????:?n?6?-?2??.???*?j?&?)?"??y??i?fY?%I??8??( ?b?!????>=??>???>9??>?m?>5M?>?,?>1 ?>??>-˲>???>)??>?i?>%I?>?(?>!?>=?s>9?c>5MS>1 C>-?2>)?">%I>!>9??=1 ?=)??=!?=1 C=!=!?<?!?A?b?? ??( ??0 ??8?A?%I?EQ?fY??a??i??q??y?? ?)?"?I?$?j?&???(???*?˲,??.? ?0?-?2?M?4?n?6???8???:????A?1 C?QE?rG??$I??,K??4M?????>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>???>1 C=1 ?=%I>1 C>=?s>%I?>???>1 ?>?m?>=??>b?%I??y???*?n?6?1 C????*>1 C>?m[>=?s>b?>%I?>?y?>???>n۶>1 ?>??m?>z??>=??>?b??0 ?%I??a??y?I?$???*? ?0?n?6???1 ?>%I?>1 C>1 ?=1 ?=1 C>%I?>1 ?>=??>%I???*?1 C??m[?=?s???????????????????????1 ?=1 C>%I?>1 ?>=??>%I???*?1 C??m[?=?s???????????????????????????????????????????=?s??m[?1 C???*?%I?=??>1 ?>%I?>1 C>1 ?=1 ?=1 C>%I?>1 ?>=??>%I???*?1 C??m[?=?s???????????????????????????????????????????=?s??m[?1 C???*?%I?=??>1 ?>%I?>1 C>1 ?=?? >9?c>HY?>??>?>??>???>9??>?[?>3j?>?j?HY ?? ????????? ???%???*??W/??3?PW8?٭Js6>?R>يk> ?>HY?>W??>??>???>Ӯ?>Js?>|?>??>V??>??>?(?>?t?> ??>ي?>?[?>? ?>P??>LY?.j??8?:???-?9_??f#??G(?S-???1?!"6?υ:???>?ZC?rG?K"K?$O??R?7?V?e|Z??(^?R?a??Se???h??j??Fl???m??1o?ģp??r?,?s???t? Vv?`?w??"y??z?[?{??F}?T?~?????=W?>Js6>?R>يk> ?>HY?>W??>??>???>Ӯ?>Js?>|?>??>V??>??>?(?>?t?> ??>ي?>?[?>? ?>P??> ????G?w??HY ??? ?Z=????W???G? ????????/ ?V"?au$???&?h?(???*???,?Ӯ.?#?0?ޛ2?1?4?yG9???>??/D?skI??N?2S?\X?~]?R?a?Uf??j??1o?,?s?`?w?[?{???operator()interp1argsortsortMatrixRowsByIndicesapplyColorMapN2cv8colormap8ColorMapEN2cv8colormap6AutumnEN2cv8colormap4BoneEN2cv8colormap4CoolEN2cv8colormap3HotEN2cv8colormap3HSVEN2cv8colormap3JetEN2cv8colormap5OceanEN2cv8colormap4PinkEN2cv8colormap7RainbowEN2cv8colormap6SpringEN2cv8colormap6SummerEN2cv8colormap6WinterE!?) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).This type is not implemented yet.This LBPH model is not computed yet. Did you call the train method?Empty 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.The number of samples (src) must equal the number of labels (labels). Was len(samples)=%d, len(labels)=%d.DataType<_Tp>::type == type() && cols == 1The images are expected as InputArray::STD_VECTOR_MAT (a std::vector) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).This FaceRecognizer (%s) does not support updating, you have to use FaceRecognizer::train to update it.In 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.???g??????????n???????ب??0?????0??P?????0??updatesaveloadtrainasRowMatrixpredictoperator<<operator<<trainpredicttrainpush_backelbphistcpredictN2cv14FaceRecognizerEN2cv10EigenfacesEN2cv11FisherfacesEN2cv4LBPHE?x???y??pz??0{???{??hw??0x??subspaceProjectsubspaceReconstructsaveloadoperator<<operator<<ldaargsortsortMatrixColumnsByIndicescomputeasRowMatrixWrong shape of input matrix! Expected a matrix with one row or column./home/fit/opencv-2.4.7/modules/contrib/src/lda.cppWrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).Wrong mean shape for the given data matrix. Expected %d, but was %d.Wrong mean shape for the given eigenvector matrix. Expected %d, but was %d.cv::sortColumnsByIndices only works on integer indices!At least two classes are needed to perform a LDA. Reason: Only one class was given!The number of samples must equal the number of labels. Given %d labels, %d samples. 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