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org.bytedeco.opencv.global.opencv_dnn Maven / Gradle / Ivy
// Targeted by JavaCPP version 1.5.4: DO NOT EDIT THIS FILE
package org.bytedeco.opencv.global;
import org.bytedeco.opencv.opencv_dnn.*;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;
import static org.bytedeco.javacpp.presets.javacpp.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import org.bytedeco.opencv.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
public class opencv_dnn extends org.bytedeco.opencv.presets.opencv_dnn {
static { Loader.load(); }
// Targeting ..\opencv_dnn\MatShapeVector.java
// Targeting ..\opencv_dnn\MatShapeVectorVector.java
// Targeting ..\opencv_dnn\RangeVectorVector.java
// Targeting ..\opencv_dnn\MatPointerVector.java
// Targeting ..\opencv_dnn\IntFloatPair.java
// Parsed from
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// #ifndef OPENCV_DNN_HPP
// #define OPENCV_DNN_HPP
// This is an umbrella header to include into you project.
// We are free to change headers layout in dnn subfolder, so please include
// this header for future compatibility
/** \defgroup dnn Deep Neural Network module
\{
This module contains:
- API for new layers creation, layers are building bricks of neural networks;
- set of built-in most-useful Layers;
- API to construct and modify comprehensive neural networks from layers;
- functionality for loading serialized networks models from different frameworks.
Functionality of this module is designed only for forward pass computations (i.e. network testing).
A network training is in principle not supported.
\}
*/
/** \example samples/dnn/classification.cpp
Check \ref tutorial_dnn_googlenet "the corresponding tutorial" for more details
*/
/** \example samples/dnn/colorization.cpp
*/
/** \example samples/dnn/object_detection.cpp
Check \ref tutorial_dnn_yolo "the corresponding tutorial" for more details
*/
/** \example samples/dnn/openpose.cpp
*/
/** \example samples/dnn/segmentation.cpp
*/
/** \example samples/dnn/text_detection.cpp
*/
// #include
// #endif /* OPENCV_DNN_HPP */
// Parsed from
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// #ifndef OPENCV_DNN_VERSION_HPP
// #define OPENCV_DNN_VERSION_HPP
/** Use with major OpenCV version only. */
public static final int OPENCV_DNN_API_VERSION = 20200609;
// #if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_INLINE_NS
// #define CV__DNN_INLINE_NS __CV_CAT(dnn4_v, OPENCV_DNN_API_VERSION)
// #define CV__DNN_INLINE_NS_BEGIN namespace CV__DNN_INLINE_NS {
// #define CV__DNN_INLINE_NS_END }
// #else
// #define CV__DNN_INLINE_NS_BEGIN
// #define CV__DNN_INLINE_NS_END
// #endif
// #endif // OPENCV_DNN_VERSION_HPP
// Parsed from
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// #include
// #include
// #include
// #include
// #ifndef OPENCV_DNN_DNN_DICT_HPP
// #define OPENCV_DNN_DNN_DICT_HPP
// Targeting ..\opencv_dnn\DictValue.java
// Targeting ..\opencv_dnn\Dict.java
/** \} */
// #endif
// Parsed from
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// #ifndef OPENCV_DNN_DNN_ALL_LAYERS_HPP
// #define OPENCV_DNN_DNN_ALL_LAYERS_HPP
// #include
// Targeting ..\opencv_dnn\BlankLayer.java
// Targeting ..\opencv_dnn\ConstLayer.java
// Targeting ..\opencv_dnn\LSTMLayer.java
// Targeting ..\opencv_dnn\RNNLayer.java
// Targeting ..\opencv_dnn\BaseConvolutionLayer.java
// Targeting ..\opencv_dnn\ConvolutionLayer.java
// Targeting ..\opencv_dnn\DeconvolutionLayer.java
// Targeting ..\opencv_dnn\LRNLayer.java
// Targeting ..\opencv_dnn\PoolingLayer.java
// Targeting ..\opencv_dnn\SoftmaxLayer.java
// Targeting ..\opencv_dnn\InnerProductLayer.java
// Targeting ..\opencv_dnn\MVNLayer.java
// Targeting ..\opencv_dnn\ReshapeLayer.java
// Targeting ..\opencv_dnn\FlattenLayer.java
// Targeting ..\opencv_dnn\ConcatLayer.java
// Targeting ..\opencv_dnn\SplitLayer.java
// Targeting ..\opencv_dnn\SliceLayer.java
// Targeting ..\opencv_dnn\PermuteLayer.java
// Targeting ..\opencv_dnn\ShuffleChannelLayer.java
// Targeting ..\opencv_dnn\PaddingLayer.java
// Targeting ..\opencv_dnn\ActivationLayer.java
// Targeting ..\opencv_dnn\ReLULayer.java
// Targeting ..\opencv_dnn\ReLU6Layer.java
// Targeting ..\opencv_dnn\ChannelsPReLULayer.java
// Targeting ..\opencv_dnn\ELULayer.java
// Targeting ..\opencv_dnn\TanHLayer.java
// Targeting ..\opencv_dnn\SwishLayer.java
// Targeting ..\opencv_dnn\MishLayer.java
// Targeting ..\opencv_dnn\SigmoidLayer.java
// Targeting ..\opencv_dnn\BNLLLayer.java
// Targeting ..\opencv_dnn\AbsLayer.java
// Targeting ..\opencv_dnn\PowerLayer.java
// Targeting ..\opencv_dnn\CropLayer.java
// Targeting ..\opencv_dnn\EltwiseLayer.java
// Targeting ..\opencv_dnn\BatchNormLayer.java
// Targeting ..\opencv_dnn\MaxUnpoolLayer.java
// Targeting ..\opencv_dnn\ScaleLayer.java
// Targeting ..\opencv_dnn\ShiftLayer.java
// Targeting ..\opencv_dnn\DataAugmentationLayer.java
// Targeting ..\opencv_dnn\CorrelationLayer.java
// Targeting ..\opencv_dnn\AccumLayer.java
// Targeting ..\opencv_dnn\FlowWarpLayer.java
// Targeting ..\opencv_dnn\PriorBoxLayer.java
// Targeting ..\opencv_dnn\ReorgLayer.java
// Targeting ..\opencv_dnn\RegionLayer.java
// Targeting ..\opencv_dnn\DetectionOutputLayer.java
// Targeting ..\opencv_dnn\NormalizeBBoxLayer.java
// Targeting ..\opencv_dnn\ResizeLayer.java
// Targeting ..\opencv_dnn\InterpLayer.java
// Targeting ..\opencv_dnn\ProposalLayer.java
// Targeting ..\opencv_dnn\CropAndResizeLayer.java
/** \}
* \} */
// #endif
// Parsed from
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// #ifndef OPENCV_DNN_DNN_HPP
// #define OPENCV_DNN_DNN_HPP
// #include
// #include
// #include "opencv2/core/async.hpp"
// #include "../dnn/version.hpp"
// #include
/** \addtogroup dnn
* \{ */
/**
* \brief Enum of computation backends supported by layers.
* @see Net::setPreferableBackend
*/
/** enum cv::dnn::Backend */
public static final int
/** DNN_BACKEND_DEFAULT equals to DNN_BACKEND_INFERENCE_ENGINE if
* OpenCV is built with Intel's Inference Engine library or
* DNN_BACKEND_OPENCV otherwise. */
DNN_BACKEND_DEFAULT = 0,
DNN_BACKEND_HALIDE = 1,
/** Intel's Inference Engine computational backend
* @see setInferenceEngineBackendType */
DNN_BACKEND_INFERENCE_ENGINE = 2,
DNN_BACKEND_OPENCV = 3,
DNN_BACKEND_VKCOM = 4,
DNN_BACKEND_CUDA = 5;
// #ifdef __OPENCV_BUILD
// #endif
/**
* \brief Enum of target devices for computations.
* @see Net::setPreferableTarget
*/
/** enum cv::dnn::Target */
public static final int
DNN_TARGET_CPU = 0,
DNN_TARGET_OPENCL = 1,
DNN_TARGET_OPENCL_FP16 = 2,
DNN_TARGET_MYRIAD = 3,
DNN_TARGET_VULKAN = 4,
/** FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin. */
DNN_TARGET_FPGA = 5,
DNN_TARGET_CUDA = 6,
DNN_TARGET_CUDA_FP16 = 7;
@Namespace("cv::dnn") public static native @ByVal @Cast("std::vector >*") IntIntPairVector getAvailableBackends();
@Namespace("cv::dnn") public static native @Cast("cv::dnn::Target*") @StdVector IntPointer getAvailableTargets(@Cast("cv::dnn::Backend") int be);
// Targeting ..\opencv_dnn\LayerParams.java
// Targeting ..\opencv_dnn\BackendNode.java
// Targeting ..\opencv_dnn\BackendWrapper.java
// Targeting ..\opencv_dnn\Layer.java
// Targeting ..\opencv_dnn\Net.java
/** \brief Reads a network model stored in Darknet model files.
* @param cfgFile path to the .cfg file with text description of the network architecture.
* @param darknetModel path to the .weights file with learned network.
* @return Network object that ready to do forward, throw an exception in failure cases.
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Str BytePointer cfgFile, @Str BytePointer darknetModel/*=cv::String()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Str BytePointer cfgFile);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Str String cfgFile, @Str String darknetModel/*=cv::String()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Str String cfgFile);
/** \brief Reads a network model stored in Darknet model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Cast("uchar*") @StdVector ByteBuffer bufferCfg,
@Cast("uchar*") @StdVector ByteBuffer bufferModel/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Cast("uchar*") @StdVector ByteBuffer bufferCfg);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Cast("uchar*") @StdVector byte[] bufferCfg,
@Cast("uchar*") @StdVector byte[] bufferModel/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Cast("uchar*") @StdVector byte[] bufferCfg);
/** \brief Reads a network model stored in Darknet model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param lenCfg Number of bytes to read from bufferCfg
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @param lenModel Number of bytes to read from bufferModel
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Cast("const char*") BytePointer bufferCfg, @Cast("size_t") long lenCfg,
@Cast("const char*") BytePointer bufferModel/*=NULL*/, @Cast("size_t") long lenModel/*=0*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(@Cast("const char*") BytePointer bufferCfg, @Cast("size_t") long lenCfg);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(String bufferCfg, @Cast("size_t") long lenCfg,
String bufferModel/*=NULL*/, @Cast("size_t") long lenModel/*=0*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromDarknet(String bufferCfg, @Cast("size_t") long lenCfg);
/** \brief Reads a network model stored in Caffe framework's format.
* @param prototxt path to the .prototxt file with text description of the network architecture.
* @param caffeModel path to the .caffemodel file with learned network.
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Str BytePointer prototxt, @Str BytePointer caffeModel/*=cv::String()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Str BytePointer prototxt);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Str String prototxt, @Str String caffeModel/*=cv::String()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Str String prototxt);
/** \brief Reads a network model stored in Caffe model in memory.
* @param bufferProto buffer containing the content of the .prototxt file
* @param bufferModel buffer containing the content of the .caffemodel file
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Cast("uchar*") @StdVector ByteBuffer bufferProto,
@Cast("uchar*") @StdVector ByteBuffer bufferModel/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Cast("uchar*") @StdVector ByteBuffer bufferProto);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Cast("uchar*") @StdVector byte[] bufferProto,
@Cast("uchar*") @StdVector byte[] bufferModel/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Cast("uchar*") @StdVector byte[] bufferProto);
/** \brief Reads a network model stored in Caffe model in memory.
* \details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
* @param bufferProto buffer containing the content of the .prototxt file
* @param lenProto length of bufferProto
* @param bufferModel buffer containing the content of the .caffemodel file
* @param lenModel length of bufferModel
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Cast("const char*") BytePointer bufferProto, @Cast("size_t") long lenProto,
@Cast("const char*") BytePointer bufferModel/*=NULL*/, @Cast("size_t") long lenModel/*=0*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(@Cast("const char*") BytePointer bufferProto, @Cast("size_t") long lenProto);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(String bufferProto, @Cast("size_t") long lenProto,
String bufferModel/*=NULL*/, @Cast("size_t") long lenModel/*=0*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromCaffe(String bufferProto, @Cast("size_t") long lenProto);
/** \brief Reads a network model stored in TensorFlow framework's format.
* @param model path to the .pb file with binary protobuf description of the network architecture
* @param config path to the .pbtxt file that contains text graph definition in protobuf format.
* Resulting Net object is built by text graph using weights from a binary one that
* let us make it more flexible.
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Str BytePointer model, @Str BytePointer config/*=cv::String()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Str BytePointer model);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Str String model, @Str String config/*=cv::String()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Str String model);
/** \brief Reads a network model stored in TensorFlow framework's format.
* @param bufferModel buffer containing the content of the pb file
* @param bufferConfig buffer containing the content of the pbtxt file
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Cast("uchar*") @StdVector ByteBuffer bufferModel,
@Cast("uchar*") @StdVector ByteBuffer bufferConfig/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Cast("uchar*") @StdVector ByteBuffer bufferModel);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Cast("uchar*") @StdVector byte[] bufferModel,
@Cast("uchar*") @StdVector byte[] bufferConfig/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Cast("uchar*") @StdVector byte[] bufferModel);
/** \brief Reads a network model stored in TensorFlow framework's format.
* \details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
* @param bufferModel buffer containing the content of the pb file
* @param lenModel length of bufferModel
* @param bufferConfig buffer containing the content of the pbtxt file
* @param lenConfig length of bufferConfig
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Cast("const char*") BytePointer bufferModel, @Cast("size_t") long lenModel,
@Cast("const char*") BytePointer bufferConfig/*=NULL*/, @Cast("size_t") long lenConfig/*=0*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(@Cast("const char*") BytePointer bufferModel, @Cast("size_t") long lenModel);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(String bufferModel, @Cast("size_t") long lenModel,
String bufferConfig/*=NULL*/, @Cast("size_t") long lenConfig/*=0*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTensorflow(String bufferModel, @Cast("size_t") long lenModel);
/**
* \brief Reads a network model stored in Torch7 framework's format.
* @param model path to the file, dumped from Torch by using torch.save() function.
* @param isBinary specifies whether the network was serialized in ascii mode or binary.
* @param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch.
* @return Net object.
*
* \note Ascii mode of Torch serializer is more preferable, because binary mode extensively use {@code long} type of C language,
* which has various bit-length on different systems.
*
* The loading file must contain serialized nn.Module object
* with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
*
* List of supported layers (i.e. object instances derived from Torch nn.Module class):
* - nn.Sequential
* - nn.Parallel
* - nn.Concat
* - nn.Linear
* - nn.SpatialConvolution
* - nn.SpatialMaxPooling, nn.SpatialAveragePooling
* - nn.ReLU, nn.TanH, nn.Sigmoid
* - nn.Reshape
* - nn.SoftMax, nn.LogSoftMax
*
* Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTorch(@Str BytePointer model, @Cast("bool") boolean isBinary/*=true*/, @Cast("bool") boolean evaluate/*=true*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTorch(@Str BytePointer model);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTorch(@Str String model, @Cast("bool") boolean isBinary/*=true*/, @Cast("bool") boolean evaluate/*=true*/);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromTorch(@Str String model);
/**
* \brief Read deep learning network represented in one of the supported formats.
* @param model [in] Binary file contains trained weights. The following file
* extensions are expected for models from different frameworks:
* * {@code *.caffemodel} (Caffe, http://caffe.berkeleyvision.org/)
* * {@code *.pb} (TensorFlow, https://www.tensorflow.org/)
* * {@code *.t7} | {@code *.net} (Torch, http://torch.ch/)
* * {@code *.weights} (Darknet, https://pjreddie.com/darknet/)
* * {@code *.bin} (DLDT, https://software.intel.com/openvino-toolkit)
* * {@code *.onnx} (ONNX, https://onnx.ai/)
* @param config [in] Text file contains network configuration. It could be a
* file with the following extensions:
* * {@code *.prototxt} (Caffe, http://caffe.berkeleyvision.org/)
* * {@code *.pbtxt} (TensorFlow, https://www.tensorflow.org/)
* * {@code *.cfg} (Darknet, https://pjreddie.com/darknet/)
* * {@code *.xml} (DLDT, https://software.intel.com/openvino-toolkit)
* @param framework [in] Explicit framework name tag to determine a format.
* @return Net object.
*
* This function automatically detects an origin framework of trained model
* and calls an appropriate function such \ref readNetFromCaffe, \ref readNetFromTensorflow,
* \ref readNetFromTorch or \ref readNetFromDarknet. An order of \p model and \p config
* arguments does not matter.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str BytePointer model, @Str BytePointer config/*=""*/, @Str BytePointer framework/*=""*/);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str BytePointer model);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str String model, @Str String config/*=""*/, @Str String framework/*=""*/);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str String model);
/**
* \brief Read deep learning network represented in one of the supported formats.
* \details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
* @param framework [in] Name of origin framework.
* @param bufferModel [in] A buffer with a content of binary file with weights
* @param bufferConfig [in] A buffer with a content of text file contains network configuration.
* @return Net object.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str BytePointer framework, @Cast("uchar*") @StdVector BytePointer bufferModel);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str String framework, @Cast("uchar*") @StdVector ByteBuffer bufferModel,
@Cast("uchar*") @StdVector ByteBuffer bufferConfig/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str String framework, @Cast("uchar*") @StdVector ByteBuffer bufferModel);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str BytePointer framework, @Cast("uchar*") @StdVector byte[] bufferModel,
@Cast("uchar*") @StdVector byte[] bufferConfig/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str BytePointer framework, @Cast("uchar*") @StdVector byte[] bufferModel);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str String framework, @Cast("uchar*") @StdVector BytePointer bufferModel,
@Cast("uchar*") @StdVector BytePointer bufferConfig/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str String framework, @Cast("uchar*") @StdVector BytePointer bufferModel);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str BytePointer framework, @Cast("uchar*") @StdVector ByteBuffer bufferModel,
@Cast("uchar*") @StdVector ByteBuffer bufferConfig/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str BytePointer framework, @Cast("uchar*") @StdVector ByteBuffer bufferModel);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str String framework, @Cast("uchar*") @StdVector byte[] bufferModel,
@Cast("uchar*") @StdVector byte[] bufferConfig/*=std::vector()*/);
@Namespace("cv::dnn") public static native @ByVal Net readNet(@Str String framework, @Cast("uchar*") @StdVector byte[] bufferModel);
/** \brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
* \warning This function has the same limitations as readNetFromTorch().
*/
@Namespace("cv::dnn") public static native @ByVal Mat readTorchBlob(@Str BytePointer filename, @Cast("bool") boolean isBinary/*=true*/);
@Namespace("cv::dnn") public static native @ByVal Mat readTorchBlob(@Str BytePointer filename);
@Namespace("cv::dnn") public static native @ByVal Mat readTorchBlob(@Str String filename, @Cast("bool") boolean isBinary/*=true*/);
@Namespace("cv::dnn") public static native @ByVal Mat readTorchBlob(@Str String filename);
/** \brief Load a network from Intel's Model Optimizer intermediate representation.
* @param xml [in] XML configuration file with network's topology.
* @param bin [in] Binary file with trained weights.
* @return Net object.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromModelOptimizer(@Str BytePointer xml, @Str BytePointer bin);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromModelOptimizer(@Str String xml, @Str String bin);
/** \brief Load a network from Intel's Model Optimizer intermediate representation.
* @param bufferModelConfig [in] Buffer contains XML configuration with network's topology.
* @param bufferWeights [in] Buffer contains binary data with trained weights.
* @return Net object.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromModelOptimizer(@Cast("uchar*") @StdVector ByteBuffer bufferModelConfig, @Cast("uchar*") @StdVector ByteBuffer bufferWeights);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromModelOptimizer(@Cast("uchar*") @StdVector byte[] bufferModelConfig, @Cast("uchar*") @StdVector byte[] bufferWeights);
/** \brief Load a network from Intel's Model Optimizer intermediate representation.
* @param bufferModelConfigPtr [in] Pointer to buffer which contains XML configuration with network's topology.
* @param bufferModelConfigSize [in] Binary size of XML configuration data.
* @param bufferWeightsPtr [in] Pointer to buffer which contains binary data with trained weights.
* @param bufferWeightsSize [in] Binary size of trained weights data.
* @return Net object.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromModelOptimizer(@Cast("const uchar*") BytePointer bufferModelConfigPtr, @Cast("size_t") long bufferModelConfigSize,
@Cast("const uchar*") BytePointer bufferWeightsPtr, @Cast("size_t") long bufferWeightsSize);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromModelOptimizer(@Cast("const uchar*") ByteBuffer bufferModelConfigPtr, @Cast("size_t") long bufferModelConfigSize,
@Cast("const uchar*") ByteBuffer bufferWeightsPtr, @Cast("size_t") long bufferWeightsSize);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromModelOptimizer(@Cast("const uchar*") byte[] bufferModelConfigPtr, @Cast("size_t") long bufferModelConfigSize,
@Cast("const uchar*") byte[] bufferWeightsPtr, @Cast("size_t") long bufferWeightsSize);
/** \brief Reads a network model ONNX .
* @param onnxFile path to the .onnx file with text description of the network architecture.
* @return Network object that ready to do forward, throw an exception in failure cases.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromONNX(@Str BytePointer onnxFile);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromONNX(@Str String onnxFile);
/** \brief Reads a network model from ONNX
* in-memory buffer.
* @param buffer memory address of the first byte of the buffer.
* @param sizeBuffer size of the buffer.
* @return Network object that ready to do forward, throw an exception
* in failure cases.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromONNX(@Cast("const char*") BytePointer buffer, @Cast("size_t") long sizeBuffer);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromONNX(String buffer, @Cast("size_t") long sizeBuffer);
/** \brief Reads a network model from ONNX
* in-memory buffer.
* @param buffer in-memory buffer that stores the ONNX model bytes.
* @return Network object that ready to do forward, throw an exception
* in failure cases.
*/
@Namespace("cv::dnn") public static native @ByVal Net readNetFromONNX(@Cast("uchar*") @StdVector ByteBuffer buffer);
@Namespace("cv::dnn") public static native @ByVal Net readNetFromONNX(@Cast("uchar*") @StdVector byte[] buffer);
/** \brief Creates blob from .pb file.
* @param path to the .pb file with input tensor.
* @return Mat.
*/
@Namespace("cv::dnn") public static native @ByVal Mat readTensorFromONNX(@Str BytePointer path);
@Namespace("cv::dnn") public static native @ByVal Mat readTensorFromONNX(@Str String path);
/** \brief Creates 4-dimensional blob from image. Optionally resizes and crops \p image from center,
* subtract \p mean values, scales values by \p scalefactor, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if \p image has BGR ordering and \p swapRB is true.
* @param scalefactor multiplier for \p image values.
* @param swapRB flag which indicates that swap first and last channels
* in 3-channel image is necessary.
* @param crop flag which indicates whether image will be cropped after resize or not
* @param ddepth Depth of output blob. Choose CV_32F or CV_8U.
* \details if \p crop is true, input image is resized so one side after resize is equal to corresponding
* dimension in \p size and another one is equal or larger. Then, crop from the center is performed.
* If \p crop is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*/
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImage(@ByVal Mat image, double scalefactor/*=1.0*/, @Const @ByRef(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImage(@ByVal Mat image);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImage(@ByVal UMat image, double scalefactor/*=1.0*/, @Const @ByRef(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImage(@ByVal UMat image);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImage(@ByVal GpuMat image, double scalefactor/*=1.0*/, @Const @ByRef(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImage(@ByVal GpuMat image);
/** \brief Creates 4-dimensional blob from image.
* \details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
*/
@Namespace("cv::dnn") public static native void blobFromImage(@ByVal Mat image, @ByVal Mat blob, double scalefactor/*=1.0*/,
@Const @ByRef(nullValue = "cv::Size()") Size size, @Const @ByRef(nullValue = "cv::Scalar()") Scalar mean,
@Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/, int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImage(@ByVal Mat image, @ByVal Mat blob);
@Namespace("cv::dnn") public static native void blobFromImage(@ByVal UMat image, @ByVal UMat blob, double scalefactor/*=1.0*/,
@Const @ByRef(nullValue = "cv::Size()") Size size, @Const @ByRef(nullValue = "cv::Scalar()") Scalar mean,
@Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/, int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImage(@ByVal UMat image, @ByVal UMat blob);
@Namespace("cv::dnn") public static native void blobFromImage(@ByVal GpuMat image, @ByVal GpuMat blob, double scalefactor/*=1.0*/,
@Const @ByRef(nullValue = "cv::Size()") Size size, @Const @ByRef(nullValue = "cv::Scalar()") Scalar mean,
@Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/, int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImage(@ByVal GpuMat image, @ByVal GpuMat blob);
/** \brief Creates 4-dimensional blob from series of images. Optionally resizes and
* crops \p images from center, subtract \p mean values, scales values by \p scalefactor,
* swap Blue and Red channels.
* @param images input images (all with 1-, 3- or 4-channels).
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if \p image has BGR ordering and \p swapRB is true.
* @param scalefactor multiplier for \p images values.
* @param swapRB flag which indicates that swap first and last channels
* in 3-channel image is necessary.
* @param crop flag which indicates whether image will be cropped after resize or not
* @param ddepth Depth of output blob. Choose CV_32F or CV_8U.
* \details if \p crop is true, input image is resized so one side after resize is equal to corresponding
* dimension in \p size and another one is equal or larger. Then, crop from the center is performed.
* If \p crop is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*/
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImages(@ByVal MatVector images, double scalefactor/*=1.0*/,
@ByVal(nullValue = "cv::Size()") Size size, @Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImages(@ByVal MatVector images);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImages(@ByVal UMatVector images, double scalefactor/*=1.0*/,
@ByVal(nullValue = "cv::Size()") Size size, @Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImages(@ByVal UMatVector images);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImages(@ByVal GpuMatVector images, double scalefactor/*=1.0*/,
@ByVal(nullValue = "cv::Size()") Size size, @Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native @ByVal Mat blobFromImages(@ByVal GpuMatVector images);
/** \brief Creates 4-dimensional blob from series of images.
* \details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
*/
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal MatVector images, @ByVal Mat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal MatVector images, @ByVal Mat blob);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal UMatVector images, @ByVal Mat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal UMatVector images, @ByVal Mat blob);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal GpuMatVector images, @ByVal Mat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal GpuMatVector images, @ByVal Mat blob);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal MatVector images, @ByVal UMat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal MatVector images, @ByVal UMat blob);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal UMatVector images, @ByVal UMat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal UMatVector images, @ByVal UMat blob);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal GpuMatVector images, @ByVal UMat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal GpuMatVector images, @ByVal UMat blob);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal MatVector images, @ByVal GpuMat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal MatVector images, @ByVal GpuMat blob);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal UMatVector images, @ByVal GpuMat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal UMatVector images, @ByVal GpuMat blob);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal GpuMatVector images, @ByVal GpuMat blob,
double scalefactor/*=1.0*/, @ByVal(nullValue = "cv::Size()") Size size,
@Const @ByRef(nullValue = "cv::Scalar()") Scalar mean, @Cast("bool") boolean swapRB/*=false*/, @Cast("bool") boolean crop/*=false*/,
int ddepth/*=CV_32F*/);
@Namespace("cv::dnn") public static native void blobFromImages(@ByVal GpuMatVector images, @ByVal GpuMat blob);
/** \brief Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure
* (std::vector).
* @param blob_ [in] 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from
* which you would like to extract the images.
* @param images_ [out] array of 2D Mat containing the images extracted from the blob in floating point precision
* (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension
* of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).
*/
@Namespace("cv::dnn") public static native void imagesFromBlob(@Const @ByRef Mat blob_, @ByVal MatVector images_);
@Namespace("cv::dnn") public static native void imagesFromBlob(@Const @ByRef Mat blob_, @ByVal UMatVector images_);
@Namespace("cv::dnn") public static native void imagesFromBlob(@Const @ByRef Mat blob_, @ByVal GpuMatVector images_);
/** \brief Convert all weights of Caffe network to half precision floating point.
* @param src Path to origin model from Caffe framework contains single
* precision floating point weights (usually has {@code .caffemodel} extension).
* @param dst Path to destination model with updated weights.
* @param layersTypes Set of layers types which parameters will be converted.
* By default, converts only Convolutional and Fully-Connected layers'
* weights.
*
* \note Shrinked model has no origin float32 weights so it can't be used
* in origin Caffe framework anymore. However the structure of data
* is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
* So the resulting model may be used there.
*/
@Namespace("cv::dnn") public static native void shrinkCaffeModel(@Str BytePointer src, @Str BytePointer dst,
@Const @ByRef(nullValue = "std::vector()") StringVector layersTypes);
@Namespace("cv::dnn") public static native void shrinkCaffeModel(@Str BytePointer src, @Str BytePointer dst);
@Namespace("cv::dnn") public static native void shrinkCaffeModel(@Str String src, @Str String dst,
@Const @ByRef(nullValue = "std::vector()") StringVector layersTypes);
@Namespace("cv::dnn") public static native void shrinkCaffeModel(@Str String src, @Str String dst);
/** \brief Create a text representation for a binary network stored in protocol buffer format.
* @param model [in] A path to binary network.
* @param output [in] A path to output text file to be created.
*
* \note To reduce output file size, trained weights are not included.
*/
@Namespace("cv::dnn") public static native void writeTextGraph(@Str BytePointer model, @Str BytePointer output);
@Namespace("cv::dnn") public static native void writeTextGraph(@Str String model, @Str String output);
/** \brief Performs non maximum suppression given boxes and corresponding scores.
* @param bboxes a set of bounding boxes to apply NMS.
* @param scores a set of corresponding confidences.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
* @param eta a coefficient in adaptive threshold formula: {@code nms\_threshold_{i+1}=eta\cdot nms\_threshold_i}.
* @param top_k if {@code >0}, keep at most \p top_k picked indices.
*/
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef RectVector bboxes, @StdVector FloatPointer scores,
float score_threshold, float nms_threshold,
@StdVector IntPointer indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef RectVector bboxes, @StdVector FloatPointer scores,
float score_threshold, float nms_threshold,
@StdVector IntPointer indices);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef RectVector bboxes, @StdVector FloatBuffer scores,
float score_threshold, float nms_threshold,
@StdVector IntBuffer indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef RectVector bboxes, @StdVector FloatBuffer scores,
float score_threshold, float nms_threshold,
@StdVector IntBuffer indices);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef RectVector bboxes, @StdVector float[] scores,
float score_threshold, float nms_threshold,
@StdVector int[] indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef RectVector bboxes, @StdVector float[] scores,
float score_threshold, float nms_threshold,
@StdVector int[] indices);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef Rect2dVector bboxes, @StdVector FloatPointer scores,
float score_threshold, float nms_threshold,
@StdVector IntPointer indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef Rect2dVector bboxes, @StdVector FloatPointer scores,
float score_threshold, float nms_threshold,
@StdVector IntPointer indices);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef Rect2dVector bboxes, @StdVector FloatBuffer scores,
float score_threshold, float nms_threshold,
@StdVector IntBuffer indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef Rect2dVector bboxes, @StdVector FloatBuffer scores,
float score_threshold, float nms_threshold,
@StdVector IntBuffer indices);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef Rect2dVector bboxes, @StdVector float[] scores,
float score_threshold, float nms_threshold,
@StdVector int[] indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native void NMSBoxes(@Const @ByRef Rect2dVector bboxes, @StdVector float[] scores,
float score_threshold, float nms_threshold,
@StdVector int[] indices);
@Namespace("cv::dnn") public static native @Name("NMSBoxes") void NMSBoxesRotated(@StdVector RotatedRect bboxes, @StdVector FloatPointer scores,
float score_threshold, float nms_threshold,
@StdVector IntPointer indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native @Name("NMSBoxes") void NMSBoxesRotated(@StdVector RotatedRect bboxes, @StdVector FloatPointer scores,
float score_threshold, float nms_threshold,
@StdVector IntPointer indices);
@Namespace("cv::dnn") public static native @Name("NMSBoxes") void NMSBoxesRotated(@StdVector RotatedRect bboxes, @StdVector FloatBuffer scores,
float score_threshold, float nms_threshold,
@StdVector IntBuffer indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native @Name("NMSBoxes") void NMSBoxesRotated(@StdVector RotatedRect bboxes, @StdVector FloatBuffer scores,
float score_threshold, float nms_threshold,
@StdVector IntBuffer indices);
@Namespace("cv::dnn") public static native @Name("NMSBoxes") void NMSBoxesRotated(@StdVector RotatedRect bboxes, @StdVector float[] scores,
float score_threshold, float nms_threshold,
@StdVector int[] indices,
float eta/*=1.f*/, int top_k/*=0*/);
@Namespace("cv::dnn") public static native @Name("NMSBoxes") void NMSBoxesRotated(@StdVector RotatedRect bboxes, @StdVector float[] scores,
float score_threshold, float nms_threshold,
@StdVector int[] indices);
// Targeting ..\opencv_dnn\Model.java
// Targeting ..\opencv_dnn\ClassificationModel.java
// Targeting ..\opencv_dnn\KeypointsModel.java
// Targeting ..\opencv_dnn\SegmentationModel.java
// Targeting ..\opencv_dnn\DetectionModel.java
/** \} */
// #include
// #include
/** @deprecated Include this header directly from application. Automatic inclusion will be removed */
// #include
// #endif /* OPENCV_DNN_DNN_HPP */
// Parsed from
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// #ifndef OPENCV_DNN_LAYER_HPP
// #define OPENCV_DNN_LAYER_HPP
// #include
// Targeting ..\opencv_dnn\LayerFactory.java
/** \}
* \} */
// #endif
// Parsed from
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// #ifndef OPENCV_DNN_DNN_SHAPE_UTILS_HPP
// #define OPENCV_DNN_DNN_SHAPE_UTILS_HPP
// #include
// #include // CV_MAX_DIM
// #include
// #include
// #include
// Targeting ..\opencv_dnn\_Range.java
@Namespace("cv::dnn") public static native @ByVal Mat slice(@Const @ByRef Mat m, @Const @ByRef _Range r0);
@Namespace("cv::dnn") public static native @ByVal Mat slice(@Const @ByRef Mat m, @Const @ByRef _Range r0, @Const @ByRef _Range r1);
@Namespace("cv::dnn") public static native @ByVal Mat slice(@Const @ByRef Mat m, @Const @ByRef _Range r0, @Const @ByRef _Range r1, @Const @ByRef _Range r2);
@Namespace("cv::dnn") public static native @ByVal Mat slice(@Const @ByRef Mat m, @Const @ByRef _Range r0, @Const @ByRef _Range r1, @Const @ByRef _Range r2, @Const @ByRef _Range r3);
@Namespace("cv::dnn") public static native @ByVal Mat getPlane(@Const @ByRef Mat m, int n, int cn);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer shape(@Const IntPointer dims, int n);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer shape(@Const IntBuffer dims, int n);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer shape(@Const int[] dims, int n);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer shape(@Const @ByRef Mat mat);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer shape(@Const @ByRef MatSize sz);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer shape(@Const @ByRef UMat mat);
// #if 0 // issues with MatExpr wrapped into InputArray
// #endif
@Namespace("cv::dnn") public static native @Cast("bool") boolean is_neg(int i);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer shape(int a0, int a1/*=-1*/, int a2/*=-1*/, int a3/*=-1*/);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer shape(int a0);
@Namespace("cv::dnn") public static native int total(@Const @StdVector @ByRef IntPointer shape, int start/*=-1*/, int end/*=-1*/);
@Namespace("cv::dnn") public static native int total(@Const @StdVector @ByRef IntPointer shape);
@Namespace("cv::dnn") public static native @StdVector @ByVal IntPointer concat(@Const @StdVector @ByRef IntPointer a, @Const @StdVector @ByRef IntPointer b);
@Namespace("cv::dnn") public static native @StdString BytePointer toString(@Const @StdVector @ByRef IntPointer shape, @Str BytePointer name/*=""*/);
@Namespace("cv::dnn") public static native @StdString BytePointer toString(@Const @StdVector @ByRef IntPointer shape);
@Namespace("cv::dnn") public static native @StdString String toString(@Const @StdVector @ByRef IntPointer shape, @Str String name/*=""*/);
@Namespace("cv::dnn") public static native void print(@Const @StdVector @ByRef IntPointer shape, @Str BytePointer name/*=""*/);
@Namespace("cv::dnn") public static native void print(@Const @StdVector @ByRef IntPointer shape);
@Namespace("cv::dnn") public static native void print(@Const @StdVector @ByRef IntPointer shape, @Str String name/*=""*/);
@Namespace("cv::dnn") public static native @Cast("std::ostream*") @ByRef @Name("operator <<") Pointer shiftLeft(@Cast("std::ostream*") @ByRef Pointer out, @Const @StdVector @ByRef IntPointer shape);
@Namespace("cv::dnn") public static native int clamp(int ax, int dims);
@Namespace("cv::dnn") public static native int clamp(int ax, @Const @StdVector @ByRef IntPointer shape);
@Namespace("cv::dnn") public static native @ByVal Range clamp(@Const @ByRef Range r, int axisSize);
// #endif
}