モジュール
	Partial List of Implemented Layers

	Utilities for New Layers Registration

クラス
struct	cv::dnn::DictValue
	This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. [詳解]

class	cv::dnn::Dict
	This class implements name-value dictionary, values are instances of DictValue. [詳解]

class	cv::dnn::LayerParams
	This class provides all data needed to initialize layer. [詳解]

class	cv::dnn::BackendNode
	Derivatives of this class encapsulates functions of certain backends. [詳解]

class	cv::dnn::BackendWrapper
	Derivatives of this class wraps cv::Mat for different backends and targets. [詳解]

class	cv::dnn::Layer
	This interface class allows to build new Layers - are building blocks of networks. [詳解]

class	cv::dnn::Net
	This class allows to create and manipulate comprehensive artificial neural networks. [詳解]

class	cv::dnn::Model
	This class is presented high-level API for neural networks. [詳解]

class	cv::dnn::ClassificationModel
	This class represents high-level API for classification models. [詳解]

class	cv::dnn::KeypointsModel
	This class represents high-level API for keypoints models [詳解]

class	cv::dnn::SegmentationModel
	This class represents high-level API for segmentation models [詳解]

class	cv::dnn::DetectionModel
	This class represents high-level API for object detection networks. [詳解]

class	cv::dnn::TextRecognitionModel
	This class represents high-level API for text recognition networks. [詳解]

class	cv::dnn::TextDetectionModel
	Base class for text detection networks [詳解]

class	cv::dnn::TextDetectionModel_EAST
	This class represents high-level API for text detection DL networks compatible with EAST model. [詳解]

class	cv::dnn::TextDetectionModel_DB
	This class represents high-level API for text detection DL networks compatible with DB model. [詳解]

型定義
typedef std::vector< int >	cv::dnn::MatShape

列挙型
enum	cv::dnn::Backend { cv::dnn::DNN_BACKEND_DEFAULT = 0 , DNN_BACKEND_HALIDE , cv::dnn::DNN_BACKEND_INFERENCE_ENGINE , DNN_BACKEND_OPENCV , DNN_BACKEND_VKCOM , DNN_BACKEND_CUDA }
	Enum of computation backends supported by layers. [詳解]

enum	cv::dnn::Target { DNN_TARGET_CPU = 0 , DNN_TARGET_OPENCL , DNN_TARGET_OPENCL_FP16 , DNN_TARGET_MYRIAD , DNN_TARGET_VULKAN , cv::dnn::DNN_TARGET_FPGA , DNN_TARGET_CUDA , DNN_TARGET_CUDA_FP16 , DNN_TARGET_HDDL }
	Enum of target devices for computations. [詳解]

関数
CV_EXPORTS std::vector< std::pair< Backend, Target > >	cv::dnn::getAvailableBackends ()

CV_EXPORTS_W std::vector< Target >	cv::dnn::getAvailableTargets (dnn::Backend be)

CV_EXPORTS void	cv::dnn::enableModelDiagnostics (bool isDiagnosticsMode)
	Enables detailed logging of the DNN model loading with CV DNN API. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromDarknet (const String &cfgFile, const String &darknetModel=String())
	Reads a network model stored in Darknet model files. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromDarknet (const std::vector< uchar > &bufferCfg, const std::vector< uchar > &bufferModel=std::vector< uchar >())
	Reads a network model stored in Darknet model files. [詳解]

CV_EXPORTS Net	cv::dnn::readNetFromDarknet (const char bufferCfg, size_t lenCfg, const char bufferModel=NULL, size_t lenModel=0)
	Reads a network model stored in Darknet model files. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromCaffe (const String &prototxt, const String &caffeModel=String())
	Reads a network model stored in Caffe framework's format. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromCaffe (const std::vector< uchar > &bufferProto, const std::vector< uchar > &bufferModel=std::vector< uchar >())
	Reads a network model stored in Caffe model in memory. [詳解]

CV_EXPORTS Net	cv::dnn::readNetFromCaffe (const char bufferProto, size_t lenProto, const char bufferModel=NULL, size_t lenModel=0)
	Reads a network model stored in Caffe model in memory. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromTensorflow (const String &model, const String &config=String())
	Reads a network model stored in TensorFlow framework's format. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromTensorflow (const std::vector< uchar > &bufferModel, const std::vector< uchar > &bufferConfig=std::vector< uchar >())
	Reads a network model stored in TensorFlow framework's format. [詳解]

CV_EXPORTS Net	cv::dnn::readNetFromTensorflow (const char bufferModel, size_t lenModel, const char bufferConfig=NULL, size_t lenConfig=0)
	Reads a network model stored in TensorFlow framework's format. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromTorch (const String &model, bool isBinary=true, bool evaluate=true)
	Reads a network model stored in Torch7 framework's format. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNet (const String &model, const String &config="", const String &framework="")
	Read deep learning network represented in one of the supported formats. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNet (const String &framework, const std::vector< uchar > &bufferModel, const std::vector< uchar > &bufferConfig=std::vector< uchar >())
	Read deep learning network represented in one of the supported formats. [詳解]

CV_EXPORTS_W Mat	cv::dnn::readTorchBlob (const String &filename, bool isBinary=true)
	Loads blob which was serialized as torch.Tensor object of Torch7 framework. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromModelOptimizer (const String &xml, const String &bin)
	Load a network from Intel's Model Optimizer intermediate representation. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromModelOptimizer (const std::vector< uchar > &bufferModelConfig, const std::vector< uchar > &bufferWeights)
	Load a network from Intel's Model Optimizer intermediate representation. [詳解]

CV_EXPORTS Net	cv::dnn::readNetFromModelOptimizer (const uchar bufferModelConfigPtr, size_t bufferModelConfigSize, const uchar bufferWeightsPtr, size_t bufferWeightsSize)
	Load a network from Intel's Model Optimizer intermediate representation. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromONNX (const String &onnxFile)
	Reads a network model ONNX. [詳解]

CV_EXPORTS Net	cv::dnn::readNetFromONNX (const char *buffer, size_t sizeBuffer)
	Reads a network model from ONNX in-memory buffer. [詳解]

CV_EXPORTS_W Net	cv::dnn::readNetFromONNX (const std::vector< uchar > &buffer)
	Reads a network model from ONNX in-memory buffer. [詳解]

CV_EXPORTS_W Mat	cv::dnn::readTensorFromONNX (const String &path)
	Creates blob from .pb file. [詳解]

CV_EXPORTS_W Mat	cv::dnn::blobFromImage (InputArray image, double scalefactor=1.0, const Size &size=Size(), const Scalar &mean=Scalar(), bool swapRB=false, bool crop=false, int ddepth=CV_32F)
	Creates 4-dimensional blob from image. Optionally resizes and crops `image` from center, subtract `mean` values, scales values by `scalefactor`, swap Blue and Red channels. [詳解]

CV_EXPORTS void	cv::dnn::blobFromImage (InputArray image, OutputArray blob, double scalefactor=1.0, const Size &size=Size(), const Scalar &mean=Scalar(), bool swapRB=false, bool crop=false, int ddepth=CV_32F)
	Creates 4-dimensional blob from image. [詳解]

CV_EXPORTS_W Mat	cv::dnn::blobFromImages (InputArrayOfArrays images, double scalefactor=1.0, Size size=Size(), const Scalar &mean=Scalar(), bool swapRB=false, bool crop=false, int ddepth=CV_32F)
	Creates 4-dimensional blob from series of images. Optionally resizes and crops `images` from center, subtract `mean` values, scales values by `scalefactor`, swap Blue and Red channels. [詳解]

CV_EXPORTS void	cv::dnn::blobFromImages (InputArrayOfArrays images, OutputArray blob, double scalefactor=1.0, Size size=Size(), const Scalar &mean=Scalar(), bool swapRB=false, bool crop=false, int ddepth=CV_32F)
	Creates 4-dimensional blob from series of images. [詳解]

CV_EXPORTS_W void	cv::dnn::imagesFromBlob (const cv::Mat &blob_, OutputArrayOfArrays images_)
	Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vector<cv::Mat>). [詳解]

CV_EXPORTS_W void	cv::dnn::shrinkCaffeModel (const String &src, const String &dst, const std::vector< String > &layersTypes=std::vector< String >())
	Convert all weights of Caffe network to half precision floating point. [詳解]

CV_EXPORTS_W void	cv::dnn::writeTextGraph (const String &model, const String &output)
	Create a text representation for a binary network stored in protocol buffer format. [詳解]

CV_EXPORTS void	cv::dnn::NMSBoxes (const std::vector< Rect > &bboxes, const std::vector< float > &scores, const float score_threshold, const float nms_threshold, CV_OUT std::vector< int > &indices, const float eta=1.f, const int top_k=0)
	Performs non maximum suppression given boxes and corresponding scores. [詳解]

CV_EXPORTS_W void	cv::dnn::NMSBoxes (const std::vector< Rect2d > &bboxes, const std::vector< float > &scores, const float score_threshold, const float nms_threshold, CV_OUT std::vector< int > &indices, const float eta=1.f, const int top_k=0)

変数
class CV_EXPORTS	cv::dnn::ActivationLayer

詳解

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.

列挙型詳解

◆ Backend

enum cv::dnn::Backend

Enum of computation backends supported by layers.

参照: Net::setPreferableBackend

列挙値

DNN_BACKEND_DEFAULT

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_INFERENCE_ENGINE

Intel's Inference Engine computational backend

参照: setInferenceEngineBackendType

◆ Target

enum cv::dnn::Target

Enum of target devices for computations.

参照: Net::setPreferableTarget

列挙値
DNN_TARGET_FPGA	FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin.

関数詳解

◆ blobFromImage() [1/2]

CV_EXPORTS_W Mat cv::dnn::blobFromImage	(	InputArray	image,
		double	scalefactor = `1.0`,
		const Size &	size = `Size()`,
		const Scalar &	mean = `Scalar()`,
		bool	swapRB = `false`,
		bool	crop = `false`,
		int	ddepth = `CV_32F`
	)

Creates 4-dimensional blob from image. Optionally resizes and crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.

引数

image	input image (with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if `image` has BGR ordering and `swapRB` is true.
scalefactor	multiplier for `image` values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

戻り値: 4-dimensional Mat with NCHW dimensions order.

◆ blobFromImage() [2/2]

CV_EXPORTS void cv::dnn::blobFromImage	(	InputArray	image,
		OutputArray	blob,
		double	scalefactor = `1.0`,
		const Size &	size = `Size()`,
		const Scalar &	mean = `Scalar()`,
		bool	swapRB = `false`,
		bool	crop = `false`,
		int	ddepth = `CV_32F`
	)

Creates 4-dimensional blob from image.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ blobFromImages() [1/2]

CV_EXPORTS_W Mat cv::dnn::blobFromImages	(	InputArrayOfArrays	images,
		double	scalefactor = `1.0`,
		Size	size = `Size()`,
		const Scalar &	mean = `Scalar()`,
		bool	swapRB = `false`,
		bool	crop = `false`,
		int	ddepth = `CV_32F`
	)

Creates 4-dimensional blob from series of images. Optionally resizes and crops images from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.

引数

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if `image` has BGR ordering and `swapRB` is true.
scalefactor	multiplier for `images` values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

戻り値: 4-dimensional Mat with NCHW dimensions order.

◆ blobFromImages() [2/2]

CV_EXPORTS void cv::dnn::blobFromImages	(	InputArrayOfArrays	images,
		OutputArray	blob,
		double	scalefactor = `1.0`,
		Size	size = `Size()`,
		const Scalar &	mean = `Scalar()`,
		bool	swapRB = `false`,
		bool	crop = `false`,
		int	ddepth = `CV_32F`
	)

Creates 4-dimensional blob from series of images.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ enableModelDiagnostics()

CV_EXPORTS void cv::dnn::enableModelDiagnostics ( bool isDiagnosticsMode )

Enables detailed logging of the DNN model loading with CV DNN API.

引数

[in] isDiagnosticsMode Indicates whether diagnostic mode should be set.

Diagnostic mode provides detailed logging of the model loading stage to explore potential problems (ex.: not implemented layer type).

覚え書き: In diagnostic mode series of assertions will be skipped, it can lead to the expected application crash.

◆ imagesFromBlob()

CV_EXPORTS_W void cv::dnn::imagesFromBlob	(	const cv::Mat &	blob_,
		OutputArrayOfArrays	images_
	)

Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vector<cv::Mat>).

引数

[in]	blob_	4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from which you would like to extract the images.
[out]	images_	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).

◆ NMSBoxes()

CV_EXPORTS void cv::dnn::NMSBoxes	(	const std::vector< Rect > &	bboxes,
		const std::vector< float > &	scores,
		const float	score_threshold,
		const float	nms_threshold,
		CV_OUT std::vector< int > &	indices,
		const float	eta = `1.f`,
		const int	top_k = `0`
	)

Performs non maximum suppression given boxes and corresponding scores.

引数

bboxes	a set of bounding boxes to apply NMS.
scores	a set of corresponding confidences.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
eta	a coefficient in adaptive threshold formula: $nms\_threshold_{i+1}=eta\cdot nms\_threshold_i$ .
top_k	if `>0`, keep at most `top_k` picked indices.

◆ readNet() [1/2]

CV_EXPORTS_W Net cv::dnn::readNet	(	const String &	framework,
		const std::vector< uchar > &	bufferModel,
		const std::vector< uchar > &	bufferConfig = `std::vector< uchar >()`
	)

Read deep learning network represented in one of the supported formats.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

引数

[in]	framework	Name of origin framework.
[in]	bufferModel	A buffer with a content of binary file with weights
[in]	bufferConfig	A buffer with a content of text file contains network configuration.

戻り値: Net object.

◆ readNet() [2/2]

CV_EXPORTS_W Net cv::dnn::readNet	(	const String &	model,
		const String &	config = `""`,
		const String &	framework = `""`
	)

Read deep learning network represented in one of the supported formats.

引数

[in]	model	Binary file contains trained weights. The following file extensions are expected for models from different frameworks: `.caffemodel` (Caffe, http://caffe.berkeleyvision.org/) `.pb` (TensorFlow, https://www.tensorflow.org/) `.t7` \| `.net` (Torch, http://torch.ch/) `.weights` (Darknet, https://pjreddie.com/darknet/) `.bin` (DLDT, https://software.intel.com/openvino-toolkit) `*.onnx` (ONNX, https://onnx.ai/)
[in]	config	Text file contains network configuration. It could be a file with the following extensions: `.prototxt` (Caffe, http://caffe.berkeleyvision.org/) `.pbtxt` (TensorFlow, https://www.tensorflow.org/) `.cfg` (Darknet, https://pjreddie.com/darknet/) `.xml` (DLDT, https://software.intel.com/openvino-toolkit)
[in]	framework	Explicit framework name tag to determine a format.

戻り値: Net object.

This function automatically detects an origin framework of trained model and calls an appropriate function such readNetFromCaffe, readNetFromTensorflow, readNetFromTorch or readNetFromDarknet. An order of model and config arguments does not matter.

◆ readNetFromCaffe() [1/3]

CV_EXPORTS Net cv::dnn::readNetFromCaffe	(	const char *	bufferProto,
		size_t	lenProto,
		const char *	bufferModel = `NULL`,
		size_t	lenModel = `0`
	)

Reads a network model stored in Caffe model in memory.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

引数

bufferProto	buffer containing the content of the .prototxt file
lenProto	length of bufferProto
bufferModel	buffer containing the content of the .caffemodel file
lenModel	length of bufferModel

戻り値: Net object.

◆ readNetFromCaffe() [2/3]

CV_EXPORTS_W Net cv::dnn::readNetFromCaffe	(	const std::vector< uchar > &	bufferProto,
		const std::vector< uchar > &	bufferModel = `std::vector< uchar >()`
	)

Reads a network model stored in Caffe model in memory.

引数

bufferProto	buffer containing the content of the .prototxt file
bufferModel	buffer containing the content of the .caffemodel file

戻り値: Net object.

◆ readNetFromCaffe() [3/3]

CV_EXPORTS_W Net cv::dnn::readNetFromCaffe	(	const String &	prototxt,
		const String &	caffeModel = `String()`
	)

Reads a network model stored in Caffe framework's format.

引数

prototxt	path to the .prototxt file with text description of the network architecture.
caffeModel	path to the .caffemodel file with learned network.

戻り値: Net object.

◆ readNetFromDarknet() [1/3]

CV_EXPORTS Net cv::dnn::readNetFromDarknet	(	const char *	bufferCfg,
		size_t	lenCfg,
		const char *	bufferModel = `NULL`,
		size_t	lenModel = `0`
	)

Reads a network model stored in Darknet model files.

引数

bufferCfg	A buffer contains a content of .cfg file with text description of the network architecture.
lenCfg	Number of bytes to read from bufferCfg
bufferModel	A buffer contains a content of .weights file with learned network.
lenModel	Number of bytes to read from bufferModel

戻り値: Net object.

◆ readNetFromDarknet() [2/3]

CV_EXPORTS_W Net cv::dnn::readNetFromDarknet	(	const std::vector< uchar > &	bufferCfg,
		const std::vector< uchar > &	bufferModel = `std::vector< uchar >()`
	)

Reads a network model stored in Darknet model files.

引数

bufferCfg	A buffer contains a content of .cfg file with text description of the network architecture.
bufferModel	A buffer contains a content of .weights file with learned network.

戻り値: Net object.

◆ readNetFromDarknet() [3/3]

CV_EXPORTS_W Net cv::dnn::readNetFromDarknet	(	const String &	cfgFile,
		const String &	darknetModel = `String()`
	)

Reads a network model stored in Darknet model files.

引数

cfgFile	path to the .cfg file with text description of the network architecture.
darknetModel	path to the .weights file with learned network.

戻り値: Network object that ready to do forward, throw an exception in failure cases.; Net object.

◆ readNetFromModelOptimizer() [1/3]

CV_EXPORTS_W Net cv::dnn::readNetFromModelOptimizer	(	const std::vector< uchar > &	bufferModelConfig,
		const std::vector< uchar > &	bufferWeights
	)

Load a network from Intel's Model Optimizer intermediate representation.

引数

[in]	bufferModelConfig	Buffer contains XML configuration with network's topology.
[in]	bufferWeights	Buffer contains binary data with trained weights.

戻り値: Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

◆ readNetFromModelOptimizer() [2/3]

CV_EXPORTS_W Net cv::dnn::readNetFromModelOptimizer	(	const String &	xml,
		const String &	bin
	)

Load a network from Intel's Model Optimizer intermediate representation.

引数

[in]	xml	XML configuration file with network's topology.
[in]	bin	Binary file with trained weights.

戻り値: Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

◆ readNetFromModelOptimizer() [3/3]

CV_EXPORTS Net cv::dnn::readNetFromModelOptimizer	(	const uchar *	bufferModelConfigPtr,
		size_t	bufferModelConfigSize,
		const uchar *	bufferWeightsPtr,
		size_t	bufferWeightsSize
	)

Load a network from Intel's Model Optimizer intermediate representation.

引数

[in]	bufferModelConfigPtr	Pointer to buffer which contains XML configuration with network's topology.
[in]	bufferModelConfigSize	Binary size of XML configuration data.
[in]	bufferWeightsPtr	Pointer to buffer which contains binary data with trained weights.
[in]	bufferWeightsSize	Binary size of trained weights data.

戻り値: Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

◆ readNetFromONNX() [1/3]

CV_EXPORTS Net cv::dnn::readNetFromONNX	(	const char *	buffer,
		size_t	sizeBuffer
	)

Reads a network model from ONNX in-memory buffer.

引数

buffer	memory address of the first byte of the buffer.
sizeBuffer	size of the buffer.

戻り値: Network object that ready to do forward, throw an exception in failure cases.

◆ readNetFromONNX() [2/3]

CV_EXPORTS_W Net cv::dnn::readNetFromONNX ( const std::vector< uchar > & buffer )

Reads a network model from ONNX in-memory buffer.

引数

buffer in-memory buffer that stores the ONNX model bytes.

戻り値: Network object that ready to do forward, throw an exception in failure cases.

◆ readNetFromONNX() [3/3]

CV_EXPORTS_W Net cv::dnn::readNetFromONNX ( const String & onnxFile )

Reads a network model ONNX.

引数

onnxFile path to the .onnx file with text description of the network architecture.

戻り値: Network object that ready to do forward, throw an exception in failure cases.

◆ readNetFromTensorflow() [1/3]

CV_EXPORTS Net cv::dnn::readNetFromTensorflow	(	const char *	bufferModel,
		size_t	lenModel,
		const char *	bufferConfig = `NULL`,
		size_t	lenConfig = `0`
	)

Reads a network model stored in TensorFlow framework's format.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

引数

bufferModel	buffer containing the content of the pb file
lenModel	length of bufferModel
bufferConfig	buffer containing the content of the pbtxt file
lenConfig	length of bufferConfig

◆ readNetFromTensorflow() [2/3]

CV_EXPORTS_W Net cv::dnn::readNetFromTensorflow	(	const std::vector< uchar > &	bufferModel,
		const std::vector< uchar > &	bufferConfig = `std::vector< uchar >()`
	)

Reads a network model stored in TensorFlow framework's format.

引数

bufferModel	buffer containing the content of the pb file
bufferConfig	buffer containing the content of the pbtxt file

戻り値: Net object.

◆ readNetFromTensorflow() [3/3]

CV_EXPORTS_W Net cv::dnn::readNetFromTensorflow	(	const String &	model,
		const String &	config = `String()`
	)

Reads a network model stored in TensorFlow framework's format.

引数

model	path to the .pb file with binary protobuf description of the network architecture
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.

戻り値: Net object.

◆ readNetFromTorch()

CV_EXPORTS_W Net cv::dnn::readNetFromTorch	(	const String &	model,
		bool	isBinary = `true`,
		bool	evaluate = `true`
	)

Reads a network model stored in Torch7 framework's format.

引数

model	path to the file, dumped from Torch by using torch.save() function.
isBinary	specifies whether the network was serialized in ascii mode or binary.
evaluate	specifies testing phase of network. If true, it's similar to evaluate() method in Torch.

戻り値: Net object.

覚え書き: Ascii mode of Torch serializer is more preferable, because binary mode extensively use 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.

◆ readTensorFromONNX()

CV_EXPORTS_W Mat cv::dnn::readTensorFromONNX ( const String & path )

Creates blob from .pb file.

引数

path	to the .pb file with input tensor.

戻り値: Mat.

◆ readTorchBlob()

CV_EXPORTS_W Mat cv::dnn::readTorchBlob	(	const String &	filename,
		bool	isBinary = `true`
	)

Loads blob which was serialized as torch.Tensor object of Torch7 framework.

警告: This function has the same limitations as readNetFromTorch().

◆ shrinkCaffeModel()

CV_EXPORTS_W void cv::dnn::shrinkCaffeModel	(	const String &	src,
		const String &	dst,
		const std::vector< String > &	layersTypes = `std::vector< String >()`
	)

Convert all weights of Caffe network to half precision floating point.

引数

src	Path to origin model from Caffe framework contains single precision floating point weights (usually has `.caffemodel` extension).
dst	Path to destination model with updated weights.
layersTypes	Set of layers types which parameters will be converted. By default, converts only Convolutional and Fully-Connected layers' weights.

覚え書き: 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.

◆ writeTextGraph()

CV_EXPORTS_W void cv::dnn::writeTextGraph	(	const String &	model,
		const String &	output
	)

Create a text representation for a binary network stored in protocol buffer format.

引数

[in]	model	A path to binary network.
[in]	output	A path to output text file to be created.

覚え書き: To reduce output file size, trained weights are not included.

モジュール

クラス

型定義

列挙型

関数

変数

詳解

列挙型詳解

◆ Backend

◆ Target

関数詳解

◆ blobFromImage() [1/2]

◆ blobFromImage() [2/2]

◆ blobFromImages() [1/2]

◆ blobFromImages() [2/2]

◆ enableModelDiagnostics()

◆ imagesFromBlob()

◆ NMSBoxes()

◆ readNet() [1/2]

◆ readNet() [2/2]

◆ readNetFromCaffe() [1/3]

◆ readNetFromCaffe() [2/3]

◆ readNetFromCaffe() [3/3]

◆ readNetFromDarknet() [1/3]

◆ readNetFromDarknet() [2/3]

◆ readNetFromDarknet() [3/3]

◆ readNetFromModelOptimizer() [1/3]

◆ readNetFromModelOptimizer() [2/3]

◆ readNetFromModelOptimizer() [3/3]

◆ readNetFromONNX() [1/3]

◆ readNetFromONNX() [2/3]

◆ readNetFromONNX() [3/3]

◆ readNetFromTensorflow() [1/3]

◆ readNetFromTensorflow() [2/3]

◆ readNetFromTensorflow() [3/3]

◆ readNetFromTorch()

◆ readTensorFromONNX()

◆ readTorchBlob()

◆ shrinkCaffeModel()

◆ writeTextGraph()