Deep Neural Network module

Topics
	Partial List of Implemented Layers
	Utilities for New Layers Registration

Detailed Description

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.

Classes
struct	cv::dnn::Arg
struct	cv::dnn::ArgData
class	cv::dnn::BackendNode
	Derivatives of this class encapsulates functions of certain backends. More...
class	cv::dnn::BackendWrapper
	Derivatives of this class wraps cv::Mat for different backends and targets. More...
class	cv::dnn::ClassificationModel
	This class represents high-level API for classification models. More...
class	cv::dnn::DetectionModel
	This class represents high-level API for object detection networks. More...
class	cv::dnn::Dict
	This class implements name-value dictionary, values are instances of DictValue. More...
struct	cv::dnn::DictValue
	This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. More...
class	cv::dnn::Graph
	Represents graph or subgraph of a model. The graph (in mathematical terms it's rather a multigraph) is represented as a topologically-sorted linear sequence of operations. Each operation is a smart pointer to a Layer (some of its derivative class instance), which includes a list of inputs and outputs, as well as an optional list of subgraphs (e.g. 'If' contains 2 subgraphs). More...
struct	cv::dnn::Image2BlobParams
	Processing params of image to blob. More...
class	cv::dnn::KeypointsModel
	This class represents high-level API for keypoints models. More...
class	cv::dnn::Layer
	This interface class allows to build new Layers - are building blocks of networks. More...
class	cv::dnn::LayerParams
	This class provides all data needed to initialize layer. More...
class	cv::dnn::Model
	This class is presented high-level API for neural networks. More...
class	cv::dnn::Net
	This class allows to create and manipulate comprehensive artificial neural networks. More...
class	cv::dnn::SegmentationModel
	This class represents high-level API for segmentation models. More...
class	cv::dnn::TextDetectionModel
	Base class for text detection networks. More...
class	cv::dnn::TextDetectionModel_DB
	This class represents high-level API for text detection DL networks compatible with DB model. More...
class	cv::dnn::TextDetectionModel_EAST
	This class represents high-level API for text detection DL networks compatible with EAST model. More...
class	cv::dnn::TextRecognitionModel
	This class represents high-level API for text recognition networks. More...

Typedefs
typedef std::map< std::string, std::vector< LayerFactory::Constructor > >	cv::dnn::LayerFactory_Impl
typedef int	cv::dnn::MatType

Enumerations
enum	cv::dnn::ArgKind {   cv::dnn::DNN_ARG_EMPTY =0 ,   cv::dnn::DNN_ARG_CONST =1 ,   cv::dnn::DNN_ARG_INPUT =2 ,   cv::dnn::DNN_ARG_OUTPUT =3 ,   cv::dnn::DNN_ARG_TEMP =4 ,   cv::dnn::DNN_ARG_PATTERN =5 }
enum	cv::dnn::Backend {   cv::dnn::DNN_BACKEND_DEFAULT = 0 ,   cv::dnn::DNN_BACKEND_INFERENCE_ENGINE = 2 ,   cv::dnn::DNN_BACKEND_OPENCV ,   cv::dnn::DNN_BACKEND_VKCOM ,   cv::dnn::DNN_BACKEND_CUDA ,   cv::dnn::DNN_BACKEND_WEBNN ,   cv::dnn::DNN_BACKEND_TIMVX ,   cv::dnn::DNN_BACKEND_CANN }
	Enum of computation backends supported by layers. More...
enum	cv::dnn::EngineType {   cv::dnn::ENGINE_CLASSIC =1 ,   cv::dnn::ENGINE_NEW =2 ,   cv::dnn::ENGINE_AUTO =3 }
enum	cv::dnn::ImagePaddingMode {   cv::dnn::DNN_PMODE_NULL = 0 ,   cv::dnn::DNN_PMODE_CROP_CENTER = 1 ,   cv::dnn::DNN_PMODE_LETTERBOX = 2 }
	Enum of image processing mode. To facilitate the specialization pre-processing requirements of the dnn model. For example, the letter box often used in the Yolo series of models. More...
enum	cv::dnn::ModelFormat {   cv::dnn::DNN_MODEL_GENERIC = 0 ,   cv::dnn::DNN_MODEL_ONNX = 1 ,   cv::dnn::DNN_MODEL_TF = 2 ,   cv::dnn::DNN_MODEL_TFLITE = 3 ,   cv::dnn::DNN_MODEL_CAFFE = 4 }
enum	cv::dnn::ProfilingMode {   cv::dnn::DNN_PROFILE_NONE = 0 ,   cv::dnn::DNN_PROFILE_SUMMARY = 1 ,   cv::dnn::DNN_PROFILE_DETAILED = 2 }
enum class	cv::dnn::SoftNMSMethod {   cv::dnn::SoftNMSMethod::SOFTNMS_LINEAR = 1 ,   cv::dnn::SoftNMSMethod::SOFTNMS_GAUSSIAN = 2 }
	Enum of Soft NMS methods. More...
enum	cv::dnn::Target {   cv::dnn::DNN_TARGET_CPU = 0 ,   cv::dnn::DNN_TARGET_OPENCL ,   cv::dnn::DNN_TARGET_OPENCL_FP16 ,   cv::dnn::DNN_TARGET_MYRIAD ,   cv::dnn::DNN_TARGET_VULKAN ,   cv::dnn::DNN_TARGET_FPGA ,   cv::dnn::DNN_TARGET_CUDA ,   cv::dnn::DNN_TARGET_CUDA_FP16 ,   cv::dnn::DNN_TARGET_HDDL ,   cv::dnn::DNN_TARGET_NPU ,   cv::dnn::DNN_TARGET_CPU_FP16 }
	Enum of target devices for computations. More...
enum	cv::dnn::TracingMode {   cv::dnn::DNN_TRACE_NONE = 0 ,   cv::dnn::DNN_TRACE_ALL = 1 ,   cv::dnn::DNN_TRACE_OP = 2 }

Functions
std::string	cv::dnn::argKindToString (ArgKind kind)
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.
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.
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.
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.
Mat	cv::dnn::blobFromImagesWithParams (InputArrayOfArrays images, const Image2BlobParams &param=Image2BlobParams())
	Creates 4-dimensional blob from series of images with given params.
void	cv::dnn::blobFromImagesWithParams (InputArrayOfArrays images, OutputArray blob, const Image2BlobParams &param=Image2BlobParams())
Mat	cv::dnn::blobFromImageWithParams (InputArray image, const Image2BlobParams &param=Image2BlobParams())
	Creates 4-dimensional blob from image with given params.
void	cv::dnn::blobFromImageWithParams (InputArray image, OutputArray blob, const Image2BlobParams &param=Image2BlobParams())
void	cv::dnn::enableModelDiagnostics (bool isDiagnosticsMode)
	Enables detailed logging of the DNN model loading with CV DNN API.
std::vector< std::pair< Backend, Target > >	cv::dnn::getAvailableBackends ()
std::vector< Target >	cv::dnn::getAvailableTargets (dnn::Backend be)
LayerFactory_Impl &	cv::dnn::getLayerFactoryImpl ()
Mutex &	cv::dnn::getLayerFactoryMutex ()
	Get the mutex guarding LayerFactory_Impl, see getLayerFactoryImpl() function.
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>).
std::string	cv::dnn::modelFormatToString (ModelFormat modelFormat)
void	cv::dnn::NMSBoxes (const std::vector< Rect > &bboxes, const std::vector< float > &scores, const float score_threshold, const float nms_threshold, std::vector< int > &indices, const float eta=1.f, const int top_k=0)
	Performs non maximum suppression given boxes and corresponding scores.
void	cv::dnn::NMSBoxes (const std::vector< Rect2d > &bboxes, const std::vector< float > &scores, const float score_threshold, const float nms_threshold, std::vector< int > &indices, const float eta=1.f, const int top_k=0)
void	cv::dnn::NMSBoxes (const std::vector< RotatedRect > &bboxes, const std::vector< float > &scores, const float score_threshold, const float nms_threshold, std::vector< int > &indices, const float eta=1.f, const int top_k=0)
void	cv::dnn::NMSBoxesBatched (const std::vector< Rect > &bboxes, const std::vector< float > &scores, const std::vector< int > &class_ids, const float score_threshold, const float nms_threshold, std::vector< int > &indices, const float eta=1.f, const int top_k=0)
	Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
void	cv::dnn::NMSBoxesBatched (const std::vector< Rect2d > &bboxes, const std::vector< float > &scores, const std::vector< int > &class_ids, const float score_threshold, const float nms_threshold, std::vector< int > &indices, const float eta=1.f, const int top_k=0)
Net	cv::dnn::readNet (const String &framework, const std::vector< uchar > &bufferModel, const std::vector< uchar > &bufferConfig=std::vector< uchar >(), int engine=ENGINE_AUTO)
	Read deep learning network represented in one of the supported formats.
Net	cv::dnn::readNet (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config="", const String &framework="", int engine=ENGINE_AUTO)
	Read deep learning network represented in one of the supported formats.
Net	cv::dnn::readNetFromCaffe (const char *bufferProto, size_t lenProto, const char *bufferModel=NULL, size_t lenModel=0, int engine=ENGINE_AUTO)
	Reads a network model stored in Caffe model in memory.
Net	cv::dnn::readNetFromCaffe (const std::vector< uchar > &bufferProto, const std::vector< uchar > &bufferModel=std::vector< uchar >(), int engine=ENGINE_AUTO)
	Reads a network model stored in Caffe model in memory.
Net	cv::dnn::readNetFromCaffe (CV_WRAP_FILE_PATH const String &prototxt, CV_WRAP_FILE_PATH const String &caffeModel=String(), int engine=ENGINE_AUTO)
	Reads a network model stored in Caffe framework's format.
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.
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.
Net	cv::dnn::readNetFromDarknet (CV_WRAP_FILE_PATH const String &cfgFile, CV_WRAP_FILE_PATH const String &darknetModel=String())
	Reads a network model stored in Darknet model files.
Net	cv::dnn::readNetFromModelOptimizer (const std::vector< uchar > &bufferModelConfig, const std::vector< uchar > &bufferWeights)
	Load a network from Intel's Model Optimizer intermediate representation.
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.
Net	cv::dnn::readNetFromModelOptimizer (CV_WRAP_FILE_PATH const String &xml, CV_WRAP_FILE_PATH const String &bin="")
	Load a network from Intel's Model Optimizer intermediate representation.
Net	cv::dnn::readNetFromONNX (const char *buffer, size_t sizeBuffer, int engine=ENGINE_AUTO)
	Reads a network model from ONNX in-memory buffer.
Net	cv::dnn::readNetFromONNX (const std::vector< uchar > &buffer, int engine=ENGINE_AUTO)
	Reads a network model from ONNX in-memory buffer.
Net	cv::dnn::readNetFromONNX (CV_WRAP_FILE_PATH const String &onnxFile, int engine=ENGINE_AUTO)
	Reads a network model ONNX.
Net	cv::dnn::readNetFromTensorflow (const char *bufferModel, size_t lenModel, const char *bufferConfig=NULL, size_t lenConfig=0, int engine=ENGINE_AUTO, const std::vector< String > &extraOutputs=std::vector< String >())
	Reads a network model stored in TensorFlow framework's format.
Net	cv::dnn::readNetFromTensorflow (const std::vector< uchar > &bufferModel, const std::vector< uchar > &bufferConfig=std::vector< uchar >(), int engine=ENGINE_AUTO, const std::vector< String > &extraOutputs=std::vector< String >())
	Reads a network model stored in TensorFlow framework's format.
Net	cv::dnn::readNetFromTensorflow (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config=String(), int engine=ENGINE_AUTO, const std::vector< String > &extraOutputs=std::vector< String >())
	Reads a network model stored in TensorFlow framework's format.
Net	cv::dnn::readNetFromTFLite (const char *bufferModel, size_t lenModel, int engine=ENGINE_AUTO)
	Reads a network model stored in TFLite framework's format.
Net	cv::dnn::readNetFromTFLite (const std::vector< uchar > &bufferModel, int engine=ENGINE_AUTO)
	Reads a network model stored in TFLite framework's format.
Net	cv::dnn::readNetFromTFLite (CV_WRAP_FILE_PATH const String &model, int engine=ENGINE_AUTO)
	Reads a network model stored in TFLite framework's format.
Mat	cv::dnn::readTensorFromONNX (CV_WRAP_FILE_PATH const String &path)
	Creates blob from .pb file.
void	cv::dnn::shrinkCaffeModel (CV_WRAP_FILE_PATH const String &src, CV_WRAP_FILE_PATH const String &dst, const std::vector< String > &layersTypes=std::vector< String >())
	Convert all weights of Caffe network to half precision floating point.
void	cv::dnn::softNMSBoxes (const std::vector< Rect > &bboxes, const std::vector< float > &scores, std::vector< float > &updated_scores, const float score_threshold, const float nms_threshold, std::vector< int > &indices, size_t top_k=0, const float sigma=0.5, SoftNMSMethod method=SoftNMSMethod::SOFTNMS_GAUSSIAN)
	Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503.
void	cv::dnn::writeTextGraph (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &output)
	Create a text representation for a binary network stored in protocol buffer format.

Typedef Documentation

LayerFactory_Impl

typedef std::map<std::string, std::vector<LayerFactory::Constructor> > cv::dnn::LayerFactory_Impl

#include <opencv2/dnn/layer_reg.private.hpp>

MatType

typedef int cv::dnn::MatType

#include <opencv2/dnn/dnn.hpp>

Enumeration Type Documentation

ArgKind

enum cv::dnn::ArgKind

#include <opencv2/dnn/dnn.hpp>

Enumerator
DNN_ARG_EMPTY  Python: cv.dnn.DNN_ARG_EMPTY	valid only for Arg.idx==0. It's "no-arg"
DNN_ARG_CONST  Python: cv.dnn.DNN_ARG_CONST	a constant argument.
DNN_ARG_INPUT  Python: cv.dnn.DNN_ARG_INPUT	input of the whole model. Before Net::forward() or in Net::forward() all inputs must be set
DNN_ARG_OUTPUT  Python: cv.dnn.DNN_ARG_OUTPUT	output of the model.
DNN_ARG_TEMP  Python: cv.dnn.DNN_ARG_TEMP	intermediate result, a result of some operation and input to some other operation(s).
DNN_ARG_PATTERN  Python: cv.dnn.DNN_ARG_PATTERN	not used for now

Backend

enum cv::dnn::Backend

#include <opencv2/dnn/dnn.hpp>

Enum of computation backends supported by layers.

See also

Net::setPreferableBackend

Enumerator
DNN_BACKEND_DEFAULT  Python: cv.dnn.DNN_BACKEND_DEFAULT	DNN_BACKEND_DEFAULT equals to OPENCV_DNN_BACKEND_DEFAULT, which can be defined using CMake or a configuration parameter.
DNN_BACKEND_INFERENCE_ENGINE  Python: cv.dnn.DNN_BACKEND_INFERENCE_ENGINE	Intel OpenVINO computational backend Note Tutorial how to build OpenCV with OpenVINO: OpenCV usage with OpenVINO
DNN_BACKEND_OPENCV  Python: cv.dnn.DNN_BACKEND_OPENCV
DNN_BACKEND_VKCOM  Python: cv.dnn.DNN_BACKEND_VKCOM
DNN_BACKEND_CUDA  Python: cv.dnn.DNN_BACKEND_CUDA
DNN_BACKEND_WEBNN  Python: cv.dnn.DNN_BACKEND_WEBNN
DNN_BACKEND_TIMVX  Python: cv.dnn.DNN_BACKEND_TIMVX
DNN_BACKEND_CANN  Python: cv.dnn.DNN_BACKEND_CANN

EngineType

enum cv::dnn::EngineType

#include <opencv2/dnn/dnn.hpp>

Enumerator
ENGINE_CLASSIC  Python: cv.dnn.ENGINE_CLASSIC	Force use the old dnn engine similar to 4.x branch.
ENGINE_NEW  Python: cv.dnn.ENGINE_NEW	Force use the new dnn engine. The engine does not support non CPU back-ends for now.
ENGINE_AUTO  Python: cv.dnn.ENGINE_AUTO	Try to use the new engine and then fall back to the classic version.

ImagePaddingMode

enum cv::dnn::ImagePaddingMode

#include <opencv2/dnn/dnn.hpp>

Enum of image processing mode. To facilitate the specialization pre-processing requirements of the dnn model. For example, the letter box often used in the Yolo series of models.

See also

Image2BlobParams

Enumerator
DNN_PMODE_NULL  Python: cv.dnn.DNN_PMODE_NULL
DNN_PMODE_CROP_CENTER  Python: cv.dnn.DNN_PMODE_CROP_CENTER
DNN_PMODE_LETTERBOX  Python: cv.dnn.DNN_PMODE_LETTERBOX

ModelFormat

enum cv::dnn::ModelFormat

#include <opencv2/dnn/dnn.hpp>

Enumerator
DNN_MODEL_GENERIC  Python: cv.dnn.DNN_MODEL_GENERIC	Some generic model format.
DNN_MODEL_ONNX  Python: cv.dnn.DNN_MODEL_ONNX	ONNX model.
DNN_MODEL_TF  Python: cv.dnn.DNN_MODEL_TF	TF model.
DNN_MODEL_TFLITE  Python: cv.dnn.DNN_MODEL_TFLITE	TFLite model.
DNN_MODEL_CAFFE  Python: cv.dnn.DNN_MODEL_CAFFE	Caffe model.

ProfilingMode

enum cv::dnn::ProfilingMode

#include <opencv2/dnn/dnn.hpp>

Enumerator
DNN_PROFILE_NONE  Python: cv.dnn.DNN_PROFILE_NONE	Don't do any profiling.
DNN_PROFILE_SUMMARY  Python: cv.dnn.DNN_PROFILE_SUMMARY	Collect the summary statistics by layer type (e.g. all "Conv2D" or all "Add") and print it in the end, sorted by the execution time (most expensive layers first). Note that it may introduce some overhead and cause slowdown, especially in the case of non-CPU backends.
DNN_PROFILE_DETAILED  Python: cv.dnn.DNN_PROFILE_DETAILED	Print execution time of each single layer. Note that it may introduce some overhead and cause slowdown, especially in the case of non-CPU backends.

SoftNMSMethod

enum class cv::dnn::SoftNMSMethod

strong

#include <opencv2/dnn/dnn.hpp>

Enum of Soft NMS methods.

See also

softNMSBoxes

Enumerator
SOFTNMS_LINEAR
SOFTNMS_GAUSSIAN

Target

enum cv::dnn::Target

#include <opencv2/dnn/dnn.hpp>

Enum of target devices for computations.

See also

Net::setPreferableTarget

Enumerator
DNN_TARGET_CPU  Python: cv.dnn.DNN_TARGET_CPU
DNN_TARGET_OPENCL  Python: cv.dnn.DNN_TARGET_OPENCL
DNN_TARGET_OPENCL_FP16  Python: cv.dnn.DNN_TARGET_OPENCL_FP16
DNN_TARGET_MYRIAD  Python: cv.dnn.DNN_TARGET_MYRIAD
DNN_TARGET_VULKAN  Python: cv.dnn.DNN_TARGET_VULKAN
DNN_TARGET_FPGA  Python: cv.dnn.DNN_TARGET_FPGA	FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin.
DNN_TARGET_CUDA  Python: cv.dnn.DNN_TARGET_CUDA
DNN_TARGET_CUDA_FP16  Python: cv.dnn.DNN_TARGET_CUDA_FP16
DNN_TARGET_HDDL  Python: cv.dnn.DNN_TARGET_HDDL
DNN_TARGET_NPU  Python: cv.dnn.DNN_TARGET_NPU
DNN_TARGET_CPU_FP16  Python: cv.dnn.DNN_TARGET_CPU_FP16

TracingMode

enum cv::dnn::TracingMode

#include <opencv2/dnn/dnn.hpp>

Enumerator
DNN_TRACE_NONE  Python: cv.dnn.DNN_TRACE_NONE	Don't trace anything.
DNN_TRACE_ALL  Python: cv.dnn.DNN_TRACE_ALL	Print all executed operations along with the output tensors, more or less compatible with ONNX Runtime.
DNN_TRACE_OP  Python: cv.dnn.DNN_TRACE_OP	Print all executed operations. Types and shapes of all inputs and outputs are printed, but the content is not.

Function Documentation

argKindToString()

std::string cv::dnn::argKindToString

(

ArgKind

kind

)

#include <opencv2/dnn/dnn.hpp>

blobFromImage() [1/2]

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 )

Python:
	cv.dnn.blobFromImage(	image[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
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.
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.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [2/2]

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 )

Python:
	cv.dnn.blobFromImage(	image[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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]

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 )

Python:
	cv.dnn.blobFromImages(	images[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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.

Parameters

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.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [2/2]

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 )

Python:
	cv.dnn.blobFromImages(	images[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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.

blobFromImagesWithParams() [1/2]

Mat cv::dnn::blobFromImagesWithParams	(	InputArrayOfArrays	images,
		const Image2BlobParams &	param = Image2BlobParams() )

Python:
	cv.dnn.blobFromImagesWithParams(	images[, param]	) ->	retval
	cv.dnn.blobFromImagesWithParams(	images[, blob[, param]]	) ->	blob

#include <opencv2/dnn/dnn.hpp>

Creates 4-dimensional blob from series of images with given params.

This function is an extension of blobFromImages to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.

Parameters

images	input image (all with 1-, 3- or 4-channels).
param	struct of Image2BlobParams, contains all parameters needed by processing of image to blob.

Returns

4-dimensional Mat.

blobFromImagesWithParams() [2/2]

void cv::dnn::blobFromImagesWithParams	(	InputArrayOfArrays	images,
		OutputArray	blob,
		const Image2BlobParams &	param = Image2BlobParams() )

Python:
	cv.dnn.blobFromImagesWithParams(	images[, param]	) ->	retval
	cv.dnn.blobFromImagesWithParams(	images[, blob[, param]]	) ->	blob

#include <opencv2/dnn/dnn.hpp>

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

blobFromImageWithParams() [1/2]

Mat cv::dnn::blobFromImageWithParams	(	InputArray	image,
		const Image2BlobParams &	param = Image2BlobParams() )

Python:
	cv.dnn.blobFromImageWithParams(	image[, param]	) ->	retval
	cv.dnn.blobFromImageWithParams(	image[, blob[, param]]	) ->	blob

#include <opencv2/dnn/dnn.hpp>

Creates 4-dimensional blob from image with given params.

This function is an extension of blobFromImage to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.

Parameters

image	input image (all with 1-, 3- or 4-channels).
param	struct of Image2BlobParams, contains all parameters needed by processing of image to blob.

Returns

4-dimensional Mat.

blobFromImageWithParams() [2/2]

void cv::dnn::blobFromImageWithParams	(	InputArray	image,
		OutputArray	blob,
		const Image2BlobParams &	param = Image2BlobParams() )

Python:
	cv.dnn.blobFromImageWithParams(	image[, param]	) ->	retval
	cv.dnn.blobFromImageWithParams(	image[, blob[, param]]	) ->	blob

#include <opencv2/dnn/dnn.hpp>

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

enableModelDiagnostics()

void cv::dnn::enableModelDiagnostics

(

bool

isDiagnosticsMode

)

#include <opencv2/dnn/dnn.hpp>

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

Parameters

[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).

Note

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

getAvailableBackends()

std::vector< std::pair< Backend, Target > > cv::dnn::getAvailableBackends

(

)

#include <opencv2/dnn/dnn.hpp>

getAvailableTargets()

std::vector< Target > cv::dnn::getAvailableTargets

(

dnn::Backend

be

)

Python:
	cv.dnn.getAvailableTargets(	be	) ->	retval

#include <opencv2/dnn/dnn.hpp>

getLayerFactoryImpl()

LayerFactory_Impl & cv::dnn::getLayerFactoryImpl

(

)

#include <opencv2/dnn/layer_reg.private.hpp>

Register layer types of DNN model.

Note

In order to thread-safely access the factory, see getLayerFactoryMutex() function.

getLayerFactoryMutex()

Mutex & cv::dnn::getLayerFactoryMutex

(

)

#include <opencv2/dnn/layer_reg.private.hpp>

Get the mutex guarding LayerFactory_Impl, see getLayerFactoryImpl() function.

imagesFromBlob()

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

Python:
	cv.dnn.imagesFromBlob(	blob_[, images_]	) ->	images_

#include <opencv2/dnn/dnn.hpp>

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

Parameters

[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).

modelFormatToString()

std::string cv::dnn::modelFormatToString

(

ModelFormat

modelFormat

)

#include <opencv2/dnn/dnn.hpp>

NMSBoxes() [1/3]

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

Python:
	cv.dnn.NMSBoxes(	bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]	) ->	indices
	cv.dnn.NMSBoxesRotated(	bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]	) ->	indices

#include <opencv2/dnn/dnn.hpp>

Performs non maximum suppression given boxes and corresponding scores.

Parameters

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.

NMSBoxes() [2/3]

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

Python:
	cv.dnn.NMSBoxes(	bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]	) ->	indices
	cv.dnn.NMSBoxesRotated(	bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]	) ->	indices

#include <opencv2/dnn/dnn.hpp>

NMSBoxes() [3/3]

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

Python:
	cv.dnn.NMSBoxes(	bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]	) ->	indices
	cv.dnn.NMSBoxesRotated(	bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]	) ->	indices

#include <opencv2/dnn/dnn.hpp>

NMSBoxesBatched() [1/2]

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

Python:
	cv.dnn.NMSBoxesBatched(	bboxes, scores, class_ids, score_threshold, nms_threshold[, eta[, top_k]]	) ->	indices

#include <opencv2/dnn/dnn.hpp>

Performs batched non maximum suppression on given boxes and corresponding scores across different classes.

Parameters

bboxes	a set of bounding boxes to apply NMS.
scores	a set of corresponding confidences.
class_ids	a set of corresponding class ids. Ids are integer and usually start from 0.
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.

NMSBoxesBatched() [2/2]

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

Python:
	cv.dnn.NMSBoxesBatched(	bboxes, scores, class_ids, score_threshold, nms_threshold[, eta[, top_k]]	) ->	indices

#include <opencv2/dnn/dnn.hpp>

readNet() [1/2]

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

Python:
	cv.dnn.readNet(	model[, config[, framework[, engine]]]	) ->	retval
	cv.dnn.readNet(	framework, bufferModel[, bufferConfig[, engine]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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.

Parameters

[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.
	engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

readNet() [2/2]

Net cv::dnn::readNet	(	CV_WRAP_FILE_PATH const String &	model,
		CV_WRAP_FILE_PATH const String &	config = "",
		const String &	framework = "",
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNet(	model[, config[, framework[, engine]]]	) ->	retval
	cv.dnn.readNet(	framework, bufferModel[, bufferConfig[, engine]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

[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/) *.weights (Darknet, https://pjreddie.com/darknet/) *.bin | *.onnx (OpenVINO, 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 (OpenVINO, https://software.intel.com/openvino-toolkit)
[in]	framework	Explicit framework name tag to determine a format.
[in]	engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

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

readNetFromCaffe() [1/3]

Net cv::dnn::readNetFromCaffe	(	const char *	bufferProto,
		size_t	lenProto,
		const char *	bufferModel = NULL,
		size_t	lenModel = 0,
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromCaffe(	prototxt[, caffeModel[, engine]]	) ->	retval
	cv.dnn.readNetFromCaffe(	bufferProto[, bufferModel[, engine]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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.

Parameters

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
engine	select DNN engine to be used. With auto selection the new engine is used. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromCaffe() [2/3]

Net cv::dnn::readNetFromCaffe	(	const std::vector< uchar > &	bufferProto,
		const std::vector< uchar > &	bufferModel = std::vector< uchar >(),
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromCaffe(	prototxt[, caffeModel[, engine]]	) ->	retval
	cv.dnn.readNetFromCaffe(	bufferProto[, bufferModel[, engine]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Reads a network model stored in Caffe model in memory.

Parameters

bufferProto	buffer containing the content of the .prototxt file
bufferModel	buffer containing the content of the .caffemodel file
engine	select DNN engine to be used. With auto selection the new engine is used. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromCaffe() [3/3]

Net cv::dnn::readNetFromCaffe	(	CV_WRAP_FILE_PATH const String &	prototxt,
		CV_WRAP_FILE_PATH const String &	caffeModel = String(),
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromCaffe(	prototxt[, caffeModel[, engine]]	) ->	retval
	cv.dnn.readNetFromCaffe(	bufferProto[, bufferModel[, engine]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

prototxt	path to the .prototxt file with text description of the network architecture.
caffeModel	path to the .caffemodel file with learned network.
engine	select DNN engine to be used. With auto selection the new engine is used. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromDarknet() [1/3]

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

Python:
	cv.dnn.readNetFromDarknet(	cfgFile[, darknetModel]	) ->	retval
	cv.dnn.readNetFromDarknet(	bufferCfg[, bufferModel]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Reads a network model stored in Darknet model files.

Parameters

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

Returns

Net object.

readNetFromDarknet() [2/3]

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

Python:
	cv.dnn.readNetFromDarknet(	cfgFile[, darknetModel]	) ->	retval
	cv.dnn.readNetFromDarknet(	bufferCfg[, bufferModel]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Reads a network model stored in Darknet model files.

Parameters

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.

Returns

Net object.

readNetFromDarknet() [3/3]

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

Python:
	cv.dnn.readNetFromDarknet(	cfgFile[, darknetModel]	) ->	retval
	cv.dnn.readNetFromDarknet(	bufferCfg[, bufferModel]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Reads a network model stored in Darknet model files.

Parameters

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

Returns

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

readNetFromModelOptimizer() [1/3]

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

Python:
	cv.dnn.readNetFromModelOptimizer(	xml[, bin]	) ->	retval
	cv.dnn.readNetFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

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

Returns

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

readNetFromModelOptimizer() [2/3]

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

Python:
	cv.dnn.readNetFromModelOptimizer(	xml[, bin]	) ->	retval
	cv.dnn.readNetFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

[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.

Returns

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

readNetFromModelOptimizer() [3/3]

Net cv::dnn::readNetFromModelOptimizer	(	CV_WRAP_FILE_PATH const String &	xml,
		CV_WRAP_FILE_PATH const String &	bin = "" )

Python:
	cv.dnn.readNetFromModelOptimizer(	xml[, bin]	) ->	retval
	cv.dnn.readNetFromModelOptimizer(	bufferModelConfig, bufferWeights	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

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

Returns

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

readNetFromONNX() [1/3]

Net cv::dnn::readNetFromONNX	(	const char *	buffer,
		size_t	sizeBuffer,
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromONNX(	onnxFile[, engine]	) ->	retval
	cv.dnn.readNetFromONNX(	buffer[, engine]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Reads a network model from ONNX in-memory buffer.

Parameters

buffer	memory address of the first byte of the buffer.
sizeBuffer	size of the buffer.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic.

Returns

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

readNetFromONNX() [2/3]

Net cv::dnn::readNetFromONNX	(	const std::vector< uchar > &	buffer,
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromONNX(	onnxFile[, engine]	) ->	retval
	cv.dnn.readNetFromONNX(	buffer[, engine]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Reads a network model from ONNX in-memory buffer.

Parameters

buffer	in-memory buffer that stores the ONNX model bytes.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

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

readNetFromONNX() [3/3]

Net cv::dnn::readNetFromONNX	(	CV_WRAP_FILE_PATH const String &	onnxFile,
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromONNX(	onnxFile[, engine]	) ->	retval
	cv.dnn.readNetFromONNX(	buffer[, engine]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Reads a network model ONNX.

Parameters

onnxFile	path to the .onnx file with text description of the network architecture.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

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

readNetFromTensorflow() [1/3]

Net cv::dnn::readNetFromTensorflow	(	const char *	bufferModel,
		size_t	lenModel,
		const char *	bufferConfig = NULL,
		size_t	lenConfig = 0,
		int	engine = ENGINE_AUTO,
		const std::vector< String > &	extraOutputs = std::vector< String >() )

Python:
	cv.dnn.readNetFromTensorflow(	model[, config[, engine[, extraOutputs]]]	) ->	retval
	cv.dnn.readNetFromTensorflow(	bufferModel[, bufferConfig[, engine[, extraOutputs]]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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.

Parameters

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
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

readNetFromTensorflow() [2/3]

Net cv::dnn::readNetFromTensorflow	(	const std::vector< uchar > &	bufferModel,
		const std::vector< uchar > &	bufferConfig = std::vector< uchar >(),
		int	engine = ENGINE_AUTO,
		const std::vector< String > &	extraOutputs = std::vector< String >() )

Python:
	cv.dnn.readNetFromTensorflow(	model[, config[, engine[, extraOutputs]]]	) ->	retval
	cv.dnn.readNetFromTensorflow(	bufferModel[, bufferConfig[, engine[, extraOutputs]]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

bufferModel	buffer containing the content of the pb file
bufferConfig	buffer containing the content of the pbtxt file
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTensorflow() [3/3]

Net cv::dnn::readNetFromTensorflow	(	CV_WRAP_FILE_PATH const String &	model,
		CV_WRAP_FILE_PATH const String &	config = String(),
		int	engine = ENGINE_AUTO,
		const std::vector< String > &	extraOutputs = std::vector< String >() )

Python:
	cv.dnn.readNetFromTensorflow(	model[, config[, engine[, extraOutputs]]]	) ->	retval
	cv.dnn.readNetFromTensorflow(	bufferModel[, bufferConfig[, engine[, extraOutputs]]]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

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.
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTFLite() [1/3]

Net cv::dnn::readNetFromTFLite	(	const char *	bufferModel,
		size_t	lenModel,
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromTFLite(	model[, engine]	) ->	retval
	cv.dnn.readNetFromTFLite(	bufferModel[, engine]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Reads a network model stored in TFLite 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.

Parameters

bufferModel	buffer containing the content of the tflite file
lenModel	length of bufferModel
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

readNetFromTFLite() [2/3]

Net cv::dnn::readNetFromTFLite	(	const std::vector< uchar > &	bufferModel,
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromTFLite(	model[, engine]	) ->	retval
	cv.dnn.readNetFromTFLite(	bufferModel[, engine]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

bufferModel	buffer containing the content of the tflite file
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTFLite() [3/3]

Net cv::dnn::readNetFromTFLite	(	CV_WRAP_FILE_PATH const String &	model,
		int	engine = ENGINE_AUTO )

Python:
	cv.dnn.readNetFromTFLite(	model[, engine]	) ->	retval
	cv.dnn.readNetFromTFLite(	bufferModel[, engine]	) ->	retval

#include <opencv2/dnn/dnn.hpp>

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

Parameters

model	path to the .tflite file with binary flatbuffers description of the network architecture
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readTensorFromONNX()

Mat cv::dnn::readTensorFromONNX

(

CV_WRAP_FILE_PATH const String &

path

)

Python:
	cv.dnn.readTensorFromONNX(	path	) ->	retval

#include <opencv2/dnn/dnn.hpp>

Creates blob from .pb file.

Parameters

path	to the .pb file with input tensor.

Returns

Mat.

shrinkCaffeModel()

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

Python:
	cv.dnn.shrinkCaffeModel(	src, dst[, layersTypes]	) ->	None

#include <opencv2/dnn/dnn.hpp>

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

Parameters

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.

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.

softNMSBoxes()

void cv::dnn::softNMSBoxes	(	const std::vector< Rect > &	bboxes,
		const std::vector< float > &	scores,
		std::vector< float > &	updated_scores,
		const float	score_threshold,
		const float	nms_threshold,
		std::vector< int > &	indices,
		size_t	top_k = 0,
		const float	sigma = 0.5,
		SoftNMSMethod	method = SoftNMSMethod::SOFTNMS_GAUSSIAN )

Python:
	cv.dnn.softNMSBoxes(	bboxes, scores, score_threshold, nms_threshold[, top_k[, sigma[, method]]]	) ->	updated_scores, indices

#include <opencv2/dnn/dnn.hpp>

Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503.

Parameters

bboxes	a set of bounding boxes to apply Soft NMS.
scores	a set of corresponding confidences.
updated_scores	a set of corresponding updated 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.
top_k	keep at most top_k picked indices.
sigma	parameter of Gaussian weighting.
method	Gaussian or linear.

See also

SoftNMSMethod

writeTextGraph()

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

Python:
	cv.dnn.writeTextGraph(	model, output	) ->	None

#include <opencv2/dnn/dnn.hpp>

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

Parameters

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

Note

To reduce output file size, trained weights are not included.