cv::dnn Namespace Reference

Namespaces
namespace	accessor
namespace	details

Classes
struct	_Range
class	AbsLayer
class	AccumLayer
class	AcoshLayer
class	AcosLayer
struct	ActivationInt8Params
class	ActivationLayer
class	ActivationLayerInt8
class	AffineGridLayer
struct	Arg
struct	ArgData
class	ArgLayer
	ArgMax/ArgMin layer. More...
class	AsinhLayer
class	AsinLayer
class	AtanhLayer
class	AtanLayer
class	AttentionLayer
class	AttentionOnnxAiLayer
class	AveragePoolLayer
class	BackendNode
	Derivatives of this class encapsulates functions of certain backends. More...
class	BackendWrapper
	Derivatives of this class wraps cv::Mat for different backends and targets. More...
class	BaseConvolutionLayer
class	BatchNorm2Layer
class	BatchNormLayer
class	BatchNormLayerInt8
class	BitShiftLayer
class	BlackmanWindowLayer
class	BlankLayer
class	BNLLLayer
class	Cast2Layer
class	CastLayer
class	CeilLayer
class	CeluLayer
class	CenterCropPadLayer
class	ChannelsPReLULayer
class	ClassificationModel
	This class represents high-level API for classification models. More...
class	ClipLayer
class	CompareLayer
class	Concat2Layer
class	ConcatLayer
class	ConstantOfShapeLayer
class	ConstLayer
class	Conv2Int8Layer
struct	Conv2Int8Params
class	Conv2Layer
class	ConvolutionLayer
class	ConvolutionLayerInt8
class	ConvTranspose2Layer
class	CorrelationLayer
class	CoshLayer
class	CosLayer
class	CropAndResizeLayer
class	CropLayer
class	CumSumLayer
class	DataAugmentationLayer
class	DeconvolutionLayer
class	DepthToSpaceLayer
class	DequantizeLayer
class	DequantizeLinearLayer
class	DetectionModel
	This class represents high-level API for object detection networks. More...
class	DetectionOutputLayer
	Detection output layer. More...
class	DetLayer
class	DFTLayer
class	Dict
	This class implements name-value dictionary, values are instances of DictValue. More...
struct	DictValue
	This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. More...
class	EinsumLayer
	This function performs array summation based on the Einstein summation convention. The function allows for concise expressions of various mathematical operations using subscripts. More...
class	Eltwise2Int8Layer
struct	Eltwise2Int8Params
class	EltwiseLayer
	Element wise operation on inputs. More...
class	EltwiseLayerInt8
class	ELULayer
class	ErfLayer
class	Expand2Layer
class	ExpandLayer
class	ExpLayer
class	EyeLikeLayer
class	FlattenLayer
class	FloorLayer
class	FlowWarpLayer
class	Gather2Layer
class	GatherElementsLayer
	GatherElements layer GatherElements takes two inputs data and indices of the same rank r >= 1 and an optional attribute axis and works such that: output[i][j][k] = data[index[i][j][k]][j][k] if axis = 0 and r = 3 output[i][j][k] = data[i][index[i][j][k]][k] if axis = 1 and r = 3 output[i][j][k] = data[i][j][index[i][j][k]] if axis = 2 and r = 3. More...
class	GatherLayer
	Gather layer. More...
class	GatherNDLayer
	GatherND layer. More...
class	GeluApproximationLayer
class	GeluLayer
class	GemmLayer
class	GlobalAveragePoolLayer
class	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...
class	GridSampleLayer
class	GroupNormLayer
class	GRULayer
	GRU recurrent one-layer. More...
class	HammingWindowLayer
class	HannWindowLayer
class	HardmaxLayer
class	HardSigmoidLayer
class	HardSwishLayer
class	IfLayer
struct	Image2BlobParams
	Processing params of image to blob. More...
struct	InnerProductInt8Params
class	InnerProductLayer
class	InnerProductLayerInt8
class	InstanceNormLayer
class	InterpLayer
	Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2. More...
class	IsInfLayer
class	IsNaNLayer
class	KeypointsModel
	This class represents high-level API for keypoints models. More...
class	Layer
	This interface class allows to build new Layers - are building blocks of networks. More...
class	LayerFactory
	Layer factory allows to create instances of registered layers. More...
class	LayerNorm2Layer
class	LayerNormLayer
class	LayerParams
	This class provides all data needed to initialize layer. More...
class	LogLayer
class	LoopLayer
class	LRNLayer
class	LSTM2Layer
class	LSTMLayer
	LSTM recurrent layer. More...
class	MatMulInt8Layer
struct	MatMulInt8Params
class	MatMulLayer
class	MaxPoolLayer
class	MaxUnpoolLayer
class	MishLayer
class	Model
	This class is presented high-level API for neural networks. More...
class	MVNLayer
class	NaryEltwiseLayer
class	NegativeLogLikelihoodLossLayer
class	Net
	This class allows to create and manipulate comprehensive artificial neural networks. More...
class	NonMaxSuppressionLayer
class	NonZeroLayer
class	NormalizeBBoxLayer
	\( L_p \) - normalization layer. More...
class	NotLayer
class	OneHotLayer
class	Pad2Layer
class	PaddingLayer
	Adds extra values for specific axes. More...
class	PermuteLayer
class	Pool2Int8Layer
struct	Pool2Int8Params
class	PoolingLayer
class	PoolingLayerInt8
class	PowerLayer
class	PriorBoxLayer
class	ProposalLayer
class	QuantizeLayer
class	QuantizeLinearLayer
class	RandomNormalLikeLayer
class	RangeLayer
class	ReciprocalLayer
class	Reduce2Layer
class	ReduceLayer
class	RegionLayer
class	ReLU6Layer
class	ReLULayer
class	ReorgLayer
class	RequantizeLayer
class	Reshape2Layer
class	ReshapeLayer
class	Resize2Layer
class	ResizeLayer
	Resize input 4-dimensional blob by nearest neighbor or bilinear strategy. More...
class	RMSNormLayer
class	RNNLayer
	Classical recurrent layer. More...
class	RoiAlignLayer
class	RotaryEmbeddingLayer
class	RoundLayer
class	ScaleLayer
class	ScaleLayerInt8
class	ScatterLayer
class	ScatterNDLayer
class	SDPALayer
class	SegmentationModel
	This class represents high-level API for segmentation models. More...
class	SeluLayer
class	ShapeLayer
class	ShiftLayer
class	ShiftLayerInt8
class	ShrinkLayer
class	ShuffleChannelLayer
class	SigmoidLayer
class	SignLayer
class	SinhLayer
class	SinLayer
class	SizeLayer
class	Slice2Layer
class	SliceLayer
class	SoftmaxCrossEntropyLossLayer
class	SoftmaxLayer
class	SoftmaxLayerInt8
class	SoftplusLayer
class	SoftsignLayer
class	SpaceToDepthLayer
class	Split2Layer
class	SplitLayer
class	SqrtLayer
class	SqueezeLayer
class	SwishLayer
class	TanHLayer
class	TanLayer
class	TextDetectionModel
	Base class for text detection networks. More...
class	TextDetectionModel_DB
	This class represents high-level API for text detection DL networks compatible with DB model. More...
class	TextDetectionModel_EAST
	This class represents high-level API for text detection DL networks compatible with EAST model. More...
class	TextRecognitionModel
	This class represents high-level API for text recognition networks. More...
class	ThresholdedReluLayer
class	Tile2Layer
class	TileLayer
class	Tokenizer
	High-level tokenizer wrapper for DNN usage. More...
class	TopK2Layer
class	TopKLayer
class	TransformLayoutLayer
class	TransposeLayer
class	TriluLayer
class	UniqueLayer
class	UnsqueezeLayer

Typedefs
typedef void(*	ActivationFunc) (const void *input, void *output, size_t len, const float *params)
typedef std::map< std::string, std::vector< LayerFactory::Constructor > >	LayerFactory_Impl
typedef int	MatType

Enumerations
enum	ActivationType {   ACTIV_NONE = 0 ,   ACTIV_MISH ,   ACTIV_SWISH ,   ACTIV_SIGMOID ,   ACTIV_TANH ,   ACTIV_ELU ,   ACTIV_HARDSWISH ,   ACTIV_HARDSIGMOID ,   ACTIV_GELU ,   ACTIV_GELU_APPROX ,   ACTIV_RELU ,   ACTIV_CLIP }
enum	ArgKind {   DNN_ARG_EMPTY =0 ,   DNN_ARG_CONST =1 ,   DNN_ARG_INPUT =2 ,   DNN_ARG_OUTPUT =3 ,   DNN_ARG_TEMP =4 ,   DNN_ARG_PATTERN =5 }
enum	AutoPadding {   AUTO_PAD_NONE = 0 ,   AUTO_PAD_SAME_UPPER = 1 ,   AUTO_PAD_SAME_LOWER = 2 ,   AUTO_PAD_VALID = 3 }
enum	Backend {   DNN_BACKEND_DEFAULT = 0 ,   DNN_BACKEND_INFERENCE_ENGINE = 2 ,   DNN_BACKEND_OPENCV ,   DNN_BACKEND_VKCOM ,   DNN_BACKEND_CUDA ,   DNN_BACKEND_WEBNN ,   DNN_BACKEND_TIMVX ,   DNN_BACKEND_CANN }
	Enum of computation backends supported by layers. More...
enum	EngineType {   ENGINE_CLASSIC =1 ,   ENGINE_NEW =2 ,   ENGINE_AUTO =3 ,   ENGINE_ORT =4 }
enum	ImagePaddingMode {   DNN_PMODE_NULL = 0 ,   DNN_PMODE_CROP_CENTER = 1 ,   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	LossReduction {   LOSS_REDUCTION_NONE = 0 ,   LOSS_REDUCTION_MEAN = 1 ,   LOSS_REDUCTION_SUM = 2 }
enum	ModelFormat {   DNN_MODEL_GENERIC = 0 ,   DNN_MODEL_ONNX = 1 ,   DNN_MODEL_TF = 2 ,   DNN_MODEL_TFLITE = 3 }
enum	ProfilingMode {   DNN_PROFILE_NONE = 0 ,   DNN_PROFILE_SUMMARY = 1 ,   DNN_PROFILE_DETAILED = 2 }
enum class	SoftNMSMethod {   SoftNMSMethod::SOFTNMS_LINEAR = 1 ,   SoftNMSMethod::SOFTNMS_GAUSSIAN = 2 }
	Enum of Soft NMS methods. More...
enum	Target {   DNN_TARGET_CPU = 0 ,   DNN_TARGET_OPENCL ,   DNN_TARGET_OPENCL_FP16 ,   DNN_TARGET_MYRIAD ,   DNN_TARGET_VULKAN ,   DNN_TARGET_FPGA ,   DNN_TARGET_CUDA ,   DNN_TARGET_CUDA_FP16 ,   DNN_TARGET_HDDL ,   DNN_TARGET_NPU ,   DNN_TARGET_CPU_FP16 }
	Enum of target devices for computations. More...
enum	TracingMode {   DNN_TRACE_NONE = 0 ,   DNN_TRACE_ALL = 1 ,   DNN_TRACE_OP = 2 }

Functions
std::string	argKindToString (ArgKind kind)
Mat	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	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	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	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	blobFromImagesWithParams (InputArrayOfArrays images, const Image2BlobParams &param=Image2BlobParams())
	Creates 4-dimensional blob from series of images with given params.
void	blobFromImagesWithParams (InputArrayOfArrays images, OutputArray blob, const Image2BlobParams &param=Image2BlobParams())
Mat	blobFromImageWithParams (InputArray image, const Image2BlobParams &param=Image2BlobParams())
	Creates 4-dimensional blob from image with given params.
void	blobFromImageWithParams (InputArray image, OutputArray blob, const Image2BlobParams &param=Image2BlobParams())
static MatShape	concat (const MatShape &a, const MatShape &b)
void	enableModelDiagnostics (bool isDiagnosticsMode)
	Enables detailed logging of the DNN model loading with CV DNN API.
ActivationFunc	getActivationFunc (int activationType)
std::vector< std::pair< Backend, Target > >	getAvailableBackends ()
std::vector< Target >	getAvailableTargets (dnn::Backend be)
cv::String	getInferenceEngineBackendType ()
	Returns Inference Engine internal backend API.
cv::String	getInferenceEngineCPUType ()
	Returns Inference Engine CPU type.
cv::String	getInferenceEngineVPUType ()
	Returns Inference Engine VPU type.
LayerFactory_Impl &	getLayerFactoryImpl ()
Mutex &	getLayerFactoryMutex ()
	Get the mutex guarding LayerFactory_Impl, see getLayerFactoryImpl() function.
static Mat	getPlane (const Mat &m, int n, int cn)
void	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>).
static bool	isAllOnes (const MatShape &inputShape, int startPos, int endPos)
std::string	modelFormatToString (ModelFormat modelFormat)
void	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	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	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	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	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)
static int	normalize_axis (int axis, const MatShape &shape)
static int	normalize_axis (int axis, int dims)
	Converts axis from [-dims; dims) (similar to Python's slice notation) to [0; dims) range.
static Range	normalize_axis_range (const Range &r, int axisSize)
template<typename _Tp>
static std::ostream &	operator<< (std::ostream &out, const std::vector< _Tp > &shape)
template<typename _Tp>
static void	print (const std::vector< _Tp > &shape, const String &name="")
Net	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	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	readNetFromModelOptimizer (const std::vector< uchar > &bufferModelConfig, const std::vector< uchar > &bufferWeights)
	Load a network from Intel's Model Optimizer intermediate representation.
Net	readNetFromModelOptimizer (const uchar *bufferModelConfigPtr, size_t bufferModelConfigSize, const uchar *bufferWeightsPtr, size_t bufferWeightsSize)
	Load a network from Intel's Model Optimizer intermediate representation.
Net	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	readNetFromONNX (const char *buffer, size_t sizeBuffer, int engine=ENGINE_AUTO)
	Reads a network model from ONNX in-memory buffer.
Net	readNetFromONNX (const std::vector< uchar > &buffer, int engine=ENGINE_AUTO)
	Reads a network model from ONNX in-memory buffer.
Net	readNetFromONNX (CV_WRAP_FILE_PATH const String &onnxFile, int engine=ENGINE_AUTO)
	Reads a network model ONNX.
Net	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	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	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	readNetFromTFLite (const char *bufferModel, size_t lenModel, int engine=ENGINE_AUTO)
	Reads a network model stored in TFLite framework's format.
Net	readNetFromTFLite (const std::vector< uchar > &bufferModel, int engine=ENGINE_AUTO)
	Reads a network model stored in TFLite framework's format.
Net	readNetFromTFLite (CV_WRAP_FILE_PATH const String &model, int engine=ENGINE_AUTO)
	Reads a network model stored in TFLite framework's format.
Mat	readTensorFromONNX (CV_WRAP_FILE_PATH const String &path)
	Creates blob from .pb file.
void	releaseHDDLPlugin ()
	Release a HDDL plugin.
void	resetMyriadDevice ()
	Release a Myriad device (binded by OpenCV).
cv::String	setInferenceEngineBackendType (const cv::String &newBackendType)
	Specify Inference Engine internal backend API.
static MatShape	shape (const int *dims, const int n)
static MatShape	shape (const Mat &mat)
static MatShape	shape (const UMat &mat)
static MatShape	shape (int a0, int a1=-1, int a2=-1, int a3=-1)
void	skipModelImport (bool skip)
	Skip model import after diagnostic run in readNet() functions.
static Mat	slice (const Mat &m, const _Range &r0)
static Mat	slice (const Mat &m, const _Range &r0, const _Range &r1)
static Mat	slice (const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2)
static Mat	slice (const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2, const _Range &r3)
void	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.
static std::string	toString (const MatShape &shape, const String &name="")
template<typename _Tp>
static std::string	toString (const std::vector< _Tp > &shape, const String &name="")
static size_t	total (const Mat &mat, int start=-1, int end=-1)
static size_t	total (const MatShape &shape, int start=-1, int end=-1)
void	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.

Function Documentation

concat()

MatShape cv::dnn::concat ( const MatShape & a, const MatShape & b )

inlinestatic

getInferenceEngineBackendType()

cv::String cv::dnn::getInferenceEngineBackendType

(

)

Python:
	cv.dnn.getInferenceEngineBackendType(		) ->	retval

Returns Inference Engine internal backend API.

See values of CV_DNN_BACKEND_INFERENCE_ENGINE_* macros.

OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE runtime parameter (environment variable) is ignored since 4.6.0.

Deprecated

getInferenceEngineCPUType()

cv::String cv::dnn::getInferenceEngineCPUType

(

)

Python:
	cv.dnn.getInferenceEngineCPUType(		) ->	retval

Returns Inference Engine CPU type.

Specify OpenVINO plugin: CPU or ARM.

getInferenceEngineVPUType()

cv::String cv::dnn::getInferenceEngineVPUType

(

)

Python:
	cv.dnn.getInferenceEngineVPUType(		) ->	retval

Returns Inference Engine VPU type.

See values of CV_DNN_INFERENCE_ENGINE_VPU_TYPE_* macros.

getPlane()

Mat cv::dnn::getPlane ( const Mat & m, int n, int cn )

inlinestatic

isAllOnes()

bool cv::dnn::isAllOnes ( const MatShape & inputShape, int startPos, int endPos )

inlinestatic

normalize_axis() [1/2]

int cv::dnn::normalize_axis ( int axis, const MatShape & shape )

inlinestatic

normalize_axis() [2/2]

int cv::dnn::normalize_axis ( int axis, int dims )

inlinestatic

Converts axis from [-dims; dims) (similar to Python's slice notation) to [0; dims) range.

normalize_axis_range()

Range cv::dnn::normalize_axis_range ( const Range & r, int axisSize )

inlinestatic

operator<<()

template<typename _Tp>

std::ostream & cv::dnn::operator<< ( std::ostream & out, const std::vector< _Tp > & shape )

inlinestatic

print()

template<typename _Tp>

void cv::dnn::print ( const std::vector< _Tp > & shape, const String & name = "" )

inlinestatic

releaseHDDLPlugin()

void cv::dnn::releaseHDDLPlugin

(

)

Python:
	cv.dnn.releaseHDDLPlugin(		) ->	None

Release a HDDL plugin.

resetMyriadDevice()

void cv::dnn::resetMyriadDevice

(

)

Python:
	cv.dnn.resetMyriadDevice(		) ->	None

Release a Myriad device (binded by OpenCV).

Single Myriad device cannot be shared across multiple processes which uses Inference Engine's Myriad plugin.

setInferenceEngineBackendType()

cv::String cv::dnn::setInferenceEngineBackendType

(

const cv::String &

newBackendType

)

Python:
	cv.dnn.setInferenceEngineBackendType(	newBackendType	) ->	retval

Specify Inference Engine internal backend API.

See values of CV_DNN_BACKEND_INFERENCE_ENGINE_* macros.

Returns

previous value of internal backend API

Deprecated

shape() [1/4]

MatShape cv::dnn::shape ( const int * dims, const int n )

inlinestatic

shape() [2/4]

MatShape cv::dnn::shape ( const Mat & mat )

inlinestatic

shape() [3/4]

MatShape cv::dnn::shape ( const UMat & mat )

inlinestatic

shape() [4/4]

MatShape cv::dnn::shape ( int a0, int a1 = -1, int a2 = -1, int a3 = -1 )

inlinestatic

skipModelImport()

void cv::dnn::skipModelImport

(

bool

skip

)

Skip model import after diagnostic run in readNet() functions.

Parameters

[in]

skip

Indicates whether to skip the import.

This is an internal OpenCV function not intended for users.

slice() [1/4]

Mat cv::dnn::slice ( const Mat & m, const _Range & r0 )

inlinestatic

slice() [2/4]

Mat cv::dnn::slice ( const Mat & m, const _Range & r0, const _Range & r1 )

inlinestatic

slice() [3/4]

Mat cv::dnn::slice ( const Mat & m, const _Range & r0, const _Range & r1, const _Range & r2 )

inlinestatic

slice() [4/4]

Mat cv::dnn::slice ( const Mat & m, const _Range & r0, const _Range & r1, const _Range & r2, const _Range & r3 )

inlinestatic

toString() [1/2]

std::string cv::dnn::toString ( const MatShape & shape, const String & name = "" )

inlinestatic

toString() [2/2]

template<typename _Tp>

std::string cv::dnn::toString ( const std::vector< _Tp > & shape, const String & name = "" )

inlinestatic

total() [1/2]

size_t cv::dnn::total ( const Mat & mat, int start = -1, int end = -1 )

inlinestatic

total() [2/2]

size_t cv::dnn::total ( const MatShape & shape, int start = -1, int end = -1 )

inlinestatic