cv::dnn Namespace Reference

Namespaces
namespace	details

Classes
struct	_Range
class	AbsLayer
class	ActivationLayer
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	BatchNormLayer
class	BlankLayer
class	BNLLLayer
class	ChannelsPReLULayer
class	ConcatLayer
class	ConstLayer
class	ConvolutionLayer
class	CropAndResizeLayer
class	CropLayer
class	DeconvolutionLayer
class	DetectionOutputLayer
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	EltwiseLayer
class	ELULayer
class	FlattenLayer
class	InnerProductLayer
class	InterpLayer
	Bilinear resize layer from https://github.com/cdmh/deeplab-public. 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	LayerParams
	This class provides all data needed to initialize layer. More...
class	LRNLayer
class	LSTMLayer
	LSTM recurrent layer. More...
class	MaxUnpoolLayer
class	MVNLayer
class	Net
	This class allows to create and manipulate comprehensive artificial neural networks. More...
class	NormalizeBBoxLayer
	\( L_p \) - normalization layer. More...
class	PaddingLayer
	Adds extra values for specific axes. More...
class	PermuteLayer
class	PoolingLayer
class	PowerLayer
class	PriorBoxLayer
class	ProposalLayer
class	RegionLayer
class	ReLU6Layer
class	ReLULayer
class	ReorgLayer
class	ReshapeLayer
class	ResizeLayer
	Resize input 4-dimensional blob by nearest neighbor or bilinear strategy. More...
class	RNNLayer
	Classical recurrent layer. More...
class	ScaleLayer
class	ShiftLayer
class	ShuffleChannelLayer
class	SigmoidLayer
class	SliceLayer
class	SoftmaxLayer
class	SplitLayer
class	TanHLayer

Typedefs
typedef std::vector< int >	MatShape

Enumerations
enum	Backend {   DNN_BACKEND_DEFAULT,   DNN_BACKEND_HALIDE,   DNN_BACKEND_INFERENCE_ENGINE,   DNN_BACKEND_OPENCV,   DNN_BACKEND_VKCOM }
	Enum of computation backends supported by layers. More...
enum	Target {   DNN_TARGET_CPU,   DNN_TARGET_OPENCL,   DNN_TARGET_OPENCL_FP16,   DNN_TARGET_MYRIAD,   DNN_TARGET_VULKAN,   DNN_TARGET_FPGA }
	Enum of target devices for computations. More...

Functions
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.
int	clamp (int ax, int dims)
int	clamp (int ax, const MatShape &shape)
Range	clamp (const Range &r, int axisSize)
static MatShape	concat (const MatShape &a, const MatShape &b)
std::vector< std::pair < Backend, Target > >	getAvailableBackends ()
std::vector< Target >	getAvailableTargets (Backend be)
cv::String	getInferenceEngineVPUType ()
	Returns Inference Engine VPU type.
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>).
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)
static std::ostream &	operator<< (std::ostream &out, const MatShape &shape)
static void	print (const MatShape &shape, const String &name="")
Net	readNet (const String &model, const String &config="", const String &framework="")
	Read deep learning network represented in one of the supported formats.
Net	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.
Net	readNetFromCaffe (const String &prototxt, const String &caffeModel=String())
	Reads a network model stored in Caffe framework's format.
Net	readNetFromCaffe (const std::vector< uchar > &bufferProto, const std::vector< uchar > &bufferModel=std::vector< uchar >())
	Reads a network model stored in Caffe model in memory.
Net	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.
Net	readNetFromDarknet (const String &cfgFile, const String &darknetModel=String())
	Reads a network model stored in Darknet model files.
Net	readNetFromDarknet (const std::vector< uchar > &bufferCfg, const std::vector< uchar > &bufferModel=std::vector< uchar >())
	Reads a network model stored in Darknet model files.
Net	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	readNetFromModelOptimizer (const String &xml, const String &bin)
	Load a network from Intel's Model Optimizer intermediate representation.
Net	readNetFromONNX (const String &onnxFile)
	Reads a network model ONNX.
Net	readNetFromTensorflow (const String &model, const String &config=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 >())
	Reads a network model stored in TensorFlow framework's format.
Net	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.
Net	readNetFromTorch (const String &model, bool isBinary=true, bool evaluate=true)
	Reads a network model stored in Torch7 framework's format.
Mat	readTensorFromONNX (const String &path)
	Creates blob from .pb file.
Mat	readTorchBlob (const String &filename, bool isBinary=true)
	Loads blob which was serialized as torch.Tensor object of Torch7 framework.
void	resetMyriadDevice ()
	Release a Myriad device (binded by OpenCV).
static MatShape	shape (const int *dims, const int n)
static MatShape	shape (const Mat &mat)
static MatShape	shape (const MatSize &sz)
static MatShape	shape (const UMat &mat)
static MatShape	shape (int a0, int a1=-1, int a2=-1, int a3=-1)
void	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.
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)
static std::string	toString (const MatShape &shape, const String &name="")
static int	total (const MatShape &shape, int start=-1, int end=-1)
void	writeTextGraph (const String &model, const String &output)
	Create a text representation for a binary network stored in protocol buffer format.

Function Documentation

int cv::dnn::clamp ( int  ax, int  dims  )

inline

int cv::dnn::clamp ( int  ax, const MatShape &  shape  )

inline

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

inline

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

inlinestatic

cv::String cv::dnn::getInferenceEngineVPUType

(

)

Returns Inference Engine VPU type.

See values of CV_DNN_INFERENCE_ENGINE_VPU_TYPE_* macros.

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

inlinestatic

static std::ostream& cv::dnn::operator<< ( std::ostream &  out, const MatShape &  shape  )

inlinestatic

static void cv::dnn::print ( const MatShape &  shape, const String &  name = ""  )

inlinestatic

void cv::dnn::resetMyriadDevice

(

)

Release a Myriad device (binded by OpenCV).

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

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

inlinestatic

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

inlinestatic

static MatShape cv::dnn::shape ( const MatSize &  sz )

inlinestatic

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

inlinestatic

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

inlinestatic

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

inlinestatic

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

inlinestatic

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

inlinestatic

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

inlinestatic

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

inlinestatic

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

inlinestatic