dnn.hpp File Reference

#include <vector>
#include <opencv2/core.hpp>
#include "opencv2/core/async.hpp"
#include "../dnn/version.hpp"
#include <opencv2/dnn/dict.hpp>
#include <opencv2/dnn/layer.hpp>
#include <opencv2/dnn/dnn.inl.hpp>
#include <opencv2/dnn/utils/inference_engine.hpp>

Include dependency graph for dnn.hpp:

This graph shows which files directly or indirectly include this file:

Classes
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...
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...

Namespaces
namespace	cv
	"black box" representation of the file storage associated with a file on disk.
namespace	cv::dnn
namespace	cv::dnn::accessor

Typedefs
typedef std::vector< int >	cv::dnn::MatShape
typedef int	cv::dnn::MatType

Enumerations
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::DataLayout {   cv::dnn::DNN_LAYOUT_UNKNOWN = 0 ,   cv::dnn::DNN_LAYOUT_ND = 1 ,   cv::dnn::DNN_LAYOUT_NCHW = 2 ,   cv::dnn::DNN_LAYOUT_NCDHW = 3 ,   cv::dnn::DNN_LAYOUT_NHWC = 4 ,   cv::dnn::DNN_LAYOUT_NDHWC = 5 ,   cv::dnn::DNN_LAYOUT_PLANAR = 6 }
	Enum of data layout for model inference. More...
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 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...

Functions
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)
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>).
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 >())
	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="")
	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)
	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 >())
	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())
	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)
	Reads a network model from ONNX in-memory buffer.
Net	cv::dnn::readNetFromONNX (const std::vector< uchar > &buffer)
	Reads a network model from ONNX in-memory buffer.
Net	cv::dnn::readNetFromONNX (CV_WRAP_FILE_PATH const String &onnxFile)
	Reads a network model ONNX.
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.
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.
Net	cv::dnn::readNetFromTensorflow (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config=String())
	Reads a network model stored in TensorFlow framework's format.
Net	cv::dnn::readNetFromTFLite (const char *bufferModel, size_t lenModel)
	Reads a network model stored in TFLite framework's format.
Net	cv::dnn::readNetFromTFLite (const std::vector< uchar > &bufferModel)
	Reads a network model stored in TFLite framework's format.
Net	cv::dnn::readNetFromTFLite (CV_WRAP_FILE_PATH const String &model)
	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.