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OpenCV
3.4.20
Open Source Computer Vision
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#include <vector>
#include <opencv2/core.hpp>
#include "opencv2/core/async.hpp"
#include <opencv2/dnn/dict.hpp>
#include <opencv2/dnn/layer.hpp>
#include <opencv2/dnn/dnn.inl.hpp>
#include <opencv2/dnn/utils/inference_engine.hpp>
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::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::Net |
This class allows to create and manipulate comprehensive artificial neural networks. More... | |
Namespaces | |
cv | |
cv::dnn | |
Typedefs | |
typedef std::vector< int > | cv::dnn::MatShape |
Enumerations | |
enum | cv::dnn::Backend { cv::dnn::DNN_BACKEND_DEFAULT = 0, cv::dnn::DNN_BACKEND_HALIDE, cv::dnn::DNN_BACKEND_INFERENCE_ENGINE, cv::dnn::DNN_BACKEND_OPENCV } |
Enum of computation backends supported by layers. 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_FPGA } |
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. More... | |
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. More... | |
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. More... | |
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. More... | |
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>). More... | |
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. More... | |
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) |
Net | cv::dnn::readNet (const String &model, const String &config="", const String &framework="") |
Read deep learning network represented in one of the supported formats. More... | |
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. More... | |
Net | cv::dnn::readNetFromCaffe (const String &prototxt, const String &caffeModel=String()) |
Reads a network model stored in Caffe framework's format. More... | |
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. More... | |
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. More... | |
Net | cv::dnn::readNetFromDarknet (const String &cfgFile, const String &darknetModel=String()) |
Reads a network model stored in Darknet model files. More... | |
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. More... | |
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. More... | |
Net | cv::dnn::readNetFromModelOptimizer (const String &xml, const String &bin) |
Load a network from Intel's Model Optimizer intermediate representation. More... | |
Net | cv::dnn::readNetFromModelOptimizer (const std::vector< uchar > &bufferModelConfig, const std::vector< uchar > &bufferWeights) |
Load a network from Intel's Model Optimizer intermediate representation. More... | |
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. More... | |
Net | cv::dnn::readNetFromONNX (const String &onnxFile) |
Reads a network model ONNX. More... | |
Net | cv::dnn::readNetFromONNX (const char *buffer, size_t sizeBuffer) |
Reads a network model from ONNX in-memory buffer. More... | |
Net | cv::dnn::readNetFromONNX (const std::vector< uchar > &buffer) |
Reads a network model from ONNX in-memory buffer. More... | |
Net | cv::dnn::readNetFromTensorflow (const String &model, const String &config=String()) |
Reads a network model stored in TensorFlow framework's format. More... | |
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. More... | |
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. More... | |
Net | cv::dnn::readNetFromTorch (const String &model, bool isBinary=true, bool evaluate=true) |
Reads a network model stored in Torch7 framework's format. More... | |
Mat | cv::dnn::readTensorFromONNX (const String &path) |
Creates blob from .pb file. More... | |
Mat | cv::dnn::readTorchBlob (const String &filename, bool isBinary=true) |
Loads blob which was serialized as torch.Tensor object of Torch7 framework. More... | |
void | cv::dnn::shrinkCaffeModel (const String &src, const String &dst, const std::vector< String > &layersTypes=std::vector< String >()) |
Convert all weights of Caffe network to half precision floating point. More... | |
void | cv::dnn::writeTextGraph (const String &model, const String &output) |
Create a text representation for a binary network stored in protocol buffer format. More... | |