| OpenCV 5.0.0alpha Open Source Computer Vision

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Classes | Namespaces | Typedefs | Enumerations | Functions
dnn.hpp File Reference
`#include <ostream>`
`#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
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::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...

## Namespaces
namespace | cv
namespace | cv::dnn
namespace | cv::dnn::accessor

## Typedefs
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)
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.