dnn.hpp#

#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 dnn.hpp:

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…

class cv::dnn::Tokenizer

High-level tokenizer wrapper for DNN usage. More…

Namespaces#

namespace cv

namespace cv::dnn

namespace cv::dnn::accessor

Enumerations#

enum cv::dnn::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 cv::dnn::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 cv::dnn::EngineType {
ENGINE_CLASSIC =1,
ENGINE_NEW =2,
ENGINE_AUTO =3,
ENGINE_ORT =4
}

enum cv::dnn::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 cv::dnn::ModelFormat {
DNN_MODEL_GENERIC = 0,
DNN_MODEL_ONNX = 1,
DNN_MODEL_TF = 2,
DNN_MODEL_TFLITE = 3
}

enum cv::dnn::ProfilingMode {
DNN_PROFILE_NONE = 0,
DNN_PROFILE_SUMMARY = 1,
DNN_PROFILE_DETAILED = 2
}

enum cv::dnn::SoftNMSMethod {
SOFTNMS_LINEAR = 1,
SOFTNMS_GAUSSIAN = 2
}

Enum of Soft NMS methods. More…

enum cv::dnn::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 cv::dnn::TracingMode {
DNN_TRACE_NONE = 0,
DNN_TRACE_ALL = 1,
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::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::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.