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std::string | argKindToString (ArgKind kind) |
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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.
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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.
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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.
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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.
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Mat | blobFromImagesWithParams (InputArrayOfArrays images, const Image2BlobParams ¶m=Image2BlobParams()) |
| Creates 4-dimensional blob from series of images with given params.
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void | blobFromImagesWithParams (InputArrayOfArrays images, OutputArray blob, const Image2BlobParams ¶m=Image2BlobParams()) |
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Mat | blobFromImageWithParams (InputArray image, const Image2BlobParams ¶m=Image2BlobParams()) |
| Creates 4-dimensional blob from image with given params.
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void | blobFromImageWithParams (InputArray image, OutputArray blob, const Image2BlobParams ¶m=Image2BlobParams()) |
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static MatShape | concat (const MatShape &a, const MatShape &b) |
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void | enableModelDiagnostics (bool isDiagnosticsMode) |
| Enables detailed logging of the DNN model loading with CV DNN API.
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std::vector< std::pair< Backend, Target > > | getAvailableBackends () |
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std::vector< Target > | getAvailableTargets (dnn::Backend be) |
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cv::String | getInferenceEngineBackendType () |
| Returns Inference Engine internal backend API.
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cv::String | getInferenceEngineCPUType () |
| Returns Inference Engine CPU type.
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cv::String | getInferenceEngineVPUType () |
| Returns Inference Engine VPU type.
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LayerFactory_Impl & | getLayerFactoryImpl () |
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Mutex & | getLayerFactoryMutex () |
| Get the mutex guarding LayerFactory_Impl, see getLayerFactoryImpl() function.
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static Mat | getPlane (const Mat &m, int n, int cn) |
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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>).
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static bool | isAllOnes (const MatShape &inputShape, int startPos, int endPos) |
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std::string | modelFormatToString (ModelFormat modelFormat) |
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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.
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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) |
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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) |
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void | 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.
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void | 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) |
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static int | normalize_axis (int axis, const MatShape &shape) |
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static int | normalize_axis (int axis, int dims) |
| Converts axis from [-dims; dims) (similar to Python's slice notation) to [0; dims) range.
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static Range | normalize_axis_range (const Range &r, int axisSize) |
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template<typename _Tp > |
static std::ostream & | operator<< (std::ostream &out, const std::vector< _Tp > &shape) |
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static std::ostream & | operator<< (std::ostream &strm, const MatShape &shape) |
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template<typename _Tp > |
static void | print (const std::vector< _Tp > &shape, const String &name="") |
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Net | 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.
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Net | 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.
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Net | 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.
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Net | 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.
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Net | 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.
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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.
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Net | readNetFromDarknet (const std::vector< uchar > &bufferCfg, const std::vector< uchar > &bufferModel=std::vector< uchar >()) |
| Reads a network model stored in Darknet model files.
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Net | 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.
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Net | readNetFromModelOptimizer (const std::vector< uchar > &bufferModelConfig, const std::vector< uchar > &bufferWeights) |
| Load a network from Intel's Model Optimizer intermediate representation.
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Net | readNetFromModelOptimizer (const uchar *bufferModelConfigPtr, size_t bufferModelConfigSize, const uchar *bufferWeightsPtr, size_t bufferWeightsSize) |
| Load a network from Intel's Model Optimizer intermediate representation.
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Net | 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.
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Net | readNetFromONNX (const char *buffer, size_t sizeBuffer, int engine=ENGINE_AUTO) |
| Reads a network model from ONNX in-memory buffer.
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Net | readNetFromONNX (const std::vector< uchar > &buffer, int engine=ENGINE_AUTO) |
| Reads a network model from ONNX in-memory buffer.
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Net | readNetFromONNX (CV_WRAP_FILE_PATH const String &onnxFile, int engine=ENGINE_AUTO) |
| Reads a network model ONNX.
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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.
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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.
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Net | 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.
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Net | readNetFromTFLite (const char *bufferModel, size_t lenModel, int engine=ENGINE_AUTO) |
| Reads a network model stored in TFLite framework's format.
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Net | readNetFromTFLite (const std::vector< uchar > &bufferModel, int engine=ENGINE_AUTO) |
| Reads a network model stored in TFLite framework's format.
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Net | readNetFromTFLite (CV_WRAP_FILE_PATH const String &model, int engine=ENGINE_AUTO) |
| Reads a network model stored in TFLite framework's format.
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Mat | readTensorFromONNX (CV_WRAP_FILE_PATH const String &path) |
| Creates blob from .pb file.
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void | releaseHDDLPlugin () |
| Release a HDDL plugin.
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void | resetMyriadDevice () |
| Release a Myriad device (binded by OpenCV).
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cv::String | setInferenceEngineBackendType (const cv::String &newBackendType) |
| Specify Inference Engine internal backend API.
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static MatShape | shape (const int *dims, const int n) |
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static MatShape | shape (const Mat &mat) |
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static MatShape | shape (const UMat &mat) |
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static MatShape | shape (int a0, int a1=-1, int a2=-1, int a3=-1) |
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void | 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.
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void | skipModelImport (bool skip) |
| Skip model import after diagnostic run in readNet() functions.
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static Mat | slice (const Mat &m, const _Range &r0) |
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static Mat | slice (const Mat &m, const _Range &r0, const _Range &r1) |
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static Mat | slice (const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2) |
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static Mat | slice (const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2, const _Range &r3) |
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void | 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.
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static std::string | toString (const MatShape &shape, const String &name="") |
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template<typename _Tp > |
static std::string | toString (const std::vector< _Tp > &shape, const String &name="") |
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static int | total (const Mat &mat, int start=-1, int end=-1) |
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static int | total (const MatShape &shape, int start=-1, int end=-1) |
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void | 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.
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