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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.
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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.
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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.
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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.
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Mat | cv::dnn::blobFromImagesWithParams (InputArrayOfArrays images, const Image2BlobParams ¶m=Image2BlobParams()) |
| Creates 4-dimensional blob from series of images with given params.
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void | cv::dnn::blobFromImagesWithParams (InputArrayOfArrays images, OutputArray blob, const Image2BlobParams ¶m=Image2BlobParams()) |
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Mat | cv::dnn::blobFromImageWithParams (InputArray image, const Image2BlobParams ¶m=Image2BlobParams()) |
| Creates 4-dimensional blob from image with given params.
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void | cv::dnn::blobFromImageWithParams (InputArray image, OutputArray blob, const Image2BlobParams ¶m=Image2BlobParams()) |
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void | cv::dnn::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 > > | cv::dnn::getAvailableBackends () |
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std::vector< Target > | cv::dnn::getAvailableTargets (dnn::Backend be) |
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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>).
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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.
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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) |
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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) |
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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.
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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) |
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Net | cv::dnn::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 | 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.
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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.
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Net | cv::dnn::readNetFromONNX (const char *buffer, size_t sizeBuffer) |
| Reads a network model from ONNX in-memory buffer.
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Net | cv::dnn::readNetFromONNX (const std::vector< uchar > &buffer) |
| Reads a network model from ONNX in-memory buffer.
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Net | cv::dnn::readNetFromONNX (CV_WRAP_FILE_PATH const String &onnxFile) |
| Reads a network model ONNX.
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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.
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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.
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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.
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Net | cv::dnn::readNetFromTFLite (const char *bufferModel, size_t lenModel) |
| Reads a network model stored in TFLite framework's format.
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Net | cv::dnn::readNetFromTFLite (const std::vector< uchar > &bufferModel) |
| Reads a network model stored in TFLite framework's format.
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Net | cv::dnn::readNetFromTFLite (CV_WRAP_FILE_PATH const String &model) |
| Reads a network model stored in TFLite framework's format.
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Net | cv::dnn::readNetFromTorch (CV_WRAP_FILE_PATH const String &model, bool isBinary=true, bool evaluate=true) |
| Reads a network model stored in Torch7 framework's format.
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Mat | cv::dnn::readTensorFromONNX (CV_WRAP_FILE_PATH const String &path) |
| Creates blob from .pb file.
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Mat | cv::dnn::readTorchBlob (const String &filename, bool isBinary=true) |
| Loads blob which was serialized as torch.Tensor object of Torch7 framework.
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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.
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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.
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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.
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