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Cv::Dnn

Singleton Methods

all_ones?(input_shape: Std::Vector≺int≻, start_pos: Integer, end_pos: Integer) -> TrueClass

blob_from_image(image: Cv::InputArray, scalefactor: Float, size: Cv::Size, mean: Cv::Scalar, swap_rb: TrueClass, crop: TrueClass, ddepth: Integer) -> Cv::Mat

blob_from_image(image: Cv::InputArray, blob: Cv::OutputArray, scalefactor: Float, size: Cv::Size, mean: Cv::Scalar, swap_rb: TrueClass, crop: TrueClass, ddepth: Integer) -> NilClass

blob_from_image_with_params(image: Cv::InputArray, param: Cv::Dnn::Image2BlobParams) -> Cv::Mat

blob_from_image_with_params(image: Cv::InputArray, blob: Cv::OutputArray, param: Cv::Dnn::Image2BlobParams) -> NilClass

blob_from_images(images: Cv::InputArray, scalefactor: Float, size: Cv::Size, mean: Cv::Scalar, swap_rb: TrueClass, crop: TrueClass, ddepth: Integer) -> Cv::Mat

blob_from_images(images: Cv::InputArray, blob: Cv::OutputArray, scalefactor: Float, size: Cv::Size, mean: Cv::Scalar, swap_rb: TrueClass, crop: TrueClass, ddepth: Integer) -> NilClass

blob_from_images_with_params(images: Cv::InputArray, param: Cv::Dnn::Image2BlobParams) -> Cv::Mat

blob_from_images_with_params(images: Cv::InputArray, blob: Cv::OutputArray, param: Cv::Dnn::Image2BlobParams) -> NilClass

concat(a: Std::Vector≺int≻, b: Std::Vector≺int≻) -> Std::Vector≺int≻

enable_model_diagnostics(is_diagnostics_mode: TrueClass) -> NilClass

get_available_backends -> Std::Vector≺pair≺enum cv꞉꞉dnn꞉꞉dnn4V20241223꞉꞉Backend‚ enum cv꞉꞉dnn꞉꞉dnn4V20241223꞉꞉Target≻≻

get_available_targets(be: Cv::Dnn::Backend) -> Std::Vector≺enum cv꞉꞉dnn꞉꞉dnn4V20241223꞉꞉Target≻

get_plane(m: Cv::Mat, n: Integer, cn: Integer) -> Cv::Mat

images_from_blob(blob_: Cv::Mat, images_: Cv::OutputArray) -> NilClass

nms_boxes(bboxes: Std::Vector≺cv꞉꞉Rect_≺int≻≻, scores: Std::Vector≺float≻, score_threshold: Float, nms_threshold: Float, indices: Std::Vector≺int≻, eta: Float, top_k: Integer) -> NilClass

nms_boxes(bboxes: Std::Vector≺cv꞉꞉Rect_≺double≻≻, scores: Std::Vector≺float≻, score_threshold: Float, nms_threshold: Float, indices: Std::Vector≺int≻, eta: Float, top_k: Integer) -> NilClass

nms_boxes(bboxes: Std::Vector≺cv꞉꞉RotatedRect≻, scores: Std::Vector≺float≻, score_threshold: Float, nms_threshold: Float, indices: Std::Vector≺int≻, eta: Float, top_k: Integer) -> NilClass

nms_boxes_batched(bboxes: Std::Vector≺cv꞉꞉Rect_≺double≻≻, scores: Std::Vector≺float≻, class_ids: Std::Vector≺int≻, score_threshold: Float, nms_threshold: Float, indices: Std::Vector≺int≻, eta: Float, top_k: Integer) -> NilClass

nms_boxes_batched(bboxes: Std::Vector≺cv꞉꞉Rect_≺int≻≻, scores: Std::Vector≺float≻, class_ids: Std::Vector≺int≻, score_threshold: Float, nms_threshold: Float, indices: Std::Vector≺int≻, eta: Float, top_k: Integer) -> NilClass

normalize_axis(axis: Integer, dims: Integer) -> Integer

normalize_axis(axis: Integer, shape: Std::Vector≺int≻) -> Integer

normalize_axis_range(r: Cv::Range, axis_size: Integer) -> Cv::Range

read_net(framework: String, buffer_model: Std::Vector≺unsigned char≻, buffer_config: Std::Vector≺unsigned char≻) -> Cv::Dnn::Net

read_net(model: String, config: String, framework: String) -> Cv::Dnn::Net

read_net_from_caffe(buffer_proto: Std::Vector≺unsigned char≻, buffer_model: Std::Vector≺unsigned char≻) -> Cv::Dnn::Net

read_net_from_caffe(prototxt: String, caffe_model: String) -> Cv::Dnn::Net

read_net_from_caffe(buffer_proto: String, len_proto: Integer, buffer_model: String, len_model: Integer) -> Cv::Dnn::Net

read_net_from_darknet(buffer_cfg: Std::Vector≺unsigned char≻, buffer_model: Std::Vector≺unsigned char≻) -> Cv::Dnn::Net

read_net_from_darknet(cfg_file: String, darknet_model: String) -> Cv::Dnn::Net

read_net_from_darknet(buffer_cfg: String, len_cfg: Integer, buffer_model: String, len_model: Integer) -> Cv::Dnn::Net

read_net_from_model_optimizer(buffer_model_config: Std::Vector≺unsigned char≻, buffer_weights: Std::Vector≺unsigned char≻) -> Cv::Dnn::Net

read_net_from_model_optimizer(xml: String, bin: String) -> Cv::Dnn::Net

read_net_from_model_optimizer(buffer_model_config_ptr: Rice::Pointer≺unsigned char≻, buffer_model_config_size: Integer, buffer_weights_ptr: Rice::Pointer≺unsigned char≻, buffer_weights_size: Integer) -> Cv::Dnn::Net

read_net_from_onnx(buffer: Std::Vector≺unsigned char≻) -> Cv::Dnn::Net

read_net_from_onnx(onnx_file: String) -> Cv::Dnn::Net

read_net_from_onnx(buffer: String, size_buffer: Integer) -> Cv::Dnn::Net

read_net_from_tensorflow(model: String, config: String) -> Cv::Dnn::Net

read_net_from_tensorflow(buffer_model: Std::Vector≺unsigned char≻, buffer_config: Std::Vector≺unsigned char≻) -> Cv::Dnn::Net

read_net_from_tensorflow(buffer_model: String, len_model: Integer, buffer_config: String, len_config: Integer) -> Cv::Dnn::Net

read_net_from_tf_lite(model: String) -> Cv::Dnn::Net

read_net_from_tf_lite(buffer_model: Std::Vector≺unsigned char≻) -> Cv::Dnn::Net

read_net_from_tf_lite(buffer_model: String, len_model: Integer) -> Cv::Dnn::Net

read_net_from_torch(model: String, is_binary: TrueClass, evaluate: TrueClass) -> Cv::Dnn::Net

read_tensor_from_onnx(path: String) -> Cv::Mat

read_torch_blob(filename: String, is_binary: TrueClass) -> Cv::Mat

shape(sz: Cv::MatSize) -> Std::Vector≺int≻

shape(mat: Cv::Mat) -> Std::Vector≺int≻

shape(mat: Cv::UMat) -> Std::Vector≺int≻

shape(dims: Rice::Pointer≺int≻, n: Integer) -> Std::Vector≺int≻

shape(a0: Integer, a1: Integer, a2: Integer, a3: Integer) -> Std::Vector≺int≻

shrink_caffe_model(src: String, dst: String, layers_types: Std::Vector≺string≻) -> NilClass

slice(m: Cv::Mat, r0: Cv::Dnn::Range) -> Cv::Mat

slice(m: Cv::Mat, r0: Cv::Dnn::Range, r1: Cv::Dnn::Range) -> Cv::Mat

slice(m: Cv::Mat, r0: Cv::Dnn::Range, r1: Cv::Dnn::Range, r2: Cv::Dnn::Range) -> Cv::Mat

slice(m: Cv::Mat, r0: Cv::Dnn::Range, r1: Cv::Dnn::Range, r2: Cv::Dnn::Range, r3: Cv::Dnn::Range) -> Cv::Mat

soft_nms_boxes(bboxes: Std::Vector≺cv꞉꞉Rect_≺int≻≻, scores: Std::Vector≺float≻, updated_scores: Std::Vector≺float≻, score_threshold: Float, nms_threshold: Float, indices: Std::Vector≺int≻, top_k: Integer, sigma: Float, method: Cv::Dnn::SoftNMSMethod) -> NilClass

total(mat: Cv::Mat, start: Integer, end: Integer) -> Integer

total(shape: Std::Vector≺int≻, start: Integer, end: Integer) -> Integer

write_text_graph(model: String, output: String) -> NilClass