OpenCVForUnity.DnnModule.Dnn Class Reference

Static Public Member Functions
static Mat	blobFromImage (Mat image)
	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.
static Mat	blobFromImage (Mat image, double scalefactor)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in Vec2d size)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in Vec2d size, in Vec4d mean)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB, bool crop)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB, bool crop, int ddepth)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in(double width, double height) size)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB, bool crop)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB, bool crop, int ddepth)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, Size size)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, Size size, Scalar mean)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, Size size, Scalar mean, bool swapRB)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop)
	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.
static Mat	blobFromImage (Mat image, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop, int ddepth)
	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.
static Mat	blobFromImages (List< Mat > images)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in Vec2d size)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in Vec2d size, in Vec4d mean)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB, bool crop)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB, bool crop, int ddepth)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in(double width, double height) size)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB, bool crop)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB, bool crop, int ddepth)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, Size size)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, Size size, Scalar mean)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, Size size, Scalar mean, bool swapRB)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop)
	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.
static Mat	blobFromImages (List< Mat > images, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop, int ddepth)
	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.
static Mat	blobFromImagesWithParams (List< Mat > images)
	Creates 4-dimensional blob from series of images with given params.
static Mat	blobFromImagesWithParams (List< Mat > images, Image2BlobParams param)
	Creates 4-dimensional blob from series of images with given params.
static void	blobFromImagesWithParams (List< Mat > images, Mat blob)
static void	blobFromImagesWithParams (List< Mat > images, Mat blob, Image2BlobParams param)
static Mat	blobFromImageWithParams (Mat image)
	Creates 4-dimensional blob from image with given params.
static Mat	blobFromImageWithParams (Mat image, Image2BlobParams param)
	Creates 4-dimensional blob from image with given params.
static void	blobFromImageWithParams (Mat image, Mat blob)
static void	blobFromImageWithParams (Mat image, Mat blob, Image2BlobParams param)
static List< int >	getAvailableTargets (int be)
static string	getInferenceEngineBackendType ()
	Returns Inference Engine internal backend API.
static string	getInferenceEngineCPUType ()
	Returns Inference Engine CPU type.
static string	getInferenceEngineVPUType ()
	Returns Inference Engine VPU type.
static void	imagesFromBlob (Mat blob_, List< Mat > images_)
	Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vector<cv::Mat>).
static void	NMSBoxes (MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
	Performs non maximum suppression given boxes and corresponding scores.
static void	NMSBoxes (MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
	Performs non maximum suppression given boxes and corresponding scores.
static void	NMSBoxes (MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
	Performs non maximum suppression given boxes and corresponding scores.
static void	NMSBoxesBatched (MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices)
	Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
static void	NMSBoxesBatched (MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
	Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
static void	NMSBoxesBatched (MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
	Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
static void	NMSBoxesRotated (MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
static void	NMSBoxesRotated (MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
static void	NMSBoxesRotated (MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
static Net	readNet (string framework, MatOfByte bufferModel)
	Read deep learning network represented in one of the supported formats.
static Net	readNet (string framework, MatOfByte bufferModel, MatOfByte bufferConfig)
	Read deep learning network represented in one of the supported formats.
static Net	readNet (string framework, MatOfByte bufferModel, MatOfByte bufferConfig, int engine)
	Read deep learning network represented in one of the supported formats.
static Net	readNet (string model)
	Read deep learning network represented in one of the supported formats.
static Net	readNet (string model, string config)
	Read deep learning network represented in one of the supported formats.
static Net	readNet (string model, string config, string framework)
	Read deep learning network represented in one of the supported formats.
static Net	readNet (string model, string config, string framework, int engine)
	Read deep learning network represented in one of the supported formats.
static Net	readNetFromModelOptimizer (MatOfByte bufferModelConfig, MatOfByte bufferWeights)
	Load a network from Intel's Model Optimizer intermediate representation.
static Net	readNetFromModelOptimizer (string xml)
	Load a network from Intel's Model Optimizer intermediate representation.
static Net	readNetFromModelOptimizer (string xml, string bin)
	Load a network from Intel's Model Optimizer intermediate representation.
static Net	readNetFromONNX (MatOfByte buffer)
	Reads a network model from ONNX in-memory buffer.
static Net	readNetFromONNX (MatOfByte buffer, int engine)
	Reads a network model from ONNX in-memory buffer.
static Net	readNetFromONNX (string onnxFile)
	Reads a network model ONNX.
static Net	readNetFromONNX (string onnxFile, int engine)
	Reads a network model ONNX.
static Net	readNetFromTensorflow (MatOfByte bufferModel)
	Reads a network model stored in TensorFlow framework's format.
static Net	readNetFromTensorflow (MatOfByte bufferModel, MatOfByte bufferConfig)
	Reads a network model stored in TensorFlow framework's format.
static Net	readNetFromTensorflow (MatOfByte bufferModel, MatOfByte bufferConfig, int engine)
	Reads a network model stored in TensorFlow framework's format.
static Net	readNetFromTensorflow (MatOfByte bufferModel, MatOfByte bufferConfig, int engine, List< string > extraOutputs)
	Reads a network model stored in TensorFlow framework's format.
static Net	readNetFromTensorflow (string model)
	Reads a network model stored in TensorFlow framework's format.
static Net	readNetFromTensorflow (string model, string config)
	Reads a network model stored in TensorFlow framework's format.
static Net	readNetFromTensorflow (string model, string config, int engine)
	Reads a network model stored in TensorFlow framework's format.
static Net	readNetFromTensorflow (string model, string config, int engine, List< string > extraOutputs)
	Reads a network model stored in TensorFlow framework's format.
static Net	readNetFromTFLite (MatOfByte bufferModel)
	Reads a network model stored in TFLite framework's format.
static Net	readNetFromTFLite (MatOfByte bufferModel, int engine)
	Reads a network model stored in TFLite framework's format.
static Net	readNetFromTFLite (string model)
	Reads a network model stored in TFLite framework's format.
static Net	readNetFromTFLite (string model, int engine)
	Reads a network model stored in TFLite framework's format.
static Mat	readTensorFromONNX (string path)
	Creates blob from .pb file.
static void	releaseHDDLPlugin ()
	Release a HDDL plugin.
static void	resetMyriadDevice ()
	Release a Myriad device (binded by OpenCV).
static string	setInferenceEngineBackendType (string newBackendType)
	Specify Inference Engine internal backend API.
static void	softNMSBoxes (MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices)
	Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503.
static void	softNMSBoxes (MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k)
	Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503.
static void	softNMSBoxes (MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k, float sigma)
	Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503.
static void	writeTextGraph (string model, string output)
	Create a text representation for a binary network stored in protocol buffer format.

Static Public Attributes
const int	ACTIV_CLIP = 0 + 11
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_ELU = 0 + 5
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_GELU = 0 + 8
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_GELU_APPROX = 0 + 9
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_HARDSIGMOID = 0 + 7
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_HARDSWISH = 0 + 6
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_MISH = 0 + 1
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_NONE = 0
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_RELU = 0 + 10
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_SIGMOID = 0 + 3
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_SWISH = 0 + 2
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	ACTIV_TANH = 0 + 4
	C++: enum ActivationType (cv.dnn.ActivationType)
const int	AUTO_PAD_NONE = 0
	C++: enum AutoPadding (cv.dnn.AutoPadding)
const int	AUTO_PAD_SAME_LOWER = 2
	C++: enum AutoPadding (cv.dnn.AutoPadding)
const int	AUTO_PAD_SAME_UPPER = 1
	C++: enum AutoPadding (cv.dnn.AutoPadding)
const int	AUTO_PAD_VALID = 3
	C++: enum AutoPadding (cv.dnn.AutoPadding)
const int	DNN_ARG_CONST = 1
	C++: enum ArgKind (cv.dnn.ArgKind)
const int	DNN_ARG_EMPTY = 0
	C++: enum ArgKind (cv.dnn.ArgKind)
const int	DNN_ARG_INPUT = 2
	C++: enum ArgKind (cv.dnn.ArgKind)
const int	DNN_ARG_OUTPUT = 3
	C++: enum ArgKind (cv.dnn.ArgKind)
const int	DNN_ARG_PATTERN = 5
	C++: enum ArgKind (cv.dnn.ArgKind)
const int	DNN_ARG_TEMP = 4
	C++: enum ArgKind (cv.dnn.ArgKind)
const int	DNN_BACKEND_CANN = 2 + 6
	C++: enum Backend (cv.dnn.Backend)
const int	DNN_BACKEND_CUDA = 2 + 3
	C++: enum Backend (cv.dnn.Backend)
const int	DNN_BACKEND_DEFAULT = 0
	C++: enum Backend (cv.dnn.Backend)
const int	DNN_BACKEND_INFERENCE_ENGINE = 2
	C++: enum Backend (cv.dnn.Backend)
const int	DNN_BACKEND_OPENCV = 2 + 1
	C++: enum Backend (cv.dnn.Backend)
const int	DNN_BACKEND_TIMVX = 2 + 5
	C++: enum Backend (cv.dnn.Backend)
const int	DNN_BACKEND_VKCOM = 2 + 2
	C++: enum Backend (cv.dnn.Backend)
const int	DNN_BACKEND_WEBNN = 2 + 4
	C++: enum Backend (cv.dnn.Backend)
const int	DNN_MODEL_GENERIC = 0
	C++: enum ModelFormat (cv.dnn.ModelFormat)
const int	DNN_MODEL_ONNX = 1
	C++: enum ModelFormat (cv.dnn.ModelFormat)
const int	DNN_MODEL_TF = 2
	C++: enum ModelFormat (cv.dnn.ModelFormat)
const int	DNN_MODEL_TFLITE = 3
	C++: enum ModelFormat (cv.dnn.ModelFormat)
const int	DNN_PMODE_CROP_CENTER = 1
	C++: enum ImagePaddingMode (cv.dnn.ImagePaddingMode)
const int	DNN_PMODE_LETTERBOX = 2
	C++: enum ImagePaddingMode (cv.dnn.ImagePaddingMode)
const int	DNN_PMODE_NULL = 0
	C++: enum ImagePaddingMode (cv.dnn.ImagePaddingMode)
const int	DNN_PROFILE_DETAILED = 2
	C++: enum ProfilingMode (cv.dnn.ProfilingMode)
const int	DNN_PROFILE_NONE = 0
	C++: enum ProfilingMode (cv.dnn.ProfilingMode)
const int	DNN_PROFILE_SUMMARY = 1
	C++: enum ProfilingMode (cv.dnn.ProfilingMode)
const int	DNN_TARGET_CPU = 0
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_CPU_FP16 = 0 + 10
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_CUDA = 0 + 6
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_CUDA_FP16 = 0 + 7
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_FPGA = 0 + 5
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_HDDL = 0 + 8
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_MYRIAD = 0 + 3
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_NPU = 0 + 9
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_OPENCL = 0 + 1
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_OPENCL_FP16 = 0 + 2
	C++: enum Target (cv.dnn.Target)
const int	DNN_TARGET_VULKAN = 0 + 4
	C++: enum Target (cv.dnn.Target)
const int	DNN_TRACE_ALL = 1
	C++: enum TracingMode (cv.dnn.TracingMode)
const int	DNN_TRACE_NONE = 0
	C++: enum TracingMode (cv.dnn.TracingMode)
const int	DNN_TRACE_OP = 2
	C++: enum TracingMode (cv.dnn.TracingMode)
const int	ENGINE_AUTO = 3
	C++: enum EngineType (cv.dnn.EngineType)
const int	ENGINE_CLASSIC = 1
	C++: enum EngineType (cv.dnn.EngineType)
const int	ENGINE_NEW = 2
	C++: enum EngineType (cv.dnn.EngineType)
const int	ENGINE_ORT = 4
	C++: enum EngineType (cv.dnn.EngineType)
const int	LOSS_REDUCTION_MEAN = 1
	C++: enum LossReduction (cv.dnn.LossReduction)
const int	LOSS_REDUCTION_NONE = 0
	C++: enum LossReduction (cv.dnn.LossReduction)
const int	LOSS_REDUCTION_SUM = 2
	C++: enum LossReduction (cv.dnn.LossReduction)
const int	OPERATION_ADD = 0 + 18
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_AND = 0
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_BITSHIFT = 0 + 9
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_BITWISE_AND = 0 + 21
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_BITWISE_OR = 0 + 22
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_BITWISE_XOR = 0 + 23
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_DIV = 0 + 19
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_EQUAL = 0 + 1
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_FMOD = 0 + 14
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_GREATER = 0 + 2
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_GREATER_EQUAL = 0 + 3
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_LESS = 0 + 4
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_LESS_EQUAL = 0 + 5
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_MAX = 0 + 10
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_MEAN = 0 + 11
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_MIN = 0 + 12
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_MOD = 0 + 13
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_OR = 0 + 6
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_POW = 0 + 7
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_PROD = 0 + 15
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_SUB = 0 + 16
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_SUM = 0 + 17
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_WHERE = 0 + 20
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	OPERATION_XOR = 0 + 8
	C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)
const int	ReduceType_L1 = 4
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_L2 = 5
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_LOG_SUM = 8
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_LOG_SUM_EXP = 9
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_MAX = 0
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_MEAN = 2
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_MIN = 1
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_PROD = 6
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_SUM = 3
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	ReduceType_SUM_SQUARE = 7
	C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)
const int	SoftNMSMethod_SOFTNMS_GAUSSIAN = 2
	C++: enum SoftNMSMethod (cv.dnn.SoftNMSMethod)
const int	SoftNMSMethod_SOFTNMS_LINEAR = 1
	C++: enum SoftNMSMethod (cv.dnn.SoftNMSMethod)

Member Function Documentation

blobFromImage() [1/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [2/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [3/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in Vec2d size )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [4/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in Vec2d size, in Vec4d mean )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [5/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [6/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB, bool crop )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [7/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB, bool crop, int ddepth )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [8/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in(double width, double height) size )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [9/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [10/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [11/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB, bool crop )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [12/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB, bool crop, int ddepth )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [13/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, Size size )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [14/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, Size size, Scalar mean )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [15/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, Size size, Scalar mean, bool swapRB )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [16/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImage() [17/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImage ( Mat image, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop, int ddepth )

static

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.

Parameters

image	input image (with 1-, 3- or 4-channels).
scalefactor	multiplier for images values.
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [1/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [2/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [3/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in Vec2d size )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [4/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in Vec2d size, in Vec4d mean )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [5/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [6/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB, bool crop )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [7/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in Vec2d size, in Vec4d mean, bool swapRB, bool crop, int ddepth )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [8/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in(double width, double height) size )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [9/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [10/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [11/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB, bool crop )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [12/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, in(double width, double height) size, in(double v0, double v1, double v2, double v3) mean, bool swapRB, bool crop, int ddepth )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [13/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, Size size )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [14/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, Size size, Scalar mean )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [15/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, Size size, Scalar mean, bool swapRB )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [16/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImages() [17/17]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImages ( List< Mat > images, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop, int ddepth )

static

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.

Parameters

images	input images (all with 1-, 3- or 4-channels).
size	spatial size for output image
mean	scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
scalefactor	multiplier for images values.
swapRB	flag which indicates that swap first and last channels in 3-channel image is necessary.
crop	flag which indicates whether image will be cropped after resize or not
ddepth	Depth of output blob. Choose CV_32F or CV_8U.

if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.

Returns

4-dimensional Mat with NCHW dimensions order.

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor.

blobFromImagesWithParams() [1/4]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImagesWithParams ( List< Mat > images )

static

Creates 4-dimensional blob from series of images with given params.

This function is an extension of blobFromImages to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.

Parameters

images	input image (all with 1-, 3- or 4-channels).
param	struct of Image2BlobParams, contains all parameters needed by processing of image to blob.

Returns

4-dimensional Mat.

blobFromImagesWithParams() [2/4]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImagesWithParams ( List< Mat > images, Image2BlobParams param )

static

Creates 4-dimensional blob from series of images with given params.

This function is an extension of blobFromImages to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.

Parameters

images	input image (all with 1-, 3- or 4-channels).
param	struct of Image2BlobParams, contains all parameters needed by processing of image to blob.

Returns

4-dimensional Mat.

blobFromImagesWithParams() [3/4]

static void OpenCVForUnity.DnnModule.Dnn.blobFromImagesWithParams ( List< Mat > images, Mat blob )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

blobFromImagesWithParams() [4/4]

static void OpenCVForUnity.DnnModule.Dnn.blobFromImagesWithParams ( List< Mat > images, Mat blob, Image2BlobParams param )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

blobFromImageWithParams() [1/4]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImageWithParams ( Mat image )

static

Creates 4-dimensional blob from image with given params.

This function is an extension of blobFromImage to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.

Parameters

image	input image (all with 1-, 3- or 4-channels).
param	struct of Image2BlobParams, contains all parameters needed by processing of image to blob.

Returns

4-dimensional Mat.

blobFromImageWithParams() [2/4]

static Mat OpenCVForUnity.DnnModule.Dnn.blobFromImageWithParams ( Mat image, Image2BlobParams param )

static

Creates 4-dimensional blob from image with given params.

This function is an extension of blobFromImage to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.

Parameters

image	input image (all with 1-, 3- or 4-channels).
param	struct of Image2BlobParams, contains all parameters needed by processing of image to blob.

Returns

4-dimensional Mat.

blobFromImageWithParams() [3/4]

static void OpenCVForUnity.DnnModule.Dnn.blobFromImageWithParams ( Mat image, Mat blob )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

blobFromImageWithParams() [4/4]

static void OpenCVForUnity.DnnModule.Dnn.blobFromImageWithParams ( Mat image, Mat blob, Image2BlobParams param )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

getAvailableTargets()

static List< int > OpenCVForUnity.DnnModule.Dnn.getAvailableTargets ( int be )

static

getInferenceEngineBackendType()

static string OpenCVForUnity.DnnModule.Dnn.getInferenceEngineBackendType ( )

static

Returns Inference Engine internal backend API.

See values of CV_DNN_BACKEND_INFERENCE_ENGINE_* macros.

OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE runtime parameter (environment variable) is ignored since 4.6.0.

Deprecated

getInferenceEngineCPUType()

static string OpenCVForUnity.DnnModule.Dnn.getInferenceEngineCPUType ( )

static

Returns Inference Engine CPU type.

Specify OpenVINO plugin: CPU or ARM.

getInferenceEngineVPUType()

static string OpenCVForUnity.DnnModule.Dnn.getInferenceEngineVPUType ( )

static

Returns Inference Engine VPU type.

See values of CV_DNN_INFERENCE_ENGINE_VPU_TYPE_* macros.

imagesFromBlob()

static void OpenCVForUnity.DnnModule.Dnn.imagesFromBlob ( Mat blob_, List< Mat > images_ )

static

Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vector<cv::Mat>).

Parameters

blob_	4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from which you would like to extract the images.
images_	array of 2D Mat containing the images extracted from the blob in floating point precision (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).

NMSBoxes() [1/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxes ( MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices )

static

Performs non maximum suppression given boxes and corresponding scores.

Parameters

bboxes	a set of bounding boxes to apply NMS.
scores	a set of corresponding confidences.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
eta	a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
top_k	if >0, keep at most top_k picked indices.

NMSBoxes() [2/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxes ( MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta )

static

Performs non maximum suppression given boxes and corresponding scores.

Parameters

bboxes	a set of bounding boxes to apply NMS.
scores	a set of corresponding confidences.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
eta	a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
top_k	if >0, keep at most top_k picked indices.

NMSBoxes() [3/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxes ( MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k )

static

Performs non maximum suppression given boxes and corresponding scores.

Parameters

bboxes	a set of bounding boxes to apply NMS.
scores	a set of corresponding confidences.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
eta	a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
top_k	if >0, keep at most top_k picked indices.

NMSBoxesBatched() [1/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxesBatched ( MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices )

static

Performs batched non maximum suppression on given boxes and corresponding scores across different classes.

Parameters

bboxes	a set of bounding boxes to apply NMS.
scores	a set of corresponding confidences.
class_ids	a set of corresponding class ids. Ids are integer and usually start from 0.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
eta	a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
top_k	if >0, keep at most top_k picked indices.

NMSBoxesBatched() [2/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxesBatched ( MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta )

static

Performs batched non maximum suppression on given boxes and corresponding scores across different classes.

Parameters

bboxes	a set of bounding boxes to apply NMS.
scores	a set of corresponding confidences.
class_ids	a set of corresponding class ids. Ids are integer and usually start from 0.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
eta	a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
top_k	if >0, keep at most top_k picked indices.

NMSBoxesBatched() [3/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxesBatched ( MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k )

static

Performs batched non maximum suppression on given boxes and corresponding scores across different classes.

Parameters

bboxes	a set of bounding boxes to apply NMS.
scores	a set of corresponding confidences.
class_ids	a set of corresponding class ids. Ids are integer and usually start from 0.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
eta	a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
top_k	if >0, keep at most top_k picked indices.

NMSBoxesRotated() [1/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxesRotated ( MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices )

static

NMSBoxesRotated() [2/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxesRotated ( MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta )

static

NMSBoxesRotated() [3/3]

static void OpenCVForUnity.DnnModule.Dnn.NMSBoxesRotated ( MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k )

static

readNet() [1/7]

static Net OpenCVForUnity.DnnModule.Dnn.readNet ( string framework, MatOfByte bufferModel )

static

Read deep learning network represented in one of the supported formats.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

framework	Name of origin framework.
bufferModel	A buffer with a content of binary file with weights
bufferConfig	A buffer with a content of text file contains network configuration.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

readNet() [2/7]

static Net OpenCVForUnity.DnnModule.Dnn.readNet ( string framework, MatOfByte bufferModel, MatOfByte bufferConfig )

static

Read deep learning network represented in one of the supported formats.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

framework	Name of origin framework.
bufferModel	A buffer with a content of binary file with weights
bufferConfig	A buffer with a content of text file contains network configuration.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

readNet() [3/7]

static Net OpenCVForUnity.DnnModule.Dnn.readNet ( string framework, MatOfByte bufferModel, MatOfByte bufferConfig, int engine )

static

Read deep learning network represented in one of the supported formats.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

framework	Name of origin framework.
bufferModel	A buffer with a content of binary file with weights
bufferConfig	A buffer with a content of text file contains network configuration.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

readNet() [4/7]

static Net OpenCVForUnity.DnnModule.Dnn.readNet ( string model )

static

Read deep learning network represented in one of the supported formats.

Parameters

model	Binary file contains trained weights. The following file extensions are expected for models from different frameworks: *.pb (TensorFlow, https://www.tensorflow.org/) *.bin | *.onnx (OpenVINO, https://software.intel.com/openvino-toolkit) *.onnx (ONNX, https://onnx.ai/)
config	Text file contains network configuration. It could be a file with the following extensions: *.pbtxt (TensorFlow, https://www.tensorflow.org/) *.xml (OpenVINO, https://software.intel.com/openvino-toolkit)
framework	Explicit framework name tag to determine a format.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

This function automatically detects an origin framework of trained model and calls an appropriate function such readNetFromTensorflow, readNetFromONNX. An order of model and config arguments does not matter.

readNet() [5/7]

static Net OpenCVForUnity.DnnModule.Dnn.readNet ( string model, string config )

static

Read deep learning network represented in one of the supported formats.

Parameters

model	Binary file contains trained weights. The following file extensions are expected for models from different frameworks: *.pb (TensorFlow, https://www.tensorflow.org/) *.bin | *.onnx (OpenVINO, https://software.intel.com/openvino-toolkit) *.onnx (ONNX, https://onnx.ai/)
config	Text file contains network configuration. It could be a file with the following extensions: *.pbtxt (TensorFlow, https://www.tensorflow.org/) *.xml (OpenVINO, https://software.intel.com/openvino-toolkit)
framework	Explicit framework name tag to determine a format.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

This function automatically detects an origin framework of trained model and calls an appropriate function such readNetFromTensorflow, readNetFromONNX. An order of model and config arguments does not matter.

readNet() [6/7]

static Net OpenCVForUnity.DnnModule.Dnn.readNet ( string model, string config, string framework )

static

Read deep learning network represented in one of the supported formats.

Parameters

model	Binary file contains trained weights. The following file extensions are expected for models from different frameworks: *.pb (TensorFlow, https://www.tensorflow.org/) *.bin | *.onnx (OpenVINO, https://software.intel.com/openvino-toolkit) *.onnx (ONNX, https://onnx.ai/)
config	Text file contains network configuration. It could be a file with the following extensions: *.pbtxt (TensorFlow, https://www.tensorflow.org/) *.xml (OpenVINO, https://software.intel.com/openvino-toolkit)
framework	Explicit framework name tag to determine a format.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

This function automatically detects an origin framework of trained model and calls an appropriate function such readNetFromTensorflow, readNetFromONNX. An order of model and config arguments does not matter.

readNet() [7/7]

static Net OpenCVForUnity.DnnModule.Dnn.readNet ( string model, string config, string framework, int engine )

static

Read deep learning network represented in one of the supported formats.

Parameters

model	Binary file contains trained weights. The following file extensions are expected for models from different frameworks: *.pb (TensorFlow, https://www.tensorflow.org/) *.bin | *.onnx (OpenVINO, https://software.intel.com/openvino-toolkit) *.onnx (ONNX, https://onnx.ai/)
config	Text file contains network configuration. It could be a file with the following extensions: *.pbtxt (TensorFlow, https://www.tensorflow.org/) *.xml (OpenVINO, https://software.intel.com/openvino-toolkit)
framework	Explicit framework name tag to determine a format.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now. Use ENGINE_CLASSIC if you want to use other back-ends.

Returns

Net object.

This function automatically detects an origin framework of trained model and calls an appropriate function such readNetFromTensorflow, readNetFromONNX. An order of model and config arguments does not matter.

readNetFromModelOptimizer() [1/3]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromModelOptimizer ( MatOfByte bufferModelConfig, MatOfByte bufferWeights )

static

Load a network from Intel's Model Optimizer intermediate representation.

Parameters

bufferModelConfig	Buffer contains XML configuration with network's topology.
bufferWeights	Buffer contains binary data with trained weights.

Returns

Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

readNetFromModelOptimizer() [2/3]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromModelOptimizer ( string xml )

static

Load a network from Intel's Model Optimizer intermediate representation.

Parameters

xml	XML configuration file with network's topology.
bin	Binary file with trained weights.

Returns

Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

readNetFromModelOptimizer() [3/3]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromModelOptimizer ( string xml, string bin )

static

Load a network from Intel's Model Optimizer intermediate representation.

Parameters

xml	XML configuration file with network's topology.
bin	Binary file with trained weights.

Returns

Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

readNetFromONNX() [1/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromONNX ( MatOfByte buffer )

static

Reads a network model from ONNX in-memory buffer.

Parameters

buffer	in-memory buffer that stores the ONNX model bytes.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Network object that ready to do forward, throw an exception in failure cases.

readNetFromONNX() [2/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromONNX ( MatOfByte buffer, int engine )

static

Reads a network model from ONNX in-memory buffer.

Parameters

buffer	in-memory buffer that stores the ONNX model bytes.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Network object that ready to do forward, throw an exception in failure cases.

readNetFromONNX() [3/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromONNX ( string onnxFile )

static

Reads a network model ONNX.

Parameters

onnxFile	path to the .onnx file with text description of the network architecture.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Network object that ready to do forward, throw an exception in failure cases.

readNetFromONNX() [4/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromONNX ( string onnxFile, int engine )

static

Reads a network model ONNX.

Parameters

onnxFile	path to the .onnx file with text description of the network architecture.
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Network object that ready to do forward, throw an exception in failure cases.

readNetFromTensorflow() [1/8]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTensorflow ( MatOfByte bufferModel )

static

Reads a network model stored in TensorFlow framework's format.

Parameters

bufferModel	buffer containing the content of the pb file
bufferConfig	buffer containing the content of the pbtxt file
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTensorflow() [2/8]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTensorflow ( MatOfByte bufferModel, MatOfByte bufferConfig )

static

Reads a network model stored in TensorFlow framework's format.

Parameters

bufferModel	buffer containing the content of the pb file
bufferConfig	buffer containing the content of the pbtxt file
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTensorflow() [3/8]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTensorflow ( MatOfByte bufferModel, MatOfByte bufferConfig, int engine )

static

Reads a network model stored in TensorFlow framework's format.

Parameters

bufferModel	buffer containing the content of the pb file
bufferConfig	buffer containing the content of the pbtxt file
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTensorflow() [4/8]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTensorflow ( MatOfByte bufferModel, MatOfByte bufferConfig, int engine, List< string > extraOutputs )

static

Reads a network model stored in TensorFlow framework's format.

Parameters

bufferModel	buffer containing the content of the pb file
bufferConfig	buffer containing the content of the pbtxt file
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTensorflow() [5/8]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTensorflow ( string model )

static

Reads a network model stored in TensorFlow framework's format.

Parameters

model	path to the .pb file with binary protobuf description of the network architecture
config	path to the .pbtxt file that contains text graph definition in protobuf format. Resulting Net object is built by text graph using weights from a binary one that let us make it more flexible.
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTensorflow() [6/8]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTensorflow ( string model, string config )

static

Reads a network model stored in TensorFlow framework's format.

Parameters

model	path to the .pb file with binary protobuf description of the network architecture
config	path to the .pbtxt file that contains text graph definition in protobuf format. Resulting Net object is built by text graph using weights from a binary one that let us make it more flexible.
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTensorflow() [7/8]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTensorflow ( string model, string config, int engine )

static

Reads a network model stored in TensorFlow framework's format.

Parameters

model	path to the .pb file with binary protobuf description of the network architecture
config	path to the .pbtxt file that contains text graph definition in protobuf format. Resulting Net object is built by text graph using weights from a binary one that let us make it more flexible.
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTensorflow() [8/8]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTensorflow ( string model, string config, int engine, List< string > extraOutputs )

static

Reads a network model stored in TensorFlow framework's format.

Parameters

model	path to the .pb file with binary protobuf description of the network architecture
config	path to the .pbtxt file that contains text graph definition in protobuf format. Resulting Net object is built by text graph using weights from a binary one that let us make it more flexible.
engine	select DNN engine to be used. With auto selection the new engine is used.
extraOutputs	specify model outputs explicitly, in addition to the outputs the graph analyzer finds. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTFLite() [1/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTFLite ( MatOfByte bufferModel )

static

Reads a network model stored in TFLite framework's format.

Parameters

bufferModel	buffer containing the content of the tflite file
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTFLite() [2/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTFLite ( MatOfByte bufferModel, int engine )

static

Reads a network model stored in TFLite framework's format.

Parameters

bufferModel	buffer containing the content of the tflite file
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTFLite() [3/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTFLite ( string model )

static

Reads a network model stored in TFLite framework's format.

Parameters

model	path to the .tflite file with binary flatbuffers description of the network architecture
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readNetFromTFLite() [4/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTFLite ( string model, int engine )

static

Reads a network model stored in TFLite framework's format.

Parameters

model	path to the .tflite file with binary flatbuffers description of the network architecture
engine	select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Net object.

readTensorFromONNX()

static Mat OpenCVForUnity.DnnModule.Dnn.readTensorFromONNX ( string path )

static

Creates blob from .pb file.

Parameters

path	to the .pb file with input tensor.

Returns

Mat.

releaseHDDLPlugin()

static void OpenCVForUnity.DnnModule.Dnn.releaseHDDLPlugin ( )

static

Release a HDDL plugin.

resetMyriadDevice()

static void OpenCVForUnity.DnnModule.Dnn.resetMyriadDevice ( )

static

Release a Myriad device (binded by OpenCV).

Single Myriad device cannot be shared across multiple processes which uses Inference Engine's Myriad plugin.

setInferenceEngineBackendType()

static string OpenCVForUnity.DnnModule.Dnn.setInferenceEngineBackendType ( string newBackendType )

static

Specify Inference Engine internal backend API.

See values of CV_DNN_BACKEND_INFERENCE_ENGINE_* macros.

Returns

previous value of internal backend API

Deprecated

softNMSBoxes() [1/3]

static void OpenCVForUnity.DnnModule.Dnn.softNMSBoxes ( MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices )

static

Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503.

Parameters

bboxes	a set of bounding boxes to apply Soft NMS.
scores	a set of corresponding confidences.
updated_scores	a set of corresponding updated confidences.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
top_k	keep at most top_k picked indices.
sigma	parameter of Gaussian weighting.
method	Gaussian or linear.

SoftNMSMethod

softNMSBoxes() [2/3]

static void OpenCVForUnity.DnnModule.Dnn.softNMSBoxes ( MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k )

static

Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503.

Parameters

bboxes	a set of bounding boxes to apply Soft NMS.
scores	a set of corresponding confidences.
updated_scores	a set of corresponding updated confidences.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
top_k	keep at most top_k picked indices.
sigma	parameter of Gaussian weighting.
method	Gaussian or linear.

SoftNMSMethod

softNMSBoxes() [3/3]

static void OpenCVForUnity.DnnModule.Dnn.softNMSBoxes ( MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k, float sigma )

static

Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503.

Parameters

bboxes	a set of bounding boxes to apply Soft NMS.
scores	a set of corresponding confidences.
updated_scores	a set of corresponding updated confidences.
score_threshold	a threshold used to filter boxes by score.
nms_threshold	a threshold used in non maximum suppression.
indices	the kept indices of bboxes after NMS.
top_k	keep at most top_k picked indices.
sigma	parameter of Gaussian weighting.
method	Gaussian or linear.

SoftNMSMethod

writeTextGraph()

static void OpenCVForUnity.DnnModule.Dnn.writeTextGraph ( string model, string output )

static

Create a text representation for a binary network stored in protocol buffer format.

Parameters

model	A path to binary network.
output	A path to output text file to be created.

Note

To reduce output file size, trained weights are not included.

Member Data Documentation

ACTIV_CLIP

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_CLIP = 0 + 11

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_ELU

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_ELU = 0 + 5

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_GELU

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_GELU = 0 + 8

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_GELU_APPROX

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_GELU_APPROX = 0 + 9

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_HARDSIGMOID

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_HARDSIGMOID = 0 + 7

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_HARDSWISH

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_HARDSWISH = 0 + 6

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_MISH

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_MISH = 0 + 1

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_NONE

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_NONE = 0

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_RELU

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_RELU = 0 + 10

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_SIGMOID

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_SIGMOID = 0 + 3

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_SWISH

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_SWISH = 0 + 2

static

C++: enum ActivationType (cv.dnn.ActivationType)

ACTIV_TANH

const int OpenCVForUnity.DnnModule.Dnn.ACTIV_TANH = 0 + 4

static

C++: enum ActivationType (cv.dnn.ActivationType)

AUTO_PAD_NONE

const int OpenCVForUnity.DnnModule.Dnn.AUTO_PAD_NONE = 0

static

C++: enum AutoPadding (cv.dnn.AutoPadding)

AUTO_PAD_SAME_LOWER

const int OpenCVForUnity.DnnModule.Dnn.AUTO_PAD_SAME_LOWER = 2

static

C++: enum AutoPadding (cv.dnn.AutoPadding)

AUTO_PAD_SAME_UPPER

const int OpenCVForUnity.DnnModule.Dnn.AUTO_PAD_SAME_UPPER = 1

static

C++: enum AutoPadding (cv.dnn.AutoPadding)

AUTO_PAD_VALID

const int OpenCVForUnity.DnnModule.Dnn.AUTO_PAD_VALID = 3

static

C++: enum AutoPadding (cv.dnn.AutoPadding)

DNN_ARG_CONST

const int OpenCVForUnity.DnnModule.Dnn.DNN_ARG_CONST = 1

static

C++: enum ArgKind (cv.dnn.ArgKind)

DNN_ARG_EMPTY

const int OpenCVForUnity.DnnModule.Dnn.DNN_ARG_EMPTY = 0

static

C++: enum ArgKind (cv.dnn.ArgKind)

DNN_ARG_INPUT

const int OpenCVForUnity.DnnModule.Dnn.DNN_ARG_INPUT = 2

static

C++: enum ArgKind (cv.dnn.ArgKind)

DNN_ARG_OUTPUT

const int OpenCVForUnity.DnnModule.Dnn.DNN_ARG_OUTPUT = 3

static

C++: enum ArgKind (cv.dnn.ArgKind)

DNN_ARG_PATTERN

const int OpenCVForUnity.DnnModule.Dnn.DNN_ARG_PATTERN = 5

static

C++: enum ArgKind (cv.dnn.ArgKind)

DNN_ARG_TEMP

const int OpenCVForUnity.DnnModule.Dnn.DNN_ARG_TEMP = 4

static

C++: enum ArgKind (cv.dnn.ArgKind)

DNN_BACKEND_CANN

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_CANN = 2 + 6

static

C++: enum Backend (cv.dnn.Backend)

DNN_BACKEND_CUDA

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_CUDA = 2 + 3

static

C++: enum Backend (cv.dnn.Backend)

DNN_BACKEND_DEFAULT

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_DEFAULT = 0

static

C++: enum Backend (cv.dnn.Backend)

DNN_BACKEND_INFERENCE_ENGINE

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_INFERENCE_ENGINE = 2

static

C++: enum Backend (cv.dnn.Backend)

DNN_BACKEND_OPENCV

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_OPENCV = 2 + 1

static

C++: enum Backend (cv.dnn.Backend)

DNN_BACKEND_TIMVX

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_TIMVX = 2 + 5

static

C++: enum Backend (cv.dnn.Backend)

DNN_BACKEND_VKCOM

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_VKCOM = 2 + 2

static

C++: enum Backend (cv.dnn.Backend)

DNN_BACKEND_WEBNN

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_WEBNN = 2 + 4

static

C++: enum Backend (cv.dnn.Backend)

DNN_MODEL_GENERIC

const int OpenCVForUnity.DnnModule.Dnn.DNN_MODEL_GENERIC = 0

static

C++: enum ModelFormat (cv.dnn.ModelFormat)

DNN_MODEL_ONNX

const int OpenCVForUnity.DnnModule.Dnn.DNN_MODEL_ONNX = 1

static

C++: enum ModelFormat (cv.dnn.ModelFormat)

DNN_MODEL_TF

const int OpenCVForUnity.DnnModule.Dnn.DNN_MODEL_TF = 2

static

C++: enum ModelFormat (cv.dnn.ModelFormat)

DNN_MODEL_TFLITE

const int OpenCVForUnity.DnnModule.Dnn.DNN_MODEL_TFLITE = 3

static

C++: enum ModelFormat (cv.dnn.ModelFormat)

DNN_PMODE_CROP_CENTER

const int OpenCVForUnity.DnnModule.Dnn.DNN_PMODE_CROP_CENTER = 1

static

C++: enum ImagePaddingMode (cv.dnn.ImagePaddingMode)

DNN_PMODE_LETTERBOX

const int OpenCVForUnity.DnnModule.Dnn.DNN_PMODE_LETTERBOX = 2

static

C++: enum ImagePaddingMode (cv.dnn.ImagePaddingMode)

DNN_PMODE_NULL

const int OpenCVForUnity.DnnModule.Dnn.DNN_PMODE_NULL = 0

static

C++: enum ImagePaddingMode (cv.dnn.ImagePaddingMode)

DNN_PROFILE_DETAILED

const int OpenCVForUnity.DnnModule.Dnn.DNN_PROFILE_DETAILED = 2

static

C++: enum ProfilingMode (cv.dnn.ProfilingMode)

DNN_PROFILE_NONE

const int OpenCVForUnity.DnnModule.Dnn.DNN_PROFILE_NONE = 0

static

C++: enum ProfilingMode (cv.dnn.ProfilingMode)

DNN_PROFILE_SUMMARY

const int OpenCVForUnity.DnnModule.Dnn.DNN_PROFILE_SUMMARY = 1

static

C++: enum ProfilingMode (cv.dnn.ProfilingMode)

DNN_TARGET_CPU

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_CPU = 0

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_CPU_FP16

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_CPU_FP16 = 0 + 10

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_CUDA

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_CUDA = 0 + 6

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_CUDA_FP16

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_CUDA_FP16 = 0 + 7

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_FPGA

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_FPGA = 0 + 5

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_HDDL

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_HDDL = 0 + 8

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_MYRIAD

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_MYRIAD = 0 + 3

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_NPU

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_NPU = 0 + 9

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_OPENCL

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_OPENCL = 0 + 1

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_OPENCL_FP16

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_OPENCL_FP16 = 0 + 2

static

C++: enum Target (cv.dnn.Target)

DNN_TARGET_VULKAN

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_VULKAN = 0 + 4

static

C++: enum Target (cv.dnn.Target)

DNN_TRACE_ALL

const int OpenCVForUnity.DnnModule.Dnn.DNN_TRACE_ALL = 1

static

C++: enum TracingMode (cv.dnn.TracingMode)

DNN_TRACE_NONE

const int OpenCVForUnity.DnnModule.Dnn.DNN_TRACE_NONE = 0

static

C++: enum TracingMode (cv.dnn.TracingMode)

DNN_TRACE_OP

const int OpenCVForUnity.DnnModule.Dnn.DNN_TRACE_OP = 2

static

C++: enum TracingMode (cv.dnn.TracingMode)

ENGINE_AUTO

const int OpenCVForUnity.DnnModule.Dnn.ENGINE_AUTO = 3

static

C++: enum EngineType (cv.dnn.EngineType)

ENGINE_CLASSIC

const int OpenCVForUnity.DnnModule.Dnn.ENGINE_CLASSIC = 1

static

C++: enum EngineType (cv.dnn.EngineType)

ENGINE_NEW

const int OpenCVForUnity.DnnModule.Dnn.ENGINE_NEW = 2

static

C++: enum EngineType (cv.dnn.EngineType)

ENGINE_ORT

const int OpenCVForUnity.DnnModule.Dnn.ENGINE_ORT = 4

static

C++: enum EngineType (cv.dnn.EngineType)

LOSS_REDUCTION_MEAN

const int OpenCVForUnity.DnnModule.Dnn.LOSS_REDUCTION_MEAN = 1

static

C++: enum LossReduction (cv.dnn.LossReduction)

LOSS_REDUCTION_NONE

const int OpenCVForUnity.DnnModule.Dnn.LOSS_REDUCTION_NONE = 0

static

C++: enum LossReduction (cv.dnn.LossReduction)

LOSS_REDUCTION_SUM

const int OpenCVForUnity.DnnModule.Dnn.LOSS_REDUCTION_SUM = 2

static

C++: enum LossReduction (cv.dnn.LossReduction)

OPERATION_ADD

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_ADD = 0 + 18

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_AND

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_AND = 0

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_BITSHIFT

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_BITSHIFT = 0 + 9

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_BITWISE_AND

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_BITWISE_AND = 0 + 21

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_BITWISE_OR

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_BITWISE_OR = 0 + 22

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_BITWISE_XOR

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_BITWISE_XOR = 0 + 23

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_DIV

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_DIV = 0 + 19

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_EQUAL

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_EQUAL = 0 + 1

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_FMOD

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_FMOD = 0 + 14

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_GREATER

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_GREATER = 0 + 2

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_GREATER_EQUAL

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_GREATER_EQUAL = 0 + 3

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_LESS

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_LESS = 0 + 4

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_LESS_EQUAL

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_LESS_EQUAL = 0 + 5

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_MAX

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_MAX = 0 + 10

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_MEAN

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_MEAN = 0 + 11

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_MIN

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_MIN = 0 + 12

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_MOD

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_MOD = 0 + 13

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_OR

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_OR = 0 + 6

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_POW

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_POW = 0 + 7

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_PROD

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_PROD = 0 + 15

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_SUB

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_SUB = 0 + 16

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_SUM

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_SUM = 0 + 17

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_WHERE

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_WHERE = 0 + 20

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

OPERATION_XOR

const int OpenCVForUnity.DnnModule.Dnn.OPERATION_XOR = 0 + 8

static

C++: enum OPERATION (cv.dnn.NaryEltwiseLayer.OPERATION)

ReduceType_L1

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_L1 = 4

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_L2

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_L2 = 5

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_LOG_SUM

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_LOG_SUM = 8

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_LOG_SUM_EXP

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_LOG_SUM_EXP = 9

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_MAX

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_MAX = 0

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_MEAN

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_MEAN = 2

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_MIN

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_MIN = 1

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_PROD

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_PROD = 6

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_SUM

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_SUM = 3

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

ReduceType_SUM_SQUARE

const int OpenCVForUnity.DnnModule.Dnn.ReduceType_SUM_SQUARE = 7

static

C++: enum ReduceType (cv.dnn.Reduce2Layer.ReduceType)

SoftNMSMethod_SOFTNMS_GAUSSIAN

const int OpenCVForUnity.DnnModule.Dnn.SoftNMSMethod_SOFTNMS_GAUSSIAN = 2

static

C++: enum SoftNMSMethod (cv.dnn.SoftNMSMethod)

SoftNMSMethod_SOFTNMS_LINEAR

const int OpenCVForUnity.DnnModule.Dnn.SoftNMSMethod_SOFTNMS_LINEAR = 1

static

C++: enum SoftNMSMethod (cv.dnn.SoftNMSMethod)

---

The documentation for this class was generated from the following files:

• OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Core/org/opencv/dnn/Dnn.cs
• OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Extensions/org/opencv/dnn/Dnn_Struct.cs
• OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Extensions/org/opencv/dnn/Dnn_ValueTuple.cs