Static Public Member Functions
static List< int >	getAvailableTargets (int be)

static Net	readNetFromDarknet (string cfgFile, string darknetModel)
	Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files. More...

static Net	readNetFromDarknet (string cfgFile)
	Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files. More...

static Net	readNetFromDarknet (MatOfByte bufferCfg, MatOfByte bufferModel)
	Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files. More...

static Net	readNetFromDarknet (MatOfByte bufferCfg)
	Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files. More...

static Net	readNetFromCaffe (string prototxt, string caffeModel)
	Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format. More...

static Net	readNetFromCaffe (string prototxt)
	Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format. More...

static Net	readNetFromCaffe (MatOfByte bufferProto, MatOfByte bufferModel)
	Reads a network model stored in Caffe model in memory. More...

static Net	readNetFromCaffe (MatOfByte bufferProto)
	Reads a network model stored in Caffe model in memory. More...

static Net	readNetFromTensorflow (string model, string config)
	Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format. More...

static Net	readNetFromTensorflow (string model)
	Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format. More...

static Net	readNetFromTensorflow (MatOfByte bufferModel, MatOfByte bufferConfig)
	Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format. More...

static Net	readNetFromTensorflow (MatOfByte bufferModel)
	Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format. More...

static Net	readNetFromTFLite (string model)
	Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format. More...

static Net	readNetFromTFLite (MatOfByte bufferModel)
	Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format. More...

static Net	readNetFromTorch (string model, bool isBinary, bool evaluate)
	Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format. More...

static Net	readNetFromTorch (string model, bool isBinary)
	Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format. More...

static Net	readNetFromTorch (string model)
	Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format. More...

static Net	readNet (string model, string config, string framework)
	Read deep learning network represented in one of the supported formats. More...

static Net	readNet (string model, string config)
	Read deep learning network represented in one of the supported formats. More...

static Net	readNet (string model)
	Read deep learning network represented in one of the supported formats. More...

static Net	readNet (string framework, MatOfByte bufferModel, MatOfByte bufferConfig)
	Read deep learning network represented in one of the supported formats. More...

static Net	readNet (string framework, MatOfByte bufferModel)
	Read deep learning network represented in one of the supported formats. More...

static Mat	readTorchBlob (string filename, bool isBinary)
	Loads blob which was serialized as torch.Tensor object of Torch7 framework. More...

static Mat	readTorchBlob (string filename)
	Loads blob which was serialized as torch.Tensor object of Torch7 framework. More...

static Net	readNetFromModelOptimizer (string xml, string bin)
	Load a network from Intel's Model Optimizer intermediate representation. More...

static Net	readNetFromModelOptimizer (string xml)
	Load a network from Intel's Model Optimizer intermediate representation. More...

static Net	readNetFromModelOptimizer (MatOfByte bufferModelConfig, MatOfByte bufferWeights)
	Load a network from Intel's Model Optimizer intermediate representation. More...

static Net	readNetFromONNX (string onnxFile)
	Reads a network model <a href="https://onnx.ai/">ONNX</a>. More...

static Net	readNetFromONNX (MatOfByte buffer)
	Reads a network model from <a href="https://onnx.ai/">ONNX</a> in-memory buffer. More...

static Mat	readTensorFromONNX (string path)
	Creates blob from .pb file. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

static Mat	blobFromImageWithParams (Mat image, Image2BlobParams param)
	Creates 4-dimensional blob from image with given params. More...

static Mat	blobFromImageWithParams (Mat image)
	Creates 4-dimensional blob from image with given params. More...

static void	blobFromImageWithParams (Mat image, Mat blob, Image2BlobParams param)

static void	blobFromImageWithParams (Mat image, Mat blob)

static Mat	blobFromImagesWithParams (List< Mat > images, Image2BlobParams param)
	Creates 4-dimensional blob from series of images with given params. More...

static Mat	blobFromImagesWithParams (List< Mat > images)
	Creates 4-dimensional blob from series of images with given params. More...

static void	blobFromImagesWithParams (List< Mat > images, Mat blob, Image2BlobParams param)

static void	blobFromImagesWithParams (List< Mat > images, Mat blob)

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>). More...

static void	shrinkCaffeModel (string src, string dst, List< string > layersTypes)
	Convert all weights of Caffe network to half precision floating point. More...

static void	shrinkCaffeModel (string src, string dst)
	Convert all weights of Caffe network to half precision floating point. More...

static void	writeTextGraph (string model, string output)
	Create a text representation for a binary network stored in protocol buffer format. More...

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. More...

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. More...

static void	NMSBoxes (MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
	Performs non maximum suppression given boxes and corresponding scores. More...

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

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)

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. More...

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. More...

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. More...

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. More...

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. More...

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. More...

static string	getInferenceEngineBackendType ()
	Returns Inference Engine internal backend API. More...

static string	setInferenceEngineBackendType (string newBackendType)
	Specify Inference Engine internal backend API. More...

static void	resetMyriadDevice ()
	Release a Myriad device (binded by OpenCV). More...

static string	getInferenceEngineVPUType ()
	Returns Inference Engine VPU type. More...

static string	getInferenceEngineCPUType ()
	Returns Inference Engine CPU type. More...

static void	releaseHDDLPlugin ()
	Release a HDDL plugin. More...

Public Attributes
const int	DNN_BACKEND_DEFAULT = 0

const int	DNN_BACKEND_HALIDE = 0 + 1

const int	DNN_BACKEND_INFERENCE_ENGINE = 0 + 2

const int	DNN_BACKEND_OPENCV = 0 + 3

const int	DNN_BACKEND_VKCOM = 0 + 4

const int	DNN_BACKEND_CUDA = 0 + 5

const int	DNN_BACKEND_WEBNN = 0 + 6

const int	DNN_BACKEND_TIMVX = 0 + 7

const int	DNN_BACKEND_CANN = 0 + 8

const int	DNN_LAYOUT_UNKNOWN = 0

const int	DNN_LAYOUT_ND = 1

const int	DNN_LAYOUT_NCHW = 2

const int	DNN_LAYOUT_NCDHW = 3

const int	DNN_LAYOUT_NHWC = 4

const int	DNN_LAYOUT_NDHWC = 5

const int	DNN_LAYOUT_PLANAR = 6

const int	DNN_PMODE_NULL = 0

const int	DNN_PMODE_CROP_CENTER = 1

const int	DNN_PMODE_LETTERBOX = 2

const int	SoftNMSMethod_SOFTNMS_LINEAR = 1

const int	SoftNMSMethod_SOFTNMS_GAUSSIAN = 2

const int	DNN_TARGET_CPU = 0

const int	DNN_TARGET_OPENCL = 0 + 1

const int	DNN_TARGET_OPENCL_FP16 = 0 + 2

const int	DNN_TARGET_MYRIAD = 0 + 3

const int	DNN_TARGET_VULKAN = 0 + 4

const int	DNN_TARGET_FPGA = 0 + 5

const int	DNN_TARGET_CUDA = 0 + 6

const int	DNN_TARGET_CUDA_FP16 = 0 + 7

const int	DNN_TARGET_HDDL = 0 + 8

const int	DNN_TARGET_NPU = 0 + 9

const int	DNN_TARGET_CPU_FP16 = 0 + 10

Member Function Documentation

◆ blobFromImage() [1/7]

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.

◆ blobFromImage() [2/7]

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() [3/7]

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() [4/7]

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() [5/7]

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() [6/7]

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() [7/7]

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.

◆ blobFromImages() [1/7]

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.

◆ blobFromImages() [2/7]

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() [3/7]

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() [4/7]

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() [5/7]

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() [6/7]

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() [7/7]

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.

◆ blobFromImagesWithParams() [1/4]

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() [2/4]

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() [3/4]

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.

◆ blobFromImagesWithParams() [4/4]

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.

◆ blobFromImageWithParams() [1/4]

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() [2/4]

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() [3/4]

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.

◆ blobFromImageWithParams() [4/4]

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.

◆ 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

[in]	blob_	4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from which you would like to extract the images.
[out]	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,
		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.

◆ 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
	)

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,
		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.

◆ 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
	)

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,
		float	eta,
		int	top_k
	)

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
	)

static

◆ readNet() [1/5]

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

[in]	model	Binary file contains trained weights. The following file extensions are expected for models from different frameworks: `.caffemodel` (Caffe, http://caffe.berkeleyvision.org/) `.pb` (TensorFlow, https://www.tensorflow.org/) `.t7` \| `.net` (Torch, http://torch.ch/) `.weights` (Darknet, https://pjreddie.com/darknet/) `.bin` \| `.onnx` (OpenVINO, https://software.intel.com/openvino-toolkit) `.onnx` (ONNX, https://onnx.ai/)
[in]	config	Text file contains network configuration. It could be a file with the following extensions: `.prototxt` (Caffe, http://caffe.berkeleyvision.org/) `.pbtxt` (TensorFlow, https://www.tensorflow.org/) `.cfg` (Darknet, https://pjreddie.com/darknet/) `.xml` (OpenVINO, https://software.intel.com/openvino-toolkit)
[in]	framework	Explicit framework name tag to determine a format.

Returns: Net object.

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

◆ readNet() [2/5]

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

static

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

Parameters

[in]	model	Binary file contains trained weights. The following file extensions are expected for models from different frameworks: `.caffemodel` (Caffe, http://caffe.berkeleyvision.org/) `.pb` (TensorFlow, https://www.tensorflow.org/) `.t7` \| `.net` (Torch, http://torch.ch/) `.weights` (Darknet, https://pjreddie.com/darknet/) `.bin` \| `.onnx` (OpenVINO, https://software.intel.com/openvino-toolkit) `.onnx` (ONNX, https://onnx.ai/)
[in]	config	Text file contains network configuration. It could be a file with the following extensions: `.prototxt` (Caffe, http://caffe.berkeleyvision.org/) `.pbtxt` (TensorFlow, https://www.tensorflow.org/) `.cfg` (Darknet, https://pjreddie.com/darknet/) `.xml` (OpenVINO, https://software.intel.com/openvino-toolkit)
[in]	framework	Explicit framework name tag to determine a format.

Returns: Net object.

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

◆ readNet() [3/5]

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

static

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

Parameters

[in]	model	Binary file contains trained weights. The following file extensions are expected for models from different frameworks: `.caffemodel` (Caffe, http://caffe.berkeleyvision.org/) `.pb` (TensorFlow, https://www.tensorflow.org/) `.t7` \| `.net` (Torch, http://torch.ch/) `.weights` (Darknet, https://pjreddie.com/darknet/) `.bin` \| `.onnx` (OpenVINO, https://software.intel.com/openvino-toolkit) `.onnx` (ONNX, https://onnx.ai/)
[in]	config	Text file contains network configuration. It could be a file with the following extensions: `.prototxt` (Caffe, http://caffe.berkeleyvision.org/) `.pbtxt` (TensorFlow, https://www.tensorflow.org/) `.cfg` (Darknet, https://pjreddie.com/darknet/) `.xml` (OpenVINO, https://software.intel.com/openvino-toolkit)
[in]	framework	Explicit framework name tag to determine a format.

Returns: Net object.

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

◆ readNet() [4/5]

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

[in]	framework	Name of origin framework.
[in]	bufferModel	A buffer with a content of binary file with weights
[in]	bufferConfig	A buffer with a content of text file contains network configuration.

Returns: Net object.

◆ readNet() [5/5]

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

[in]	framework	Name of origin framework.
[in]	bufferModel	A buffer with a content of binary file with weights
[in]	bufferConfig	A buffer with a content of text file contains network configuration.

Returns: Net object.

◆ readNetFromCaffe() [1/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromCaffe	(	string	prototxt,
		string	caffeModel
	)

static

Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.

Parameters

prototxt	path to the .prototxt file with text description of the network architecture.
caffeModel	path to the .caffemodel file with learned network.

Returns: Net object.

◆ readNetFromCaffe() [2/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromCaffe ( string prototxt )

static

Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.

Parameters

prototxt	path to the .prototxt file with text description of the network architecture.
caffeModel	path to the .caffemodel file with learned network.

Returns: Net object.

◆ readNetFromCaffe() [3/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromCaffe	(	MatOfByte	bufferProto,
		MatOfByte	bufferModel
	)

static

Reads a network model stored in Caffe model in memory.

Parameters

bufferProto	buffer containing the content of the .prototxt file
bufferModel	buffer containing the content of the .caffemodel file

Returns: Net object.

◆ readNetFromCaffe() [4/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromCaffe ( MatOfByte bufferProto )

static

Reads a network model stored in Caffe model in memory.

Parameters

bufferProto	buffer containing the content of the .prototxt file
bufferModel	buffer containing the content of the .caffemodel file

Returns: Net object.

◆ readNetFromDarknet() [1/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromDarknet	(	string	cfgFile,
		string	darknetModel
	)

static

Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.

Parameters

cfgFile	path to the .cfg file with text description of the network architecture.
darknetModel	path to the .weights file with learned network.

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

◆ readNetFromDarknet() [2/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromDarknet ( string cfgFile )

static

Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.

Parameters

cfgFile	path to the .cfg file with text description of the network architecture.
darknetModel	path to the .weights file with learned network.

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

◆ readNetFromDarknet() [3/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromDarknet	(	MatOfByte	bufferCfg,
		MatOfByte	bufferModel
	)

static

Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.

Parameters

bufferCfg	A buffer contains a content of .cfg file with text description of the network architecture.
bufferModel	A buffer contains a content of .weights file with learned network.

Returns: Net object.

◆ readNetFromDarknet() [4/4]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromDarknet ( MatOfByte bufferCfg )

static

Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.

Parameters

bufferCfg	A buffer contains a content of .cfg file with text description of the network architecture.
bufferModel	A buffer contains a content of .weights file with learned network.

Returns: Net object.

◆ readNetFromModelOptimizer() [1/3]

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

static

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

Parameters

[in]	xml	XML configuration file with network's topology.
[in]	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() [2/3]

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

static

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

Parameters

[in]	xml	XML configuration file with network's topology.
[in]	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	(	MatOfByte	bufferModelConfig,
		MatOfByte	bufferWeights
	)

static

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

Parameters

[in]	bufferModelConfig	Buffer contains XML configuration with network's topology.
[in]	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.

◆ readNetFromONNX() [1/2]

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

static

Reads a network model <a href="https://onnx.ai/">ONNX</a>.

Parameters

onnxFile path to the .onnx file with text description of the network architecture.

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

◆ readNetFromONNX() [2/2]

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

static

Reads a network model from <a href="https://onnx.ai/">ONNX</a> in-memory buffer.

Parameters

buffer in-memory buffer that stores the ONNX model bytes.

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

◆ readNetFromTensorflow() [1/4]

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

static

Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> 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.

Returns: Net object.

◆ readNetFromTensorflow() [2/4]

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

static

Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> 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.

Returns: Net object.

◆ readNetFromTensorflow() [3/4]

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

static

Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.

Parameters

bufferModel	buffer containing the content of the pb file
bufferConfig	buffer containing the content of the pbtxt file

Returns: Net object.

◆ readNetFromTensorflow() [4/4]

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

static

Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.

Parameters

bufferModel	buffer containing the content of the pb file
bufferConfig	buffer containing the content of the pbtxt file

Returns: Net object.

◆ readNetFromTFLite() [1/2]

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

static

Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.

Parameters

model path to the .tflite file with binary flatbuffers description of the network architecture

Returns: Net object.

◆ readNetFromTFLite() [2/2]

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

static

Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.

Parameters

bufferModel buffer containing the content of the tflite file

Returns: Net object.

◆ readNetFromTorch() [1/3]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTorch	(	string	model,
		bool	isBinary,
		bool	evaluate
	)

static

Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.

Parameters

model	path to the file, dumped from Torch by using torch.save() function.
isBinary	specifies whether the network was serialized in ascii mode or binary.
evaluate	specifies testing phase of network. If true, it's similar to evaluate() method in Torch.

Returns: Net object.

Note: Ascii mode of Torch serializer is more preferable, because binary mode extensively use long type of C language, which has various bit-length on different systems.

The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.

List of supported layers (i.e. object instances derived from Torch nn.Module class):

nn.Sequential
nn.Parallel
nn.Concat
nn.Linear
nn.SpatialConvolution
nn.SpatialMaxPooling, nn.SpatialAveragePooling
nn.ReLU, nn.TanH, nn.Sigmoid
nn.Reshape
nn.SoftMax, nn.LogSoftMax

Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.

◆ readNetFromTorch() [2/3]

static Net OpenCVForUnity.DnnModule.Dnn.readNetFromTorch	(	string	model,
		bool	isBinary
	)

static

Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.

Parameters

model	path to the file, dumped from Torch by using torch.save() function.
isBinary	specifies whether the network was serialized in ascii mode or binary.
evaluate	specifies testing phase of network. If true, it's similar to evaluate() method in Torch.

Returns: Net object.

Note: Ascii mode of Torch serializer is more preferable, because binary mode extensively use long type of C language, which has various bit-length on different systems.

The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.

List of supported layers (i.e. object instances derived from Torch nn.Module class):

nn.Sequential
nn.Parallel
nn.Concat
nn.Linear
nn.SpatialConvolution
nn.SpatialMaxPooling, nn.SpatialAveragePooling
nn.ReLU, nn.TanH, nn.Sigmoid
nn.Reshape
nn.SoftMax, nn.LogSoftMax

Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.

◆ readNetFromTorch() [3/3]

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

static

Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.

Parameters

model	path to the file, dumped from Torch by using torch.save() function.
isBinary	specifies whether the network was serialized in ascii mode or binary.
evaluate	specifies testing phase of network. If true, it's similar to evaluate() method in Torch.

Returns: Net object.

Note: Ascii mode of Torch serializer is more preferable, because binary mode extensively use long type of C language, which has various bit-length on different systems.

The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.

List of supported layers (i.e. object instances derived from Torch nn.Module class):

nn.Sequential
nn.Parallel
nn.Concat
nn.Linear
nn.SpatialConvolution
nn.SpatialMaxPooling, nn.SpatialAveragePooling
nn.ReLU, nn.TanH, nn.Sigmoid
nn.Reshape
nn.SoftMax, nn.LogSoftMax

Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.

◆ 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.

◆ readTorchBlob() [1/2]

static Mat OpenCVForUnity.DnnModule.Dnn.readTorchBlob	(	string	filename,
		bool	isBinary
	)

static

Loads blob which was serialized as torch.Tensor object of Torch7 framework.

Warning: This function has the same limitations as readNetFromTorch().

◆ readTorchBlob() [2/2]

static Mat OpenCVForUnity.DnnModule.Dnn.readTorchBlob ( string filename )

static

Loads blob which was serialized as torch.Tensor object of Torch7 framework.

Warning: This function has the same limitations as readNetFromTorch().

◆ 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:

◆ shrinkCaffeModel() [1/2]

static void OpenCVForUnity.DnnModule.Dnn.shrinkCaffeModel	(	string	src,
		string	dst,
		List< string >	layersTypes
	)

static

Convert all weights of Caffe network to half precision floating point.

Parameters

src	Path to origin model from Caffe framework contains single precision floating point weights (usually has `.caffemodel` extension).
dst	Path to destination model with updated weights.
layersTypes	Set of layers types which parameters will be converted. By default, converts only Convolutional and Fully-Connected layers' weights.

Note: Shrinked model has no origin float32 weights so it can't be used in origin Caffe framework anymore. However the structure of data is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe. So the resulting model may be used there.

◆ shrinkCaffeModel() [2/2]

static void OpenCVForUnity.DnnModule.Dnn.shrinkCaffeModel	(	string	src,
		string	dst
	)

static

Convert all weights of Caffe network to half precision floating point.

Parameters

src	Path to origin model from Caffe framework contains single precision floating point weights (usually has `.caffemodel` extension).
dst	Path to destination model with updated weights.
layersTypes	Set of layers types which parameters will be converted. By default, converts only Convolutional and Fully-Connected layers' weights.

Note: Shrinked model has no origin float32 weights so it can't be used in origin Caffe framework anymore. However the structure of data is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe. So the resulting model may be used there.

◆ softNMSBoxes() [1/3]

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.

See also: 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.

See also: 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
	)

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.

See also: 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

[in]	model	A path to binary network.
[in]	output	A path to output text file to be created. @note To reduce output file size, trained weights are not included.

Member Data Documentation

◆ DNN_BACKEND_CANN

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

◆ DNN_BACKEND_CUDA

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

◆ DNN_BACKEND_DEFAULT

const int OpenCVForUnity.DnnModule.Dnn.DNN_BACKEND_DEFAULT = 0

◆ DNN_BACKEND_HALIDE

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

◆ DNN_BACKEND_INFERENCE_ENGINE

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

◆ DNN_BACKEND_OPENCV

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

◆ DNN_BACKEND_TIMVX

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

◆ DNN_BACKEND_VKCOM

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

◆ DNN_BACKEND_WEBNN

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

◆ DNN_LAYOUT_NCDHW

const int OpenCVForUnity.DnnModule.Dnn.DNN_LAYOUT_NCDHW = 3

◆ DNN_LAYOUT_NCHW

const int OpenCVForUnity.DnnModule.Dnn.DNN_LAYOUT_NCHW = 2

◆ DNN_LAYOUT_ND

const int OpenCVForUnity.DnnModule.Dnn.DNN_LAYOUT_ND = 1

◆ DNN_LAYOUT_NDHWC

const int OpenCVForUnity.DnnModule.Dnn.DNN_LAYOUT_NDHWC = 5

◆ DNN_LAYOUT_NHWC

const int OpenCVForUnity.DnnModule.Dnn.DNN_LAYOUT_NHWC = 4

◆ DNN_LAYOUT_PLANAR

const int OpenCVForUnity.DnnModule.Dnn.DNN_LAYOUT_PLANAR = 6

◆ DNN_LAYOUT_UNKNOWN

const int OpenCVForUnity.DnnModule.Dnn.DNN_LAYOUT_UNKNOWN = 0

◆ DNN_PMODE_CROP_CENTER

const int OpenCVForUnity.DnnModule.Dnn.DNN_PMODE_CROP_CENTER = 1

◆ DNN_PMODE_LETTERBOX

const int OpenCVForUnity.DnnModule.Dnn.DNN_PMODE_LETTERBOX = 2

◆ DNN_PMODE_NULL

const int OpenCVForUnity.DnnModule.Dnn.DNN_PMODE_NULL = 0

◆ DNN_TARGET_CPU

const int OpenCVForUnity.DnnModule.Dnn.DNN_TARGET_CPU = 0

◆ DNN_TARGET_CPU_FP16

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

◆ DNN_TARGET_CUDA

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

◆ DNN_TARGET_CUDA_FP16

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

◆ DNN_TARGET_FPGA

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

◆ DNN_TARGET_HDDL

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

◆ DNN_TARGET_MYRIAD

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

◆ DNN_TARGET_NPU

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

◆ DNN_TARGET_OPENCL

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

◆ DNN_TARGET_OPENCL_FP16

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

◆ DNN_TARGET_VULKAN

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

◆ SoftNMSMethod_SOFTNMS_GAUSSIAN

const int OpenCVForUnity.DnnModule.Dnn.SoftNMSMethod_SOFTNMS_GAUSSIAN = 2

◆ SoftNMSMethod_SOFTNMS_LINEAR

const int OpenCVForUnity.DnnModule.Dnn.SoftNMSMethod_SOFTNMS_LINEAR = 1

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

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/dnn/Dnn.cs

Static Public Member Functions

Public Attributes

Member Function Documentation

◆ blobFromImage() [1/7]

◆ blobFromImage() [2/7]

◆ blobFromImage() [3/7]

◆ blobFromImage() [4/7]

◆ blobFromImage() [5/7]

◆ blobFromImage() [6/7]

◆ blobFromImage() [7/7]

◆ blobFromImages() [1/7]

◆ blobFromImages() [2/7]

◆ blobFromImages() [3/7]

◆ blobFromImages() [4/7]

◆ blobFromImages() [5/7]

◆ blobFromImages() [6/7]

◆ blobFromImages() [7/7]

◆ blobFromImagesWithParams() [1/4]

◆ blobFromImagesWithParams() [2/4]

◆ blobFromImagesWithParams() [3/4]

◆ blobFromImagesWithParams() [4/4]

◆ blobFromImageWithParams() [1/4]

◆ blobFromImageWithParams() [2/4]

◆ blobFromImageWithParams() [3/4]

◆ blobFromImageWithParams() [4/4]

◆ getAvailableTargets()

◆ getInferenceEngineBackendType()

◆ getInferenceEngineCPUType()

◆ getInferenceEngineVPUType()

◆ imagesFromBlob()

◆ NMSBoxes() [1/3]

◆ NMSBoxes() [2/3]

◆ NMSBoxes() [3/3]

◆ NMSBoxesBatched() [1/3]

◆ NMSBoxesBatched() [2/3]

◆ NMSBoxesBatched() [3/3]

◆ NMSBoxesRotated() [1/3]

◆ NMSBoxesRotated() [2/3]

◆ NMSBoxesRotated() [3/3]

◆ readNet() [1/5]

◆ readNet() [2/5]

◆ readNet() [3/5]

◆ readNet() [4/5]

◆ readNet() [5/5]

◆ readNetFromCaffe() [1/4]

◆ readNetFromCaffe() [2/4]

◆ readNetFromCaffe() [3/4]

◆ readNetFromCaffe() [4/4]

◆ readNetFromDarknet() [1/4]

◆ readNetFromDarknet() [2/4]

◆ readNetFromDarknet() [3/4]

◆ readNetFromDarknet() [4/4]

◆ readNetFromModelOptimizer() [1/3]

◆ readNetFromModelOptimizer() [2/3]

◆ readNetFromModelOptimizer() [3/3]

◆ readNetFromONNX() [1/2]

◆ readNetFromONNX() [2/2]

◆ readNetFromTensorflow() [1/4]

◆ readNetFromTensorflow() [2/4]

◆ readNetFromTensorflow() [3/4]

◆ readNetFromTensorflow() [4/4]

◆ readNetFromTFLite() [1/2]

◆ readNetFromTFLite() [2/2]

◆ readNetFromTorch() [1/3]

◆ readNetFromTorch() [2/3]

◆ readNetFromTorch() [3/3]

◆ readTensorFromONNX()

◆ readTorchBlob() [1/2]

◆ readTorchBlob() [2/2]

◆ releaseHDDLPlugin()

◆ resetMyriadDevice()

◆ setInferenceEngineBackendType()

◆ shrinkCaffeModel() [1/2]

◆ shrinkCaffeModel() [2/2]

◆ softNMSBoxes() [1/3]

◆ softNMSBoxes() [2/3]

◆ softNMSBoxes() [3/3]

◆ writeTextGraph()

Member Data Documentation

◆ DNN_BACKEND_CANN