cv::dnn::SegmentationModel Class Reference

This class represents high-level API for segmentation models. More...

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::SegmentationModel:

Public Member Functions
	SegmentationModel (const Net &network)
	Create model from deep learning network.
	SegmentationModel (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config="")
	Create segmentation model from network represented in one of the supported formats. An order of model and config arguments does not matter.
void	segment (InputArray frame, OutputArray mask)
	Given the input frame, create input blob, run net.
Public Member Functions inherited from cv::dnn::Model
	Model ()
	Model (const Model &)=default
	Model (const Net &network)
	Create model from deep learning network.
	Model (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config="")
	Create model from deep learning network represented in one of the supported formats. An order of model and config arguments does not matter.
	Model (Model &&)=default
Model &	enableWinograd (bool useWinograd)
Impl *	getImpl () const
Impl &	getImplRef () const
Net &	getNetwork_ ()
Net &	getNetwork_ () const
	operator Net & () const
Model &	operator= (const Model &)=default
Model &	operator= (Model &&)=default
void	predict (InputArray frame, OutputArrayOfArrays outs) const
	Given the input frame, create input blob, run net and return the output blobs.
Model &	setInputCrop (bool crop)
	Set flag crop for frame.
Model &	setInputMean (const Scalar &mean)
	Set mean value for frame.
void	setInputParams (double scale=1.0, const Size &size=Size(), const Scalar &mean=Scalar(), bool swapRB=false, bool crop=false)
	Set preprocessing parameters for frame.
Model &	setInputScale (const Scalar &scale)
	Set scalefactor value for frame.
Model &	setInputSize (const Size &size)
	Set input size for frame.
Model &	setInputSize (int width, int height)
Model &	setInputSwapRB (bool swapRB)
	Set flag swapRB for frame.
Model &	setOutputNames (const std::vector< String > &outNames)
	Set output names for frame.
Model &	setPreferableBackend (dnn::Backend backendId)
Model &	setPreferableTarget (dnn::Target targetId)

Additional Inherited Members
Protected Attributes inherited from cv::dnn::Model
Ptr< Impl >	impl

Detailed Description

This class represents high-level API for segmentation models.

SegmentationModel allows to set params for preprocessing input image. SegmentationModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and returns the class prediction for each pixel.

Constructor & Destructor Documentation

SegmentationModel() [1/2]

cv::dnn::SegmentationModel::SegmentationModel	(	CV_WRAP_FILE_PATH const String &	model,
		CV_WRAP_FILE_PATH const String &	config = ""
	)

Python:
	cv.dnn.SegmentationModel(	model[, config]	) ->	<dnn_SegmentationModel object>
	cv.dnn.SegmentationModel(	network	) ->	<dnn_SegmentationModel object>

Create segmentation model from network represented in one of the supported formats. An order of model and config arguments does not matter.

Parameters

[in]	model	Binary file contains trained weights.
[in]	config	Text file contains network configuration.

SegmentationModel() [2/2]

cv::dnn::SegmentationModel::SegmentationModel

(

const Net &

network

)

Python:
	cv.dnn.SegmentationModel(	model[, config]	) ->	<dnn_SegmentationModel object>
	cv.dnn.SegmentationModel(	network	) ->	<dnn_SegmentationModel object>

Create model from deep learning network.

Parameters

[in]

network

Net object.

Member Function Documentation

segment()

void cv::dnn::SegmentationModel::segment	(	InputArray	frame,
		OutputArray	mask
	)

Python:
	cv.dnn.SegmentationModel.segment(	frame[, mask]	) ->	mask

Given the input frame, create input blob, run net.

Parameters

[in]	frame	The input image.
[out]	mask	Allocated class prediction for each pixel

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

• opencv2/dnn/dnn.hpp