cv::dnn::ClassificationModel Class Reference

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

#include <opencv2/dnn/dnn.hpp>

Inheritance diagram for cv::dnn::ClassificationModel:

Collaboration diagram for cv::dnn::ClassificationModel:

Public Member Functions
	ClassificationModel ()
	ClassificationModel (const Net &network)
	Create model from deep learning network.
	ClassificationModel (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config="")
	Create classification model from network represented in one of the supported formats. An order of model and config arguments does not matter.
std::pair< int, float >	classify (InputArray frame)
	Given the input frame, create input blob, run net and return top-1 prediction.
void	classify (InputArray frame, int &classId, float &conf)
bool	getEnableSoftmaxPostProcessing () const
	Get enable/disable softmax post processing option.
ClassificationModel &	setEnableSoftmaxPostProcessing (bool enable)
	Set enable/disable softmax post processing option.
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 &	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 classification models.

ClassificationModel allows to set params for preprocessing input image. ClassificationModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return top-1 prediction.

Constructor & Destructor Documentation

ClassificationModel() [1/3]

cv::dnn::ClassificationModel::ClassificationModel

(

)

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

ClassificationModel() [2/3]

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

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

Create classification 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.

ClassificationModel() [3/3]

cv::dnn::ClassificationModel::ClassificationModel

(

const Net &

network

)

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

Create model from deep learning network.

Parameters

[in]

network

Net object.

Member Function Documentation

classify() [1/2]

std::pair< int, float > cv::dnn::ClassificationModel::classify

(

InputArray

frame

)

Python:
	cv.dnn.ClassificationModel.classify(	frame	) ->	classId, conf

Given the input frame, create input blob, run net and return top-1 prediction.

Parameters

[in]

frame

The input image.

classify() [2/2]

void cv::dnn::ClassificationModel::classify	(	InputArray	frame,
		int &	classId,
		float &	conf
	)

Python:
	cv.dnn.ClassificationModel.classify(	frame	) ->	classId, conf

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

getEnableSoftmaxPostProcessing()

bool cv::dnn::ClassificationModel::getEnableSoftmaxPostProcessing

(

)

const

Python:
	cv.dnn.ClassificationModel.getEnableSoftmaxPostProcessing(		) ->	retval

Get enable/disable softmax post processing option.

This option defaults to false, softmax post processing is not applied within the classify() function.

setEnableSoftmaxPostProcessing()

ClassificationModel & cv::dnn::ClassificationModel::setEnableSoftmaxPostProcessing

(

bool

enable

)

Python:
	cv.dnn.ClassificationModel.setEnableSoftmaxPostProcessing(	enable	) ->	retval

Set enable/disable softmax post processing option.

If this option is true, softmax is applied after forward inference within the classify() function to convert the confidences range to [0.0-1.0]. This function allows you to toggle this behavior. Please turn true when not contain softmax layer in model.

Parameters

[in]

enable

Set enable softmax post processing within the classify() function.

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

• opencv2/dnn/dnn.hpp