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:

Public Member Functions
	ClassificationModel (const String &model, 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. More...
	ClassificationModel (const Net &network)
	Create model from deep learning network. More...
std::pair< int, float >	classify (InputArray frame)
	Given the input frame, create input blob, run net and return top-1 prediction. More...
void	classify (InputArray frame, int &classId, float &conf)
Public Member Functions inherited from cv::dnn::Model
	Model ()
	Model (const Model &)=default
	Model (Model &&)=default
	Model (const String &model, 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. More...
	Model (const Net &network)
	Create model from deep learning network. More...
Impl *	getImpl () const
Impl &	getImplRef () const
Net &	getNetwork_ () const
Net &	getNetwork_ ()
	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. More...
Model &	setInputCrop (bool crop)
	Set flag crop for frame. More...
Model &	setInputMean (const Scalar &mean)
	Set mean value for frame. More...
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. More...
Model &	setInputScale (double scale)
	Set scalefactor value for frame. More...
Model &	setInputSize (const Size &size)
	Set input size for frame. More...
Model &	setInputSize (int width, int height)
Model &	setInputSwapRB (bool swapRB)
	Set flag swapRB for frame. More...
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() [1/2]

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

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

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() [2/2]

cv::dnn::ClassificationModel::ClassificationModel

(

const Net &

network

)

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

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:
	classId, conf	=	cv.dnn_ClassificationModel.classify(	frame	)

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:
	classId, conf	=	cv.dnn_ClassificationModel.classify(	frame	)

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

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

• opencv2/dnn/dnn.hpp