cv::dnn::DetectionModel Class Reference

This class represents high-level API for object detection networks. More...

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::DetectionModel:

Public Member Functions
	DetectionModel ()
	DetectionModel (const Net &network)
	Create model from deep learning network.
	DetectionModel (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config="")
	Create detection model from network represented in one of the supported formats. An order of model and config arguments does not matter.
void	detect (InputArray frame, std::vector< int > &classIds, std::vector< float > &confidences, std::vector< Rect > &boxes, float confThreshold=0.5f, float nmsThreshold=0.0f)
	Given the input frame, create input blob, run net and return result detections.
bool	getNmsAcrossClasses ()
	Getter for nmsAcrossClasses. This variable defaults to false, such that when non max suppression is used during the detect() function, it will do so only per-class.
DetectionModel &	setNmsAcrossClasses (bool value)
	nmsAcrossClasses defaults to false, such that when non max suppression is used during the detect() function, it will do so per-class. This function allows you to toggle this behaviour.
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 object detection networks.

DetectionModel allows to set params for preprocessing input image. DetectionModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return result detections. For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported.

Constructor & Destructor Documentation

DetectionModel() [1/3]

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

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

Create detection 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.

DetectionModel() [2/3]

cv::dnn::DetectionModel::DetectionModel

(

const Net &

network

)

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

Create model from deep learning network.

Parameters

[in]

network

Net object.

DetectionModel() [3/3]

cv::dnn::DetectionModel::DetectionModel

(

)

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

Member Function Documentation

detect()

void cv::dnn::DetectionModel::detect	(	InputArray	frame,
		std::vector< int > &	classIds,
		std::vector< float > &	confidences,
		std::vector< Rect > &	boxes,
		float	confThreshold = 0.5f,
		float	nmsThreshold = 0.0f )

Python:
	cv.dnn.DetectionModel.detect(	frame[, confThreshold[, nmsThreshold]]	) ->	classIds, confidences, boxes

Given the input frame, create input blob, run net and return result detections.

Parameters

[in]	frame	The input image.
[out]	classIds	Class indexes in result detection.
[out]	confidences	A set of corresponding confidences.
[out]	boxes	A set of bounding boxes.
[in]	confThreshold	A threshold used to filter boxes by confidences.
[in]	nmsThreshold	A threshold used in non maximum suppression.

getNmsAcrossClasses()

bool cv::dnn::DetectionModel::getNmsAcrossClasses

(

)

Python:
	cv.dnn.DetectionModel.getNmsAcrossClasses(		) ->	retval

Getter for nmsAcrossClasses. This variable defaults to false, such that when non max suppression is used during the detect() function, it will do so only per-class.

setNmsAcrossClasses()

DetectionModel & cv::dnn::DetectionModel::setNmsAcrossClasses

(

bool

value

)

Python:
	cv.dnn.DetectionModel.setNmsAcrossClasses(	value	) ->	retval

nmsAcrossClasses defaults to false, such that when non max suppression is used during the detect() function, it will do so per-class. This function allows you to toggle this behaviour.

Parameters

[in]

value

The new value for nmsAcrossClasses

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

• opencv2/dnn/dnn.hpp