cv::dnn::KeypointsModel Class Reference

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

#include <opencv2/dnn/dnn.hpp>

Inheritance diagram for cv::dnn::KeypointsModel:

Collaboration diagram for cv::dnn::KeypointsModel:

Public Member Functions
	KeypointsModel (const Net &network)
	Create model from deep learning network.
	KeypointsModel (CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config="")
	Create keypoints model from network represented in one of the supported formats. An order of model and config arguments does not matter.
std::vector< Point2f >	estimate (InputArray frame, float thresh=0.5)
	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 &	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 keypoints models.

KeypointsModel allows to set params for preprocessing input image. KeypointsModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and returns the x and y coordinates of each detected keypoint

Constructor & Destructor Documentation

KeypointsModel() [1/2]

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

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

Create keypoints 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.

KeypointsModel() [2/2]

cv::dnn::KeypointsModel::KeypointsModel

(

const Net &

network

)

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

Create model from deep learning network.

Parameters

[in]

network

Net object.

Member Function Documentation

estimate()

std::vector< Point2f > cv::dnn::KeypointsModel::estimate	(	InputArray	frame,
		float	thresh = 0.5
	)

Python:
	cv.dnn.KeypointsModel.estimate(	frame[, thresh]	) ->	retval

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

Parameters

[in]	frame	The input image.
	thresh	minimum confidence threshold to select a keypoint

Returns

a vector holding the x and y coordinates of each detected keypoint

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

• opencv2/dnn/dnn.hpp