cv::DISK Class Reference

DISK feature detector and descriptor, based on a DNN model. More...

#include <opencv2/features.hpp>

Public Member Functions
virtual String	getDefaultName () const CV_OVERRIDE
virtual Size	getImageSize () const =0
virtual int	getMaxKeypoints () const =0
virtual float	getScoreThreshold () const =0
virtual void	setImageSize (const Size &size)=0
virtual void	setMaxKeypoints (int maxKeypoints)=0
virtual void	setScoreThreshold (float threshold)=0
Public Member Functions inherited from cv::Feature2D
virtual	~Feature2D ()
virtual void	compute (InputArray image, std::vector< KeyPoint > &keypoints, OutputArray descriptors)
	Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).
virtual void	compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors)
virtual int	defaultNorm () const
virtual int	descriptorSize () const
virtual int	descriptorType () const
virtual void	detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray())
	Detects keypoints in an image (first variant) or image set (second variant).
virtual void	detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray())
virtual void	detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
virtual bool	empty () const CV_OVERRIDE
	Return true if detector object is empty.
virtual void	read (const FileNode &) CV_OVERRIDE
	Reads algorithm parameters from a file storage.
void	read (const String &fileName)
void	write (const String &fileName) const
virtual void	write (FileStorage &) const CV_OVERRIDE
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual void	save (const String &filename) const
void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< DISK >	create (const std::vector< uchar > &bufferModel, int maxKeypoints=-1, float scoreThreshold=0.0f, const Size &imageSize=Size(), int backendId=0, int targetId=0)
	Creates a DISK detector from an in-memory model buffer.
static Ptr< DISK >	create (const String &modelPath, int maxKeypoints=-1, float scoreThreshold=0.0f, const Size &imageSize=Size(), int backendId=0, int targetId=0)
	Creates a DISK detector.
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp>
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.
template<typename _Tp>
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp>
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

DISK feature detector and descriptor, based on a DNN model.

DISK (Deep Image Structure and Keypoints) is a learned local-feature pipeline that produces keypoints and 128-D L2-normalized descriptors via a single forward pass through a fully convolutional network. This class wraps an ONNX export of the pre-trained DISK model through cv::dnn::Net and exposes it under the standard cv::Feature2D interface so it can be used as a drop-in alternative to SIFT/ORB.

The class assumes the ONNX model has a single input named image taking an N×3×H×W float32 tensor in [0, 1] (RGB channel order) and three outputs named keypoints (N×2), scores (N) and descriptors (N×128).

Member Function Documentation

create() [1/2]

Ptr< DISK > cv::DISK::create ( const std::vector< uchar > & bufferModel, int maxKeypoints = -1, float scoreThreshold = 0.0f, const Size & imageSize = Size(), int backendId = 0, int targetId = 0 )

static

Python:
	cv.DISK.create(	modelPath[, maxKeypoints[, scoreThreshold[, imageSize[, backendId[, targetId]]]]]	) ->	retval
	cv.DISK.createFromMemory(	bufferModel[, maxKeypoints[, scoreThreshold[, imageSize[, backendId[, targetId]]]]]	) ->	retval
	cv.DISK_create(	modelPath[, maxKeypoints[, scoreThreshold[, imageSize[, backendId[, targetId]]]]]	) ->	retval
	cv.DISK_createFromMemory(	bufferModel[, maxKeypoints[, scoreThreshold[, imageSize[, backendId[, targetId]]]]]	) ->	retval

Creates a DISK detector from an in-memory model buffer.

This overload loads the DISK ONNX model from a buffer instead of a file on disk. It is intended for cases where the model is read from application resources (for example Android assets) and is not available as a path on the filesystem.

Parameters

bufferModel	A buffer containing the contents of the DISK ONNX model.
maxKeypoints	Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
scoreThreshold	Discard keypoints with network score strictly below this value.
imageSize	Target input size (width, height) fed to the network. Use Size() (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
backendId	DNN backend identifier (see cv::dnn::Backend); 0 = DNN_BACKEND_DEFAULT.
targetId	DNN target identifier (see cv::dnn::Target); 0 = DNN_TARGET_CPU.

Note

In C++ this is an overload of create. The Python/Java/Objective-C bindings expose it as createFromMemory, because Objective-C selectors are not disambiguated by argument type and would otherwise clash with the file-path create.

create() [2/2]

Ptr< DISK > cv::DISK::create ( const String & modelPath, int maxKeypoints = -1, float scoreThreshold = 0.0f, const Size & imageSize = Size(), int backendId = 0, int targetId = 0 )

static

Python:
	cv.DISK.create(	modelPath[, maxKeypoints[, scoreThreshold[, imageSize[, backendId[, targetId]]]]]	) ->	retval
	cv.DISK.createFromMemory(	bufferModel[, maxKeypoints[, scoreThreshold[, imageSize[, backendId[, targetId]]]]]	) ->	retval
	cv.DISK_create(	modelPath[, maxKeypoints[, scoreThreshold[, imageSize[, backendId[, targetId]]]]]	) ->	retval
	cv.DISK_createFromMemory(	bufferModel[, maxKeypoints[, scoreThreshold[, imageSize[, backendId[, targetId]]]]]	) ->	retval

Creates a DISK detector.

Parameters

modelPath	Path to the DISK ONNX model.
maxKeypoints	Maximum number of keypoints to return per image. The strongest responses (by network score) are kept; -1 keeps all detections.
scoreThreshold	Discard keypoints with network score strictly below this value.
imageSize	Target input size (width, height) fed to the network. Use Size() (the default) to fall back to the network's expected fixed input shape of 1024x1024. When overriding, both dimensions must be positive multiples of 16, since DISK downsamples by a factor of 16.
backendId	DNN backend identifier (see cv::dnn::Backend); 0 = DNN_BACKEND_DEFAULT.
targetId	DNN target identifier (see cv::dnn::Target); 0 = DNN_TARGET_CPU.

getDefaultName()

virtual String cv::DISK::getDefaultName ( ) const

virtual

Python:
	cv.DISK.getDefaultName(		) ->	retval

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Reimplemented from cv::Feature2D.

getImageSize()

virtual Size cv::DISK::getImageSize ( ) const

pure virtual

Python:
	cv.DISK.getImageSize(		) ->	retval

getMaxKeypoints()

virtual int cv::DISK::getMaxKeypoints ( ) const

pure virtual

Python:
	cv.DISK.getMaxKeypoints(		) ->	retval

getScoreThreshold()

virtual float cv::DISK::getScoreThreshold ( ) const

pure virtual

Python:
	cv.DISK.getScoreThreshold(		) ->	retval

setImageSize()

virtual void cv::DISK::setImageSize ( const Size & size )

pure virtual

Python:
	cv.DISK.setImageSize(	size	) ->	None

setMaxKeypoints()

virtual void cv::DISK::setMaxKeypoints ( int maxKeypoints )

pure virtual

Python:
	cv.DISK.setMaxKeypoints(	maxKeypoints	) ->	None

setScoreThreshold()

virtual void cv::DISK::setScoreThreshold ( float threshold )

pure virtual

Python:
	cv.DISK.setScoreThreshold(	threshold	) ->	None

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

• opencv2/features.hpp