cv::Algorithm Class Reference

This is a base class for all more or less complex algorithms in OpenCV. More...

#include <opencv2/core.hpp>

Inheritance diagram for cv::Algorithm:

Public Member Functions
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state. More...
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...
virtual void	save (const String &filename) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...
void	write (FileStorage &fs, const String &name) const
void	write (const Ptr< FileStorage > &fs, const String &name=String()) const

Static Public Member Functions
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file. More...
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

Protected Member Functions
void	writeFormat (FileStorage &fs) const

Detailed Description

This is a base class for all more or less complex algorithms in OpenCV.

especially for classes of algorithms, for which there can be multiple implementations. The examples are stereo correspondence (for which there are algorithms like block matching, semi-global block matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck etc.).

Here is example of SimpleBlobDetector use in your application via Algorithm interface:

    Ptr<Feature2D> sbd = SimpleBlobDetector::create();
    FileStorage fs_read("SimpleBlobDetector_params.xml", FileStorage::READ);

    if (fs_read.isOpened()) // if we have file with parameters, read them
    {
        sbd->read(fs_read.root());
        fs_read.release();
    }
    else // else modify the parameters and store them; user can later edit the file to use different parameters
    {
        fs_read.release();
        FileStorage fs_write("SimpleBlobDetector_params.xml", FileStorage::WRITE);
        sbd->write(fs_write);
        fs_write.release();
    }

    Mat result, image = imread("../data/detect_blob.png", IMREAD_COLOR);
    vector<KeyPoint> keypoints;
    sbd->detect(image, keypoints, Mat());

    drawKeypoints(image, keypoints, result);
    for (vector<KeyPoint>::iterator k = keypoints.begin(); k != keypoints.end(); ++k)
        circle(result, k->pt, (int)k->size, Scalar(0, 0, 255), 2);

    imshow("result", result);
    waitKey(0);

Constructor & Destructor Documentation

Algorithm()

cv::Algorithm::Algorithm

(

)

~Algorithm()

virtual cv::Algorithm::~Algorithm ( )

virtual

Member Function Documentation

clear()

virtual void cv::Algorithm::clear ( )

inlinevirtual

Python:
	cv.Algorithm.clear(		) ->	None

Clears the algorithm state.

Reimplemented in cv::FlannBasedMatcher, cv::line_descriptor::BinaryDescriptorMatcher, cv::DescriptorMatcher, and cv::cuda::DescriptorMatcher.

empty()

virtual bool cv::Algorithm::empty ( ) const

inlinevirtual

Python:
	cv.Algorithm.empty(		) ->	retval

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

Reimplemented in cv::DescriptorMatcher, cv::face::FaceRecognizer, cv::ml::StatModel, cv::Feature2D, cv::BaseCascadeClassifier, cv::cuda::DescriptorMatcher, and cv::face::BasicFaceRecognizer.

getDefaultName()

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

virtual

Python:
	cv.Algorithm.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 in cv::AKAZE, cv::KAZE, cv::SimpleBlobDetector, cv::GFTTDetector, cv::AgastFeatureDetector, cv::FastFeatureDetector, cv::MSER, cv::ORB, cv::BRISK, cv::SIFT, cv::AffineFeature, and cv::Feature2D.

load()

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::load ( const String &  filename, const String &  objname = String()  )

inlinestatic

Loads algorithm from the file.

Parameters

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

loadFromString()

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::loadFromString ( const String &  strModel, const String &  objname = String()  )

inlinestatic

Loads algorithm from a String.

Parameters

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

read() [1/2]

virtual void cv::Algorithm::read ( const FileNode &  fn )

inlinevirtual

Python:
	cv.Algorithm.read(	fn	) ->	None

Reads algorithm parameters from a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, cv::Tracker, cv::face::FaceRecognizer, cv::optflow::GPCForest< T >, cv::line_descriptor::BinaryDescriptor, cv::Feature2D, cv::optflow::GPCTree, cv::saliency::StaticSaliencySpectralResidual, and cv::face::BasicFaceRecognizer.

read() [2/2]

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::read ( const FileNode &  fn )

inlinestatic

Python:
	cv.Algorithm.read(	fn	) ->	None

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

cv::FileStorage fsRead("example.xml", FileStorage::READ);
Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

save()

virtual void cv::Algorithm::save ( const String &  filename ) const

virtual

Python:
	cv.Algorithm.save(	filename	) ->	None

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

write() [1/3]

virtual void cv::Algorithm::write ( FileStorage &  fs ) const

inlinevirtual

Python:
	cv.Algorithm.write(	fs	) ->	None
	cv.Algorithm.write(	fs, name	) ->	None

Stores algorithm parameters in a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, cv::Tracker, cv::face::FaceRecognizer, cv::optflow::GPCForest< T >, cv::bioinspired::Retina, cv::line_descriptor::BinaryDescriptor, cv::Feature2D, cv::optflow::GPCTree, cv::bioinspired::TransientAreasSegmentationModule, cv::saliency::StaticSaliencySpectralResidual, and cv::face::BasicFaceRecognizer.

write() [2/3]

void cv::Algorithm::write	(	FileStorage &	fs,
		const String &	name
	)		const

Python:
	cv.Algorithm.write(	fs	) ->	None
	cv.Algorithm.write(	fs, name	) ->	None

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

write() [3/3]

void cv::Algorithm::write	(	const Ptr< FileStorage > &	fs,
		const String &	name = String()
	)		const

Python:
	cv.Algorithm.write(	fs	) ->	None
	cv.Algorithm.write(	fs, name	) ->	None

Deprecated:

writeFormat()

void cv::Algorithm::writeFormat ( FileStorage &  fs ) const

protected

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

• opencv2/core.hpp