cv::face::Facemark Class Reference

Abstract base class for all facemark models. More...

#include <opencv2/face/facemark.hpp>

Collaboration diagram for cv::face::Facemark:

Public Member Functions
virtual bool	fit (InputArray image, InputArray faces, OutputArrayOfArrays landmarks)=0
	Detect facial landmarks from an image.
virtual void	loadModel (String model)=0
	A function to load the trained model before the fitting process.
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const

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

Detailed Description

Abstract base class for all facemark models.

To utilize this API in your program, please take a look at the Tutorial on Facial Landmark Detector API

Description

Facemark is a base class which provides universal access to any specific facemark algorithm. Therefore, the users should declare a desired algorithm before they can use it in their application.

Here is an example on how to declare a facemark algorithm:

// Using Facemark in your code:
Ptr<Facemark> facemark = createFacemarkLBF();

The typical pipeline for facemark detection is as follows:

• Load the trained model using Facemark::loadModel.
• Perform the fitting on an image via Facemark::fit.

Member Function Documentation

fit()

virtual bool cv::face::Facemark::fit ( InputArray image, InputArray faces, OutputArrayOfArrays landmarks )

pure virtual

Python:
	cv.face.Facemark.fit(	image, faces[, landmarks]	) ->	retval, landmarks

Detect facial landmarks from an image.

Parameters

image	Input image.
faces	Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.
landmarks	The detected landmark points for each faces.

Example of usage

Mat image = imread("image.jpg");
std::vector<Rect> faces;
std::vector<std::vector<Point2f> > landmarks;
facemark->fit(image, faces, landmarks);

loadModel()

virtual void cv::face::Facemark::loadModel ( String model )

pure virtual

Python:
	cv.face.Facemark.loadModel(	model	) ->	None

A function to load the trained model before the fitting process.

Parameters

model

A string represent the filename of a trained model.

Example of usage

facemark->loadModel("../data/lbf.model");

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

• opencv2/face/facemark.hpp