cv::face::FacemarkTrain クラス

Abstract base class for trainable facemark models [詳解]

#include <facemark_train.hpp>

cv::face::Facemarkを継承しています。

cv::face::FacemarkAAM, cv::face::FacemarkLBFに継承されています。

公開メンバ関数
virtual bool	addTrainingSample (InputArray image, InputArray landmarks)=0
	Add one training sample to the trainer. [詳解]
virtual void	training (void *parameters=0)=0
	Trains a Facemark algorithm using the given dataset. Before the training process, training samples should be added to the trainer using face::addTrainingSample function. [詳解]
virtual bool	setFaceDetector (FN_FaceDetector detector, void *userData=0)=0
	Set a user defined face detector for the Facemark algorithm. [詳解]
virtual bool	getFaces (InputArray image, OutputArray faces)=0
	Detect faces from a given image using default or user defined face detector. Some Algorithm might not provide a default face detector. [詳解]
virtual bool	getData (void *items=0)=0
	Get data from an algorithm [詳解]
基底クラス cv::face::Facemark に属する継承公開メンバ関数
virtual CV_WRAP void	loadModel (String model)=0
	A function to load the trained model before the fitting process. [詳解]
virtual CV_WRAP bool	fit (InputArray image, InputArray faces, OutputArrayOfArrays landmarks)=0
	Detect facial landmarks from an image. [詳解]
基底クラス cv::Algorithm に属する継承公開メンバ関数
virtual CV_WRAP void	clear ()
	Clears the algorithm state [詳解]
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage [詳解]
CV_WRAP void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。
virtual CV_WRAP void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage [詳解]
virtual CV_WRAP bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read [詳解]
virtual CV_WRAP void	save (const String &filename) const
virtual CV_WRAP String	getDefaultName () const

その他の継承メンバ
基底クラス cv::Algorithm に属する継承静的公開メンバ関数
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node [詳解]
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file [詳解]
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String [詳解]
基底クラス cv::Algorithm に属する継承限定公開メンバ関数
void	writeFormat (FileStorage &fs) const

詳解

Abstract base class for trainable facemark models

To utilize this API in your program, please take a look at the tutorial_table_of_content_facemark

Description

The AAM and LBF facemark models in OpenCV are derived from the abstract base class FacemarkTrain, which provides a unified access to those facemark algorithms in OpenCV.

Here is an example on how to declare facemark algorithm:

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

The typical pipeline for facemark detection is listed as follows:

• (Non-mandatory) Set a user defined face detection using FacemarkTrain::setFaceDetector. The facemark algorithms are designed to fit the facial points into a face. Therefore, the face information should be provided to the facemark algorithm. Some algorithms might provides a default face recognition function. However, the users might prefer to use their own face detector to obtains the best possible detection result.
• (Non-mandatory) Training the model for a specific algorithm using FacemarkTrain::training. In this case, the model should be automatically saved by the algorithm. If the user already have a trained model, then this part can be omitted.
• Load the trained model using Facemark::loadModel.
• Perform the fitting via the Facemark::fit.

関数詳解

addTrainingSample()

virtual bool cv::face::FacemarkTrain::addTrainingSample ( InputArray  image, InputArray  landmarks  )

pure virtual

Add one training sample to the trainer.

引数

image	Input image.
landmarks	The ground-truth of facial landmarks points corresponds to the image.

Example of usage

String imageFiles = "../data/images_train.txt";
String ptsFiles = "../data/points_train.txt";
std::vector<String> images_train;
std::vector<String> landmarks_train;
 
// load the list of dataset: image paths and landmark file paths
loadDatasetList(imageFiles,ptsFiles,images_train,landmarks_train);
 
Mat image;
std::vector<Point2f> facial_points;
for(size_t i=0;i<images_train.size();i++){
    image = imread(images_train[i].c_str());
    loadFacePoints(landmarks_train[i],facial_points);
    facemark->addTrainingSample(image, facial_points);
}

The contents in the training files should follows the standard format. Here are examples for the contents in these files. example of content in the images_train.txt

/home/user/ibug/image_003_1.jpg
/home/user/ibug/image_004_1.jpg
/home/user/ibug/image_005_1.jpg
/home/user/ibug/image_006.jpg

example of content in the points_train.txt

/home/user/ibug/image_003_1.pts
/home/user/ibug/image_004_1.pts
/home/user/ibug/image_005_1.pts
/home/user/ibug/image_006.pts

getData()

virtual bool cv::face::FacemarkTrain::getData ( void *  items = 0 )

pure virtual

Get data from an algorithm

引数

items

The obtained data, algorithm dependent.

Example of usage

Ptr<FacemarkAAM> facemark = FacemarkAAM::create();
facemark->loadModel("AAM.yml");
 
FacemarkAAM::Data data;
facemark->getData(&data);
std::vector<Point2f> s0 = data.s0;
 
cout<<s0<<endl;

getFaces()

virtual bool cv::face::FacemarkTrain::getFaces ( InputArray  image, OutputArray  faces  )

pure virtual

Detect faces from a given image using default or user defined face detector. Some Algorithm might not provide a default face detector.

引数

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.

Example of usage

std::vector<cv::Rect> faces;
facemark->getFaces(img, faces);
for(int j=0;j<faces.size();j++){
    cv::rectangle(img, faces[j], cv::Scalar(255,0,255));
}

setFaceDetector()

virtual bool cv::face::FacemarkTrain::setFaceDetector ( FN_FaceDetector  detector, void *  userData = 0  )

pure virtual

Set a user defined face detector for the Facemark algorithm.

引数

detector	The user defined face detector function
userData	Detector parameters

Example of usage

MyDetectorParameters detectorParameters(...);
facemark->setFaceDetector(myDetector, &detectorParameters);

Example of a user defined face detector

bool myDetector( InputArray image, OutputArray faces, void* userData)
{
    MyDetectorParameters* params = (MyDetectorParameters*)userData;
    // -------- do something --------
}

TODO Lifetime of detector parameters is uncontrolled. Rework interface design to "Ptr<FaceDetector>".

training()

virtual void cv::face::FacemarkTrain::training ( void *  parameters = 0 )

pure virtual

Trains a Facemark algorithm using the given dataset. Before the training process, training samples should be added to the trainer using face::addTrainingSample function.

引数

parameters

Optional extra parameters (algorithm dependent).

Example of usage

FacemarkLBF::Params params;
params.model_filename = "ibug68.model"; // filename to save the trained model
Ptr<Facemark> facemark = FacemarkLBF::create(params);
 
// add training samples (see Facemark::addTrainingSample)
 
facemark->training();

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このクラス詳解は次のファイルから抽出されました:

• facemark_train.hpp