OpenCV  4.5.1-dev
Open Source Computer Vision
Adding a new algorithm to the Facemark API

Goals

In this tutorial you will learn how to:

Explanation

class CV_EXPORTS_W FacemarkNEW : public Facemark {
public:
struct CV_EXPORTS Config {
Config();
/*read only parameters - just for example*/
double detect_thresh;
double sigma;
void read(const FileNode& /*fn*/);
void write(FileStorage& /*fs*/) const;
};
/*Builder and destructor*/
static Ptr<FacemarkNEW> create(const FacemarkNEW::Config &conf = FacemarkNEW::Config() );
virtual ~FacemarkNEW(){};
};
#include "opencv2/face.hpp"
#include "precomp.hpp"
namespace cv
{
FacemarkNEW::Config::Config(){
detect_thresh = 0.5;
sigma=0.2;
}
*this = FacemarkNEW::Config();
if (!fn["detect_thresh"].empty())
fn["detect_thresh"] >> detect_thresh;
if (!fn["sigma"].empty())
fn["sigma"] >> sigma;
}
fs << "detect_thresh" << detect_thresh;
fs << "sigma" << sigma;
}
/*implementation of the algorithm is in this class*/
class FacemarkNEWImpl : public FacemarkNEW {
public:
FacemarkNEWImpl( const FacemarkNEW::Config &conf = FacemarkNEW::Config() );
void read( const FileNode& /*fn*/ );
void write( FileStorage& /*fs*/ ) const;
void loadModel(String filename);
bool setFaceDetector(bool(*f)(InputArray , OutputArray, void * extra_params));
bool getFaces( InputArray image , OutputArray faces, void * extra_params);
Config config;
protected:
bool addTrainingSample(InputArray image, InputArray landmarks);
void training();
bool fit(InputArray image, InputArray faces, InputOutputArray landmarks, void * runtime_params);
Config config; // configurations
/*proxy to the user defined face detector function*/
bool(*faceDetector)(InputArray , OutputArray, void * );
}; // class
Ptr<FacemarkNEW> FacemarkNEW::create(const FacemarkNEW::Config &conf){
return Ptr<FacemarkNEWImpl>(new FacemarkNEWImpl(conf));
}
FacemarkNEWImpl::FacemarkNEWImpl( const FacemarkNEW::Config &conf ) :
config( conf )
{
// other initialization
}
bool FacemarkNEWImpl::addTrainingSample(InputArray image, InputArray landmarks){
// pre-process and save the new training sample
return true;
}
void FacemarkNEWImpl::training(){
printf("training\n");
}
bool FacemarkNEWImpl::fit(
InputArray image,
InputArray faces,
InputOutputArray landmarks,
void * runtime_params)
{
if(runtime_params!=0){
// do something based on the extra parameters
}
printf("fitting\n");
return 0;
}
config.read( fn );
}
config.write( fs );
}
void FacemarkNEWImpl::loadModel(String filename){
// load the model
}
bool FacemarkNEWImpl::setFaceDetector(bool(*f)(InputArray , OutputArray, void * extra_params )){
faceDetector = f;
isSetDetector = true;
return true;
}
bool FacemarkNEWImpl::getFaces( InputArray image , OutputArray roi, void * extra_params){
if(!isSetDetector){
return false;
}
if(extra_params!=0){
//extract the extra parameters
}
std::vector<Rect> & faces = *(std::vector<Rect>*)roi.getObj();
faces.clear();
faceDetector(image.getMat(), faces, extra_params);
return true;
}
}

Best Practice