61 lines
3.0 KiB
C++
61 lines
3.0 KiB
C++
|
// This file is part of OpenCV project.
|
||
|
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||
|
|
// of this distribution and at http://opencv.org/license.html.
|
||
|
|
#ifndef __OPENCV_FACE_ALIGNMENT_HPP__
|
||
|
|
#define __OPENCV_FACE_ALIGNMENT_HPP__
|
||
|
|
|
||
|
|
#include "opencv2/face/facemark_train.hpp"
|
||
|
|
|
||
|
|
namespace cv{
|
||
|
|
namespace face{
|
||
|
|
class CV_EXPORTS_W FacemarkKazemi : public Facemark
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
struct CV_EXPORTS Params
|
||
|
|
{
|
||
|
|
/**
|
||
|
|
* \brief Constructor
|
||
|
|
*/
|
||
|
|
Params();
|
||
|
|
/// cascade_depth This stores the deapth of cascade used for training.
|
||
|
|
unsigned long cascade_depth;
|
||
|
|
/// tree_depth This stores the max height of the regression tree built.
|
||
|
|
unsigned long tree_depth;
|
||
|
|
/// num_trees_per_cascade_level This stores number of trees fit per cascade level.
|
||
|
|
unsigned long num_trees_per_cascade_level;
|
||
|
|
/// learning_rate stores the learning rate in gradient boosting, also referred as shrinkage.
|
||
|
|
float learning_rate;
|
||
|
|
/// oversampling_amount stores number of initialisations used to create training samples.
|
||
|
|
unsigned long oversampling_amount;
|
||
|
|
/// num_test_coordinates stores number of test coordinates.
|
||
|
|
unsigned long num_test_coordinates;
|
||
|
|
/// lambda stores a value to calculate probability of closeness of two coordinates.
|
||
|
|
float lambda;
|
||
|
|
/// num_test_splits stores number of random test splits generated.
|
||
|
|
unsigned long num_test_splits;
|
||
|
|
/// configfile stores the name of the file containing the values of training parameters
|
||
|
|
String configfile;
|
||
|
|
};
|
||
|
|
static Ptr<FacemarkKazemi> create(const FacemarkKazemi::Params ¶meters = FacemarkKazemi::Params());
|
||
|
|
virtual ~FacemarkKazemi();
|
||
|
|
|
||
|
|
/** @brief This function is used to train the model using gradient boosting to get a cascade of regressors
|
||
|
|
*which can then be used to predict shape.
|
||
|
|
*@param images A vector of type cv::Mat which stores the images which are used in training samples.
|
||
|
|
*@param landmarks A vector of vectors of type cv::Point2f which stores the landmarks detected in a particular image.
|
||
|
|
*@param scale A size of type cv::Size to which all images and landmarks have to be scaled to.
|
||
|
|
*@param configfile A variable of type std::string which stores the name of the file storing parameters for training the model.
|
||
|
|
*@param modelFilename A variable of type std::string which stores the name of the trained model file that has to be saved.
|
||
|
|
*@returns A boolean value. The function returns true if the model is trained properly or false if it is not trained.
|
||
|
|
*/
|
||
|
|
virtual bool training(std::vector<Mat>& images, std::vector< std::vector<Point2f> >& landmarks,std::string configfile,Size scale,std::string modelFilename = "face_landmarks.dat")=0;
|
||
|
|
|
||
|
|
/// set the custom face detector
|
||
|
|
virtual bool setFaceDetector(bool(*f)(InputArray , OutputArray, void*), void* userData)=0;
|
||
|
|
/// get faces using the custom detector
|
||
|
|
virtual bool getFaces(InputArray image, OutputArray faces)=0;
|
||
|
|
};
|
||
|
|
|
||
|
|
}} // namespace
|
||
|
|
#endif
|