/*
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license. If you do not agree to this license, do not download, install,
copy or use the software.
                          License Agreement
               For Open Source Computer Vision Library
                       (3-clause BSD License)
Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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the use of this software, even if advised of the possibility of such damage.

This file was part of GSoC Project: Facemark API for OpenCV
Final report: https://gist.github.com/kurnianggoro/74de9121e122ad0bd825176751d47ecc
Student: Laksono Kurnianggoro
Mentor: Delia Passalacqua
*/

#include "precomp.hpp"
#include "opencv2/face.hpp"
#include <fstream>
#include <cmath>
#include <ctime>
#include <cstdio>
#include <cstdarg>

namespace cv {
namespace face {

#define TIMER_BEGIN { double __time__ = (double)getTickCount();
#define TIMER_NOW   ((getTickCount() - __time__) / getTickFrequency())
#define TIMER_END   }

#define SIMILARITY_TRANSFORM(x, y, scale, rotate) do {            \
    double x_tmp = scale * (rotate(0, 0)*x + rotate(0, 1)*y); \
    double y_tmp = scale * (rotate(1, 0)*x + rotate(1, 1)*y); \
    x = x_tmp; y = y_tmp;                                     \
} while(0)

FacemarkLBF::Params::Params(){

    cascade_face = "";
    shape_offset = 0.0;
    n_landmarks = 68;
    initShape_n = 10;
    stages_n=5;
    tree_n=6;
    tree_depth=5;
    bagging_overlap = 0.4;
    model_filename = "";
    save_model = true;
    verbose = true;
    seed = 0;

    int _pupils[][6] = { { 36, 37, 38, 39, 40, 41 }, { 42, 43, 44, 45, 46, 47 } };
    for (int i = 0; i < 6; i++) {
        pupils[0].push_back(_pupils[0][i]);
        pupils[1].push_back(_pupils[1][i]);
    }

    int _feats_m[] = { 500, 500, 500, 300, 300, 300, 200, 200, 200, 100 };
    double _radius_m[] = { 0.3, 0.2, 0.15, 0.12, 0.10, 0.10, 0.08, 0.06, 0.06, 0.05 };
    for (int i = 0; i < 10; i++) {
        feats_m.push_back(_feats_m[i]);
        radius_m.push_back(_radius_m[i]);
    }

    detectROI = Rect(-1,-1,-1,-1);
}

void FacemarkLBF::Params::read( const cv::FileNode& fn ){
    *this = FacemarkLBF::Params();

    if (!fn["verbose"].empty())
        fn["verbose"] >> verbose;

}

void FacemarkLBF::Params::write( cv::FileStorage& fs ) const{
    fs << "verbose" << verbose;
}

class FacemarkLBFImpl : public FacemarkLBF {
public:
    FacemarkLBFImpl( const FacemarkLBF::Params &parameters = FacemarkLBF::Params() );

    void read( const FileNode& /*fn*/ ) CV_OVERRIDE;
    void write( FileStorage& /*fs*/ ) const CV_OVERRIDE;

    void loadModel(String fs) CV_OVERRIDE;

    bool setFaceDetector(bool(*f)(InputArray , OutputArray, void * extra_params ), void* userData) CV_OVERRIDE;
    bool getFaces(InputArray image, OutputArray faces) CV_OVERRIDE;
    bool getData(void * items) CV_OVERRIDE;

    Params params;

protected:

    bool fit( InputArray image, InputArray faces, OutputArrayOfArrays landmarks ) CV_OVERRIDE;//!< from many ROIs
    bool fitImpl( const Mat image, std::vector<Point2f> & landmarks );//!< from a face

    bool addTrainingSample(InputArray image, InputArray landmarks) CV_OVERRIDE;
    void training(void* parameters) CV_OVERRIDE;

    Rect getBBox(Mat &img, const Mat_<double> shape);
    void prepareTrainingData(Mat img, std::vector<Point2f> facePoints,
        std::vector<Mat> & cropped, std::vector<Mat> & shapes, std::vector<BBox> &boxes);
    void data_augmentation(std::vector<Mat> &imgs, std::vector<Mat> &gt_shapes, std::vector<BBox> &bboxes);
    Mat getMeanShape(std::vector<Mat> &gt_shapes, std::vector<BBox> &bboxes);

    bool defaultFaceDetector(const Mat& image, std::vector<Rect>& faces);

    CascadeClassifier face_cascade;
    FN_FaceDetector faceDetector;
    void* faceDetectorData;

    /*training data*/
    std::vector<std::vector<Point2f> > data_facemarks; //original position
    std::vector<Mat> data_faces; //face ROI
    std::vector<BBox> data_boxes;
    std::vector<Mat> data_shapes; //position in the face ROI

private:
    bool isModelTrained;

    /*---------------LBF Class---------------------*/
    class LBF {
    public:
        void calcSimilarityTransform(const Mat &shape1, const Mat &shape2, double &scale, Mat &rotate);
        std::vector<Mat> getDeltaShapes(std::vector<Mat> &gt_shapes, std::vector<Mat> &current_shapes,
                                   std::vector<BBox> &bboxes, Mat &mean_shape);
        double calcVariance(const Mat &vec);
        double calcVariance(const std::vector<double> &vec);
        double calcMeanError(std::vector<Mat> &gt_shapes, std::vector<Mat> &current_shapes, int landmark_n , std::vector<int> &left, std::vector<int> &right );

    };

    /*---------------RandomTree Class---------------------*/
    class RandomTree : public LBF {
    public:
        RandomTree(){};
        ~RandomTree(){};

        void initTree(int landmark_id, int depth, std::vector<int>, std::vector<double>);
        void train(std::vector<Mat> &imgs, std::vector<Mat> &current_shapes, std::vector<BBox> &bboxes,
                   std::vector<Mat> &delta_shapes, Mat &mean_shape, std::vector<int> &index, int stage);
        void splitNode(std::vector<cv::Mat> &imgs, std::vector<cv::Mat> &current_shapes, std::vector<BBox> &bboxes,
                      cv::Mat &delta_shapes, cv::Mat &mean_shape, std::vector<int> &root, int idx, int stage);

        void write(FileStorage fs, int forestId, int i, int j);
        void read(FileStorage fs, int forestId, int i, int j);

        int depth;
        int nodes_n;
        int landmark_id;
        cv::Mat_<double> feats;
        std::vector<int> thresholds;

        std::vector<int> params_feats_m;
        std::vector<double> params_radius_m;
    };
    /*---------------RandomForest Class---------------------*/
    class RandomForest : public LBF {
    public:
        RandomForest(){};
        ~RandomForest(){};

        void initForest(int landmark_n, int trees_n, int tree_depth, double ,  std::vector<int>, std::vector<double>, bool);
        void train(std::vector<cv::Mat> &imgs, std::vector<cv::Mat> &current_shapes, \
                   std::vector<BBox> &bboxes, std::vector<cv::Mat> &delta_shapes, cv::Mat &mean_shape, int stage);
        Mat generateLBF(Mat &img, Mat &current_shape, BBox &bbox, Mat &mean_shape);

        void write(FileStorage fs, int forestId);
        void read(FileStorage fs, int forestId);

        bool verbose;
        int landmark_n;
        int trees_n, tree_depth;
        double overlap_ratio;
        std::vector<std::vector<RandomTree> > random_trees;

        std::vector<int> feats_m;
        std::vector<double> radius_m;
    };
    /*---------------Regressor Class---------------------*/
    class Regressor  : public LBF {
    protected:
        struct feature_node{
            int index;
            double value;
        };
    public:
        Regressor(){};
        ~Regressor(){};

        void initRegressor(Params);
        void trainRegressor(std::vector<cv::Mat> &imgs, std::vector<cv::Mat> &gt_shapes, \
                   std::vector<cv::Mat> &current_shapes, std::vector<BBox> &bboxes, \
                   cv::Mat &mean_shape, int start_from, Params );
        Mat globalRegressionPredict(const Mat &lbf, int stage);
        Mat predict(Mat &img, BBox &bbox);

        void write(FileStorage fs, Params config);
        void read(FileStorage fs, Params & config);

        void globalRegressionTrain(
            std::vector<Mat> &lbfs, std::vector<Mat> &delta_shapes,
            int stage, Params config
        );

        Mat supportVectorRegression(
            feature_node **x, double *y, int nsamples, int feat_size, bool verbose=0
        );

        int stages_n;
        int landmark_n;
        cv::Mat mean_shape;
        std::vector<RandomForest> random_forests;
        std::vector<cv::Mat> gl_regression_weights;

    }; // LBF

    Regressor regressor;
}; // class

/*
* Constructor
*/
Ptr<FacemarkLBF> FacemarkLBF::create(const FacemarkLBF::Params &parameters){
    return Ptr<FacemarkLBFImpl>(new FacemarkLBFImpl(parameters));
}
/*
* Constructor
*/
Ptr<Facemark> createFacemarkLBF(){
    const FacemarkLBF::Params parameters;
    return Ptr<FacemarkLBFImpl>(new FacemarkLBFImpl(parameters));
}

FacemarkLBFImpl::FacemarkLBFImpl( const FacemarkLBF::Params &parameters ) :
    faceDetector(NULL), faceDetectorData(NULL)
{
    isModelTrained = false;
    params = parameters;
}

bool FacemarkLBFImpl::setFaceDetector(bool(*f)(InputArray , OutputArray, void * extra_params ), void* userData){
    faceDetector = f;
    faceDetectorData = userData;
    return true;
}

bool FacemarkLBFImpl::getFaces(InputArray image, OutputArray faces_)
{
    if (!faceDetector)
    {
        std::vector<Rect> faces;
        defaultFaceDetector(image.getMat(), faces);
        Mat(faces).copyTo(faces_);
        return true;
    }
    return faceDetector(image, faces_, faceDetectorData);
}

bool FacemarkLBFImpl::defaultFaceDetector(const Mat& image, std::vector<Rect>& faces){
    Mat gray;

    faces.clear();

    if (image.channels() > 1)
    {
        cvtColor(image, gray, COLOR_BGR2GRAY);
    }
    else
    {
        gray = image;
    }

    equalizeHist(gray, gray);

    if (face_cascade.empty())
    {
        { /* check the cascade classifier file */
            std::ifstream infile;
            infile.open(params.cascade_face.c_str(), std::ios::in);
            if (!infile)
                CV_Error_(Error::StsBadArg, ("The cascade classifier model is not found: %s", params.cascade_face.c_str()));
        }
        face_cascade.load(params.cascade_face.c_str());
        CV_Assert(!face_cascade.empty());
    }
    face_cascade.detectMultiScale(gray, faces, 1.05, 2, CASCADE_SCALE_IMAGE, Size(30, 30) );
    return true;
}

bool FacemarkLBFImpl::getData(void * items){
    CV_UNUSED(items);
    return false;
}

bool FacemarkLBFImpl::addTrainingSample(InputArray image, InputArray landmarks){
    // FIXIT
    std::vector<Point2f> & _landmarks = *(std::vector<Point2f>*)landmarks.getObj();
    prepareTrainingData(image.getMat(), _landmarks, data_faces, data_shapes, data_boxes);
    return true;
}

void FacemarkLBFImpl::training(void* parameters){
    CV_UNUSED(parameters);

    if (data_faces.empty())
    {
        CV_Error(Error::StsBadArg, "Training data is not provided. Consider to add using addTrainingSample() function!");
    }

    if (params.cascade_face.empty() || (params.model_filename.empty() && params.save_model))
    {
        CV_Error(Error::StsBadArg, "The parameter cascade_face and model_filename should be set!");
    }

    // flip the image and swap the landmark position
    data_augmentation(data_faces, data_shapes, data_boxes);

    Mat mean_shape = getMeanShape(data_shapes, data_boxes);

    int N = (int)data_faces.size();
    int L = N*params.initShape_n;
    std::vector<Mat> imgs(L), gt_shapes(L), current_shapes(L);
    std::vector<BBox> bboxes(L);
    RNG rng(params.seed);
    for (int i = 0; i < N; i++) {
        for (int j = 0; j < params.initShape_n; j++) {
            int idx = i*params.initShape_n + j;
            int k = rng.uniform(0, N - 1);
            k = (k >= i) ? k + 1 : k; // require k != i
            imgs[idx] = data_faces[i];
            gt_shapes[idx] = data_shapes[i];
            bboxes[idx] = data_boxes[i];
            current_shapes[idx] = data_boxes[i].reproject(data_boxes[k].project(data_shapes[k]));
        }
    }

    regressor.initRegressor(params);
    regressor.trainRegressor(imgs, gt_shapes, current_shapes, bboxes, mean_shape, 0, params);

    if(params.save_model){
        FileStorage fs(params.model_filename.c_str(),FileStorage::WRITE_BASE64);
        regressor.write(fs, params);
    }

    isModelTrained = true;
}

bool FacemarkLBFImpl::fit( InputArray image, InputArray roi, OutputArrayOfArrays  _landmarks )
{
    // FIXIT
    std::vector<Rect> & faces = *(std::vector<Rect> *)roi.getObj();
    if (faces.empty()) return false;

    std::vector<std::vector<Point2f> > & landmarks =
        *(std::vector<std::vector<Point2f> >*) _landmarks.getObj();

    landmarks.resize(faces.size());

    for(unsigned i=0; i<faces.size();i++){
        params.detectROI = faces[i];
        fitImpl(image.getMat(), landmarks[i]);
    }

    return true;
}

bool FacemarkLBFImpl::fitImpl( const Mat image, std::vector<Point2f>& landmarks){
    if (landmarks.size()>0)
        landmarks.clear();

    if (!isModelTrained) {
        CV_Error(Error::StsBadArg, "The LBF model is not trained yet. Please provide a trained model.");
    }

    Mat img;
    if(image.channels()>1){
        cvtColor(image,img,COLOR_BGR2GRAY);
    }else{
        img = image;
    }

    Rect box;
    if (params.detectROI.width>0){
        box = params.detectROI;
    }else{
        std::vector<Rect> rects;

        if (!getFaces(img, rects)) return 0;
        if (rects.empty())  return 0; //failed to get face
        box = rects[0];
    }

    double min_x, min_y, max_x, max_y;
    min_x = std::max(0., (double)box.x - box.width / 2);
    max_x = std::min(img.cols - 1., (double)box.x+box.width + box.width / 2);
    min_y = std::max(0., (double)box.y - box.height / 2);
    max_y = std::min(img.rows - 1., (double)box.y + box.height + box.height / 2);

    double w = max_x - min_x;
    double h = max_y - min_y;

    BBox bbox(box.x - min_x, box.y - min_y, box.width, box.height);
    Mat crop = img(Rect((int)min_x, (int)min_y, (int)w, (int)h)).clone();
    Mat shape = regressor.predict(crop, bbox);

    if(params.detectROI.width>0){
        landmarks = Mat(shape.reshape(2)+Scalar(min_x, min_y));
        params.detectROI.width = -1;
    }else{
        landmarks = Mat(shape.reshape(2)+Scalar(min_x, min_y));
    }

    return 1;
}

void FacemarkLBFImpl::read( const cv::FileNode& fn ){
    params.read( fn );
}

void FacemarkLBFImpl::write( cv::FileStorage& fs ) const {
    params.write( fs );
}

void FacemarkLBFImpl::loadModel(String s){
    if(params.verbose) printf("loading data from : %s\n", s.c_str());
    std::ifstream infile;
    infile.open(s.c_str(), std::ios::in);
    if (!infile) {
        CV_Error(Error::StsBadArg, "No valid input file was given, please check the given filename.");
    }

    FileStorage fs(s.c_str(),FileStorage::READ);
    regressor.read(fs, params);

    isModelTrained = true;
}

Rect FacemarkLBFImpl::getBBox(Mat &img, const Mat_<double> shape) {
    std::vector<Rect> rects;

    if(!faceDetector){
        defaultFaceDetector(img, rects);
    }else{
        faceDetector(img, rects, faceDetectorData);
    }

    if (rects.size() == 0) return Rect(-1, -1, -1, -1);
    double center_x=0, center_y=0, min_x, max_x, min_y, max_y;

    min_x = shape(0, 0);
    max_x = shape(0, 0);
    min_y = shape(0, 1);
    max_y = shape(0, 1);

    for (int i = 0; i < shape.rows; i++) {
        center_x += shape(i, 0);
        center_y += shape(i, 1);
        min_x = std::min(min_x, shape(i, 0));
        max_x = std::max(max_x, shape(i, 0));
        min_y = std::min(min_y, shape(i, 1));
        max_y = std::max(max_y, shape(i, 1));
    }
    center_x /= shape.rows;
    center_y /= shape.rows;

    for (int i = 0; i < (int)rects.size(); i++) {
        Rect r = rects[i];
        if (max_x - min_x > r.width*1.5) continue;
        if (max_y - min_y > r.height*1.5) continue;
        if (abs(center_x - (r.x + r.width / 2)) > r.width / 2) continue;
        if (abs(center_y - (r.y + r.height / 2)) > r.height / 2) continue;
        return r;
    }
    return Rect(-1, -1, -1, -1);
}

void FacemarkLBFImpl::prepareTrainingData(Mat img, std::vector<Point2f> facePoints,
    std::vector<Mat> & cropped, std::vector<Mat> & shapes, std::vector<BBox> &boxes)
{
    if(img.channels()>1){
        cvtColor(img,img,COLOR_BGR2GRAY);
    }

    Mat shape;
    Mat _shape = Mat(facePoints).reshape(1);
    Rect box = getBBox(img, _shape);
    if(box.x != -1){
        _shape.convertTo(shape, CV_64FC1);
        Mat sx = shape.col(0);
        Mat sy = shape.col(1);
        double min_x, max_x, min_y, max_y;
        minMaxIdx(sx, &min_x, &max_x);
        minMaxIdx(sy, &min_y, &max_y);

        min_x = std::max(0., min_x - (double)box.width / 2.);
        max_x = std::min(img.cols - 1., max_x + (double)box.width / 2.);
        min_y = std::max(0., min_y - (double)box.height / 2.);
        max_y = std::min(img.rows - 1., max_y + (double)box.height / 2.);

        double w = max_x - min_x;
        double h = max_y - min_y;

        shape = Mat(shape.reshape(2)-Scalar(min_x, min_y)).reshape(1);

        boxes.push_back(BBox(box.x - min_x, box.y - min_y, box.width, box.height));
        Mat crop = img(Rect((int)min_x, (int)min_y, (int)w, (int)h)).clone();
        cropped.push_back(crop);
        shapes.push_back(shape);

    } // if box is valid
} // prepareTrainingData

void FacemarkLBFImpl::data_augmentation(std::vector<Mat> &imgs, std::vector<Mat> &gt_shapes, std::vector<BBox> &bboxes) {
    int N = (int)imgs.size();
    imgs.reserve(2 * N);
    gt_shapes.reserve(2 * N);
    bboxes.reserve(2 * N);
    for (int i = 0; i < N; i++) {
        Mat img_flipped;
        Mat_<double> gt_shape_flipped(gt_shapes[i].size());
        flip(imgs[i], img_flipped, 1);
        int w = img_flipped.cols - 1;
        // int h = img_flipped.rows - 1;
        for (int k = 0; k < gt_shapes[i].rows; k++) {
            gt_shape_flipped(k, 0) = w - gt_shapes[i].at<double>(k, 0);
            gt_shape_flipped(k, 1) = gt_shapes[i].at<double>(k, 1);
        }
        int x_b, y_b, w_b, h_b;
        x_b = w - (int)bboxes[i].x - (int)bboxes[i].width;
        y_b = (int)bboxes[i].y;
        w_b = (int)bboxes[i].width;
        h_b = (int)bboxes[i].height;
        BBox bbox_flipped(x_b, y_b, w_b, h_b);

        imgs.push_back(img_flipped);
        gt_shapes.push_back(gt_shape_flipped);
        bboxes.push_back(bbox_flipped);

    }
#define SWAP(shape, i, j) do { \
        double tmp = shape.at<double>(i-1, 0); \
        shape.at<double>(i-1, 0) = shape.at<double>(j-1, 0); \
        shape.at<double>(j-1, 0) = tmp; \
        tmp =  shape.at<double>(i-1, 1); \
        shape.at<double>(i-1, 1) = shape.at<double>(j-1, 1); \
        shape.at<double>(j-1, 1) = tmp; \
    } while(0)

    if (params.n_landmarks == 29) {
        for (int i = N; i < (int)gt_shapes.size(); i++) {
            SWAP(gt_shapes[i], 1, 2);
            SWAP(gt_shapes[i], 3, 4);
            SWAP(gt_shapes[i], 5, 7);
            SWAP(gt_shapes[i], 6, 8);
            SWAP(gt_shapes[i], 13, 15);
            SWAP(gt_shapes[i], 9, 10);
            SWAP(gt_shapes[i], 11, 12);
            SWAP(gt_shapes[i], 17, 18);
            SWAP(gt_shapes[i], 14, 16);
            SWAP(gt_shapes[i], 19, 20);
            SWAP(gt_shapes[i], 23, 24);
        }
    }
    else if (params.n_landmarks == 68) {
        for (int i = N; i < (int)gt_shapes.size(); i++) {
            for (int k = 1; k <= 8; k++) SWAP(gt_shapes[i], k, 18 - k);
            for (int k = 18; k <= 22; k++) SWAP(gt_shapes[i], k, 45 - k);
            for (int k = 37; k <= 40; k++) SWAP(gt_shapes[i], k, 83 - k);
            SWAP(gt_shapes[i], 42, 47);
            SWAP(gt_shapes[i], 41, 48);
            SWAP(gt_shapes[i], 32, 36);
            SWAP(gt_shapes[i], 33, 35);
            for (int k = 49; k <= 51; k++) SWAP(gt_shapes[i], k, 104 - k);
            SWAP(gt_shapes[i], 60, 56);
            SWAP(gt_shapes[i], 59, 57);
            SWAP(gt_shapes[i], 61, 65);
            SWAP(gt_shapes[i], 62, 64);
            SWAP(gt_shapes[i], 68, 66);
        }
    }
    else {
        printf("Wrong n_landmarks, currently only 29 and 68 landmark points are supported");
    }

#undef SWAP

}

FacemarkLBFImpl::BBox::BBox() {}
FacemarkLBFImpl::BBox::~BBox() {}

FacemarkLBFImpl::BBox::BBox(double _x, double _y, double w, double h) {
    x = _x;
    y = _y;
    width = w;
    height = h;
    x_center = x + w / 2.;
    y_center = y + h / 2.;
    x_scale = w / 2.;
    y_scale = h / 2.;
}

// Project absolute shape to relative shape binding to this bbox
Mat FacemarkLBFImpl::BBox::project(const Mat &shape) const {
    Mat_<double> res(shape.rows, shape.cols);
    const Mat_<double> &shape_ = (Mat_<double>)shape;
    for (int i = 0; i < shape.rows; i++) {
        res(i, 0) = (shape_(i, 0) - x_center) / x_scale;
        res(i, 1) = (shape_(i, 1) - y_center) / y_scale;
    }
    return res;
}

// Project relative shape to absolute shape binding to this bbox
Mat FacemarkLBFImpl::BBox::reproject(const Mat &shape) const {
    Mat_<double> res(shape.rows, shape.cols);
    const Mat_<double> &shape_ = (Mat_<double>)shape;
    for (int i = 0; i < shape.rows; i++) {
        res(i, 0) = shape_(i, 0)*x_scale + x_center;
        res(i, 1) = shape_(i, 1)*y_scale + y_center;
    }
    return res;
}

Mat FacemarkLBFImpl::getMeanShape(std::vector<Mat> &gt_shapes, std::vector<BBox> &bboxes) {

    int N = (int)gt_shapes.size();
    Mat mean_shape = Mat::zeros(gt_shapes[0].rows, 2, CV_64FC1);
    for (int i = 0; i < N; i++) {
        mean_shape += bboxes[i].project(gt_shapes[i]);
    }
    mean_shape /= N;
    return mean_shape;
}

// Similarity Transform, project shape2 to shape1
// p1 ~= scale * rotate * p2, p1 and p2 are vector in math
void FacemarkLBFImpl::LBF::calcSimilarityTransform(const Mat &shape1, const Mat &shape2, double &scale, Mat &rotate) {
    Mat_<double> rotate_(2, 2);
    double x1_center, y1_center, x2_center, y2_center;
    x1_center = cv::mean(shape1.col(0))[0];
    y1_center = cv::mean(shape1.col(1))[0];
    x2_center = cv::mean(shape2.col(0))[0];
    y2_center = cv::mean(shape2.col(1))[0];

    Mat temp1(shape1.rows, shape1.cols, CV_64FC1);
    Mat temp2(shape2.rows, shape2.cols, CV_64FC1);
    temp1.col(0) = shape1.col(0) - x1_center;
    temp1.col(1) = shape1.col(1) - y1_center;
    temp2.col(0) = shape2.col(0) - x2_center;
    temp2.col(1) = shape2.col(1) - y2_center;

    Mat_<double> covar1, covar2;
    Mat_<double> mean1, mean2;
    calcCovarMatrix(temp1, covar1, mean1, COVAR_COLS);
    calcCovarMatrix(temp2, covar2, mean2, COVAR_COLS);

    double s1 = sqrt(cv::norm(covar1));
    double s2 = sqrt(cv::norm(covar2));
    scale = s1 / s2;
    temp1 /= s1;
    temp2 /= s2;

    double num = temp1.col(1).dot(temp2.col(0)) - temp1.col(0).dot(temp2.col(1));
    double den = temp1.col(0).dot(temp2.col(0)) + temp1.col(1).dot(temp2.col(1));
    double normed = sqrt(num*num + den*den);
    double sin_theta = num / normed;
    double cos_theta = den / normed;
    rotate_(0, 0) = cos_theta; rotate_(0, 1) = -sin_theta;
    rotate_(1, 0) = sin_theta; rotate_(1, 1) = cos_theta;
    rotate = rotate_;
}

// Get relative delta_shapes for predicting target
std::vector<Mat> FacemarkLBFImpl::LBF::getDeltaShapes(std::vector<Mat> &gt_shapes, std::vector<Mat> &current_shapes,
                           std::vector<BBox> &bboxes, Mat &mean_shape) {
    std::vector<Mat> delta_shapes;
    int N = (int)gt_shapes.size();
    delta_shapes.resize(N);
    double scale;
    Mat_<double> rotate;
    for (int i = 0; i < N; i++) {
        delta_shapes[i] = bboxes[i].project(gt_shapes[i]) - bboxes[i].project(current_shapes[i]);
        calcSimilarityTransform(mean_shape, bboxes[i].project(current_shapes[i]), scale, rotate);
        // delta_shapes[i] = scale * delta_shapes[i] * rotate.t(); // the result is better without this part
    }
    return delta_shapes;
}

double FacemarkLBFImpl::LBF::calcVariance(const Mat &vec) {
    double m1 = cv::mean(vec)[0];
    double m2 = cv::mean(vec.mul(vec))[0];
    double variance = m2 - m1*m1;
    return variance;
}

double FacemarkLBFImpl::LBF::calcVariance(const std::vector<double> &vec) {
    if (vec.size() == 0) return 0.;
    Mat_<double> vec_(vec);
    double m1 = cv::mean(vec_)[0];
    double m2 = cv::mean(vec_.mul(vec_))[0];
    double variance = m2 - m1*m1;
    return variance;
}

double FacemarkLBFImpl::LBF::calcMeanError(std::vector<Mat> &gt_shapes, std::vector<Mat> &current_shapes, int landmark_n , std::vector<int> &left, std::vector<int> &right ) {
    int N = (int)gt_shapes.size();

    double e = 0;
    // every train data
    for (int i = 0; i < N; i++) {
        const Mat_<double> &gt_shape = (Mat_<double>)gt_shapes[i];
        const Mat_<double> &current_shape = (Mat_<double>)current_shapes[i];
        double x1, y1, x2, y2;
        x1 = x2 = y1 = y2 = 0;
        for (int j = 0; j < (int)left.size(); j++) {
            x1 += gt_shape(left[j], 0);
            y1 += gt_shape(left[j], 1);
        }
        for (int j = 0; j < (int)right.size(); j++) {
            x2 += gt_shape(right[j], 0);
            y2 += gt_shape(right[j], 1);
        }
        x1 /= left.size(); y1 /= left.size();
        x2 /= right.size(); y2 /= right.size();
        double pupils_distance = sqrt((x2 - x1)*(x2 - x1) + (y2 - y1)*(y2 - y1));
        // every landmark
        double e_ = 0;
        for (int j = 0; j < landmark_n; j++) {
            e_ += norm(gt_shape.row(j) - current_shape.row(j));
        }
        e += e_ / pupils_distance;
    }
    e /= N*landmark_n;
    return e;
}

/*---------------RandomTree Implementation---------------------*/
void FacemarkLBFImpl::RandomTree::initTree(int _landmark_id, int _depth, std::vector<int> feats_m, std::vector<double> radius_m) {
    landmark_id = _landmark_id;
    depth = _depth;
    nodes_n = 1 << depth;
    feats = Mat::zeros(nodes_n, 4, CV_64FC1);
    thresholds.resize(nodes_n);

    params_feats_m = feats_m;
    params_radius_m = radius_m;
}

void FacemarkLBFImpl::RandomTree::train(std::vector<Mat> &imgs, std::vector<Mat> &current_shapes, std::vector<BBox> &bboxes,
                       std::vector<Mat> &delta_shapes, Mat &mean_shape, std::vector<int> &index, int stage) {
    Mat_<double> delta_shapes_((int)delta_shapes.size(), 2);
    for (int i = 0; i < (int)delta_shapes.size(); i++) {
        delta_shapes_(i, 0) = delta_shapes[i].at<double>(landmark_id, 0);
        delta_shapes_(i, 1) = delta_shapes[i].at<double>(landmark_id, 1);
    }
    splitNode(imgs, current_shapes, bboxes, delta_shapes_, mean_shape, index, 1, stage);
}

void FacemarkLBFImpl::RandomTree::splitNode(std::vector<Mat> &imgs, std::vector<Mat> &current_shapes, std::vector<BBox> &bboxes,
                           Mat &delta_shapes, Mat &mean_shape, std::vector<int> &root, int idx, int stage) {

    int N = (int)root.size();
    if (N == 0) {
        thresholds[idx] = 0;
        feats.row(idx).setTo(0);
        std::vector<int> left, right;
        // split left and right child in DFS
        if (2 * idx < feats.rows / 2)
            splitNode(imgs, current_shapes, bboxes, delta_shapes, mean_shape, left, 2 * idx, stage);
        if (2 * idx + 1 < feats.rows / 2)
            splitNode(imgs, current_shapes, bboxes, delta_shapes, mean_shape, right, 2 * idx + 1, stage);
        return;
    }

    int feats_m = params_feats_m[stage];
    double radius_m = params_radius_m[stage];
    Mat_<double> candidate_feats(feats_m, 4);
    RNG rng(getTickCount());
    // generate feature pool
    for (int i = 0; i < feats_m; i++) {
        double x1, y1, x2, y2;
        x1 = rng.uniform(-1., 1.); y1 = rng.uniform(-1., 1.);
        x2 = rng.uniform(-1., 1.); y2 = rng.uniform(-1., 1.);
        if (x1*x1 + y1*y1 > 1. || x2*x2 + y2*y2 > 1.) {
            i--;
            continue;
        }
        candidate_feats[i][0] = x1 * radius_m;
        candidate_feats[i][1] = y1 * radius_m;
        candidate_feats[i][2] = x2 * radius_m;
        candidate_feats[i][3] = y2 * radius_m;
    }
    // calc features
    Mat_<int> densities(feats_m, N);
    for (int i = 0; i < N; i++) {
        double scale;
        Mat_<double> rotate;
        const Mat_<double> &current_shape = (Mat_<double>)current_shapes[root[i]];
        BBox &bbox = bboxes[root[i]];
        Mat &img = imgs[root[i]];
        calcSimilarityTransform(bbox.project(current_shape), mean_shape, scale, rotate);
        for (int j = 0; j < feats_m; j++) {
            double x1 = candidate_feats(j, 0);
            double y1 = candidate_feats(j, 1);
            double x2 = candidate_feats(j, 2);
            double y2 = candidate_feats(j, 3);
            SIMILARITY_TRANSFORM(x1, y1, scale, rotate);
            SIMILARITY_TRANSFORM(x2, y2, scale, rotate);

            x1 = x1*bbox.x_scale + current_shape(landmark_id, 0);
            y1 = y1*bbox.y_scale + current_shape(landmark_id, 1);
            x2 = x2*bbox.x_scale + current_shape(landmark_id, 0);
            y2 = y2*bbox.y_scale + current_shape(landmark_id, 1);
            x1 = max(0., min(img.cols - 1., x1)); y1 = max(0., min(img.rows - 1., y1));
            x2 = max(0., min(img.cols - 1., x2)); y2 = max(0., min(img.rows - 1., y2));
            densities(j, i) = (int)img.at<uchar>(int(y1), int(x1)) - (int)img.at<uchar>(int(y2), int(x2));
        }
    }
    Mat_<int> densities_sorted;
    cv::sort(densities, densities_sorted, SORT_EVERY_ROW + SORT_ASCENDING);
    //select a feat which reduces maximum variance
    double variance_all = (calcVariance(delta_shapes.col(0)) + calcVariance(delta_shapes.col(1)))*N;
    double variance_reduce_max = 0;
    int threshold = 0;
    int feat_id = 0;
    std::vector<double> left_x, left_y, right_x, right_y;
    left_x.reserve(N); left_y.reserve(N);
    right_x.reserve(N); right_y.reserve(N);
    for (int j = 0; j < feats_m; j++) {
        left_x.clear(); left_y.clear();
        right_x.clear(); right_y.clear();
        int threshold_ = densities_sorted(j, (int)(N*rng.uniform(0.05, 0.95)));
        for (int i = 0; i < N; i++) {
            if (densities(j, i) < threshold_) {
                left_x.push_back(delta_shapes.at<double>(root[i], 0));
                left_y.push_back(delta_shapes.at<double>(root[i], 1));
            }
            else {
                right_x.push_back(delta_shapes.at<double>(root[i], 0));
                right_y.push_back(delta_shapes.at<double>(root[i], 1));
            }
        }
        double variance_ = (calcVariance(left_x) + calcVariance(left_y))*left_x.size() + \
            (calcVariance(right_x) + calcVariance(right_y))*right_x.size();
        double variance_reduce = variance_all - variance_;
        if (variance_reduce > variance_reduce_max) {
            variance_reduce_max = variance_reduce;
            threshold = threshold_;
            feat_id = j;
        }
    }
    thresholds[idx] = threshold;
    feats(idx, 0) = candidate_feats(feat_id, 0); feats(idx, 1) = candidate_feats(feat_id, 1);
    feats(idx, 2) = candidate_feats(feat_id, 2); feats(idx, 3) = candidate_feats(feat_id, 3);
    // generate left and right child
    std::vector<int> left, right;
    left.reserve(N);
    right.reserve(N);
    for (int i = 0; i < N; i++) {
        if (densities(feat_id, i) < threshold) left.push_back(root[i]);
        else right.push_back(root[i]);
    }
    // split left and right child in DFS
    if (2 * idx < feats.rows / 2)
        splitNode(imgs, current_shapes, bboxes, delta_shapes, mean_shape, left, 2 * idx, stage);
    if (2 * idx + 1 < feats.rows / 2)
        splitNode(imgs, current_shapes, bboxes, delta_shapes, mean_shape, right, 2 * idx + 1, stage);
}

void FacemarkLBFImpl::RandomTree::write(FileStorage fs, int k, int i, int j) {

    String x;
    x = cv::format("tree_%i_%i_%i",k,i,j);
    fs << x << feats;
    x = cv::format("thresholds_%i_%i_%i",k,i,j);
    fs << x << thresholds;
}

void FacemarkLBFImpl::RandomTree::read(FileStorage fs, int k, int i, int j) {
    String x;
    x = cv::format("tree_%i_%i_%i",k,i,j);
    fs[x] >> feats;
    x = cv::format("thresholds_%i_%i_%i",k,i,j);
    fs[x] >> thresholds;
}


/*---------------RandomForest Implementation---------------------*/
void FacemarkLBFImpl::RandomForest::initForest(
    int _landmark_n,
    int _trees_n,
    int _tree_depth,
    double _overlap_ratio,
    std::vector<int>_feats_m,
    std::vector<double>_radius_m,
    bool verbose_mode
) {
    trees_n = _trees_n;
    landmark_n = _landmark_n;
    tree_depth = _tree_depth;
    overlap_ratio = _overlap_ratio;

    feats_m = _feats_m;
    radius_m = _radius_m;

    verbose = verbose_mode;

    random_trees.resize(landmark_n);
    for (int i = 0; i < landmark_n; i++) {
        random_trees[i].resize(trees_n);
        for (int j = 0; j < trees_n; j++) random_trees[i][j].initTree(i, tree_depth, feats_m, radius_m);
    }
}

void FacemarkLBFImpl::RandomForest::train(std::vector<Mat> &imgs, std::vector<Mat> &current_shapes, \
                         std::vector<BBox> &bboxes, std::vector<Mat> &delta_shapes, Mat &mean_shape, int stage) {
    int N = (int)imgs.size();
    int Q = int(N / ((1. - overlap_ratio) * trees_n));

    #ifdef _OPENMP
    #pragma omp parallel for
    #endif
    for (int i = 0; i < landmark_n; i++) {
    TIMER_BEGIN
        std::vector<int> root;
        for (int j = 0; j < trees_n; j++) {
            int start = max(0, int(floor(j*Q - j*Q*overlap_ratio)));
            int end = min(int(start + Q + 1), N);
            int L = end - start;
            root.resize(L);
            for (int k = 0; k < L; k++) root[k] = start + k;
            random_trees[i][j].train(imgs, current_shapes, bboxes, delta_shapes, mean_shape, root, stage);
        }
        if(verbose) printf("Train %2dth of %d landmark Done, it costs %.4lf s\n", i+1, landmark_n, TIMER_NOW);
    TIMER_END
    }
}

Mat FacemarkLBFImpl::RandomForest::generateLBF(Mat &img, Mat &current_shape, BBox &bbox, Mat &mean_shape) {
    Mat_<int> lbf_feat(1, landmark_n*trees_n);
    double scale;
    Mat_<double> rotate;
    calcSimilarityTransform(bbox.project(current_shape), mean_shape, scale, rotate);

    int base = 1 << (tree_depth - 1);

    #ifdef _OPENMP
    #pragma omp parallel for
    #endif
    for (int i = 0; i < landmark_n; i++) {
        for (int j = 0; j < trees_n; j++) {
            RandomTree &tree = random_trees[i][j];
            int code = 0;
            int idx = 1;
            for (int k = 1; k < tree.depth; k++) {
                double x1 = tree.feats(idx, 0);
                double y1 = tree.feats(idx, 1);
                double x2 = tree.feats(idx, 2);
                double y2 = tree.feats(idx, 3);
                SIMILARITY_TRANSFORM(x1, y1, scale, rotate);
                SIMILARITY_TRANSFORM(x2, y2, scale, rotate);

                x1 = x1*bbox.x_scale + current_shape.at<double>(i, 0);
                y1 = y1*bbox.y_scale + current_shape.at<double>(i, 1);
                x2 = x2*bbox.x_scale + current_shape.at<double>(i, 0);
                y2 = y2*bbox.y_scale + current_shape.at<double>(i, 1);
                x1 = max(0., min(img.cols - 1., x1)); y1 = max(0., min(img.rows - 1., y1));
                x2 = max(0., min(img.cols - 1., x2)); y2 = max(0., min(img.rows - 1., y2));
                int density = img.at<uchar>(int(y1), int(x1)) - img.at<uchar>(int(y2), int(x2));
                code <<= 1;
                if (density < tree.thresholds[idx]) {
                    idx = 2 * idx;
                }
                else {
                    code += 1;
                    idx = 2 * idx + 1;
                }
            }
            lbf_feat(i*trees_n + j) = (i*trees_n + j)*base + code;
        }
    }
    return lbf_feat;
}

void FacemarkLBFImpl::RandomForest::write(FileStorage fs, int k) {
    for (int i = 0; i < landmark_n; i++) {
        for (int j = 0; j < trees_n; j++) {
            random_trees[i][j].write(fs,k,i,j);
        }
    }
}

void FacemarkLBFImpl::RandomForest::read(FileStorage fs,int k)
{
    for (int i = 0; i < landmark_n; i++) {
        for (int j = 0; j < trees_n; j++) {
            random_trees[i][j].initTree(i, tree_depth, feats_m, radius_m);
            random_trees[i][j].read(fs,k,i,j);
        }
    }
}

/*---------------Regressor Implementation---------------------*/
void FacemarkLBFImpl::Regressor::initRegressor(Params config) {
    stages_n = config.stages_n;
    landmark_n = config.n_landmarks;

    random_forests.resize(stages_n);
    for (int i = 0; i < stages_n; i++)
        random_forests[i].initForest(
            config.n_landmarks,
            config.tree_n,
            config.tree_depth,
            config.bagging_overlap,
            config.feats_m,
            config.radius_m,
            config.verbose
        );

    mean_shape.create(config.n_landmarks, 2, CV_64FC1);

    gl_regression_weights.resize(stages_n);
    int F = config.n_landmarks * config.tree_n * (1 << (config.tree_depth - 1));

    for (int i = 0; i < stages_n; i++) {
        gl_regression_weights[i].create(2 * config.n_landmarks, F, CV_64FC1);
    }
}

void FacemarkLBFImpl::Regressor::trainRegressor(std::vector<Mat> &imgs, std::vector<Mat> &gt_shapes, std::vector<Mat> &current_shapes,
                        std::vector<BBox> &bboxes, Mat &mean_shape_, int start_from, Params config) {
    CV_Assert(start_from >= 0 && start_from < stages_n);
    mean_shape = mean_shape_;
    int N = (int)imgs.size();

    for (int k = start_from; k < stages_n; k++) {
        std::vector<Mat> delta_shapes = getDeltaShapes(gt_shapes, current_shapes, bboxes, mean_shape);

        // train random forest
        if(config.verbose) printf("training random forest %dth of %d stages, ",k+1, stages_n);
        TIMER_BEGIN
            random_forests[k].train(imgs, current_shapes, bboxes, delta_shapes, mean_shape, k);
            if(config.verbose) printf("costs %.4lf s\n",  TIMER_NOW);
        TIMER_END

        // generate lbf of every train data
        std::vector<Mat> lbfs;
        lbfs.resize(N);
        for (int i = 0; i < N; i++) {
            lbfs[i] = random_forests[k].generateLBF(imgs[i], current_shapes[i], bboxes[i], mean_shape);
        }

        // global regression
        if(config.verbose) printf("start train global regression of %dth stage\n", k);
        TIMER_BEGIN
            globalRegressionTrain(lbfs, delta_shapes, k, config);
            if(config.verbose) printf("end of train global regression of %dth stage, costs %.4lf s\n", k, TIMER_NOW);
        TIMER_END

        // update current_shapes
        double scale;
        Mat rotate;
        for (int i = 0; i < N; i++) {
            Mat delta_shape = globalRegressionPredict(lbfs[i], k);
            calcSimilarityTransform(bboxes[i].project(current_shapes[i]), mean_shape, scale, rotate);
            current_shapes[i] = bboxes[i].reproject(bboxes[i].project(current_shapes[i]) + scale * delta_shape * rotate.t());
        }

        // calc mean error
        double e = calcMeanError(gt_shapes, current_shapes, config.n_landmarks, config.pupils[0],config.pupils[1]);
        if(config.verbose) printf("Train %dth stage Done with Error = %lf\n", k, e);

    } // for int k
}//Regressor::training

void FacemarkLBFImpl::Regressor::globalRegressionTrain(
    std::vector<Mat> &lbfs, std::vector<Mat> &delta_shapes,
    int stage, Params config
) {

    int N = (int)lbfs.size();
    int M = lbfs[0].cols;
    int F = config.n_landmarks*config.tree_n*(1 << (config.tree_depth - 1));
    int landmark_n_ = delta_shapes[0].rows;
    feature_node **X = (feature_node **)malloc(N * sizeof(feature_node *));
    double **Y = (double **)malloc(landmark_n_ * 2 * sizeof(double *));
    for (int i = 0; i < N; i++) {
        X[i] = (feature_node *)malloc((M + 1) * sizeof(feature_node));
        for (int j = 0; j < M; j++) {
            X[i][j].index = lbfs[i].at<int>(0, j) + 1; // index starts from 1
            X[i][j].value = 1;
        }
        X[i][M].index = -1;
        X[i][M].value = -1;
    }
    for (int i = 0; i < landmark_n_; i++) {
        Y[2 * i] = (double *)malloc(N*sizeof(double));
        Y[2 * i + 1] = (double *)malloc(N*sizeof(double));
        for (int j = 0; j < N; j++) {
            Y[2 * i][j] = delta_shapes[j].at<double>(i, 0);
            Y[2 * i + 1][j] = delta_shapes[j].at<double>(i, 1);
        }
    }

    double *y;
    Mat weights;
    for(int i=0; i< landmark_n_; i++){
        y =  Y[2 * i];
        Mat wx = supportVectorRegression(X,y,N,F,config.verbose);
        weights.push_back(wx);

        y = Y[2 * i + 1];
        Mat wy = supportVectorRegression(X,y,N,F,config.verbose);
        weights.push_back(wy);
    }

    gl_regression_weights[stage] = weights;

    // free
    for (int i = 0; i < N; i++) free(X[i]);
    for (int i = 0; i < 2 * landmark_n_; i++) free(Y[i]);
    free(X);
    free(Y);
} // Regressor:globalRegressionTrain

/*adapted from the liblinear library*/
/* TODO: change feature_node to MAT
* as the index in feature_node is only used for "counter"
*/
Mat FacemarkLBFImpl::Regressor::supportVectorRegression(
    feature_node **x, double *y, int nsamples, int feat_size, bool verbose
){
    #define GETI(i) ((int) y[i])

    std::vector<double> w;
    w.resize(feat_size);

    RNG rng(0);
    int l = nsamples; // n-samples
    double C = 1./(double)nsamples;
    double p = 0;
    int w_size = feat_size; //feat size
    double eps =  0.00001;
    int i, s, iter = 0;
    int max_iter = 1000;
    int active_size = l;
    std::vector<int> index(l);

    double d, G, H;
    double Gmax_old = HUGE_VAL;
    double Gmax_new, Gnorm1_new;
    double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration
    std::vector<double> beta(l);
    std::vector<double> QD(l);

    double lambda[1], upper_bound[1];
    lambda[0] = 0.5/C;
    upper_bound[0] = HUGE_VAL;

    // Initial beta can be set here. Note that
    // -upper_bound <= beta[i] <= upper_bound
    for(i=0; i<l; i++)
        beta[i] = 0;

    for(i=0; i<w_size; i++)
        w[i] = 0;

    for(i=0; i<l; i++){
        QD[i] = 0;
        feature_node *xi = x[i];
        while(xi->index != -1){
            double val = xi->value;
            QD[i] += val*val;
            w[xi->index-1] += beta[i]*val;
            xi++;
        }
        index[i] = i;
    }

    while(iter < max_iter){
        Gmax_new = 0;
        Gnorm1_new = 0;

        for(i=0; i<active_size; i++){
            int j = i+rng.uniform(0,RAND_MAX)%(active_size-i);
            swap(index[i], index[j]);
        }

        for(s=0; s<active_size; s++){
            i = index[s];
            G = -y[i] + lambda[GETI(i)]*beta[i];
            H = QD[i] + lambda[GETI(i)];

            feature_node *xi = x[i];
            while(xi->index != -1){
                int ind = xi->index-1;
                double val = xi->value;
                G += val*w[ind];
                xi++;
            }

            double Gp = G+p;
            double Gn = G-p;
            double violation = 0;
            if(beta[i] == 0){
                if(Gp < 0)
                    violation = -Gp;
                else if(Gn > 0)
                    violation = Gn;
                else if(Gp>Gmax_old && Gn<-Gmax_old){
                    active_size--;
                    swap(index[s], index[active_size]);
                    s--;
                    continue;
                }
            }else if(beta[i] >= upper_bound[GETI(i)]){
                if(Gp > 0)
                    violation = Gp;
                else if(Gp < -Gmax_old){
                    active_size--;
                    swap(index[s], index[active_size]);
                    s--;
                    continue;
                }
            }else if(beta[i] <= -upper_bound[GETI(i)]){
                if(Gn < 0)
                    violation = -Gn;
                else if(Gn > Gmax_old){
                    active_size--;
                    swap(index[s], index[active_size]);
                    s--;
                    continue;
                }
            }else if(beta[i] > 0)
                violation = fabs(Gp);
            else
                violation = fabs(Gn);

            Gmax_new = max(Gmax_new, violation);
            Gnorm1_new += violation;

            // obtain Newton direction d
            if(Gp < H*beta[i])
                d = -Gp/H;
            else if(Gn > H*beta[i])
                d = -Gn/H;
            else
                d = -beta[i];

            if(fabs(d) < 1.0e-12)
                continue;

            double beta_old = beta[i];
            beta[i] = min(max(beta[i]+d, -upper_bound[GETI(i)]), upper_bound[GETI(i)]);
            d = beta[i]-beta_old;

            if(d != 0){
                xi = x[i];
                while(xi->index != -1){
                    w[xi->index-1] += d*xi->value;
                    xi++;
                }
            }
        }// for s<active_size

        if(iter == 0)
            Gnorm1_init = Gnorm1_new;
        iter++;

        if(Gnorm1_new <= eps*Gnorm1_init){
            if(active_size == l)
                break;
            else{
                active_size = l;
                Gmax_old = HUGE_VAL;
                continue;
            }
        }

        Gmax_old = Gmax_new;
    } //while <max_iter

    if(verbose) printf("optimization finished, #iter = %d\n", iter);
    if(iter >= max_iter && verbose)
        printf("WARNING: reaching max number of iterations\n");

    // calculate objective value
    double v = 0;
    int nSV = 0;
    for(i=0; i<w_size; i++)
        v += w[i]*w[i];
    v = 0.5*v;
    for(i=0; i<l; i++){
        v += p*fabs(beta[i]) - y[i]*beta[i] + 0.5*lambda[GETI(i)]*beta[i]*beta[i];
        if(beta[i] != 0)
            nSV++;
    }

    if(verbose) printf("Objective value = %lf\n", v);
    if(verbose) printf("nSV = %d\n",nSV);

    return Mat(Mat(w).t()).clone();

}//end

Mat FacemarkLBFImpl::Regressor::globalRegressionPredict(const Mat &lbf, int stage) {
    const Mat_<double> &weight = (Mat_<double>)gl_regression_weights[stage];
    Mat_<double> delta_shape(weight.rows / 2, 2);
    const double *w_ptr = NULL;
    const int *lbf_ptr = lbf.ptr<int>(0);

    //#pragma omp parallel for num_threads(2) private(w_ptr)
    for (int i = 0; i < delta_shape.rows; i++) {
        w_ptr = weight.ptr<double>(2 * i);
        double y = 0;
        for (int j = 0; j < lbf.cols; j++) y += w_ptr[lbf_ptr[j]];
        delta_shape(i, 0) = y;

        w_ptr = weight.ptr<double>(2 * i + 1);
        y = 0;
        for (int j = 0; j < lbf.cols; j++) y += w_ptr[lbf_ptr[j]];
        delta_shape(i, 1) = y;
    }
    return delta_shape;
} // Regressor::globalRegressionPredict

Mat FacemarkLBFImpl::Regressor::predict(Mat &img, BBox &bbox) {
    Mat current_shape = bbox.reproject(mean_shape);
    double scale;
    Mat rotate;
    Mat lbf_feat;
    for (int k = 0; k < stages_n; k++) {
        // generate lbf
        lbf_feat = random_forests[k].generateLBF(img, current_shape, bbox, mean_shape);
        // update current_shapes
        Mat delta_shape = globalRegressionPredict(lbf_feat, k);
        delta_shape = delta_shape.reshape(0, landmark_n);
        calcSimilarityTransform(bbox.project(current_shape), mean_shape, scale, rotate);
        current_shape = bbox.reproject(bbox.project(current_shape) + scale * delta_shape * rotate.t());
    }
    return current_shape;
} // Regressor::predict

void FacemarkLBFImpl::Regressor::write(FileStorage fs, Params config) {

    fs << "stages_n" << config.stages_n;
    fs << "tree_n" << config.tree_n;
    fs << "tree_depth" << config.tree_depth;
    fs << "n_landmarks" << config.n_landmarks;

    fs << "regressor_meanshape" <<mean_shape;

    // every stages
    String x;
    for (int k = 0; k < config.stages_n; k++) {
        if(config.verbose) printf("Write %dth stage\n", k);
        random_forests[k].write(fs,k);
        x = cv::format("weights_%i",k);
        fs << x << gl_regression_weights[k];
    }
}

void FacemarkLBFImpl::Regressor::read(FileStorage fs, Params & config){
    fs["stages_n"] >>  config.stages_n;
    fs["tree_n"] >>  config.tree_n;
    fs["tree_depth"] >>  config.tree_depth;
    fs["n_landmarks"] >>  config.n_landmarks;

    stages_n = config.stages_n;
    landmark_n = config.n_landmarks;

    initRegressor(config);

    fs["regressor_meanshape"] >>  mean_shape;

    // every stages
    String x;
    for (int k = 0; k < stages_n; k++) {
        random_forests[k].initForest(
            config.n_landmarks,
            config.tree_n,
            config.tree_depth,
            config.bagging_overlap,
            config.feats_m,
            config.radius_m,
            config.verbose
        );
        random_forests[k].read(fs,k);

        x = cv::format("weights_%i",k);
        fs[x] >> gl_regression_weights[k];
    }
}

#undef TIMER_BEGIN
#undef TIMER_NOW
#undef TIMER_END
#undef SIMILARITY_TRANSFORM
} /* namespace face */
} /* namespace cv */
