// 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.
#include "precomp.hpp"
#include "face_alignmentimpl.hpp"
#include <vector>
using namespace std;
namespace cv{
namespace face{
FacemarkKazemi::~FacemarkKazemi(){}
FacemarkKazemiImpl:: ~FacemarkKazemiImpl(){}
unsigned long FacemarkKazemiImpl::left(unsigned long index){
return 2*index+1;
}
unsigned long FacemarkKazemiImpl::right(unsigned long index){
return 2*index+2;
}
bool FacemarkKazemiImpl::setFaceDetector(FN_FaceDetector f, void* userData){
faceDetector = f;
faceDetectorData = userData;
//printf("face detector is configured\n");
return true;
}
bool FacemarkKazemiImpl::getFaces(InputArray image, OutputArray faces)
{
CV_Assert(faceDetector);
return faceDetector(image, faces, faceDetectorData);
}
FacemarkKazemiImpl::FacemarkKazemiImpl(const FacemarkKazemi::Params& parameters) :
faceDetector(NULL),
faceDetectorData(NULL)
{
minmeanx=8000.0;
maxmeanx=0.0;
minmeany=8000.0;
maxmeany=0.0;
isModelLoaded =false;
params = parameters;
}
FacemarkKazemi::Params::Params(){
//These variables are used for training data
//These are initialised as described in the research paper
//referenced above
cascade_depth = 15;
tree_depth = 5;
num_trees_per_cascade_level = 500;
learning_rate = float(0.1);
oversampling_amount = 20;
num_test_coordinates = 500;
lambda = float(0.1);
num_test_splits = 20;
}
bool FacemarkKazemiImpl::convertToActual(Rect r,Mat &warp){
Point2f srcTri[3],dstTri[3];
srcTri[0]=Point2f(0,0);
srcTri[1]=Point2f(1,0);
srcTri[2]=Point2f(0,1);
dstTri[0]=Point2f((float)r.x,(float)r.y);
dstTri[1]=Point2f((float)r.x+r.width,(float)r.y);
dstTri[2]=Point2f((float)r.x,(float)r.y+(float)1.3*r.height);
warp=getAffineTransform(srcTri,dstTri);
return true;
}
bool FacemarkKazemiImpl::convertToUnit(Rect r,Mat &warp){
Point2f srcTri[3],dstTri[3];
dstTri[0]=Point2f(0,0);
dstTri[1]=Point2f(1,0);
dstTri[2]=Point2f(0,1);
srcTri[0]=Point2f((float)r.x,(float)r.y);
srcTri[1]=Point2f((float)r.x+r.width,(float)r.y);
srcTri[2]=Point2f((float)r.x,(float)r.y+(float)1.3*r.height);
warp=getAffineTransform(srcTri,dstTri);
return true;
}
bool FacemarkKazemiImpl::setMeanExtreme(){
if(meanshape.empty()){
String error_message = "Model not loaded properly.No mean shape found.Aborting...";
CV_ErrorNoReturn(Error::StsBadArg, error_message);
return false;
}
for(size_t i=0;i<meanshape.size();i++){
if(meanshape[i].x>maxmeanx)
maxmeanx = meanshape[i].x;
if(meanshape[i].x<minmeanx)
minmeanx = meanshape[i].x;
if(meanshape[i].y>maxmeany)
maxmeany = meanshape[i].y;
if(meanshape[i].y<minmeany)
minmeany = meanshape[i].y;
}
return true;
}
bool FacemarkKazemiImpl::calcMeanShape (vector< vector<Point2f> >& trainlandmarks,vector<Mat>& trainimages,std::vector<Rect>& faces){
//clear the loaded meanshape
if(trainimages.empty()||trainlandmarks.size()!=trainimages.size()) {
// throw error if no data (or simply return -1?)
CV_ErrorNoReturn(Error::StsBadArg, "Number of images is not equal to corresponding landmarks. Aborting...");
}
meanshape.clear();
vector<Mat> finalimages;
vector< vector<Point2f> > finallandmarks;
float xmean[200] = {0.0};
//array to store mean of y coordinates
float ymean[200] = {0.0};
size_t k=0;
//loop to calculate mean
Mat warp_mat,src,C,D;
vector<Rect> facesp;
Rect face;
for(size_t i = 0;i < trainimages.size();i++){
src = trainimages[i].clone();
//get bounding rectangle of image for reference
//function from facemark class
facesp.clear();
if(!getFaces(src,facesp)){
continue;
}
if(facesp.size()>1||facesp.empty())
continue;
face = facesp[0];
convertToUnit(face,warp_mat);
//loop to bring points to a common reference and adding
for(k=0;k<trainlandmarks[i].size();k++){
Point2f pt=trainlandmarks[i][k];
C = (Mat_<double>(3,1) << pt.x, pt.y, 1);
D = warp_mat*C;
pt.x = float(D.at<double>(0,0));
pt.y = float(D.at<double>(1,0));
trainlandmarks[i][k] = pt;
xmean[k] = xmean[k]+pt.x;
ymean[k] = ymean[k]+pt.y;
}
finalimages.push_back(trainimages[i]);
finallandmarks.push_back(trainlandmarks[i]);
faces.push_back(face);
}
//dividing by size to get mean and initialize meanshape
for(size_t i=0;i<k;i++){
xmean[i]=xmean[i]/finalimages.size();
ymean[i]=ymean[i]/finalimages.size();
if(xmean[i]>maxmeanx)
maxmeanx = xmean[i];
if(xmean[i]<minmeanx)
minmeanx = xmean[i];
if(ymean[i]>maxmeany)
maxmeany = ymean[i];
if(ymean[i]<minmeany)
minmeany = ymean[i];
meanshape.push_back(Point2f(xmean[i],ymean[i]));
}
trainimages.clear();
trainlandmarks.clear();
trainimages = finalimages;
trainlandmarks = finallandmarks;
finalimages.clear();
finallandmarks.clear();
return true;
}
bool FacemarkKazemiImpl::scaleData( vector< vector<Point2f> > & trainlandmarks,
vector<Mat> & trainimages ,Size s)
{
if(trainimages.empty()||trainimages.size()!=trainlandmarks.size()){
// throw error if no data (or simply return -1?)
CV_ErrorNoReturn(Error::StsBadArg, "The data is not loaded properly by train function. Aborting...");
}
float scalex,scaley;
//scale all images and their landmarks according to input size
for(size_t i=0;i< trainimages.size();i++){
//calculating scale for x and y axis
scalex=float(s.width)/float(trainimages[i].cols);
scaley=float(s.height)/float(trainimages[i].rows);
resize(trainimages[i],trainimages[i],s,0,0,INTER_LINEAR_EXACT);
for (vector<Point2f>::iterator it = trainlandmarks[i].begin(); it != trainlandmarks[i].end(); it++) {
Point2f pt = (*it);
pt.x = pt.x*scalex;
pt.y = pt.y*scaley;
(*it) = pt;
}
}
return true;
}
Ptr<FacemarkKazemi> FacemarkKazemi::create(const FacemarkKazemi::Params ¶meters){
return Ptr<FacemarkKazemiImpl>(new FacemarkKazemiImpl(parameters));
}
}//cv
}//face