void FastLaplaceComper::Clear()
{
if(_cd)
{
delete _cd;
_cd = NULL;
}
_src_xfrm = SimilarityTransform();
_tgt_xfrm = SimilarityTransform();
}
// scale always baked, rotation optionally
SimilarityTransform FastLaplaceComper::_unbakedPartOfTgtXfrm()
{
if(m_rotationBaked)
{
return SimilarityTransform(_tgt_xfrm.getTranslation(), 0, pt_t(), 1);
}
else
{
return SimilarityTransform(_tgt_xfrm.getTranslation(), _tgt_xfrm.getRot(), _tgt_xfrm.getCenter(), 1);
}
}
// scaling is tricky. Have to apply scale, then rasterize.
// So scale is baked in to the mesh, but rot
// remesh if scale is different;
void FastLaplaceComper::SetTransform(double rot, pt_t center, double scale, pt_t translation, bool bake_rot)
{
m_rotationBaked = bake_rot;
bool remesh = false;
if(_tgt_xfrm.getScale() != scale)
{
remesh = true;
}
if(_tgt_xfrm.getRot() != rot && m_rotationBaked)
{
remesh = true;
}
_tgt_xfrm = SimilarityTransform(translation, rot, center, scale);
if(remesh) _generateMesh();
_computeTextureCoords();
_getBdryColourDifferences();
ComputeInterpolant();
}
vector<Mat_<double> > FernCascade::Train(const vector<Mat_<uchar> >& images,
const vector<Mat_<double> >& current_shapes,
const vector<Mat_<double> >& ground_truth_shapes,
const vector<BoundingBox> & bounding_box,
const Mat_<double>& mean_shape,
int second_level_num,
int candidate_pixel_num,
int fern_pixel_num){
Mat_<double> candidate_pixel_locations(candidate_pixel_num,2);
Mat_<int> nearest_landmark_index(candidate_pixel_num,1);
vector<Mat_<double> > regression_targets;
RNG random_generator(getTickCount());
second_level_num_ = second_level_num;
// calculate regression targets: the difference between ground truth shapes and current shapes
// candidate_pixel_locations: the locations of candidate pixels, indexed relative to its nearest landmark on mean shape
regression_targets.resize(current_shapes.size());
for(int i = 0;i < current_shapes.size();i++){
regression_targets[i] = ProjectShape(ground_truth_shapes[i],bounding_box[i])
- ProjectShape(current_shapes[i],bounding_box[i]);
Mat_<double> rotation;
double scale;
SimilarityTransform(mean_shape,ProjectShape(current_shapes[i],bounding_box[i]),rotation,scale);
transpose(rotation,rotation);
regression_targets[i] = scale * regression_targets[i] * rotation;
}
// get candidate pixel locations, please refer to 'shape-indexed features'
for(int i = 0;i < candidate_pixel_num;i++){
double x = random_generator.uniform(-1.0,1.0);
double y = random_generator.uniform(-1.0,1.0);
if(x*x + y*y > 1.0){
i--;
continue;
}
// find nearest landmark index
double min_dist = 1e10;
int min_index = 0;
for(int j = 0;j < mean_shape.rows;j++){
double temp = pow(mean_shape(j,0)-x,2.0) + pow(mean_shape(j,1)-y,2.0);
if(temp < min_dist){
min_dist = temp;
min_index = j;
}
}
candidate_pixel_locations(i,0) = x - mean_shape(min_index,0);
candidate_pixel_locations(i,1) = y - mean_shape(min_index,1);
nearest_landmark_index(i) = min_index;
}
// get densities of candidate pixels for each image
// for densities: each row is the pixel densities at each candidate pixels for an image
// Mat_<double> densities(images.size(), candidate_pixel_num);
vector<vector<double> > densities;
densities.resize(candidate_pixel_num);
for(int i = 0;i < images.size();i++){
Mat_<double> rotation;
double scale;
Mat_<double> temp = ProjectShape(current_shapes[i],bounding_box[i]);
SimilarityTransform(temp,mean_shape,rotation,scale);
for(int j = 0;j < candidate_pixel_num;j++){
double project_x = rotation(0,0) * candidate_pixel_locations(j,0) + rotation(0,1) * candidate_pixel_locations(j,1);
double project_y = rotation(1,0) * candidate_pixel_locations(j,0) + rotation(1,1) * candidate_pixel_locations(j,1);
project_x = scale * project_x * bounding_box[i].width / 2.0;
project_y = scale * project_y * bounding_box[i].height / 2.0;
int index = nearest_landmark_index(j);
int real_x = project_x + current_shapes[i](index,0);
int real_y = project_y + current_shapes[i](index,1);
real_x = std::max(0.0,std::min((double)real_x,images[i].cols-1.0));
real_y = std::max(0.0,std::min((double)real_y,images[i].rows-1.0));
densities[j].push_back((int)images[i](real_y,real_x));
}
}
// calculate the covariance between densities at each candidate pixels
Mat_<double> covariance(candidate_pixel_num,candidate_pixel_num);
Mat_<double> mean;
for(int i = 0;i < candidate_pixel_num;i++){
for(int j = i;j< candidate_pixel_num;j++){
double correlation_result = calculate_covariance(densities[i],densities[j]);
covariance(i,j) = correlation_result;
covariance(j,i) = correlation_result;
}
}
// train ferns
vector<Mat_<double> > prediction;
prediction.resize(regression_targets.size());
for(int i = 0;i < regression_targets.size();i++){
prediction[i] = Mat::zeros(mean_shape.rows,2,CV_64FC1);
}
ferns_.resize(second_level_num);
for(int i = 0;i < second_level_num;i++){
cout<<"Training ferns: "<<i+1<<" out of "<<second_level_num<<endl;
vector<Mat_<double> > temp = ferns_[i].Train(densities,covariance,candidate_pixel_locations,nearest_landmark_index,regression_targets,fern_pixel_num);
// update regression targets
for(int j = 0;j < temp.size();j++){
prediction[j] = prediction[j] + temp[j];
regression_targets[j] = regression_targets[j] - temp[j];
}
}
for(int i = 0;i < prediction.size();i++){
Mat_<double> rotation;
double scale;
SimilarityTransform(ProjectShape(current_shapes[i],bounding_box[i]),mean_shape,rotation,scale);
transpose(rotation,rotation);
prediction[i] = scale * prediction[i] * rotation;
}
return prediction;
}
void FastLaplaceComper::SetSrcTransform(double rot, pt_t center, double scale, pt_t translation)
{
_src_xfrm = SimilarityTransform(translation, rot, center, scale);
_computeTextureCoords();
}