//=============================================================================
// Basically Kabsch's algorithm but also allows the collection of points to be different in scale from each other
cv::Matx22f AlignShapesWithScale(cv::Mat_<float>& src, cv::Mat_<float> dst)
{
int n = src.rows;
// First we mean normalise both src and dst
float mean_src_x = cv::mean(src.col(0))[0];
float mean_src_y = cv::mean(src.col(1))[0];
float mean_dst_x = cv::mean(dst.col(0))[0];
float mean_dst_y = cv::mean(dst.col(1))[0];
cv::Mat_<float> src_mean_normed = src.clone();
src_mean_normed.col(0) = src_mean_normed.col(0) - mean_src_x;
src_mean_normed.col(1) = src_mean_normed.col(1) - mean_src_y;
cv::Mat_<float> dst_mean_normed = dst.clone();
dst_mean_normed.col(0) = dst_mean_normed.col(0) - mean_dst_x;
dst_mean_normed.col(1) = dst_mean_normed.col(1) - mean_dst_y;
// Find the scaling factor of each
cv::Mat src_sq;
cv::pow(src_mean_normed, 2, src_sq);
cv::Mat dst_sq;
cv::pow(dst_mean_normed, 2, dst_sq);
float s_src = sqrt(cv::sum(src_sq)[0] / n);
float s_dst = sqrt(cv::sum(dst_sq)[0] / n);
src_mean_normed = src_mean_normed / s_src;
dst_mean_normed = dst_mean_normed / s_dst;
float s = s_dst / s_src;
// Get the rotation
cv::Matx22f R = AlignShapesKabsch2D(src_mean_normed, dst_mean_normed);
cv::Matx22f A;
cv::Mat(s * R).copyTo(A);
cv::Mat_<float> aligned = (cv::Mat(cv::Mat(A) * src.t())).t();
cv::Mat_<float> offset = dst - aligned;
float t_x = cv::mean(offset.col(0))[0];
float t_y = cv::mean(offset.col(1))[0];
return A;
}
/** Orthogonal matching pursuit
* x: input signal, N * 1
* D: dictionary, N * M
* L: number of non_zero elements in output
* coeff: coefficent of each atoms in dictionary, M * 1
*/
void OMP(const cv::Mat_<double>& x, const cv::Mat_<double>& D, int L, cv::Mat_<double>& coeff){
int dim = x.rows;
int atom_num = D.cols;
coeff = Mat::zeros(atom_num, 1, CV_64FC1);
Mat_<double> residual = x.clone();
Mat_<double> selected_index(L, 1);
Mat_<double> a;
for (int i = 0; i < L; i++){
cout << "here ok 1" << endl;
Mat_<double> dot_p = D.t() * residual;
Point max_index;
minMaxLoc(abs(dot_p), NULL, NULL, NULL, &max_index);
int max_row = max_index.y;
selected_index(i) = max_row;
Mat_<double> temp(dim, i + 1);
for (int j = 0; j < i + 1; j++){
D.col(selected_index(j)).copyTo(temp.col(j));
}
Mat_<double> invert_temp;
invert(temp, invert_temp, CV_SVD);
a = invert_temp * x;
residual = x - temp * a;
}
for (int i = 0; i < L; i++){
coeff(selected_index(i)) = a(i);
}
}
// Extract summary statistics (mean, stdev, min, max) from each dimension of a descriptor, each row is a descriptor
void ExtractSummaryStatistics(const cv::Mat_<double>& descriptors, cv::Mat_<double>& sum_stats, bool use_mean, bool use_stdev, bool use_max_min)
{
// Using four summary statistics at the moment
// Means, stds, mins, maxs
int num_stats = 0;
if(use_mean)
num_stats++;
if(use_stdev)
num_stats++;
if(use_max_min)
num_stats++;
sum_stats = Mat_<double>(1, descriptors.cols * num_stats, 0.0);
for(int i = 0; i < descriptors.cols; ++i)
{
Scalar mean, stdev;
cv::meanStdDev(descriptors.col(i), mean, stdev);
int add = 0;
if(use_mean)
{
sum_stats.at<double>(0, i*num_stats + add) = mean[0];
add++;
}
if(use_stdev)
{
sum_stats.at<double>(0, i*num_stats + add) = stdev[0];
add++;
}
if(use_max_min)
{
double min, max;
cv::minMaxIdx(descriptors.col(i), &min, &max);
sum_stats.at<double>(0, i*num_stats + add) = max - min;
add++;
}
}
}
void cModel::UpDate(cv::Mat_<int>&binary, cv::Rect &bbox, cv::Mat_<float>& shape, int stage)
{
int num_point = m_Model.__head.__num_point;
int w_cols = 2 * num_point;
int w_rows = binary.cols;
cv::Mat_<float> deltashapes = cv::Mat::zeros(1, w_cols, CV_32FC1);
for (int i = 0; i < w_rows; i++){
if (binary(0, i) == 1){
deltashapes += m_Model.__w.row(stage * w_rows + i);
}
}
cv::Mat_<float> deltax = deltashapes.colRange(0, num_point).t();
deltax *= bbox.width;
cv::Mat_<float> deltay = deltashapes.colRange(num_point, w_cols).t();
deltay *= bbox.height;
shape.col(0) += deltax;
shape.col(1) += deltay;
}
/**
* Find 3D rotation and translation with homography (i.e. calibration boards position in the camera cooperate system )
* @param h**o: [Input] Estimated Homography
* @param rot: [Output] Rotation result
* @param trans: [Output] Translation result
*/
void AugmentationEnvironment::H2Rt(const cv::Mat_<double> intrisinc, const cv::Mat_<double>& h**o, cv::Mat_<double>& rot, cv::Mat_<double> &trans)
{
double coefH;
cv::Mat_<double> invcam = intrisinc.inv();
rot = invcam*h**o;
//scaling rotation matrix to right order = find the coefficient lost while calculating homography
/**
* [Cam]*[Rot]' = [H**o]*coefH
* |fx 0 u0|
* [Cam]' = | 0 fy v0|
* | 0 0 1|
*
* |R11 R12 t1|
* [Rot]' = |R21 R22 t2|
* |R31 R32 t3|
*
* |H11 H12 H13|
* [H**o]' = |H21 H22 H23|
* |H31 H32 1|
*/
coefH = 2/(cv::norm(rot.col(0)) + cv::norm(rot.col(1)));
rot = rot * coefH;
trans = rot.col(2).clone();
//The their rotation vector in the rotation matrix is produced as the cross product of other two vectors
cv::Mat tmp = rot.col(2);
rot.col(0).cross(rot.col(1)).copyTo(tmp);
//Add here to make rotation matrix a "real" rotation matrix in zhang's method
/**
* rot = UDVt
* rot_real = UVt
*/
cv::Mat_<double> D,U,Vt;
cv::SVD::compute(rot,D,U,Vt);
rot = U*Vt;
}
