double cv_emd_distance(const std::vector<double>& h1, const std::vector<double>& h2,
int histogram_cycle)
{
int n = h1.size();
#if 0
ntk_dbg_print(h1.size(), 1);
ntk_dbg_print(h2.size(), 1);
double norm1 = std::accumulate(stl_bounds(h1), 0.0);
double norm2 = std::accumulate(stl_bounds(h2), 0.0);
ntk_dbg_print(norm1, 1);
ntk_dbg_print(norm2, 1);
#endif
CvMat* sig1 = cvCreateMat(n, 3, CV_32FC1);
CvMat* sig2 = cvCreateMat(n, 3, CV_32FC1);
foreach_idx(i, h1)
{
int row = i/histogram_cycle;
int col = i%histogram_cycle;
cvSet2D(sig1, i, 0, cvScalar(h1[i]));
cvSet2D(sig1, i, 1, cvScalar(row));
cvSet2D(sig1, i, 2, cvScalar(col));
cvSet2D(sig2, i, 0, cvScalar(h2[i]));
cvSet2D(sig2, i, 1, cvScalar(row));
cvSet2D(sig2, i, 2, cvScalar(col));
}
std::vector<cv::Point2f> make_unimodal_distribution(const std::vector<cv::Point2f>& points)
{
double max_value = -FLT_MAX;
foreach_idx(i, points)
if (points[i].y > max_value)
max_value = points[i].y;
std::vector<cv::Point2f> control_points;
double current_max = -FLT_MAX;
int i = points.size()-1;
for (i = points.size()-1; i > 0 && points[i].y < max_value; --i)
{
if (points[i].y > current_max)
{
current_max = points[i].y;
control_points.insert(control_points.begin(), points[i]);
}
}
int last_index = i;
control_points.insert(control_points.begin(), points[i]);
std::vector<cv::Point2f> control_points_fwd;
current_max = -FLT_MAX;
for (i = 0; i < last_index; ++i)
{
if (points[i].y > current_max)
{
current_max = points[i].y;
control_points_fwd.push_back(points[i]);
}
}
control_points_fwd.insert(control_points_fwd.end(), stl_bounds(control_points));
return control_points_fwd;
}
std::vector<double> distrib_to_bimodal_linear(const std::vector<double>& factors)
{
double max_value = -FLT_MAX;
foreach_idx(i, factors) if (factors[i] > max_value) max_value = factors[i];
std::vector<cv::Point2f> control_points;
double current_max = -FLT_MAX;
int i = factors.size()-1;
for (i = factors.size()-1; i > 0 && factors[i] < max_value; --i)
{
if (factors[i] > current_max)
{
current_max = factors[i];
control_points.insert(control_points.begin(), cv::Point2f(i, factors[i]));
}
}
int last_index = i;
control_points.insert(control_points.begin(), cv::Point2f(i, factors[i]));
std::vector<cv::Point2f> control_points_fwd;
current_max = -FLT_MAX;
for (i = 0; i < last_index; ++i)
{
if (factors[i] > current_max)
{
current_max = factors[i];
control_points_fwd.push_back(cv::Point2f(i, factors[i]));
}
}
control_points_fwd.insert(control_points_fwd.end(), stl_bounds(control_points));
return fill_linear_values_from_control_points(control_points_fwd);
}
void SiftObjectDetector :: initializeFindObjects()
{
super::initializeFindObjects();
m_point_matches.clear();
m_image_sift_points.clear();
std::list<LocatedFeature*> points;
ntk::TimeCount tc_init("initialize", 1);
compute_feature_points(points,
m_data.image,
siftDatabase().featureType());
tc_init.elapsedMsecs(" compute feature points: ");
std::copy(stl_bounds(points), std::back_inserter(m_image_sift_points));
ntk_dbg_print(m_image_sift_points.size(), 1);
tc_init.stop();
}
