void TrainBase::view(std::string img_url, int num_pts)
{
// load image
IplImage *img = cvLoadImage(img_url.c_str(), 0);
IplImage *img_color = cvLoadImage(img_url.c_str(), 1);
if (!img) STDOUT_ERROR("Cannot open file: " + img_url);
printf("w=%d, h=%d; cw=%d, ch=%d\n", img->width, img->height,img_color->width, img_color->height);
// detect keypoints
std::vector<Keypoint> det_kpts;
StarDetector det(cvSize(img->width, img->height),8,10);
std::insert_iterator< std::vector<Keypoint> > inserter(det_kpts,det_kpts.begin());
printf("Detecting points\n");
det.DetectPoints(img, inserter);
printf("[OK] %i keypoints detected.\n", det_kpts.size());
// sort descending on response magnitude and pick num_pts
std::sort(det_kpts.begin(), det_kpts.end());
// show detected points
std::vector<features::BaseKeypoint> base_kpts;
features::BaseKeypoint helper(0,0,img);
int count = 0;
BOOST_FOREACH(Keypoint k, det_kpts) {
cvCircle(img_color, cvPoint(k.x,k.y), 3+(1<<k.s), CV_RGB(0,255,0));
helper.x = k.x; helper.y = k.y;
base_kpts.push_back(helper);
if (++count >= num_pts) break;
}
static void sample(T* tests, int cnt, int patch_sz, int type)
{
switch (type)
{
case 0: // uniform random, i.i.d.
sampleUniform(tests, patch_sz, cnt);
break;
case 1: // Gaussian random, i.i.d.
sampleGaussian(tests, patch_sz, cnt);
break;
default:
STDOUT_ERROR("Bad sample type");
}
}
