rect_t MT::track(const unsigned char *gray)
{
number_coarse = number_MLK = number_iteration = 0;
if (log != NULL) {
(*log) << "roll = " << roll * 90.0f / PI_2 << endl;
(*log) << "yaw = " << yaw * 90.0f / PI_2 << endl;
(*log) << "pitch = " << pitch * 90.0f / PI_2 << endl;
}
feature.process(gray, roll);
//the first attempt
Warp w = warp;
if (log != NULL)
(*log) << "track at " << w.t.transpose() << " " << window(w.t) << endl;
w = fine_test(w);
float e = evaluate(w);
if (e > fast_threshold) {
//use coarse searching
++number_coarse;
Warp w2 = warp;
w2.sett(fast_test(warp));
if (log != NULL)
(*log) << "search at " << w2.t.transpose() << " " << window(w2.t) << endl;
w2 = fine_test(w2);
float e2 = evaluate(w2);
if (e2 < e) {
w = w2;
e = e2;
}
}
update(w, e);
return window(w.t);
}
rect_t MT::retrack(const unsigned char *gray, const vector<rect_t> &detections)
{
number_coarse = number_MLK = number_iteration = 0;
if (log != NULL) {
(*log) << "roll = " << roll * 90.0f / PI_2 << endl;
(*log) << "yaw = " << yaw * 90.0f / PI_2 << endl;
(*log) << "pitch = " << pitch * 90.0f / PI_2 << endl;
}
feature.process(gray, 0.0f);
//the first attempt
Warp w = warp;
w.setr(Vector3f(0.0f, 0.0f, 0.0f));
if (log != NULL)
(*log) << "track at " << w.t.transpose() << " " << window(w.t) << endl;
w = fine_test(w);
float e = evaluate(w);
//use coarse searching
Warp w2 = warp;
w2.setr(Vector3f(0.0f, 0.0f, 0.0f));
w2.sett(fast_test(warp));
if (log != NULL)
(*log) << "search at " << w2.t.transpose() << " " << window(w2.t) << endl;
w2 = fine_test(w2);
float e2 = evaluate(w2);
if (e2 < e) {
w = w2;
e = e2;
}
//use re-detection
for (auto d : detections) {
Warp w3 = warp;
w3.setr(Vector3f(0.0f, 0.0f, 0.0f));
w3.sett(locate(d));
if (log != NULL)
(*log) << "detect at " << w3.t.transpose() << " " << window(w3.t) << endl;
w3 = fine_test(w3);
float e3 = evaluate(w3);
if (e3 < e) {
w = w3;
e = e3;
}
}
update(w, e);
return window(w.t);
}
Rect2f MT::track(Mat img, const vector<Rect2f> &detections)
{
if (log != NULL) {
(*log) << "roll = " << roll * 90.0f / PI_2 << endl;
(*log) << "yaw = " << yaw * 90.0f / PI_2 << endl;
(*log) << "pitch = " << pitch * 90.0f / PI_2 << endl;
}
if (!detections.empty())
roll = 0.0f;
feature.process(img, roll);
vector<Point3f> candidates;
candidates.push_back(warp.t);
candidates.push_back(fast_test(warp));
for (auto d : detections)
candidates.push_back(locate(d));
Warp best_warp(image_size);
float best_error = 1.0f;
for (auto& t : candidates) {
if (log != NULL)
(*log) << "candidate " << t << " " << window(t) << endl;
Warp w(image_size);
if (detections.empty())
w.set(warp.r);
w.set(t);
w = fine_test(w);
float e = evaluate(w);
if (log != NULL) {
(*log) << "final translation = " << w.t << " " << window(w.t) << endl;
(*log) << "final rotation = " << w.r << endl;
(*log) << "final error = " << e << endl;
}
if (e < best_error) {
best_warp = w;
best_error = e;
}
}
candidates.clear();
error = best_error;
if (error < threshold_error) {
count = 0;
warp = best_warp;
warp.euler(roll, yaw, pitch);
fine_train(warp);
}
else {
++count;
warp.t = best_warp.t * (warp.t.z / best_warp.t.z);
}
fast_train(warp);
return window(best_warp.t);
}