os << std::setprecision(3);
os << p.obj.size << " " << p.obj.channels() << "ch ";// << static_cast<const void *>(p.obj.data);
os << " = [ ";
- constexpr size_t num = 100;
+ constexpr size_t num = 10;
for (size_t i = 0; i < std::min(num, p.obj.total()); ++i)
os << p.obj.ptr<float>()[i] << ", ";
os << (num < p.obj.total() ? "... ]" : "]");
os << std::setprecision(3);
os << "<cplx> " << p.obj.size() << " " << p.obj.channels() << "ch "; // << p.obj.get_p_data();
os << " = [ ";
- constexpr int num = 100;
+ constexpr int num = 10;
for (int i = 0; i < std::min(num, p.obj.size().area()); ++i)
os << p.obj.get_p_data()[i] << ", ";
os << (num < p.obj.size().area() ? "... ]" : "]");
// obtain a sub-window for training
// TODO: Move Mats outside from here
MatScaleFeats patch_feats(1, p_num_of_feats, p_roi);
+ DEBUG_PRINT(patch_feats);
MatScaleFeats temp(1, p_num_of_feats, p_roi);
get_features(input_rgb, input_gray, p_pose.cx, p_pose.cy,
p_windows_size.width, p_windows_size.height,
(*gaussian_correlation)(*this, kf, p_model_xf, p_model_xf, p_kernel_sigma, true);
DEBUG_PRINTM(kf);
p_model_alphaf_num = p_yf * kf;
- DEBUG_PRINTM(p_model_alphaf_num);
p_model_alphaf_den = kf * (kf + p_lambda);
- DEBUG_PRINTM(p_model_alphaf_den);
}
p_model_alphaf = p_model_alphaf_num / p_model_alphaf_den;
DEBUG_PRINTM(p_model_alphaf);
DEBUG_PRINT(patch_feats.scale(i));
}
- DEBUG_PRINT(patch_feats);
kcf.fft.forward_window(patch_feats, zf, temp);
DEBUG_PRINTM(zf);
kzf = zf.mul(kcf.p_model_alphaf).sum_over_channels();
} else {
gaussian_correlation(kcf, kzf, zf, kcf.p_model_xf, kcf.p_kernel_sigma);
- DEBUG_PRINTM(kcf.p_model_alphaf);
DEBUG_PRINTM(kzf);
kzf = kzf.mul(kcf.p_model_alphaf);
- DEBUG_PRINTM(kzf);
}
kcf.fft.inverse(kzf, response);