alphaf_den = (p_xf * xfconj);
} else {
// Kernel Ridge Regression, calculate alphas (in Fourier domain)
- const uint num_scales = BIG_BATCH_MODE ? p_num_scales : 1;
cv::Size sz(Fft::freq_size(p_roi));
- ComplexMat kf(sz.height, sz.width, num_scales);
+ ComplexMat kf(sz.height, sz.width, 1);
(*gaussian_correlation)(kf, p_model_xf, p_model_xf, p_kernel_sigma, true, *this);
DEBUG_PRINTM(kf);
p_model_alphaf_num = p_yf * kf;
d.threadctxs.emplace_back(p_roi, p_num_of_feats, p_num_scales);
#endif
- gaussian_correlation.reset(
- new GaussianCorrelation(IF_BIG_BATCH(p_num_scales, 1), p_roi));
+ gaussian_correlation.reset(new GaussianCorrelation(1, p_roi));
p_current_scale = 1.;
float numel_xf_inv = 1.f / (xf.cols * xf.rows * (xf.channels() / xf.n_scales));
for (uint i = 0; i < xf.n_scales; ++i) {
cv::Mat plane = ifft_res.plane(i);
+ DEBUG_PRINT(ifft_res.plane(i));
cv::exp(-1. / (sigma * sigma) * cv::max((xf_sqr_norm[i] + yf_sqr_norm[0] - 2 * ifft_res.plane(i))
* numel_xf_inv, 0), plane);
DEBUG_PRINTM(plane);