void AdaptiveManifoldFilterN::buildManifoldsAndPerformFiltering(vector<Mat>& eta, Mat1b& cluster, int treeLevel)
{
CV_DbgAssert((int)eta.size() == jointCnNum);
//splatting
Size etaSize = eta[0].size();
CV_DbgAssert(etaSize == srcSize || etaSize == smallSize);
if (etaSize == srcSize)
{
compute_w_k(eta, w_k, sigma_r_over_sqrt_2, treeLevel);
etaFull = eta;
downsample(eta, eta);
}
else
{
upsample(eta, etaFull);
compute_w_k(etaFull, w_k, sigma_r_over_sqrt_2, treeLevel);
}
//blurring
Psi_splat_small.resize(srcCnNum);
for (int si = 0; si < srcCnNum; si++)
{
Mat tmp;
multiply(srcCn[si], w_k, tmp);
downsample(tmp, Psi_splat_small[si]);
}
downsample(w_k, Psi_splat_0_small);
vector<Mat>& Psi_splat_small_blur = Psi_splat_small;
Mat& Psi_splat_0_small_blur = Psi_splat_0_small;
float rf_ss = (float)(sigma_s_ / getResizeRatio());
float rf_sr = (float)(sigma_r_over_sqrt_2);
RFFilterPass(eta, Psi_splat_small, Psi_splat_0_small, Psi_splat_small_blur, Psi_splat_0_small_blur, rf_ss, rf_sr);
//slicing
{
Mat tmp;
for (int i = 0; i < srcCnNum; i++)
{
upsample(Psi_splat_small_blur[i], tmp);
multiply(tmp, w_k, tmp);
add(sum_w_ki_Psi_blur_[i], tmp, sum_w_ki_Psi_blur_[i]);
}
upsample(Psi_splat_0_small_blur, tmp);
multiply(tmp, w_k, tmp);
add(sum_w_ki_Psi_blur_0_, tmp, sum_w_ki_Psi_blur_0_);
}
//build new manifolds
if (treeLevel < curTreeHeight)
{
Mat1b cluster_minus, cluster_plus;
computeClusters(cluster, cluster_minus, cluster_plus);
vector<Mat> eta_minus(jointCnNum), eta_plus(jointCnNum);
{
Mat1f teta = 1.0 - w_k;
computeEta(teta, cluster_minus, eta_minus);
computeEta(teta, cluster_plus, eta_plus);
}
//free memory to continue deep recursion
eta.clear();
cluster.release();
buildManifoldsAndPerformFiltering(eta_minus, cluster_minus, treeLevel + 1);
buildManifoldsAndPerformFiltering(eta_plus, cluster_plus, treeLevel + 1);
}
}
