convolution.hpp
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#ifndef PCL_2D_CONVOLUTION_IMPL_HPP
#define PCL_2D_CONVOLUTION_IMPL_HPP
//////////////////////////////////////////////////////////////////////////////
template <typename PointT>
void
pcl::Convolution<PointT>::filter(pcl::PointCloud<PointT>& output)
{
int input_row = 0;
int input_col = 0;
// default boundary option : zero padding
output = *input_;
int iw = static_cast<int>(input_->width), ih = static_cast<int>(input_->height),
kw = static_cast<int>(kernel_.width), kh = static_cast<int>(kernel_.height);
switch (boundary_options_) {
default:
case BOUNDARY_OPTION_CLAMP: {
for (int i = 0; i < ih; i++) {
for (int j = 0; j < iw; j++) {
float intensity = 0;
for (int k = 0; k < kh; k++) {
for (int l = 0; l < kw; l++) {
int ikkh = i + k - kh / 2, jlkw = j + l - kw / 2;
if (ikkh < 0)
input_row = 0;
else if (ikkh >= ih)
input_row = ih - 1;
else
input_row = ikkh;
if (jlkw < 0)
input_col = 0;
else if (jlkw >= iw)
input_col = iw - 1;
else
input_col = jlkw;
intensity +=
kernel_(l, k).intensity * (*input_)(input_col, input_row).intensity;
}
}
output(j, i).intensity = intensity;
}
}
break;
}
case BOUNDARY_OPTION_MIRROR: {
for (int i = 0; i < ih; i++) {
for (int j = 0; j < iw; j++) {
float intensity = 0;
for (int k = 0; k < kh; k++) {
for (int l = 0; l < kw; l++) {
int ikkh = i + k - kh / 2, jlkw = j + l - kw / 2;
if (ikkh < 0)
input_row = -ikkh - 1;
else if (ikkh >= ih)
input_row = 2 * ih - 1 - ikkh;
else
input_row = ikkh;
if (jlkw < 0)
input_col = -jlkw - 1;
else if (jlkw >= iw)
input_col = 2 * iw - 1 - jlkw;
else
input_col = jlkw;
intensity +=
kernel_(l, k).intensity * ((*input_)(input_col, input_row).intensity);
}
}
output(j, i).intensity = intensity;
}
}
break;
}
case BOUNDARY_OPTION_ZERO_PADDING: {
for (int i = 0; i < ih; i++) {
for (int j = 0; j < iw; j++) {
float intensity = 0;
for (int k = 0; k < kh; k++) {
for (int l = 0; l < kw; l++) {
int ikkh = i + k - kh / 2, jlkw = j + l - kw / 2;
if (ikkh < 0 || ikkh >= ih || jlkw < 0 || jlkw >= iw)
continue;
intensity += kernel_(l, k).intensity * ((*input_)(jlkw, ikkh).intensity);
}
}
output(j, i).intensity = intensity;
}
}
break;
}
} // switch
}
#endif
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: projection_matrix.h:52
pcl::Convolution::filter
void filter(pcl::PointCloud< PointT > &output)
Performs 2D convolution of the input point cloud with the kernel.
Definition: convolution.hpp:44