fast_bilateral_omp.hpp
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2012-, Open Perception, Inc.
* Copyright (c) 2004, Sylvain Paris and Francois Sillion
* All rights reserved.
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* $Id: fast_bilateral_omp.hpp 8381 2013-01-02 23:12:44Z sdmiller $
*
*/
#ifndef PCL_FILTERS_IMPL_FAST_BILATERAL_OMP_HPP_
#define PCL_FILTERS_IMPL_FAST_BILATERAL_OMP_HPP_
#include <pcl/filters/fast_bilateral_omp.h>
#include <pcl/common/io.h>
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::FastBilateralFilterOMP<PointT>::setNumberOfThreads (unsigned int nr_threads)
{
if (nr_threads == 0)
#ifdef _OPENMP
threads_ = omp_get_num_procs();
#else
threads_ = 1;
#endif
else
threads_ = nr_threads;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::FastBilateralFilterOMP<PointT>::applyFilter (PointCloud &output)
{
if (!input_->isOrganized ())
{
PCL_ERROR ("[pcl::FastBilateralFilterOMP] Input cloud needs to be organized.\n");
return;
}
copyPointCloud (*input_, output);
float base_max = -std::numeric_limits<float>::max (),
base_min = std::numeric_limits<float>::max ();
bool found_finite = false;
for (const auto& pt: output)
{
if (std::isfinite(pt.z))
{
base_max = std::max<float>(pt.z, base_max);
base_min = std::min<float>(pt.z, base_min);
found_finite = true;
}
}
if (!found_finite)
{
PCL_WARN ("[pcl::FastBilateralFilterOMP] Given an empty cloud. Doing nothing.\n");
return;
}
#pragma omp parallel for \
default(none) \
shared(base_min, base_max, output) \
num_threads(threads_)
for (long int i = 0; i < static_cast<long int> (output.size ()); ++i)
if (!std::isfinite (output.at(i).z))
output.at(i).z = base_max;
const float base_delta = base_max - base_min;
const std::size_t padding_xy = 2;
const std::size_t padding_z = 2;
const std::size_t small_width = static_cast<std::size_t> (static_cast<float> (input_->width - 1) / sigma_s_) + 1 + 2 * padding_xy;
const std::size_t small_height = static_cast<std::size_t> (static_cast<float> (input_->height - 1) / sigma_s_) + 1 + 2 * padding_xy;
const std::size_t small_depth = static_cast<std::size_t> (base_delta / sigma_r_) + 1 + 2 * padding_z;
Array3D data (small_width, small_height, small_depth);
#if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
#pragma omp parallel for \
default(none) \
shared(base_min, data, output) \
num_threads(threads_)
#else
#pragma omp parallel for \
default(none) \
shared(base_min, data, output, small_height, small_width) \
num_threads(threads_)
#endif
for (long int i = 0; i < static_cast<long int> (small_width * small_height); ++i)
{
std::size_t small_x = static_cast<std::size_t> (i % small_width);
std::size_t small_y = static_cast<std::size_t> (i / small_width);
std::size_t start_x = static_cast<std::size_t>(
std::max ((static_cast<float> (small_x) - static_cast<float> (padding_xy) - 0.5f) * sigma_s_ + 1, 0.f));
std::size_t end_x = static_cast<std::size_t>(
std::max ((static_cast<float> (small_x) - static_cast<float> (padding_xy) + 0.5f) * sigma_s_ + 1, 0.f));
std::size_t start_y = static_cast<std::size_t>(
std::max ((static_cast<float> (small_y) - static_cast<float> (padding_xy) - 0.5f) * sigma_s_ + 1, 0.f));
std::size_t end_y = static_cast<std::size_t>(
std::max ((static_cast<float> (small_y) - static_cast<float> (padding_xy) + 0.5f) * sigma_s_ + 1, 0.f));
for (std::size_t x = start_x; x < end_x && x < input_->width; ++x)
{
for (std::size_t y = start_y; y < end_y && y < input_->height; ++y)
{
const float z = output (x,y).z - base_min;
const std::size_t small_z = static_cast<std::size_t> (static_cast<float> (z) / sigma_r_ + 0.5f) + padding_z;
Eigen::Vector2f& d = data (small_x, small_y, small_z);
d[0] += output (x,y).z;
d[1] += 1.0f;
}
}
}
std::vector<long int> offset (3);
offset[0] = &(data (1,0,0)) - &(data (0,0,0));
offset[1] = &(data (0,1,0)) - &(data (0,0,0));
offset[2] = &(data (0,0,1)) - &(data (0,0,0));
Array3D buffer (small_width, small_height, small_depth);
for (std::size_t dim = 0; dim < 3; ++dim)
{
for (std::size_t n_iter = 0; n_iter < 2; ++n_iter)
{
Array3D* current_buffer = (n_iter % 2 == 1 ? &buffer : &data);
Array3D* current_data =(n_iter % 2 == 1 ? &data : &buffer);
#if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
#pragma omp parallel for \
default(none) \
shared(current_buffer, current_data, dim, offset) \
num_threads(threads_)
#else
#pragma omp parallel for \
default(none) \
shared(current_buffer, current_data, dim, offset, small_depth, small_height, small_width) \
num_threads(threads_)
#endif
for(long int i = 0; i < static_cast<long int> ((small_width - 2)*(small_height - 2)); ++i)
{
std::size_t x = static_cast<std::size_t> (i % (small_width - 2) + 1);
std::size_t y = static_cast<std::size_t> (i / (small_width - 2) + 1);
const long int off = offset[dim];
Eigen::Vector2f* d_ptr = &(current_data->operator() (x,y,1));
Eigen::Vector2f* b_ptr = &(current_buffer->operator() (x,y,1));
for(std::size_t z = 1; z < small_depth - 1; ++z, ++d_ptr, ++b_ptr)
*d_ptr = (*(b_ptr - off) + *(b_ptr + off) + 2.0 * (*b_ptr)) / 4.0;
}
}
}
// Note: this works because there are an even number of iterations.
// If there were an odd number, we would need to end with a:
// std::swap (data, buffer);
if (early_division_)
{
for (std::vector<Eigen::Vector2f, Eigen::aligned_allocator<Eigen::Vector2f> >::iterator d = data.begin (); d != data.end (); ++d)
*d /= ((*d)[0] != 0) ? (*d)[1] : 1;
#pragma omp parallel for \
default(none) \
shared(base_min, data, output) \
num_threads(threads_)
for (long int i = 0; i < static_cast<long int> (input_->size ()); ++i)
{
std::size_t x = static_cast<std::size_t> (i % input_->width);
std::size_t y = static_cast<std::size_t> (i / input_->width);
const float z = output (x,y).z - base_min;
const Eigen::Vector2f D = data.trilinear_interpolation (static_cast<float> (x) / sigma_s_ + padding_xy,
static_cast<float> (y) / sigma_s_ + padding_xy,
z / sigma_r_ + padding_z);
output(x,y).z = D[0];
}
}
else
{
#pragma omp parallel for \
default(none) \
shared(base_min, data, output) \
num_threads(threads_)
for (long i = 0; i < static_cast<long int> (input_->size ()); ++i)
{
std::size_t x = static_cast<std::size_t> (i % input_->width);
std::size_t y = static_cast<std::size_t> (i / input_->width);
const float z = output (x,y).z - base_min;
const Eigen::Vector2f D = data.trilinear_interpolation (static_cast<float> (x) / sigma_s_ + padding_xy,
static_cast<float> (y) / sigma_s_ + padding_xy,
z / sigma_r_ + padding_z);
output (x,y).z = D[0] / D[1];
}
}
}
#endif /* PCL_FILTERS_IMPL_FAST_BILATERAL_OMP_HPP_ */
pcl::FastBilateralFilterOMP::setNumberOfThreads
void setNumberOfThreads(unsigned int nr_threads=0)
Initialize the scheduler and set the number of threads to use.
Definition: fast_bilateral_omp.hpp:48
pcl::FastBilateralFilterOMP::applyFilter
void applyFilter(PointCloud &output) override
Filter the input data and store the results into output.
Definition: fast_bilateral_omp.hpp:62
pcl::FastBilateralFilterOMP::Array3D
typename FastBilateralFilter< PointT >::Array3D Array3D
Definition: fast_bilateral_omp.h:64
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
pcl::PointCloud::at
const PointT & at(int column, int row) const
Obtain the point given by the (column, row) coordinates.
Definition: point_cloud.h:262
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:443
pcl::copyPointCloud
void copyPointCloud(const pcl::PointCloud< PointInT > &cloud_in, pcl::PointCloud< PointOutT > &cloud_out)
Copy all the fields from a given point cloud into a new point cloud.
Definition: io.hpp:144