186 lines
6.6 KiB
C++
186 lines
6.6 KiB
C++
/*
|
|
* Software License Agreement (BSD License)
|
|
*
|
|
* Point Cloud Library (PCL) - www.pointclouds.org
|
|
* Copyright (c) 2009-2012, Willow Garage, Inc.
|
|
*
|
|
* 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 Willow Garage, Inc. 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.
|
|
*
|
|
*/
|
|
|
|
|
|
#ifndef PCL_SURFACE_IMPL_BILATERAL_UPSAMPLING_H_
|
|
#define PCL_SURFACE_IMPL_BILATERAL_UPSAMPLING_H_
|
|
|
|
#include <pcl/surface/bilateral_upsampling.h>
|
|
#include <algorithm>
|
|
#include <pcl/console/print.h>
|
|
|
|
#include <Eigen/LU> // for inverse
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename PointInT, typename PointOutT> void
|
|
pcl::BilateralUpsampling<PointInT, PointOutT>::process (pcl::PointCloud<PointOutT> &output)
|
|
{
|
|
// Copy the header
|
|
output.header = input_->header;
|
|
|
|
if (!initCompute ())
|
|
{
|
|
output.width = output.height = 0;
|
|
output.clear ();
|
|
return;
|
|
}
|
|
|
|
if (input_->isOrganized () == false)
|
|
{
|
|
PCL_ERROR ("Input cloud is not organized.\n");
|
|
return;
|
|
}
|
|
|
|
// Invert projection matrix
|
|
unprojection_matrix_ = projection_matrix_.inverse ();
|
|
|
|
for (int i = 0; i < 3; ++i)
|
|
{
|
|
for (int j = 0; j < 3; ++j)
|
|
printf ("%f ", unprojection_matrix_(i, j));
|
|
|
|
printf ("\n");
|
|
}
|
|
|
|
|
|
// Perform the actual surface reconstruction
|
|
performProcessing (output);
|
|
|
|
deinitCompute ();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename PointInT, typename PointOutT> void
|
|
pcl::BilateralUpsampling<PointInT, PointOutT>::performProcessing (PointCloudOut &output)
|
|
{
|
|
output.resize (input_->size ());
|
|
float nan = std::numeric_limits<float>::quiet_NaN ();
|
|
|
|
Eigen::MatrixXf val_exp_depth_matrix;
|
|
Eigen::VectorXf val_exp_rgb_vector;
|
|
computeDistances (val_exp_depth_matrix, val_exp_rgb_vector);
|
|
|
|
for (int x = 0; x < static_cast<int> (input_->width); ++x)
|
|
for (int y = 0; y < static_cast<int> (input_->height); ++y)
|
|
{
|
|
int start_window_x = std::max (x - window_size_, 0),
|
|
start_window_y = std::max (y - window_size_, 0),
|
|
end_window_x = std::min (x + window_size_, static_cast<int> (input_->width)),
|
|
end_window_y = std::min (y + window_size_, static_cast<int> (input_->height));
|
|
|
|
float sum = 0.0f,
|
|
norm_sum = 0.0f;
|
|
|
|
for (int x_w = start_window_x; x_w < end_window_x; ++ x_w)
|
|
for (int y_w = start_window_y; y_w < end_window_y; ++ y_w)
|
|
{
|
|
float val_exp_depth = val_exp_depth_matrix (static_cast<Eigen::MatrixXf::Index> (x - x_w + window_size_),
|
|
static_cast<Eigen::MatrixXf::Index> (y - y_w + window_size_));
|
|
|
|
Eigen::VectorXf::Index d_color = static_cast<Eigen::VectorXf::Index> (
|
|
std::abs ((*input_)[y_w * input_->width + x_w].r - (*input_)[y * input_->width + x].r) +
|
|
std::abs ((*input_)[y_w * input_->width + x_w].g - (*input_)[y * input_->width + x].g) +
|
|
std::abs ((*input_)[y_w * input_->width + x_w].b - (*input_)[y * input_->width + x].b));
|
|
|
|
float val_exp_rgb = val_exp_rgb_vector (d_color);
|
|
|
|
if (std::isfinite ((*input_)[y_w*input_->width + x_w].z))
|
|
{
|
|
sum += val_exp_depth * val_exp_rgb * (*input_)[y_w*input_->width + x_w].z;
|
|
norm_sum += val_exp_depth * val_exp_rgb;
|
|
}
|
|
}
|
|
|
|
output[y*input_->width + x].r = (*input_)[y*input_->width + x].r;
|
|
output[y*input_->width + x].g = (*input_)[y*input_->width + x].g;
|
|
output[y*input_->width + x].b = (*input_)[y*input_->width + x].b;
|
|
|
|
if (norm_sum != 0.0f)
|
|
{
|
|
float depth = sum / norm_sum;
|
|
Eigen::Vector3f pc (static_cast<float> (x) * depth, static_cast<float> (y) * depth, depth);
|
|
Eigen::Vector3f pw (unprojection_matrix_ * pc);
|
|
output[y*input_->width + x].x = pw[0];
|
|
output[y*input_->width + x].y = pw[1];
|
|
output[y*input_->width + x].z = pw[2];
|
|
}
|
|
else
|
|
{
|
|
output[y*input_->width + x].x = nan;
|
|
output[y*input_->width + x].y = nan;
|
|
output[y*input_->width + x].z = nan;
|
|
}
|
|
}
|
|
|
|
output.header = input_->header;
|
|
output.width = input_->width;
|
|
output.height = input_->height;
|
|
}
|
|
|
|
|
|
template <typename PointInT, typename PointOutT> void
|
|
pcl::BilateralUpsampling<PointInT, PointOutT>::computeDistances (Eigen::MatrixXf &val_exp_depth, Eigen::VectorXf &val_exp_rgb)
|
|
{
|
|
val_exp_depth.resize (2*window_size_+1,2*window_size_+1);
|
|
val_exp_rgb.resize (3*255+1);
|
|
|
|
int j = 0;
|
|
for (int dx = -window_size_; dx < window_size_+1; ++dx)
|
|
{
|
|
int i = 0;
|
|
for (int dy = -window_size_; dy < window_size_+1; ++dy)
|
|
{
|
|
float val_exp = std::exp (- (dx*dx + dy*dy) / (2.0f * static_cast<float> (sigma_depth_ * sigma_depth_)));
|
|
val_exp_depth(i,j) = val_exp;
|
|
i++;
|
|
}
|
|
j++;
|
|
}
|
|
|
|
for (int d_color = 0; d_color < 3*255+1; d_color++)
|
|
{
|
|
float val_exp = std::exp (- d_color * d_color / (2.0f * sigma_color_ * sigma_color_));
|
|
val_exp_rgb(d_color) = val_exp;
|
|
}
|
|
}
|
|
|
|
|
|
#define PCL_INSTANTIATE_BilateralUpsampling(T,OutT) template class PCL_EXPORTS pcl::BilateralUpsampling<T,OutT>;
|
|
|
|
|
|
#endif /* PCL_SURFACE_IMPL_BILATERAL_UPSAMPLING_H_ */
|