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/*
* 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.
*
* $Id$
*
*/
#ifndef PCL_FILTERS_FAST_BILATERAL_H_
#define PCL_FILTERS_FAST_BILATERAL_H_
#include <pcl/filters/filter.h>
namespace pcl
{
/** \brief Implementation of a fast bilateral filter for smoothing depth information in organized point clouds
* Based on the following paper:
* * Sylvain Paris and Frdo Durand
* "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach"
* European Conference on Computer Vision (ECCV'06)
*
* More details on the webpage: http://people.csail.mit.edu/sparis/bf/
*/
template<typename PointT>
class FastBilateralFilter : public Filter<PointT>
{
protected:
using Filter<PointT>::input_;
typedef typename Filter<PointT>::PointCloud PointCloud;
public:
typedef boost::shared_ptr< FastBilateralFilter<PointT> > Ptr;
typedef boost::shared_ptr< const FastBilateralFilter<PointT> > ConstPtr;
/** \brief Empty constructor. */
FastBilateralFilter ()
: sigma_s_ (15.0f)
, sigma_r_ (0.05f)
, early_division_ (false)
{ }
/** \brief Empty destructor */
virtual ~FastBilateralFilter () {}
/** \brief Set the standard deviation of the Gaussian used by the bilateral filter for
* the spatial neighborhood/window.
* \param[in] sigma_s the size of the Gaussian bilateral filter window to use
*/
inline void
setSigmaS (float sigma_s)
{ sigma_s_ = sigma_s; }
/** \brief Get the size of the Gaussian bilateral filter window as set by the user. */
inline float
getSigmaS () const
{ return sigma_s_; }
/** \brief Set the standard deviation of the Gaussian used to control how much an adjacent
* pixel is downweighted because of the intensity difference (depth in our case).
* \param[in] sigma_r the standard deviation of the Gaussian for the intensity difference
*/
inline void
setSigmaR (float sigma_r)
{ sigma_r_ = sigma_r; }
/** \brief Get the standard deviation of the Gaussian for the intensity difference */
inline float
getSigmaR () const
{ return sigma_r_; }
/** \brief Filter the input data and store the results into output.
* \param[out] output the resultant point cloud
*/
virtual void
applyFilter (PointCloud &output);
protected:
float sigma_s_;
float sigma_r_;
bool early_division_;
class Array3D
{
public:
Array3D (const size_t width, const size_t height, const size_t depth)
{
x_dim_ = width;
y_dim_ = height;
z_dim_ = depth;
v_ = std::vector<Eigen::Vector2f> (width*height*depth, Eigen::Vector2f (0.0f, 0.0f));
}
inline Eigen::Vector2f&
operator () (const size_t x, const size_t y, const size_t z)
{ return v_[(x * y_dim_ + y) * z_dim_ + z]; }
inline const Eigen::Vector2f&
operator () (const size_t x, const size_t y, const size_t z) const
{ return v_[(x * y_dim_ + y) * z_dim_ + z]; }
inline void
resize (const size_t width, const size_t height, const size_t depth)
{
x_dim_ = width;
y_dim_ = height;
z_dim_ = depth;
v_.resize (x_dim_ * y_dim_ * z_dim_);
}
Eigen::Vector2f
trilinear_interpolation (const float x,
const float y,
const float z);
static inline size_t
clamp (const size_t min_value,
const size_t max_value,
const size_t x);
inline size_t
x_size () const
{ return x_dim_; }
inline size_t
y_size () const
{ return y_dim_; }
inline size_t
z_size () const
{ return z_dim_; }
inline std::vector<Eigen::Vector2f >::iterator
begin ()
{ return v_.begin (); }
inline std::vector<Eigen::Vector2f >::iterator
end ()
{ return v_.end (); }
inline std::vector<Eigen::Vector2f >::const_iterator
begin () const
{ return v_.begin (); }
inline std::vector<Eigen::Vector2f >::const_iterator
end () const
{ return v_.end (); }
private:
std::vector<Eigen::Vector2f > v_;
size_t x_dim_, y_dim_, z_dim_;
};
};
}
#ifdef PCL_NO_PRECOMPILE
#include <pcl/filters/impl/fast_bilateral.hpp>
#else
#define PCL_INSTANTIATE_FastBilateralFilter(T) template class PCL_EXPORTS pcl::FastBilateralFilter<T>;
#endif
#endif /* PCL_FILTERS_FAST_BILATERAL_H_ */
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