263 lines
12 KiB
C
263 lines
12 KiB
C
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/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2010-2011, Willow Garage, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* $Id$
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*
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*/
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#pragma once
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#include <pcl/point_cloud.h>
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#include <Eigen/Core> // for VectorXf
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#include <functional>
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namespace pcl
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{
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/** Class GaussianKernel assembles all the method for computing,
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* convolving, smoothing, gradients computing an image using
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* a gaussian kernel. The image is stored in point cloud elements
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* intensity member or rgb or...
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* \author Nizar Sallem
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* \ingroup common
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*/
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class PCL_EXPORTS GaussianKernel
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{
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public:
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static const unsigned MAX_KERNEL_WIDTH = 71;
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/** Computes the gaussian kernel and dervative assiociated to sigma.
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* The kernel and derivative width are adjusted according.
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* \param[in] sigma
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* \param[out] kernel the computed gaussian kernel
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* \param[in] kernel_width the desired kernel width upper bond
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* \throws pcl::KernelWidthTooSmallException
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*/
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void
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compute (float sigma,
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Eigen::VectorXf &kernel,
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unsigned kernel_width = MAX_KERNEL_WIDTH) const;
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/** Computes the gaussian kernel and dervative assiociated to sigma.
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* The kernel and derivative width are adjusted according.
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* \param[in] sigma
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* \param[out] kernel the computed gaussian kernel
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* \param[out] derivative the computed kernel derivative
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* \param[in] kernel_width the desired kernel width upper bond
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* \throws pcl::KernelWidthTooSmallException
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*/
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void
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compute (float sigma,
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Eigen::VectorXf &kernel,
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Eigen::VectorXf &derivative,
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unsigned kernel_width = MAX_KERNEL_WIDTH) const;
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/** Convolve a float image rows by a given kernel.
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* \param[in] kernel convolution kernel
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* \param[in] input the image to convolve
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* \param[out] output the convolved image
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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void
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convolveRows (const pcl::PointCloud<float> &input,
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const Eigen::VectorXf &kernel,
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pcl::PointCloud<float> &output) const;
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/** Convolve a float image rows by a given kernel.
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* \param[in] input the image to convolve
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* \param[in] field_accessor a field accessor
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* \param[in] kernel convolution kernel
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* \param[out] output the convolved image
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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template <typename PointT> void
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convolveRows (const pcl::PointCloud<PointT> &input,
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std::function <float (const PointT& p)> field_accessor,
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const Eigen::VectorXf &kernel,
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pcl::PointCloud<float> &output) const;
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/** Convolve a float image columns by a given kernel.
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* \param[in] input the image to convolve
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* \param[in] kernel convolution kernel
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* \param[out] output the convolved image
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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void
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convolveCols (const pcl::PointCloud<float> &input,
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const Eigen::VectorXf &kernel,
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pcl::PointCloud<float> &output) const;
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/** Convolve a float image columns by a given kernel.
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* \param[in] input the image to convolve
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* \param[in] field_accessor a field accessor
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* \param[in] kernel convolution kernel
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* \param[out] output the convolved image
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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template <typename PointT> void
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convolveCols (const pcl::PointCloud<PointT> &input,
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std::function <float (const PointT& p)> field_accessor,
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const Eigen::VectorXf &kernel,
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pcl::PointCloud<float> &output) const;
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/** Convolve a float image in the 2 directions
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* \param[in] horiz_kernel kernel for convolving rows
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* \param[in] vert_kernel kernel for convolving columns
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* \param[in] input image to convolve
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* \param[out] output the convolved image
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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inline void
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convolve (const pcl::PointCloud<float> &input,
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const Eigen::VectorXf &horiz_kernel,
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const Eigen::VectorXf &vert_kernel,
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pcl::PointCloud<float> &output) const
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{
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std::cout << ">>> convolve cpp" << std::endl;
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pcl::PointCloud<float> tmp (input.width, input.height) ;
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convolveRows (input, horiz_kernel, tmp);
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convolveCols (tmp, vert_kernel, output);
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std::cout << "<<< convolve cpp" << std::endl;
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}
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/** Convolve a float image in the 2 directions
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* \param[in] input image to convolve
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* \param[in] field_accessor a field accessor
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* \param[in] horiz_kernel kernel for convolving rows
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* \param[in] vert_kernel kernel for convolving columns
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* \param[out] output the convolved image
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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template <typename PointT> inline void
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convolve (const pcl::PointCloud<PointT> &input,
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std::function <float (const PointT& p)> field_accessor,
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const Eigen::VectorXf &horiz_kernel,
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const Eigen::VectorXf &vert_kernel,
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pcl::PointCloud<float> &output) const
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{
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std::cout << ">>> convolve hpp" << std::endl;
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pcl::PointCloud<float> tmp (input.width, input.height);
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convolveRows<PointT>(input, field_accessor, horiz_kernel, tmp);
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convolveCols(tmp, vert_kernel, output);
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std::cout << "<<< convolve hpp" << std::endl;
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}
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/** Computes float image gradients using a gaussian kernel and gaussian kernel
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* derivative.
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* \param[in] input image to compute gardients for
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* \param[in] gaussian_kernel the gaussian kernel to be used
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* \param[in] gaussian_kernel_derivative the associated derivative
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* \param[out] grad_x gradient along X direction
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* \param[out] grad_y gradient along Y direction
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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inline void
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computeGradients (const pcl::PointCloud<float> &input,
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const Eigen::VectorXf &gaussian_kernel,
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const Eigen::VectorXf &gaussian_kernel_derivative,
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pcl::PointCloud<float> &grad_x,
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pcl::PointCloud<float> &grad_y) const
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{
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convolve (input, gaussian_kernel_derivative, gaussian_kernel, grad_x);
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convolve (input, gaussian_kernel, gaussian_kernel_derivative, grad_y);
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}
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/** Computes float image gradients using a gaussian kernel and gaussian kernel
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* derivative.
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* \param[in] input image to compute gardients for
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* \param[in] field_accessor a field accessor
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* \param[in] gaussian_kernel the gaussian kernel to be used
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* \param[in] gaussian_kernel_derivative the associated derivative
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* \param[out] grad_x gradient along X direction
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* \param[out] grad_y gradient along Y direction
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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template <typename PointT> inline void
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computeGradients (const pcl::PointCloud<PointT> &input,
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std::function <float (const PointT& p)> field_accessor,
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const Eigen::VectorXf &gaussian_kernel,
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const Eigen::VectorXf &gaussian_kernel_derivative,
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pcl::PointCloud<float> &grad_x,
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pcl::PointCloud<float> &grad_y) const
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{
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convolve<PointT> (input, field_accessor, gaussian_kernel_derivative, gaussian_kernel, grad_x);
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convolve<PointT> (input, field_accessor, gaussian_kernel, gaussian_kernel_derivative, grad_y);
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}
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/** Smooth image using a gaussian kernel.
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* \param[in] input image
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* \param[in] gaussian_kernel the gaussian kernel to be used
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* \param[out] output the smoothed image
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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inline void
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smooth (const pcl::PointCloud<float> &input,
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const Eigen::VectorXf &gaussian_kernel,
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pcl::PointCloud<float> &output) const
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{
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convolve (input, gaussian_kernel, gaussian_kernel, output);
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}
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/** Smooth image using a gaussian kernel.
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* \param[in] input image
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* \param[in] field_accessor a field accessor
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* \param[in] gaussian_kernel the gaussian kernel to be used
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* \param[out] output the smoothed image
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* \note if output doesn't fit in input i.e. output.rows () < input.rows () or
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* output.cols () < input.cols () then output is resized to input sizes.
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*/
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template <typename PointT> inline void
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smooth (const pcl::PointCloud<PointT> &input,
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std::function <float (const PointT& p)> field_accessor,
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const Eigen::VectorXf &gaussian_kernel,
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pcl::PointCloud<float> &output) const
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{
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convolve<PointT> (input, field_accessor, gaussian_kernel, gaussian_kernel, output);
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}
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};
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}
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#include <pcl/common/impl/gaussian.hpp>
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