Point Cloud Library (PCL)
1.15.1
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pcl
2d
impl
convolution.hpp
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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) 2012-, Open Perception, 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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*/
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#pragma once
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#include <pcl/2d/convolution.h>
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namespace
pcl
{
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template
<
typename
Po
int
T>
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void
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Convolution<PointT>::filter
(
pcl::PointCloud<PointT>
& output)
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{
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int
input_row = 0;
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int
input_col = 0;
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// default boundary option : zero padding
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output = *
input_
;
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int
iw =
static_cast<
int
>
(
input_
->width), ih =
static_cast<
int
>
(
input_
->height),
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kw =
static_cast<
int
>
(kernel_.width), kh =
static_cast<
int
>
(kernel_.height);
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switch
(boundary_options_) {
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default
:
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case
BOUNDARY_OPTION_CLAMP
: {
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for
(
int
i = 0; i < ih; i++) {
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for
(
int
j = 0; j < iw; j++) {
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float
intensity = 0;
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for
(
int
k = 0; k < kh; k++) {
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for
(
int
l = 0; l < kw; l++) {
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int
ikkh = i + k - kh / 2, jlkw = j + l - kw / 2;
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if
(ikkh < 0)
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input_row = 0;
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else
if
(ikkh >= ih)
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input_row = ih - 1;
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else
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input_row = ikkh;
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if
(jlkw < 0)
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input_col = 0;
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else
if
(jlkw >= iw)
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input_col = iw - 1;
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else
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input_col = jlkw;
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intensity +=
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kernel_(l, k).intensity * (*input_)(input_col, input_row).intensity;
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}
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}
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output(j, i).intensity = intensity;
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}
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}
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break
;
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}
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case
BOUNDARY_OPTION_MIRROR
: {
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for
(
int
i = 0; i < ih; i++) {
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for
(
int
j = 0; j < iw; j++) {
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float
intensity = 0;
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for
(
int
k = 0; k < kh; k++) {
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for
(
int
l = 0; l < kw; l++) {
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int
ikkh = i + k - kh / 2, jlkw = j + l - kw / 2;
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if
(ikkh < 0)
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input_row = -ikkh - 1;
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else
if
(ikkh >= ih)
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input_row = 2 * ih - 1 - ikkh;
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else
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input_row = ikkh;
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if
(jlkw < 0)
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input_col = -jlkw - 1;
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else
if
(jlkw >= iw)
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input_col = 2 * iw - 1 - jlkw;
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else
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input_col = jlkw;
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intensity +=
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kernel_(l, k).intensity * ((*input_)(input_col, input_row).intensity);
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}
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}
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output(j, i).intensity = intensity;
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}
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}
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break
;
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}
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case
BOUNDARY_OPTION_ZERO_PADDING
: {
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for
(
int
i = 0; i < ih; i++) {
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for
(
int
j = 0; j < iw; j++) {
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float
intensity = 0;
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for
(
int
k = 0; k < kh; k++) {
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for
(
int
l = 0; l < kw; l++) {
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int
ikkh = i + k - kh / 2, jlkw = j + l - kw / 2;
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if
(ikkh < 0 || ikkh >= ih || jlkw < 0 || jlkw >= iw)
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continue
;
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intensity += kernel_(l, k).intensity * ((*input_)(jlkw, ikkh).intensity);
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}
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}
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output(j, i).intensity = intensity;
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}
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}
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break
;
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}
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}
// switch
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}
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}
// namespace pcl
pcl::Convolution::BOUNDARY_OPTION_ZERO_PADDING
@ BOUNDARY_OPTION_ZERO_PADDING
Definition
convolution.h:101
pcl::Convolution::BOUNDARY_OPTION_MIRROR
@ BOUNDARY_OPTION_MIRROR
Definition
convolution.h:100
pcl::Convolution::BOUNDARY_OPTION_CLAMP
@ BOUNDARY_OPTION_CLAMP
Definition
convolution.h:99
pcl::Convolution::filter
void filter(pcl::PointCloud< PointT > &output)
Performs 2D convolution of the input point cloud with the kernel.
Definition
convolution.hpp:46
pcl::PCLBase::input_
PointCloudConstPtr input_
The input point cloud dataset.
Definition
pcl_base.h:147
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition
point_cloud.h:174
pcl
Definition
convolution.h:46