point_cloud_image_extractors.hpp
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2013-, Open Perception, 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 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.
*
*/
#pragma once
#include <set>
#include <map>
#include <ctime>
#include <cstdlib>
#include <pcl/common/io.h>
#include <pcl/common/colors.h>
#include <pcl/common/point_tests.h> // for pcl::isFinite
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::io::PointCloudImageExtractor<PointT>::extract (const PointCloud& cloud, pcl::PCLImage& img) const
{
if (!cloud.isOrganized () || cloud.size () != cloud.width * cloud.height)
return (false);
bool result = this->extractImpl (cloud, img);
if (paint_nans_with_black_ && result)
{
std::size_t size = img.encoding == "mono16" ? 2 : 3;
for (std::size_t i = 0; i < cloud.size (); ++i)
if (!pcl::isFinite (cloud[i]))
std::memset (&img.data[i * size], 0, size);
}
return (result);
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::io::PointCloudImageExtractorFromNormalField<PointT>::extractImpl (const PointCloud& cloud, pcl::PCLImage& img) const
{
std::vector<pcl::PCLPointField> fields;
int field_x_idx = pcl::getFieldIndex<PointT> ("normal_x", fields);
int field_y_idx = pcl::getFieldIndex<PointT> ("normal_y", fields);
int field_z_idx = pcl::getFieldIndex<PointT> ("normal_z", fields);
if (field_x_idx == -1 || field_y_idx == -1 || field_z_idx == -1)
return (false);
const std::size_t offset_x = fields[field_x_idx].offset;
const std::size_t offset_y = fields[field_y_idx].offset;
const std::size_t offset_z = fields[field_z_idx].offset;
img.encoding = "rgb8";
img.width = cloud.width;
img.height = cloud.height;
img.step = img.width * sizeof (unsigned char) * 3;
img.data.resize (img.step * img.height);
for (std::size_t i = 0; i < cloud.size (); ++i)
{
float x;
float y;
float z;
pcl::getFieldValue<PointT, float> (cloud[i], offset_x, x);
pcl::getFieldValue<PointT, float> (cloud[i], offset_y, y);
pcl::getFieldValue<PointT, float> (cloud[i], offset_z, z);
img.data[i * 3 + 0] = static_cast<unsigned char>((x + 1.0) * 127);
img.data[i * 3 + 1] = static_cast<unsigned char>((y + 1.0) * 127);
img.data[i * 3 + 2] = static_cast<unsigned char>((z + 1.0) * 127);
}
return (true);
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::io::PointCloudImageExtractorFromRGBField<PointT>::extractImpl (const PointCloud& cloud, pcl::PCLImage& img) const
{
std::vector<pcl::PCLPointField> fields;
int field_idx = pcl::getFieldIndex<PointT> ("rgb", fields);
if (field_idx == -1)
{
field_idx = pcl::getFieldIndex<PointT> ("rgba", fields);
if (field_idx == -1)
return (false);
}
const std::size_t offset = fields[field_idx].offset;
img.encoding = "rgb8";
img.width = cloud.width;
img.height = cloud.height;
img.step = img.width * sizeof (unsigned char) * 3;
img.data.resize (img.step * img.height);
for (std::size_t i = 0; i < cloud.size (); ++i)
{
std::uint32_t val;
pcl::getFieldValue<PointT, std::uint32_t> (cloud[i], offset, val);
img.data[i * 3 + 0] = (val >> 16) & 0x0000ff;
img.data[i * 3 + 1] = (val >> 8) & 0x0000ff;
img.data[i * 3 + 2] = (val) & 0x0000ff;
}
return (true);
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::io::PointCloudImageExtractorFromLabelField<PointT>::extractImpl (const PointCloud& cloud, pcl::PCLImage& img) const
{
std::vector<pcl::PCLPointField> fields;
int field_idx = pcl::getFieldIndex<PointT> ("label", fields);
if (field_idx == -1)
return (false);
const std::size_t offset = fields[field_idx].offset;
switch (color_mode_)
{
case COLORS_MONO:
{
img.encoding = "mono16";
img.width = cloud.width;
img.height = cloud.height;
img.step = img.width * sizeof (unsigned short);
img.data.resize (img.step * img.height);
unsigned short* data = reinterpret_cast<unsigned short*>(&img.data[0]);
for (std::size_t i = 0; i < cloud.size (); ++i)
{
std::uint32_t val;
pcl::getFieldValue<PointT, std::uint32_t> (cloud[i], offset, val);
data[i] = static_cast<unsigned short>(val);
}
break;
}
case COLORS_RGB_RANDOM:
{
img.encoding = "rgb8";
img.width = cloud.width;
img.height = cloud.height;
img.step = img.width * sizeof (unsigned char) * 3;
img.data.resize (img.step * img.height);
std::srand(std::time(nullptr));
std::map<std::uint32_t, std::size_t> colormap;
for (std::size_t i = 0; i < cloud.size (); ++i)
{
std::uint32_t val;
pcl::getFieldValue<PointT, std::uint32_t> (cloud[i], offset, val);
if (colormap.count (val) == 0)
{
colormap[val] = i * 3;
img.data[i * 3 + 0] = static_cast<std::uint8_t> ((std::rand () % 256));
img.data[i * 3 + 1] = static_cast<std::uint8_t> ((std::rand () % 256));
img.data[i * 3 + 2] = static_cast<std::uint8_t> ((std::rand () % 256));
}
else
{
memcpy (&img.data[i * 3], &img.data[colormap[val]], 3);
}
}
break;
}
case COLORS_RGB_GLASBEY:
{
img.encoding = "rgb8";
img.width = cloud.width;
img.height = cloud.height;
img.step = img.width * sizeof (unsigned char) * 3;
img.data.resize (img.step * img.height);
std::srand(std::time(nullptr));
std::set<std::uint32_t> labels;
std::map<std::uint32_t, std::size_t> colormap;
// First pass: find unique labels
for (const auto& point: cloud)
{
// If we need to paint NaN points with black do not waste colors on them
if (paint_nans_with_black_ && !pcl::isFinite (point))
continue;
std::uint32_t val;
pcl::getFieldValue<PointT, std::uint32_t> (point, offset, val);
labels.insert (val);
}
// Assign Glasbey colors in ascending order of labels
// Note: the color LUT has a finite size (256 colors), therefore when
// there are more labels the colors will repeat
std::size_t color = 0;
for (const std::uint32_t &label : labels)
{
colormap[label] = color % GlasbeyLUT::size ();
++color;
}
// Second pass: copy colors from the LUT
for (std::size_t i = 0; i < cloud.size (); ++i)
{
std::uint32_t val;
pcl::getFieldValue<PointT, std::uint32_t> (cloud[i], offset, val);
memcpy (&img.data[i * 3], GlasbeyLUT::data () + colormap[val] * 3, 3);
}
break;
}
}
return (true);
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::io::PointCloudImageExtractorWithScaling<PointT>::extractImpl (const PointCloud& cloud, pcl::PCLImage& img) const
{
std::vector<pcl::PCLPointField> fields;
int field_idx = pcl::getFieldIndex<PointT> (field_name_, fields);
if (field_idx == -1)
return (false);
const std::size_t offset = fields[field_idx].offset;
img.encoding = "mono16";
img.width = cloud.width;
img.height = cloud.height;
img.step = img.width * sizeof (unsigned short);
img.data.resize (img.step * img.height);
unsigned short* data = reinterpret_cast<unsigned short*>(&img.data[0]);
float scaling_factor = scaling_factor_;
float data_min = 0.0f;
if (scaling_method_ == SCALING_FULL_RANGE)
{
float min = std::numeric_limits<float>::infinity();
float max = -std::numeric_limits<float>::infinity();
for (const auto& point: cloud)
{
float val;
pcl::getFieldValue<PointT, float> (point, offset, val);
if (val < min)
min = val;
if (val > max)
max = val;
}
scaling_factor = min == max ? 0 : std::numeric_limits<unsigned short>::max() / (max - min);
data_min = min;
}
for (std::size_t i = 0; i < cloud.size (); ++i)
{
float val;
pcl::getFieldValue<PointT, float> (cloud[i], offset, val);
if (scaling_method_ == SCALING_NO)
{
data[i] = val;
}
else if (scaling_method_ == SCALING_FULL_RANGE)
{
data[i] = (val - data_min) * scaling_factor;
}
else if (scaling_method_ == SCALING_FIXED_FACTOR)
{
data[i] = val * scaling_factor;
}
}
return (true);
}
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:398
pcl::PointCloud::isOrganized
bool isOrganized() const
Return whether a dataset is organized (e.g., arranged in a structured grid).
Definition: point_cloud.h:310
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:400
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:443
pcl::io::PointCloudImageExtractorFromLabelField::extractImpl
bool extractImpl(const PointCloud &cloud, pcl::PCLImage &img) const override
Implementation of the extract() function, has to be implemented in deriving classes.
Definition: point_cloud_image_extractors.hpp:139
pcl::io::PointCloudImageExtractorFromNormalField::extractImpl
bool extractImpl(const PointCloud &cloud, pcl::PCLImage &img) const override
Implementation of the extract() function, has to be implemented in deriving classes.
Definition: point_cloud_image_extractors.hpp:71
pcl::io::PointCloudImageExtractorFromRGBField::extractImpl
bool extractImpl(const PointCloud &cloud, pcl::PCLImage &img) const override
Implementation of the extract() function, has to be implemented in deriving classes.
Definition: point_cloud_image_extractors.hpp:107
pcl::io::PointCloudImageExtractor::extract
bool extract(const PointCloud &cloud, pcl::PCLImage &image) const
Obtain the image from the given cloud.
Definition: point_cloud_image_extractors.hpp:51
pcl::io::PointCloudImageExtractorWithScaling::extractImpl
bool extractImpl(const PointCloud &cloud, pcl::PCLImage &image) const override
Implementation of the extract() function, has to be implemented in deriving classes.
Definition: point_cloud_image_extractors.hpp:244
pcl::isFinite
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition: point_tests.h:55
pcl::PCLImage
Definition: PCLImage.h:13
pcl::PCLImage::step
uindex_t step
Definition: PCLImage.h:21
pcl::PCLImage::height
uindex_t height
Definition: PCLImage.h:16
pcl::PCLImage::encoding
std::string encoding
Definition: PCLImage.h:18
pcl::PCLImage::data
std::vector< std::uint8_t > data
Definition: PCLImage.h:23
pcl::PCLImage::width
uindex_t width
Definition: PCLImage.h:17