Point Cloud Library (PCL) 1.14.0
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point_cloud_image_extractors.hpp
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37
38#pragma once
39
40#include <set>
41#include <map>
42#include <ctime>
43#include <cstdlib>
44
45#include <pcl/common/io.h>
46#include <pcl/common/colors.h>
47#include <pcl/common/point_tests.h> // for pcl::isFinite
48
49///////////////////////////////////////////////////////////////////////////////////////////
50template <typename PointT> bool
52{
53 if (!cloud.isOrganized () || cloud.size () != cloud.width * cloud.height)
54 return (false);
55
56 bool result = this->extractImpl (cloud, img);
57
58 if (paint_nans_with_black_ && result)
59 {
60 std::size_t size = img.encoding == "mono16" ? 2 : 3;
61 for (std::size_t i = 0; i < cloud.size (); ++i)
62 if (!pcl::isFinite (cloud[i])) {
63 std::fill_n(&img.data[i * size], size, 0);
64 }
65 }
66
67 return (result);
68}
69
70///////////////////////////////////////////////////////////////////////////////////////////
71template <typename PointT> bool
73{
74 std::vector<pcl::PCLPointField> fields;
75 int field_x_idx = pcl::getFieldIndex<PointT> ("normal_x", fields);
76 int field_y_idx = pcl::getFieldIndex<PointT> ("normal_y", fields);
77 int field_z_idx = pcl::getFieldIndex<PointT> ("normal_z", fields);
78 if (field_x_idx == -1 || field_y_idx == -1 || field_z_idx == -1)
79 return (false);
80 const std::size_t offset_x = fields[field_x_idx].offset;
81 const std::size_t offset_y = fields[field_y_idx].offset;
82 const std::size_t offset_z = fields[field_z_idx].offset;
83
84 img.encoding = "rgb8";
85 img.width = cloud.width;
86 img.height = cloud.height;
87 img.step = img.width * sizeof (unsigned char) * 3;
88 img.data.resize (img.step * img.height);
89
90 for (std::size_t i = 0; i < cloud.size (); ++i)
91 {
92 float x;
93 float y;
94 float z;
95 pcl::getFieldValue<PointT, float> (cloud[i], offset_x, x);
96 pcl::getFieldValue<PointT, float> (cloud[i], offset_y, y);
97 pcl::getFieldValue<PointT, float> (cloud[i], offset_z, z);
98 img.data[i * 3 + 0] = static_cast<unsigned char>((x + 1.0) * 127);
99 img.data[i * 3 + 1] = static_cast<unsigned char>((y + 1.0) * 127);
100 img.data[i * 3 + 2] = static_cast<unsigned char>((z + 1.0) * 127);
101 }
102
103 return (true);
104}
105
106///////////////////////////////////////////////////////////////////////////////////////////
107template <typename PointT> bool
109{
110 std::vector<pcl::PCLPointField> fields;
111 int field_idx = pcl::getFieldIndex<PointT> ("rgb", fields);
112 if (field_idx == -1)
113 {
114 field_idx = pcl::getFieldIndex<PointT> ("rgba", fields);
115 if (field_idx == -1)
116 return (false);
117 }
118 const std::size_t offset = fields[field_idx].offset;
119
120 img.encoding = "rgb8";
121 img.width = cloud.width;
122 img.height = cloud.height;
123 img.step = img.width * sizeof (unsigned char) * 3;
124 img.data.resize (img.step * img.height);
125
126 for (std::size_t i = 0; i < cloud.size (); ++i)
127 {
128 std::uint32_t val;
129 pcl::getFieldValue<PointT, std::uint32_t> (cloud[i], offset, val);
130 img.data[i * 3 + 0] = (val >> 16) & 0x0000ff;
131 img.data[i * 3 + 1] = (val >> 8) & 0x0000ff;
132 img.data[i * 3 + 2] = (val) & 0x0000ff;
133 }
134
135 return (true);
136}
137
138///////////////////////////////////////////////////////////////////////////////////////////
139template <typename PointT> bool
141{
142 std::vector<pcl::PCLPointField> fields;
143 int field_idx = pcl::getFieldIndex<PointT> ("label", fields);
144 if (field_idx == -1)
145 return (false);
146 const std::size_t offset = fields[field_idx].offset;
147
148 switch (color_mode_)
149 {
150 case COLORS_MONO:
151 {
152 img.encoding = "mono16";
153 img.width = cloud.width;
154 img.height = cloud.height;
155 img.step = img.width * sizeof (unsigned short);
156 img.data.resize (img.step * img.height);
157 auto* data = reinterpret_cast<unsigned short*>(img.data.data());
158 for (std::size_t i = 0; i < cloud.size (); ++i)
159 {
160 std::uint32_t val;
161 pcl::getFieldValue<PointT, std::uint32_t> (cloud[i], offset, val);
162 data[i] = static_cast<unsigned short>(val);
163 }
164 break;
165 }
166 case COLORS_RGB_RANDOM:
167 {
168 img.encoding = "rgb8";
169 img.width = cloud.width;
170 img.height = cloud.height;
171 img.step = img.width * sizeof (unsigned char) * 3;
172 img.data.resize (img.step * img.height);
173
174 std::srand(std::time(nullptr));
175 std::map<std::uint32_t, std::size_t> colormap;
176
177 for (std::size_t i = 0; i < cloud.size (); ++i)
178 {
179 std::uint32_t val;
180 pcl::getFieldValue<PointT, std::uint32_t> (cloud[i], offset, val);
181 if (colormap.count (val) == 0)
182 {
183 colormap[val] = i * 3;
184 img.data[i * 3 + 0] = static_cast<std::uint8_t> ((std::rand () % 256));
185 img.data[i * 3 + 1] = static_cast<std::uint8_t> ((std::rand () % 256));
186 img.data[i * 3 + 2] = static_cast<std::uint8_t> ((std::rand () % 256));
187 }
188 else
189 {
190 memcpy (&img.data[i * 3], &img.data[colormap[val]], 3);
191 }
192 }
193 break;
194 }
195 case COLORS_RGB_GLASBEY:
196 {
197 img.encoding = "rgb8";
198 img.width = cloud.width;
199 img.height = cloud.height;
200 img.step = img.width * sizeof (unsigned char) * 3;
201 img.data.resize (img.step * img.height);
202
203 std::srand(std::time(nullptr));
204 std::set<std::uint32_t> labels;
205 std::map<std::uint32_t, std::size_t> colormap;
206
207 // First pass: find unique labels
208 for (const auto& point: cloud)
209 {
210 // If we need to paint NaN points with black do not waste colors on them
211 if (paint_nans_with_black_ && !pcl::isFinite (point))
212 continue;
213 std::uint32_t val;
214 pcl::getFieldValue<PointT, std::uint32_t> (point, offset, val);
215 labels.insert (val);
216 }
217
218 // Assign Glasbey colors in ascending order of labels
219 // Note: the color LUT has a finite size (256 colors), therefore when
220 // there are more labels the colors will repeat
221 std::size_t color = 0;
222 for (const std::uint32_t &label : labels)
223 {
224 colormap[label] = color % GlasbeyLUT::size ();
225 ++color;
226 }
227
228 // Second pass: copy colors from the LUT
229 for (std::size_t i = 0; i < cloud.size (); ++i)
230 {
231 std::uint32_t val;
232 pcl::getFieldValue<PointT, std::uint32_t> (cloud[i], offset, val);
233 memcpy (&img.data[i * 3], GlasbeyLUT::data () + colormap[val] * 3, 3);
234 }
235
236 break;
237 }
238 }
239
240 return (true);
241}
242
243///////////////////////////////////////////////////////////////////////////////////////////
244template <typename PointT> bool
246{
247 std::vector<pcl::PCLPointField> fields;
248 int field_idx = pcl::getFieldIndex<PointT> (field_name_, fields);
249 if (field_idx == -1)
250 return (false);
251 const std::size_t offset = fields[field_idx].offset;
252
253 img.encoding = "mono16";
254 img.width = cloud.width;
255 img.height = cloud.height;
256 img.step = img.width * sizeof (unsigned short);
257 img.data.resize (img.step * img.height);
258 auto* data = reinterpret_cast<unsigned short*>(img.data.data());
259
260 float scaling_factor = scaling_factor_;
261 float data_min = 0.0f;
262 if (scaling_method_ == SCALING_FULL_RANGE)
263 {
264 float min = std::numeric_limits<float>::infinity();
265 float max = -std::numeric_limits<float>::infinity();
266 for (const auto& point: cloud)
267 {
268 float val;
269 pcl::getFieldValue<PointT, float> (point, offset, val);
270 if (val < min)
271 min = val;
272 if (val > max)
273 max = val;
274 }
275 scaling_factor = min == max ? 0 : std::numeric_limits<unsigned short>::max() / (max - min);
276 data_min = min;
277 }
278
279 for (std::size_t i = 0; i < cloud.size (); ++i)
280 {
281 float val;
282 pcl::getFieldValue<PointT, float> (cloud[i], offset, val);
283 if (scaling_method_ == SCALING_NO)
284 {
285 data[i] = val;
286 }
287 else if (scaling_method_ == SCALING_FULL_RANGE)
288 {
289 data[i] = (val - data_min) * scaling_factor;
290 }
291 else if (scaling_method_ == SCALING_FIXED_FACTOR)
292 {
293 data[i] = val * scaling_factor;
294 }
295 }
296
297 return (true);
298}
299
PointCloud represents the base class in PCL for storing collections of 3D points.
std::uint32_t width
The point cloud width (if organized as an image-structure).
bool isOrganized() const
Return whether a dataset is organized (e.g., arranged in a structured grid).
std::uint32_t height
The point cloud height (if organized as an image-structure).
std::size_t size() const
bool extractImpl(const PointCloud &cloud, pcl::PCLImage &img) const override
Implementation of the extract() function, has to be implemented in deriving classes.
bool extractImpl(const PointCloud &cloud, pcl::PCLImage &img) const override
Implementation of the extract() function, has to be implemented in deriving classes.
bool extractImpl(const PointCloud &cloud, pcl::PCLImage &img) const override
Implementation of the extract() function, has to be implemented in deriving classes.
bool extract(const PointCloud &cloud, pcl::PCLImage &image) const
Obtain the image from the given cloud.
bool extractImpl(const PointCloud &cloud, pcl::PCLImage &image) const override
Implementation of the extract() function, has to be implemented in deriving classes.
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
uindex_t step
Definition PCLImage.h:21
uindex_t height
Definition PCLImage.h:16
std::string encoding
Definition PCLImage.h:18
std::vector< std::uint8_t > data
Definition PCLImage.h:23
uindex_t width
Definition PCLImage.h:17