Point Cloud Library (PCL) 1.12.0
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conversions.h
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39
40#pragma once
41
42#ifdef __GNUC__
43#pragma GCC system_header
44#endif
45
46#include <pcl/PCLPointField.h>
47#include <pcl/PCLPointCloud2.h>
48#include <pcl/PCLImage.h>
49#include <pcl/point_cloud.h>
50#include <pcl/type_traits.h>
51#include <pcl/for_each_type.h>
52#include <pcl/console/print.h>
53
54#include <boost/foreach.hpp>
55
56namespace pcl
57{
58 namespace detail
59 {
60 // For converting template point cloud to message.
61 template<typename PointT>
63 {
64 FieldAdder (std::vector<pcl::PCLPointField>& fields) : fields_ (fields) {};
65
66 template<typename U> void operator() ()
67 {
69 f.name = pcl::traits::name<PointT, U>::value;
70 f.offset = pcl::traits::offset<PointT, U>::value;
71 f.datatype = pcl::traits::datatype<PointT, U>::value;
72 f.count = pcl::traits::datatype<PointT, U>::size;
73 fields_.push_back (f);
74 }
75
76 std::vector<pcl::PCLPointField>& fields_;
77 };
78
79 // For converting message to template point cloud.
80 template<typename PointT>
82 {
83 FieldMapper (const std::vector<pcl::PCLPointField>& fields,
84 std::vector<FieldMapping>& map)
85 : fields_ (fields), map_ (map)
86 {
87 }
88
89 template<typename Tag> void
91 {
92 for (const auto& field : fields_)
93 {
94 if (FieldMatches<PointT, Tag>()(field))
95 {
96 FieldMapping mapping;
97 mapping.serialized_offset = field.offset;
98 mapping.struct_offset = pcl::traits::offset<PointT, Tag>::value;
99 mapping.size = sizeof (typename pcl::traits::datatype<PointT, Tag>::type);
100 map_.push_back (mapping);
101 return;
102 }
103 }
104 // Disable thrown exception per #595: http://dev.pointclouds.org/issues/595
105 PCL_WARN ("Failed to find match for field '%s'.\n", pcl::traits::name<PointT, Tag>::value);
106 //throw pcl::InvalidConversionException (ss.str ());
107 }
108
109 const std::vector<pcl::PCLPointField>& fields_;
110 std::vector<FieldMapping>& map_;
111 };
112
113 inline bool
115 {
117 }
118
119 } //namespace detail
120
121 template<typename PointT> void
122 createMapping (const std::vector<pcl::PCLPointField>& msg_fields, MsgFieldMap& field_map)
123 {
124 // Create initial 1-1 mapping between serialized data segments and struct fields
125 detail::FieldMapper<PointT> mapper (msg_fields, field_map);
126 for_each_type< typename traits::fieldList<PointT>::type > (mapper);
127
128 // Coalesce adjacent fields into single memcpy's where possible
129 if (field_map.size() > 1)
130 {
131 std::sort(field_map.begin(), field_map.end(), detail::fieldOrdering);
132 MsgFieldMap::iterator i = field_map.begin(), j = i + 1;
133 while (j != field_map.end())
134 {
135 // This check is designed to permit padding between adjacent fields.
136 /// @todo One could construct a pathological case where the struct has a
137 /// field where the serialized data has padding
138 if (j->serialized_offset - i->serialized_offset == j->struct_offset - i->struct_offset)
139 {
140 i->size += (j->struct_offset + j->size) - (i->struct_offset + i->size);
141 j = field_map.erase(j);
142 }
143 else
144 {
145 ++i;
146 ++j;
147 }
148 }
149 }
150 }
151
152 /** \brief Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
153 * \param[in] msg the PCLPointCloud2 binary blob
154 * \param[out] cloud the resultant pcl::PointCloud<T>
155 * \param[in] field_map a MsgFieldMap object
156 *
157 * \note Use fromPCLPointCloud2 (PCLPointCloud2, PointCloud<T>) directly or create you
158 * own MsgFieldMap using:
159 *
160 * \code
161 * MsgFieldMap field_map;
162 * createMapping<PointT> (msg.fields, field_map);
163 * \endcode
164 */
165 template <typename PointT> void
167 const MsgFieldMap& field_map)
168 {
169 // Copy info fields
170 cloud.header = msg.header;
171 cloud.width = msg.width;
172 cloud.height = msg.height;
173 cloud.is_dense = msg.is_dense == 1;
174
175 // Copy point data
176 std::uint32_t num_points = msg.width * msg.height;
177 cloud.resize (num_points);
178 std::uint8_t* cloud_data = reinterpret_cast<std::uint8_t*>(&cloud[0]);
179
180 // Check if we can copy adjacent points in a single memcpy. We can do so if there
181 // is exactly one field to copy and it is the same size as the source and destination
182 // point types.
183 if (field_map.size() == 1 &&
184 field_map[0].serialized_offset == 0 &&
185 field_map[0].struct_offset == 0 &&
186 field_map[0].size == msg.point_step &&
187 field_map[0].size == sizeof(PointT))
188 {
189 std::uint32_t cloud_row_step = static_cast<std::uint32_t> (sizeof (PointT) * cloud.width);
190 const std::uint8_t* msg_data = &msg.data[0];
191 // Should usually be able to copy all rows at once
192 if (msg.row_step == cloud_row_step)
193 {
194 memcpy (cloud_data, msg_data, msg.data.size ());
195 }
196 else
197 {
198 for (std::uint32_t i = 0; i < msg.height; ++i, cloud_data += cloud_row_step, msg_data += msg.row_step)
199 memcpy (cloud_data, msg_data, cloud_row_step);
200 }
201
202 }
203 else
204 {
205 // If not, memcpy each group of contiguous fields separately
206 for (index_t row = 0; row < msg.height; ++row)
207 {
208 const std::uint8_t* row_data = &msg.data[row * msg.row_step];
209 for (index_t col = 0; col < msg.width; ++col)
210 {
211 const std::uint8_t* msg_data = row_data + col * msg.point_step;
212 for (const detail::FieldMapping& mapping : field_map)
213 {
214 memcpy (cloud_data + mapping.struct_offset, msg_data + mapping.serialized_offset, mapping.size);
215 }
216 cloud_data += sizeof (PointT);
217 }
218 }
219 }
220 }
221
222 /** \brief Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object.
223 * \param[in] msg the PCLPointCloud2 binary blob
224 * \param[out] cloud the resultant pcl::PointCloud<T>
225 */
226 template<typename PointT> void
228 {
229 MsgFieldMap field_map;
230 createMapping<PointT> (msg.fields, field_map);
231 fromPCLPointCloud2 (msg, cloud, field_map);
232 }
233
234 /** \brief Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.
235 * \param[in] cloud the input pcl::PointCloud<T>
236 * \param[out] msg the resultant PCLPointCloud2 binary blob
237 */
238 template<typename PointT> void
240 {
241 // Ease the user's burden on specifying width/height for unorganized datasets
242 if (cloud.width == 0 && cloud.height == 0)
243 {
244 msg.width = cloud.size ();
245 msg.height = 1;
246 }
247 else
248 {
249 assert (cloud.size () == cloud.width * cloud.height);
250 msg.height = cloud.height;
251 msg.width = cloud.width;
252 }
253
254 // Fill point cloud binary data (padding and all)
255 std::size_t data_size = sizeof (PointT) * cloud.size ();
256 msg.data.resize (data_size);
257 if (data_size)
258 {
259 memcpy(&msg.data[0], &cloud[0], data_size);
260 }
261
262 // Fill fields metadata
263 msg.fields.clear ();
264 for_each_type<typename traits::fieldList<PointT>::type> (detail::FieldAdder<PointT>(msg.fields));
265
266 msg.header = cloud.header;
267 msg.point_step = sizeof (PointT);
268 msg.row_step = static_cast<std::uint32_t> (sizeof (PointT) * msg.width);
269 msg.is_dense = cloud.is_dense;
270 /// @todo msg.is_bigendian = ?;
271 }
272
273 /** \brief Copy the RGB fields of a PointCloud into pcl::PCLImage format
274 * \param[in] cloud the point cloud message
275 * \param[out] msg the resultant pcl::PCLImage
276 * CloudT cloud type, CloudT should be akin to pcl::PointCloud<pcl::PointXYZRGBA>
277 * \note will throw std::runtime_error if there is a problem
278 */
279 template<typename CloudT> void
280 toPCLPointCloud2 (const CloudT& cloud, pcl::PCLImage& msg)
281 {
282 // Ease the user's burden on specifying width/height for unorganized datasets
283 if (cloud.width == 0 && cloud.height == 0)
284 throw std::runtime_error("Needs to be a dense like cloud!!");
285 else
286 {
287 if (cloud.size () != cloud.width * cloud.height)
288 throw std::runtime_error("The width and height do not match the cloud size!");
289 msg.height = cloud.height;
290 msg.width = cloud.width;
291 }
292
293 // ensor_msgs::image_encodings::BGR8;
294 msg.header = cloud.header;
295 msg.encoding = "bgr8";
296 msg.step = msg.width * sizeof (std::uint8_t) * 3;
297 msg.data.resize (msg.step * msg.height);
298 for (std::size_t y = 0; y < cloud.height; y++)
299 {
300 for (std::size_t x = 0; x < cloud.width; x++)
301 {
302 std::uint8_t * pixel = &(msg.data[y * msg.step + x * 3]);
303 memcpy (pixel, &cloud (x, y).rgb, 3 * sizeof(std::uint8_t));
304 }
305 }
306 }
307
308 /** \brief Copy the RGB fields of a PCLPointCloud2 msg into pcl::PCLImage format
309 * \param cloud the point cloud message
310 * \param msg the resultant pcl::PCLImage
311 * will throw std::runtime_error if there is a problem
312 */
313 inline void
315 {
316 const auto predicate = [](const auto& field) { return field.name == "rgb"; };
317 const auto result = std::find_if(cloud.fields.cbegin (), cloud.fields.cend (), predicate);
318 if (result == cloud.fields.end ())
319 throw std::runtime_error ("No rgb field!!");
320
321 const auto rgb_index = std::distance(cloud.fields.begin (), result);
322 if (cloud.width == 0 && cloud.height == 0)
323 throw std::runtime_error ("Needs to be a dense like cloud!!");
324 else
325 {
326 msg.height = cloud.height;
327 msg.width = cloud.width;
328 }
329 int rgb_offset = cloud.fields[rgb_index].offset;
330 int point_step = cloud.point_step;
331
332 // pcl::image_encodings::BGR8;
333 msg.header = cloud.header;
334 msg.encoding = "bgr8";
335 msg.step = static_cast<std::uint32_t>(msg.width * sizeof (std::uint8_t) * 3);
336 msg.data.resize (msg.step * msg.height);
337
338 for (std::size_t y = 0; y < cloud.height; y++)
339 {
340 for (std::size_t x = 0; x < cloud.width; x++, rgb_offset += point_step)
341 {
342 std::uint8_t * pixel = &(msg.data[y * msg.step + x * 3]);
343 memcpy (pixel, &(cloud.data[rgb_offset]), 3 * sizeof (std::uint8_t));
344 }
345 }
346 }
347}
PointCloud represents the base class in PCL for storing collections of 3D points.
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
void resize(std::size_t count)
Resizes the container to contain count elements.
std::uint32_t width
The point cloud width (if organized as an image-structure).
pcl::PCLHeader header
The point cloud header.
std::uint32_t height
The point cloud height (if organized as an image-structure).
std::size_t size() const
bool fieldOrdering(const FieldMapping &a, const FieldMapping &b)
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition types.h:112
void toPCLPointCloud2(const pcl::PointCloud< PointT > &cloud, pcl::PCLPointCloud2 &msg)
Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.
void createMapping(const std::vector< pcl::PCLPointField > &msg_fields, MsgFieldMap &field_map)
std::vector< detail::FieldMapping > MsgFieldMap
Definition point_cloud.h:72
void fromPCLPointCloud2(const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map)
Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
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
::pcl::PCLHeader header
Definition PCLImage.h:14
std::uint8_t is_dense
std::vector<::pcl::PCLPointField > fields
::pcl::PCLHeader header
std::vector< std::uint8_t > data
std::uint8_t datatype
A point structure representing Euclidean xyz coordinates, and the RGB color.
FieldAdder(std::vector< pcl::PCLPointField > &fields)
Definition conversions.h:64
std::vector< pcl::PCLPointField > & fields_
Definition conversions.h:76
FieldMapper(const std::vector< pcl::PCLPointField > &fields, std::vector< FieldMapping > &map)
Definition conversions.h:83
const std::vector< pcl::PCLPointField > & fields_
std::vector< FieldMapping > & map_
std::size_t serialized_offset
Definition point_cloud.h:64