Point Cloud Library (PCL)  1.13.1-dev
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 <algorithm>
55 #include <iterator>
56 
57 namespace pcl
58 {
59  namespace detail
60  {
61  // For converting template point cloud to message.
62  template<typename PointT>
63  struct FieldAdder
64  {
65  FieldAdder (std::vector<pcl::PCLPointField>& fields) : fields_ (fields) {};
66 
67  template<typename U> void operator() ()
68  {
70  f.name = pcl::traits::name<PointT, U>::value;
71  f.offset = pcl::traits::offset<PointT, U>::value;
72  f.datatype = pcl::traits::datatype<PointT, U>::value;
73  f.count = pcl::traits::datatype<PointT, U>::size;
74  fields_.push_back (f);
75  }
76 
77  std::vector<pcl::PCLPointField>& fields_;
78  };
79 
80  // For converting message to template point cloud.
81  template<typename PointT>
82  struct FieldMapper
83  {
84  FieldMapper (const std::vector<pcl::PCLPointField>& fields,
85  std::vector<FieldMapping>& map)
86  : fields_ (fields), map_ (map)
87  {
88  }
89 
90  template<typename Tag> void
92  {
93  for (const auto& field : fields_)
94  {
95  if (FieldMatches<PointT, Tag>()(field))
96  {
97  FieldMapping mapping;
98  mapping.serialized_offset = field.offset;
99  mapping.struct_offset = pcl::traits::offset<PointT, Tag>::value;
100  mapping.size = sizeof (typename pcl::traits::datatype<PointT, Tag>::type);
101  map_.push_back (mapping);
102  return;
103  }
104  }
105  // Disable thrown exception per #595: http://dev.pointclouds.org/issues/595
106  PCL_WARN ("Failed to find match for field '%s'.\n", pcl::traits::name<PointT, Tag>::value);
107  //throw pcl::InvalidConversionException (ss.str ());
108  }
109 
110  const std::vector<pcl::PCLPointField>& fields_;
111  std::vector<FieldMapping>& map_;
112  };
113 
114  inline bool
116  {
117  return (a.serialized_offset < b.serialized_offset);
118  }
119 
120  } //namespace detail
121 
122  template<typename PointT> void
123  createMapping (const std::vector<pcl::PCLPointField>& msg_fields, MsgFieldMap& field_map)
124  {
125  // Create initial 1-1 mapping between serialized data segments and struct fields
126  detail::FieldMapper<PointT> mapper (msg_fields, field_map);
127  for_each_type< typename traits::fieldList<PointT>::type > (mapper);
128 
129  // Coalesce adjacent fields into single memcpy's where possible
130  if (field_map.size() > 1)
131  {
132  std::sort(field_map.begin(), field_map.end(), detail::fieldOrdering);
133  MsgFieldMap::iterator i = field_map.begin(), j = i + 1;
134  while (j != field_map.end())
135  {
136  // This check is designed to permit padding between adjacent fields.
137  /// @todo One could construct a pathological case where the struct has a
138  /// field where the serialized data has padding
139  if (j->serialized_offset - i->serialized_offset == j->struct_offset - i->struct_offset)
140  {
141  i->size += (j->struct_offset + j->size) - (i->struct_offset + i->size);
142  j = field_map.erase(j);
143  }
144  else
145  {
146  ++i;
147  ++j;
148  }
149  }
150  }
151  }
152 
153  /** \brief Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
154  * \param[in] msg the PCLPointCloud2 binary blob (note that the binary point data in msg.data will not be used!)
155  * \param[out] cloud the resultant pcl::PointCloud<T>
156  * \param[in] field_map a MsgFieldMap object
157  * \param[in] msg_data pointer to binary blob data, used instead of msg.data
158  *
159  * \note Use fromPCLPointCloud2 (PCLPointCloud2, PointCloud<T>) instead, except if you have a binary blob of
160  * point data that you do not want to copy into a pcl::PCLPointCloud2 in order to use fromPCLPointCloud2.
161  */
162  template <typename PointT> void
164  const MsgFieldMap& field_map, const std::uint8_t* msg_data)
165  {
166  // Copy info fields
167  cloud.header = msg.header;
168  cloud.width = msg.width;
169  cloud.height = msg.height;
170  cloud.is_dense = msg.is_dense == 1;
171 
172  // Resize cloud
173  cloud.resize (msg.width * msg.height);
174 
175  // check if there is data to copy
176  if (msg.width * msg.height == 0)
177  {
178  PCL_WARN("[pcl::fromPCLPointCloud2] No data to copy.\n");
179  return;
180  }
181 
182  // Copy point data
183  std::uint8_t* cloud_data = reinterpret_cast<std::uint8_t*>(cloud.data());
184 
185  // Check if we can copy adjacent points in a single memcpy. We can do so if there
186  // is exactly one field to copy and it is the same size as the source and destination
187  // point types.
188  if (field_map.size() == 1 &&
189  field_map[0].serialized_offset == 0 &&
190  field_map[0].struct_offset == 0 &&
191  field_map[0].size == msg.point_step &&
192  field_map[0].size == sizeof(PointT))
193  {
194  const auto cloud_row_step = (sizeof (PointT) * cloud.width);
195  // Should usually be able to copy all rows at once
196  if (msg.row_step == cloud_row_step)
197  {
198  memcpy (cloud_data, msg_data, msg.width * msg.height * sizeof(PointT));
199  }
200  else
201  {
202  for (uindex_t i = 0; i < msg.height; ++i, cloud_data += cloud_row_step, msg_data += msg.row_step)
203  memcpy (cloud_data, msg_data, cloud_row_step);
204  }
205 
206  }
207  else
208  {
209  // If not, memcpy each group of contiguous fields separately
210  for (std::size_t row = 0; row < msg.height; ++row)
211  {
212  const std::uint8_t* row_data = msg_data + row * msg.row_step;
213  for (std::size_t col = 0; col < msg.width; ++col)
214  {
215  const std::uint8_t* msg_data = row_data + col * msg.point_step;
216  for (const detail::FieldMapping& mapping : field_map)
217  {
218  std::copy(msg_data + mapping.serialized_offset, msg_data + mapping.serialized_offset + mapping.size,
219  cloud_data + mapping.struct_offset);
220  }
221  cloud_data += sizeof (PointT);
222  }
223  }
224  }
225  }
226 
227  /** \brief Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
228  * \param[in] msg the PCLPointCloud2 binary blob
229  * \param[out] cloud the resultant pcl::PointCloud<T>
230  * \param[in] field_map a MsgFieldMap object
231  *
232  * \note Use fromPCLPointCloud2 (PCLPointCloud2, PointCloud<T>) directly or create you
233  * own MsgFieldMap using:
234  *
235  * \code
236  * MsgFieldMap field_map;
237  * createMapping<PointT> (msg.fields, field_map);
238  * \endcode
239  */
240  template <typename PointT> void
242  const MsgFieldMap& field_map)
243  {
244  fromPCLPointCloud2 (msg, cloud, field_map, msg.data.data());
245  }
246 
247  /** \brief Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object.
248  * \param[in] msg the PCLPointCloud2 binary blob
249  * \param[out] cloud the resultant pcl::PointCloud<T>
250  */
251  template<typename PointT> void
253  {
254  MsgFieldMap field_map;
255  createMapping<PointT> (msg.fields, field_map);
256  fromPCLPointCloud2 (msg, cloud, field_map);
257  }
258 
259  /** \brief Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.
260  * \param[in] cloud the input pcl::PointCloud<T>
261  * \param[out] msg the resultant PCLPointCloud2 binary blob
262  */
263  template<typename PointT> void
265  {
266  // Ease the user's burden on specifying width/height for unorganized datasets
267  if (cloud.width == 0 && cloud.height == 0)
268  {
269  msg.width = cloud.size ();
270  msg.height = 1;
271  }
272  else
273  {
274  assert (cloud.size () == cloud.width * cloud.height);
275  msg.height = cloud.height;
276  msg.width = cloud.width;
277  }
278 
279  // Fill point cloud binary data (padding and all)
280  std::size_t data_size = sizeof (PointT) * cloud.size ();
281  msg.data.resize (data_size);
282  if (data_size)
283  {
284  memcpy(msg.data.data(), cloud.data(), data_size);
285  }
286 
287  // Fill fields metadata
288  msg.fields.clear ();
289  for_each_type<typename traits::fieldList<PointT>::type> (detail::FieldAdder<PointT>(msg.fields));
290 
291  msg.header = cloud.header;
292  msg.point_step = sizeof (PointT);
293  msg.row_step = (sizeof (PointT) * msg.width);
294  msg.is_dense = cloud.is_dense;
295  /// @todo msg.is_bigendian = ?;
296  }
297 
298  /** \brief Copy the RGB fields of a PointCloud into pcl::PCLImage format
299  * \param[in] cloud the point cloud message
300  * \param[out] msg the resultant pcl::PCLImage
301  * CloudT cloud type, CloudT should be akin to pcl::PointCloud<pcl::PointXYZRGBA>
302  * \note will throw std::runtime_error if there is a problem
303  */
304  template<typename CloudT> void
305  toPCLPointCloud2 (const CloudT& cloud, pcl::PCLImage& msg)
306  {
307  // Ease the user's burden on specifying width/height for unorganized datasets
308  if (cloud.width == 0 && cloud.height == 0)
309  throw std::runtime_error("Needs to be a dense like cloud!!");
310  else
311  {
312  if (cloud.size () != cloud.width * cloud.height)
313  throw std::runtime_error("The width and height do not match the cloud size!");
314  msg.height = cloud.height;
315  msg.width = cloud.width;
316  }
317 
318  // ensor_msgs::image_encodings::BGR8;
319  msg.header = cloud.header;
320  msg.encoding = "bgr8";
321  msg.step = msg.width * sizeof (std::uint8_t) * 3;
322  msg.data.resize (msg.step * msg.height);
323  for (std::size_t y = 0; y < cloud.height; y++)
324  {
325  for (std::size_t x = 0; x < cloud.width; x++)
326  {
327  std::uint8_t * pixel = &(msg.data[y * msg.step + x * 3]);
328  memcpy (pixel, &cloud (x, y).rgb, 3 * sizeof(std::uint8_t));
329  }
330  }
331  }
332 
333  /** \brief Copy the RGB fields of a PCLPointCloud2 msg into pcl::PCLImage format
334  * \param cloud the point cloud message
335  * \param msg the resultant pcl::PCLImage
336  * will throw std::runtime_error if there is a problem
337  */
338  inline void
340  {
341  const auto predicate = [](const auto& field) { return field.name == "rgb"; };
342  const auto result = std::find_if(cloud.fields.cbegin (), cloud.fields.cend (), predicate);
343  if (result == cloud.fields.end ())
344  throw std::runtime_error ("No rgb field!!");
345 
346  const auto rgb_index = std::distance(cloud.fields.begin (), result);
347  if (cloud.width == 0 && cloud.height == 0)
348  throw std::runtime_error ("Needs to be a dense like cloud!!");
349  else
350  {
351  msg.height = cloud.height;
352  msg.width = cloud.width;
353  }
354  auto rgb_offset = cloud.fields[rgb_index].offset;
355  const auto point_step = cloud.point_step;
356 
357  // pcl::image_encodings::BGR8;
358  msg.header = cloud.header;
359  msg.encoding = "bgr8";
360  msg.step = (msg.width * sizeof (std::uint8_t) * 3);
361  msg.data.resize (msg.step * msg.height);
362 
363  for (std::size_t y = 0; y < cloud.height; y++)
364  {
365  for (std::size_t x = 0; x < cloud.width; x++, rgb_offset += point_step)
366  {
367  std::uint8_t * pixel = &(msg.data[y * msg.step + x * 3]);
368  std::copy(&cloud.data[rgb_offset], &cloud.data[rgb_offset] + 3, pixel);
369  }
370  }
371  }
372 }
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
PointT * data() noexcept
Definition: point_cloud.h:447
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:403
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:462
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:398
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:392
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:400
std::size_t size() const
Definition: point_cloud.h:443
bool fieldOrdering(const FieldMapping &a, const FieldMapping &b)
Definition: conversions.h:115
float distance(const PointT &p1, const PointT &p2)
Definition: geometry.h:60
detail::int_type_t< detail::index_type_size, false > uindex_t
Type used for an unsigned index in PCL.
Definition: types.h:120
void fromPCLPointCloud2(const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map, const std::uint8_t *msg_data)
Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
Definition: conversions.h:163
void toPCLPointCloud2(const pcl::PointCloud< PointT > &cloud, pcl::PCLPointCloud2 &msg)
Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.
Definition: conversions.h:264
void createMapping(const std::vector< pcl::PCLPointField > &msg_fields, MsgFieldMap &field_map)
Definition: conversions.h:123
std::vector< detail::FieldMapping > MsgFieldMap
Definition: point_cloud.h:72
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::string name
Definition: PCLPointField.h:14
std::uint8_t datatype
Definition: PCLPointField.h:17
A point structure representing Euclidean xyz coordinates, and the RGB color.
FieldAdder(std::vector< pcl::PCLPointField > &fields)
Definition: conversions.h:65
std::vector< pcl::PCLPointField > & fields_
Definition: conversions.h:77
FieldMapper(const std::vector< pcl::PCLPointField > &fields, std::vector< FieldMapping > &map)
Definition: conversions.h:84
const std::vector< pcl::PCLPointField > & fields_
Definition: conversions.h:110
std::vector< FieldMapping > & map_
Definition: conversions.h:111
std::size_t serialized_offset
Definition: point_cloud.h:64