Point Cloud Library (PCL) 1.13.1
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spin_image.hpp
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40
41#ifndef PCL_FEATURES_IMPL_SPIN_IMAGE_H_
42#define PCL_FEATURES_IMPL_SPIN_IMAGE_H_
43
44#include <limits>
45#include <pcl/point_types.h>
46#include <pcl/exceptions.h>
47#include <pcl/features/spin_image.h>
48#include <cmath>
49
50//////////////////////////////////////////////////////////////////////////////////////////////
51template <typename PointInT, typename PointNT, typename PointOutT>
53 unsigned int image_width, double support_angle_cos, unsigned int min_pts_neighb) :
54 input_normals_ (), rotation_axes_cloud_ (),
55 is_angular_ (false), rotation_axis_ (), use_custom_axis_(false), use_custom_axes_cloud_ (false),
56 is_radial_ (false), support_angle_cos_ (support_angle_cos),
57 min_pts_neighb_ (min_pts_neighb)
58{
59 if (0.0 > support_angle_cos || support_angle_cos > 1.0) { // may be permit negative cosine?
60 throw PCLException ("Cosine of support angle should be between 0 and 1", "spin_image.hpp", "SpinImageEstimation");
61 }
62 setImageWidth(image_width);
63
64 feature_name_ = "SpinImageEstimation";
65}
66
67
68//////////////////////////////////////////////////////////////////////////////////////////////
69template <typename PointInT, typename PointNT, typename PointOutT> Eigen::ArrayXXd
71{
72 assert (image_width_ > 0);
73 assert (support_angle_cos_ <= 1.0 && support_angle_cos_ >= 0.0); // may be permit negative cosine?
74
75 const Eigen::Vector3f origin_point ((*input_)[index].getVector3fMap ());
76
77 Eigen::Vector3f origin_normal;
78 origin_normal =
79 input_normals_ ?
80 (*input_normals_)[index].getNormalVector3fMap () :
81 Eigen::Vector3f (); // just a placeholder; should never be used!
82
83 const Eigen::Vector3f rotation_axis = use_custom_axis_ ?
84 rotation_axis_.getNormalVector3fMap () :
85 use_custom_axes_cloud_ ?
86 (*rotation_axes_cloud_)[index].getNormalVector3fMap () :
87 origin_normal;
88
89 Eigen::ArrayXXd m_matrix (Eigen::ArrayXXd::Zero (image_width_+1, 2*image_width_+1));
90 Eigen::ArrayXXd m_averAngles (Eigen::ArrayXXd::Zero (image_width_+1, 2*image_width_+1));
91
92 // OK, we are interested in the points of the cylinder of height 2*r and
93 // base radius r, where r = m_dBinSize * in_iImageWidth
94 // it can be embedded to the sphere of radius sqrt(2) * m_dBinSize * in_iImageWidth
95 // suppose that points are uniformly distributed, so we lose ~40%
96 // according to the volumes ratio
97 double bin_size = 0.0;
98 if (is_radial_)
99 bin_size = search_radius_ / image_width_;
100 else
101 bin_size = search_radius_ / image_width_ / sqrt(2.0);
102
103 pcl::Indices nn_indices;
104 std::vector<float> nn_sqr_dists;
105 const int neighb_cnt = this->searchForNeighbors (index, search_radius_, nn_indices, nn_sqr_dists);
106 if (neighb_cnt < static_cast<int> (min_pts_neighb_))
107 {
108 throw PCLException (
109 "Too few points for spin image, use setMinPointCountInNeighbourhood() to decrease the threshold or use larger feature radius",
110 "spin_image.hpp", "computeSiForPoint");
111 }
112
113 // for all neighbor points
114 for (int i_neigh = 0; i_neigh < neighb_cnt ; i_neigh++)
115 {
116 // first, skip the points with distant normals
117 double cos_between_normals = -2.0; // should be initialized if used
118 if (support_angle_cos_ > 0.0 || is_angular_) // not bogus
119 {
120 cos_between_normals = origin_normal.dot ((*input_normals_)[nn_indices[i_neigh]].getNormalVector3fMap ());
121 if (std::abs (cos_between_normals) > (1.0 + 10*std::numeric_limits<float>::epsilon ())) // should be okay for numeric stability
122 {
123 PCL_ERROR ("[pcl::%s::computeSiForPoint] Normal for the point %d and/or the point %d are not normalized, dot ptoduct is %f.\n",
124 getClassName ().c_str (), nn_indices[i_neigh], index, cos_between_normals);
125 throw PCLException ("Some normals are not normalized",
126 "spin_image.hpp", "computeSiForPoint");
127 }
128 cos_between_normals = std::max (-1.0, std::min (1.0, cos_between_normals));
129
130 if (std::abs (cos_between_normals) < support_angle_cos_ ) // allow counter-directed normals
131 {
132 continue;
133 }
134
135 if (cos_between_normals < 0.0)
136 {
137 cos_between_normals = -cos_between_normals; // the normal is not used explicitly from now
138 }
139 }
140
141 // now compute the coordinate in cylindric coordinate system associated with the origin point
142 const Eigen::Vector3f direction (
143 (*surface_)[nn_indices[i_neigh]].getVector3fMap () - origin_point);
144 const double direction_norm = direction.norm ();
145 if (std::abs(direction_norm) < 10*std::numeric_limits<double>::epsilon ())
146 continue; // ignore the point itself; it does not contribute really
147 assert (direction_norm > 0.0);
148
149 // the angle between the normal vector and the direction to the point
150 double cos_dir_axis = direction.dot(rotation_axis) / direction_norm;
151 if (std::abs(cos_dir_axis) > (1.0 + 10*std::numeric_limits<float>::epsilon())) // should be okay for numeric stability
152 {
153 PCL_ERROR ("[pcl::%s::computeSiForPoint] Rotation axis for the point %d are not normalized, dot ptoduct is %f.\n",
154 getClassName ().c_str (), index, cos_dir_axis);
155 throw PCLException ("Some rotation axis is not normalized",
156 "spin_image.hpp", "computeSiForPoint");
157 }
158 cos_dir_axis = std::max (-1.0, std::min (1.0, cos_dir_axis));
159
160 // compute coordinates w.r.t. the reference frame
161 double beta = std::numeric_limits<double>::signaling_NaN ();
162 double alpha = std::numeric_limits<double>::signaling_NaN ();
163 if (is_radial_) // radial spin image structure
164 {
165 beta = asin (cos_dir_axis); // yes, arc sine! to get the angle against tangent, not normal!
166 alpha = direction_norm;
167 }
168 else // rectangular spin-image structure
169 {
170 beta = direction_norm * cos_dir_axis;
171 alpha = direction_norm * sqrt (1.0 - cos_dir_axis*cos_dir_axis);
172
173 if (std::abs (beta) >= bin_size * image_width_ || alpha >= bin_size * image_width_)
174 {
175 continue; // outside the cylinder
176 }
177 }
178
179 assert (alpha >= 0.0);
180 assert (alpha <= bin_size * image_width_ + 20 * std::numeric_limits<float>::epsilon () );
181
182
183 // bilinear interpolation
184 double beta_bin_size = is_radial_ ? (M_PI / 2 / image_width_) : bin_size;
185 int beta_bin = static_cast<int>(std::floor (beta / beta_bin_size)) + static_cast<int>(image_width_);
186 assert (0 <= beta_bin && beta_bin < m_matrix.cols ());
187 int alpha_bin = static_cast<int>(std::floor (alpha / bin_size));
188 assert (0 <= alpha_bin && alpha_bin < m_matrix.rows ());
189
190 if (alpha_bin == static_cast<int> (image_width_)) // border points
191 {
192 alpha_bin--;
193 // HACK: to prevent a > 1
194 alpha = bin_size * (alpha_bin + 1) - std::numeric_limits<double>::epsilon ();
195 }
196 if (beta_bin == static_cast<int>(2*image_width_) ) // border points
197 {
198 beta_bin--;
199 // HACK: to prevent b > 1
200 beta = beta_bin_size * (beta_bin - static_cast<int>(image_width_) + 1) - std::numeric_limits<double>::epsilon ();
201 }
202
203 double a = alpha/bin_size - static_cast<double>(alpha_bin);
204 double b = beta/beta_bin_size - static_cast<double>(beta_bin-static_cast<int>(image_width_));
205
206 assert (0 <= a && a <= 1);
207 assert (0 <= b && b <= 1);
208
209 m_matrix (alpha_bin, beta_bin) += (1-a) * (1-b);
210 m_matrix (alpha_bin+1, beta_bin) += a * (1-b);
211 m_matrix (alpha_bin, beta_bin+1) += (1-a) * b;
212 m_matrix (alpha_bin+1, beta_bin+1) += a * b;
213
214 if (is_angular_)
215 {
216 m_averAngles (alpha_bin, beta_bin) += (1-a) * (1-b) * std::acos (cos_between_normals);
217 m_averAngles (alpha_bin+1, beta_bin) += a * (1-b) * std::acos (cos_between_normals);
218 m_averAngles (alpha_bin, beta_bin+1) += (1-a) * b * std::acos (cos_between_normals);
219 m_averAngles (alpha_bin+1, beta_bin+1) += a * b * std::acos (cos_between_normals);
220 }
221 }
222
223 if (is_angular_)
224 {
225 // transform sum to average
226 m_matrix = m_averAngles / (m_matrix + std::numeric_limits<double>::epsilon ()); // +eps to avoid division by zero
227 }
228 else if (neighb_cnt > 1) // to avoid division by zero, also no need to divide by 1
229 {
230 // normalization
231 m_matrix /= m_matrix.sum();
232 }
233
234 return m_matrix;
235}
236
237
238//////////////////////////////////////////////////////////////////////////////////////////////
239template <typename PointInT, typename PointNT, typename PointOutT> bool
241{
243 {
244 PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
245 return (false);
246 }
247
248 // Check if input normals are set
249 if (!input_normals_)
250 {
251 PCL_ERROR ("[pcl::%s::initCompute] No input dataset containing normals was given!\n", getClassName ().c_str ());
253 return (false);
254 }
255
256 // Check if the size of normals is the same as the size of the surface
257 if (input_normals_->size () != input_->size ())
258 {
259 PCL_ERROR ("[pcl::%s::initCompute] ", getClassName ().c_str ());
260 PCL_ERROR ("The number of points in the input dataset differs from ");
261 PCL_ERROR ("the number of points in the dataset containing the normals!\n");
263 return (false);
264 }
265
266 // We need a positive definite search radius to continue
267 if (search_radius_ == 0)
268 {
269 PCL_ERROR ("[pcl::%s::initCompute] Need a search radius different than 0!\n", getClassName ().c_str ());
271 return (false);
272 }
273 if (k_ != 0)
274 {
275 PCL_ERROR ("[pcl::%s::initCompute] K-nearest neighbor search for spin images not implemented. Used a search radius instead!\n", getClassName ().c_str ());
277 return (false);
278 }
279 // If the surface won't be set, make fake surface and fake surface normals
280 // if we wouldn't do it here, the following method would alarm that no surface normals is given
281 if (!surface_)
282 {
283 surface_ = input_;
284 fake_surface_ = true;
285 }
286
287 //if (fake_surface_ && !input_normals_)
288 // input_normals_ = normals_; // normals_ is set, as checked earlier
289
290 assert(!(use_custom_axis_ && use_custom_axes_cloud_));
291
292 if (!use_custom_axis_ && !use_custom_axes_cloud_ // use input normals as rotation axes
293 && !input_normals_)
294 {
295 PCL_ERROR ("[pcl::%s::initCompute] No normals for input cloud were given!\n", getClassName ().c_str ());
296 // Cleanup
298 return (false);
299 }
300
301 if ((is_angular_ || support_angle_cos_ > 0.0) // support angle is not bogus NOTE this is for randomly-flipped normals
302 && !input_normals_)
303 {
304 PCL_ERROR ("[pcl::%s::initCompute] No normals for input cloud were given!\n", getClassName ().c_str ());
305 // Cleanup
307 return (false);
308 }
309
310 if (use_custom_axes_cloud_
311 && rotation_axes_cloud_->size () == input_->size ())
312 {
313 PCL_ERROR ("[pcl::%s::initCompute] Rotation axis cloud have different size from input!\n", getClassName ().c_str ());
314 // Cleanup
316 return (false);
317 }
318
319 return (true);
320}
321
322
323//////////////////////////////////////////////////////////////////////////////////////////////
324template <typename PointInT, typename PointNT, typename PointOutT> void
326{
327 for (std::size_t i_input = 0; i_input < indices_->size (); ++i_input)
328 {
329 Eigen::ArrayXXd res = computeSiForPoint (indices_->at (i_input));
330
331 // Copy into the resultant cloud
332 for (Eigen::Index iRow = 0; iRow < res.rows () ; iRow++)
333 {
334 for (Eigen::Index iCol = 0; iCol < res.cols () ; iCol++)
335 {
336 output[i_input].histogram[ iRow*res.cols () + iCol ] = static_cast<float> (res (iRow, iCol));
337 }
338 }
339 }
340}
341
342#define PCL_INSTANTIATE_SpinImageEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::SpinImageEstimation<T,NT,OutT>;
343
344#endif // PCL_FEATURES_IMPL_SPIN_IMAGE_H_
345
Feature represents the base feature class.
Definition feature.h:107
std::string feature_name_
The feature name.
Definition feature.h:220
virtual bool deinitCompute()
This method should get called after ending the actual computation.
Definition feature.hpp:181
A base class for all pcl exceptions which inherits from std::runtime_error.
Definition exceptions.h:64
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition spin_image.h:103
Eigen::ArrayXXd computeSiForPoint(int index) const
Computes a spin-image for the point of the scan.
void setImageWidth(unsigned int bin_count)
Sets spin-image resolution.
Definition spin_image.h:134
SpinImageEstimation(unsigned int image_width=8, double support_angle_cos=0.0, unsigned int min_pts_neighb=0)
Constructs empty spin image estimator.
bool initCompute() override
initializes computations specific to spin-image.
void computeFeature(PointCloudOut &output) override
Estimate the Spin Image descriptors at a set of points given by setInputWithNormals() using the surfa...
Defines all the PCL implemented PointT point type structures.
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
#define M_PI
Definition pcl_macros.h:201