Point Cloud Library (PCL)  1.9.0-dev
harris_6d.hpp
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37 
38 #ifndef PCL_HARRIS_KEYPOINT_6D_IMPL_H_
39 #define PCL_HARRIS_KEYPOINT_6D_IMPL_H_
40 
41 #include <pcl/keypoints/harris_6d.h>
42 #include <pcl/common/io.h>
43 #include <pcl/filters/passthrough.h>
44 #include <pcl/filters/extract_indices.h>
45 #include <pcl/features/normal_3d.h>
46 //#include <pcl/features/fast_intensity_gradient.h>
47 #include <pcl/features/intensity_gradient.h>
48 #include <pcl/features/integral_image_normal.h>
49 
50 template <typename PointInT, typename PointOutT, typename NormalT> void
52 {
53  threshold_= threshold;
54 }
55 
56 template <typename PointInT, typename PointOutT, typename NormalT> void
58 {
59  search_radius_ = radius;
60 }
61 
62 template <typename PointInT, typename PointOutT, typename NormalT> void
64 {
65  refine_ = do_refine;
66 }
67 
68 template <typename PointInT, typename PointOutT, typename NormalT> void
70 {
71  nonmax_ = nonmax;
72 }
73 
74 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
75 template <typename PointInT, typename PointOutT, typename NormalT> void
76 pcl::HarrisKeypoint6D<PointInT, PointOutT, NormalT>::calculateCombinedCovar (const std::vector<int>& neighbors, float* coefficients) const
77 {
78  memset (coefficients, 0, sizeof (float) * 21);
79  unsigned count = 0;
80  for (std::vector<int>::const_iterator iIt = neighbors.begin(); iIt != neighbors.end(); ++iIt)
81  {
82  if (pcl_isfinite (normals_->points[*iIt].normal_x) && pcl_isfinite (intensity_gradients_->points[*iIt].gradient [0]))
83  {
84  coefficients[ 0] += normals_->points[*iIt].normal_x * normals_->points[*iIt].normal_x;
85  coefficients[ 1] += normals_->points[*iIt].normal_x * normals_->points[*iIt].normal_y;
86  coefficients[ 2] += normals_->points[*iIt].normal_x * normals_->points[*iIt].normal_z;
87  coefficients[ 3] += normals_->points[*iIt].normal_x * intensity_gradients_->points[*iIt].gradient [0];
88  coefficients[ 4] += normals_->points[*iIt].normal_x * intensity_gradients_->points[*iIt].gradient [1];
89  coefficients[ 5] += normals_->points[*iIt].normal_x * intensity_gradients_->points[*iIt].gradient [2];
90 
91  coefficients[ 6] += normals_->points[*iIt].normal_y * normals_->points[*iIt].normal_y;
92  coefficients[ 7] += normals_->points[*iIt].normal_y * normals_->points[*iIt].normal_z;
93  coefficients[ 8] += normals_->points[*iIt].normal_y * intensity_gradients_->points[*iIt].gradient [0];
94  coefficients[ 9] += normals_->points[*iIt].normal_y * intensity_gradients_->points[*iIt].gradient [1];
95  coefficients[10] += normals_->points[*iIt].normal_y * intensity_gradients_->points[*iIt].gradient [2];
96 
97  coefficients[11] += normals_->points[*iIt].normal_z * normals_->points[*iIt].normal_z;
98  coefficients[12] += normals_->points[*iIt].normal_z * intensity_gradients_->points[*iIt].gradient [0];
99  coefficients[13] += normals_->points[*iIt].normal_z * intensity_gradients_->points[*iIt].gradient [1];
100  coefficients[14] += normals_->points[*iIt].normal_z * intensity_gradients_->points[*iIt].gradient [2];
101 
102  coefficients[15] += intensity_gradients_->points[*iIt].gradient [0] * intensity_gradients_->points[*iIt].gradient [0];
103  coefficients[16] += intensity_gradients_->points[*iIt].gradient [0] * intensity_gradients_->points[*iIt].gradient [1];
104  coefficients[17] += intensity_gradients_->points[*iIt].gradient [0] * intensity_gradients_->points[*iIt].gradient [2];
105 
106  coefficients[18] += intensity_gradients_->points[*iIt].gradient [1] * intensity_gradients_->points[*iIt].gradient [1];
107  coefficients[19] += intensity_gradients_->points[*iIt].gradient [1] * intensity_gradients_->points[*iIt].gradient [2];
108 
109  coefficients[20] += intensity_gradients_->points[*iIt].gradient [2] * intensity_gradients_->points[*iIt].gradient [2];
110 
111  ++count;
112  }
113  }
114  if (count > 0)
115  {
116  float norm = 1.0 / float (count);
117  coefficients[ 0] *= norm;
118  coefficients[ 1] *= norm;
119  coefficients[ 2] *= norm;
120  coefficients[ 3] *= norm;
121  coefficients[ 4] *= norm;
122  coefficients[ 5] *= norm;
123  coefficients[ 6] *= norm;
124  coefficients[ 7] *= norm;
125  coefficients[ 8] *= norm;
126  coefficients[ 9] *= norm;
127  coefficients[10] *= norm;
128  coefficients[11] *= norm;
129  coefficients[12] *= norm;
130  coefficients[13] *= norm;
131  coefficients[14] *= norm;
132  coefficients[15] *= norm;
133  coefficients[16] *= norm;
134  coefficients[17] *= norm;
135  coefficients[18] *= norm;
136  coefficients[19] *= norm;
137  coefficients[20] *= norm;
138  }
139 }
140 
141 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
142 template <typename PointInT, typename PointOutT, typename NormalT> void
144 {
145  if (normals_->empty ())
146  {
147  normals_->reserve (surface_->size ());
148  if (!surface_->isOrganized ())
149  {
151  normal_estimation.setInputCloud (surface_);
152  normal_estimation.setRadiusSearch (search_radius_);
153  normal_estimation.compute (*normals_);
154  }
155  else
156  {
159  normal_estimation.setInputCloud (surface_);
160  normal_estimation.setNormalSmoothingSize (5.0);
161  normal_estimation.compute (*normals_);
162  }
163  }
164 
166  cloud->resize (surface_->size ());
167 #ifdef _OPENMP
168  #pragma omp parallel for num_threads(threads_) default(shared)
169 #endif
170  for (unsigned idx = 0; idx < surface_->size (); ++idx)
171  {
172  cloud->points [idx].x = surface_->points [idx].x;
173  cloud->points [idx].y = surface_->points [idx].y;
174  cloud->points [idx].z = surface_->points [idx].z;
175  //grayscale = 0.2989 * R + 0.5870 * G + 0.1140 * B
176 
177  cloud->points [idx].intensity = 0.00390625 * (0.114 * float(surface_->points [idx].b) + 0.5870 * float(surface_->points [idx].g) + 0.2989 * float(surface_->points [idx].r));
178  }
179  pcl::copyPointCloud (*surface_, *cloud);
180 
182  grad_est.setInputCloud (cloud);
183  grad_est.setInputNormals (normals_);
184  grad_est.setRadiusSearch (search_radius_);
185  grad_est.compute (*intensity_gradients_);
186 
187 #ifdef _OPENMP
188  #pragma omp parallel for num_threads(threads_) default (shared)
189 #endif
190  for (unsigned idx = 0; idx < intensity_gradients_->size (); ++idx)
191  {
192  float len = intensity_gradients_->points [idx].gradient_x * intensity_gradients_->points [idx].gradient_x +
193  intensity_gradients_->points [idx].gradient_y * intensity_gradients_->points [idx].gradient_y +
194  intensity_gradients_->points [idx].gradient_z * intensity_gradients_->points [idx].gradient_z ;
195 
196  // Suat: ToDo: remove this magic number or expose using set/get
197  if (len > 200.0)
198  {
199  len = 1.0 / sqrt (len);
200  intensity_gradients_->points [idx].gradient_x *= len;
201  intensity_gradients_->points [idx].gradient_y *= len;
202  intensity_gradients_->points [idx].gradient_z *= len;
203  }
204  else
205  {
206  intensity_gradients_->points [idx].gradient_x = 0;
207  intensity_gradients_->points [idx].gradient_y = 0;
208  intensity_gradients_->points [idx].gradient_z = 0;
209  }
210  }
211 
212  boost::shared_ptr<pcl::PointCloud<PointOutT> > response (new pcl::PointCloud<PointOutT> ());
213  response->points.reserve (input_->points.size());
214  responseTomasi(*response);
215 
216  // just return the response
217  if (!nonmax_)
218  {
219  output = *response;
220  // we do not change the denseness in this case
221  output.is_dense = input_->is_dense;
222  for (size_t i = 0; i < response->size (); ++i)
223  keypoints_indices_->indices.push_back (i);
224  }
225  else
226  {
227  output.points.clear ();
228  output.points.reserve (response->points.size());
229 
230 #ifdef _OPENMP
231  #pragma omp parallel for num_threads(threads_) default(shared)
232 #endif
233  for (size_t idx = 0; idx < response->points.size (); ++idx)
234  {
235  if (!isFinite (response->points[idx]) || response->points[idx].intensity < threshold_)
236  continue;
237 
238  std::vector<int> nn_indices;
239  std::vector<float> nn_dists;
240  tree_->radiusSearch (idx, search_radius_, nn_indices, nn_dists);
241  bool is_maxima = true;
242  for (std::vector<int>::const_iterator iIt = nn_indices.begin(); iIt != nn_indices.end(); ++iIt)
243  {
244  if (response->points[idx].intensity < response->points[*iIt].intensity)
245  {
246  is_maxima = false;
247  break;
248  }
249  }
250  if (is_maxima)
251 #ifdef _OPENMP
252  #pragma omp critical
253 #endif
254  {
255  output.points.push_back (response->points[idx]);
256  keypoints_indices_->indices.push_back (idx);
257  }
258  }
259 
260  if (refine_)
261  refineCorners (output);
262 
263  output.height = 1;
264  output.width = static_cast<uint32_t> (output.points.size());
265  output.is_dense = true;
266  }
267 }
268 
269 template <typename PointInT, typename PointOutT, typename NormalT> void
271 {
272  // get the 6x6 covar-mat
273  PointOutT pointOut;
274  PCL_ALIGN (16) float covar [21];
275  Eigen::SelfAdjointEigenSolver <Eigen::Matrix<float, 6, 6> > solver;
276  Eigen::Matrix<float, 6, 6> covariance;
277 
278 #ifdef _OPENMP
279  #pragma omp parallel for default (shared) private (pointOut, covar, covariance, solver) num_threads(threads_)
280 #endif
281  for (unsigned pIdx = 0; pIdx < input_->size (); ++pIdx)
282  {
283  const PointInT& pointIn = input_->points [pIdx];
284  pointOut.intensity = 0.0; //std::numeric_limits<float>::quiet_NaN ();
285  if (isFinite (pointIn))
286  {
287  std::vector<int> nn_indices;
288  std::vector<float> nn_dists;
289  tree_->radiusSearch (pointIn, search_radius_, nn_indices, nn_dists);
290  calculateCombinedCovar (nn_indices, covar);
291 
292  float trace = covar [0] + covar [6] + covar [11] + covar [15] + covar [18] + covar [20];
293  if (trace != 0)
294  {
295  covariance.coeffRef ( 0) = covar [ 0];
296  covariance.coeffRef ( 1) = covar [ 1];
297  covariance.coeffRef ( 2) = covar [ 2];
298  covariance.coeffRef ( 3) = covar [ 3];
299  covariance.coeffRef ( 4) = covar [ 4];
300  covariance.coeffRef ( 5) = covar [ 5];
301 
302  covariance.coeffRef ( 7) = covar [ 6];
303  covariance.coeffRef ( 8) = covar [ 7];
304  covariance.coeffRef ( 9) = covar [ 8];
305  covariance.coeffRef (10) = covar [ 9];
306  covariance.coeffRef (11) = covar [10];
307 
308  covariance.coeffRef (14) = covar [11];
309  covariance.coeffRef (15) = covar [12];
310  covariance.coeffRef (16) = covar [13];
311  covariance.coeffRef (17) = covar [14];
312 
313  covariance.coeffRef (21) = covar [15];
314  covariance.coeffRef (22) = covar [16];
315  covariance.coeffRef (23) = covar [17];
316 
317  covariance.coeffRef (28) = covar [18];
318  covariance.coeffRef (29) = covar [19];
319 
320  covariance.coeffRef (35) = covar [20];
321 
322  covariance.coeffRef ( 6) = covar [ 1];
323 
324  covariance.coeffRef (12) = covar [ 2];
325  covariance.coeffRef (13) = covar [ 7];
326 
327  covariance.coeffRef (18) = covar [ 3];
328  covariance.coeffRef (19) = covar [ 8];
329  covariance.coeffRef (20) = covar [12];
330 
331  covariance.coeffRef (24) = covar [ 4];
332  covariance.coeffRef (25) = covar [ 9];
333  covariance.coeffRef (26) = covar [13];
334  covariance.coeffRef (27) = covar [16];
335 
336  covariance.coeffRef (30) = covar [ 5];
337  covariance.coeffRef (31) = covar [10];
338  covariance.coeffRef (32) = covar [14];
339  covariance.coeffRef (33) = covar [17];
340  covariance.coeffRef (34) = covar [19];
341 
342  solver.compute (covariance);
343  pointOut.intensity = solver.eigenvalues () [3];
344  }
345  }
346 
347  pointOut.x = pointIn.x;
348  pointOut.y = pointIn.y;
349  pointOut.z = pointIn.z;
350 #ifdef _OPENMP
351  #pragma omp critical
352 #endif
353 
354  output.points.push_back(pointOut);
355  }
356  output.height = input_->height;
357  output.width = input_->width;
358 }
359 
360 template <typename PointInT, typename PointOutT, typename NormalT> void
362 {
364  search.setInputCloud(surface_);
365 
366  Eigen::Matrix3f nnT;
367  Eigen::Matrix3f NNT;
368  Eigen::Vector3f NNTp;
369  const Eigen::Vector3f* normal;
370  const Eigen::Vector3f* point;
371  float diff;
372  const unsigned max_iterations = 10;
373  for (typename PointCloudOut::iterator cornerIt = corners.begin(); cornerIt != corners.end(); ++cornerIt)
374  {
375  unsigned iterations = 0;
376  do {
377  NNT.setZero();
378  NNTp.setZero();
379  PointInT corner;
380  corner.x = cornerIt->x;
381  corner.y = cornerIt->y;
382  corner.z = cornerIt->z;
383  std::vector<int> nn_indices;
384  std::vector<float> nn_dists;
385  search.radiusSearch (corner, search_radius_, nn_indices, nn_dists);
386  for (std::vector<int>::const_iterator iIt = nn_indices.begin(); iIt != nn_indices.end(); ++iIt)
387  {
388  normal = reinterpret_cast<const Eigen::Vector3f*> (&(normals_->points[*iIt].normal_x));
389  point = reinterpret_cast<const Eigen::Vector3f*> (&(surface_->points[*iIt].x));
390  nnT = (*normal) * (normal->transpose());
391  NNT += nnT;
392  NNTp += nnT * (*point);
393  }
394  if (NNT.determinant() != 0)
395  *(reinterpret_cast<Eigen::Vector3f*>(&(cornerIt->x))) = NNT.inverse () * NNTp;
396 
397  diff = (cornerIt->x - corner.x) * (cornerIt->x - corner.x) +
398  (cornerIt->y - corner.y) * (cornerIt->y - corner.y) +
399  (cornerIt->z - corner.z) * (cornerIt->z - corner.z);
400 
401  } while (diff > 1e-6 && ++iterations < max_iterations);
402  }
403 }
404 
405 #define PCL_INSTANTIATE_HarrisKeypoint6D(T,U,N) template class PCL_EXPORTS pcl::HarrisKeypoint6D<T,U,N>;
406 #endif // #ifndef PCL_HARRIS_KEYPOINT_6D_IMPL_H_
407 
void calculateCombinedCovar(const std::vector< int > &neighbors, float *coefficients) const
Definition: harris_6d.hpp:76
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:62
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:54
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
void refineCorners(PointCloudOut &corners) const
Definition: harris_6d.hpp:361
void setNonMaxSupression(bool=false)
whether non maxima suppression should be applied or the response for each point should be returned ...
Definition: harris_6d.hpp:69
iterator end()
Definition: point_cloud.h:443
void responseTomasi(PointCloudOut &output) const
Definition: harris_6d.hpp:270
PCL_EXPORTS void copyPointCloud(const pcl::PCLPointCloud2 &cloud_in, const std::vector< int > &indices, pcl::PCLPointCloud2 &cloud_out)
Extract the indices of a given point cloud as a new point cloud.
void setRadiusSearch(double radius)
Set the sphere radius that is to be used for determining the nearest neighbors used for the feature e...
Definition: feature.h:200
VectorType::iterator iterator
Definition: point_cloud.h:440
NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point...
Definition: normal_3d.h:242
void detectKeypoints(PointCloudOut &output)
Abstract key point detection method.
Definition: harris_6d.hpp:143
uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:415
boost::shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:428
uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:413
Surface normal estimation on organized data using integral images.
void setRadius(float radius)
set the radius for normal estimation and non maxima supression.
Definition: harris_6d.hpp:57
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: normal_3d.h:333
void setRefine(bool do_refine)
whether the detected key points should be refined or not.
Definition: harris_6d.hpp:63
PointCloud represents the base class in PCL for storing collections of 3D points. ...
void setNormalEstimationMethod(NormalEstimationMethod normal_estimation_method)
Set the normal estimation method.
IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position...
void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
Provide a pointer to the input dataset.
Definition: kdtree.hpp:77
void setThreshold(float threshold)
set the threshold value for detecting corners.
Definition: harris_6d.hpp:51
virtual void setInputCloud(const typename PointCloudIn::ConstPtr &cloud)
Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method) ...
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
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:418
int radiusSearch(const PointT &point, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
Search for all the nearest neighbors of the query point in a given radius.
Definition: kdtree.hpp:97
iterator begin()
Definition: point_cloud.h:442
void resize(size_t n)
Resize the cloud.
Definition: point_cloud.h:455
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition: feature.hpp:189
void setInputNormals(const PointCloudNConstPtr &normals)
Provide a pointer to the input dataset that contains the point normals of the XYZ dataset...
Definition: feature.h:344
void setNormalSmoothingSize(float normal_smoothing_size)
Set the normal smoothing size.