Point Cloud Library (PCL)  1.11.1-dev
trajkovic_3d.hpp
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37 
38 #ifndef PCL_TRAJKOVIC_KEYPOINT_3D_IMPL_H_
39 #define PCL_TRAJKOVIC_KEYPOINT_3D_IMPL_H_
40 
41 #include <pcl/features/integral_image_normal.h>
42 
43 
44 namespace pcl
45 {
46 
47 template <typename PointInT, typename PointOutT, typename NormalT> bool
49 {
51  return (false);
52 
53  keypoints_indices_.reset (new pcl::PointIndices);
54  keypoints_indices_->indices.reserve (input_->size ());
55 
56  if (indices_->size () != input_->size ())
57  {
58  PCL_ERROR ("[pcl::%s::initCompute] %s doesn't support setting indices!\n", name_.c_str ());
59  return (false);
60  }
61 
62  if ((window_size_%2) == 0)
63  {
64  PCL_ERROR ("[pcl::%s::initCompute] Window size must be odd!\n", name_.c_str ());
65  return (false);
66  }
67 
68  if (window_size_ < 3)
69  {
70  PCL_ERROR ("[pcl::%s::initCompute] Window size must be >= 3x3!\n", name_.c_str ());
71  return (false);
72  }
73 
74  half_window_size_ = window_size_ / 2;
75 
76  if (!normals_)
77  {
78  NormalsPtr normals (new Normals ());
81  normal_estimation.setInputCloud (input_);
82  normal_estimation.setNormalSmoothingSize (5.0);
83  normal_estimation.compute (*normals);
84  normals_ = normals;
85  }
86 
87  if (normals_->size () != input_->size ())
88  {
89  PCL_ERROR ("[pcl::%s::initCompute] normals given, but the number of normals does not match the number of input points!\n", name_.c_str ());
90  return (false);
91  }
92 
93  return (true);
94 }
95 
96 
97 template <typename PointInT, typename PointOutT, typename NormalT> void
99 {
100  response_.reset (new pcl::PointCloud<float> (input_->width, input_->height));
101  const Normals &normals = *normals_;
102  const PointCloudIn &input = *input_;
103  pcl::PointCloud<float>& response = *response_;
104  const int w = static_cast<int> (input_->width) - half_window_size_;
105  const int h = static_cast<int> (input_->height) - half_window_size_;
106 
107  if (method_ == FOUR_CORNERS)
108  {
109 #if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
110 #pragma omp parallel for \
111  default(none) \
112  shared(input, normals, response) \
113  num_threads(threads_)
114 #else
115 #pragma omp parallel for \
116  default(none) \
117  shared(h, input, normals, response, w) \
118  num_threads(threads_)
119 #endif
120  for(int j = half_window_size_; j < h; ++j)
121  {
122  for(int i = half_window_size_; i < w; ++i)
123  {
124  if (!isFinite (input (i,j))) continue;
125  const NormalT &center = normals (i,j);
126  if (!isFinite (center)) continue;
127 
128  int count = 0;
129  const NormalT &up = getNormalOrNull (i, j-half_window_size_, count);
130  const NormalT &down = getNormalOrNull (i, j+half_window_size_, count);
131  const NormalT &left = getNormalOrNull (i-half_window_size_, j, count);
132  const NormalT &right = getNormalOrNull (i+half_window_size_, j, count);
133  // Get rid of isolated points
134  if (!count) continue;
135 
136  float sn1 = squaredNormalsDiff (up, center);
137  float sn2 = squaredNormalsDiff (down, center);
138  float r1 = sn1 + sn2;
139  float r2 = squaredNormalsDiff (right, center) + squaredNormalsDiff (left, center);
140 
141  float d = std::min (r1, r2);
142  if (d < first_threshold_) continue;
143 
144  sn1 = std::sqrt (sn1);
145  sn2 = std::sqrt (sn2);
146  float b1 = normalsDiff (right, up) * sn1;
147  b1+= normalsDiff (left, down) * sn2;
148  float b2 = normalsDiff (right, down) * sn2;
149  b2+= normalsDiff (left, up) * sn1;
150  float B = std::min (b1, b2);
151  float A = r2 - r1 - 2*B;
152 
153  response (i,j) = ((B < 0) && ((B + A) > 0)) ? r1 - ((B*B)/A) : d;
154  }
155  }
156  }
157  else
158  {
159 #if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
160 #pragma omp parallel for \
161  default(none) \
162  shared(input, normals, response) \
163  num_threads(threads_)
164 #else
165 #pragma omp parallel for \
166  default(none) \
167  shared(h, input, normals, response, w) \
168  num_threads(threads_)
169 #endif
170  for(int j = half_window_size_; j < h; ++j)
171  {
172  for(int i = half_window_size_; i < w; ++i)
173  {
174  if (!isFinite (input (i,j))) continue;
175  const NormalT &center = normals (i,j);
176  if (!isFinite (center)) continue;
177 
178  int count = 0;
179  const NormalT &up = getNormalOrNull (i, j-half_window_size_, count);
180  const NormalT &down = getNormalOrNull (i, j+half_window_size_, count);
181  const NormalT &left = getNormalOrNull (i-half_window_size_, j, count);
182  const NormalT &right = getNormalOrNull (i+half_window_size_, j, count);
183  const NormalT &upleft = getNormalOrNull (i-half_window_size_, j-half_window_size_, count);
184  const NormalT &upright = getNormalOrNull (i+half_window_size_, j-half_window_size_, count);
185  const NormalT &downleft = getNormalOrNull (i-half_window_size_, j+half_window_size_, count);
186  const NormalT &downright = getNormalOrNull (i+half_window_size_, j+half_window_size_, count);
187  // Get rid of isolated points
188  if (!count) continue;
189 
190  std::vector<float> r (4,0);
191 
192  r[0] = squaredNormalsDiff (up, center);
193  r[0]+= squaredNormalsDiff (down, center);
194 
195  r[1] = squaredNormalsDiff (upright, center);
196  r[1]+= squaredNormalsDiff (downleft, center);
197 
198  r[2] = squaredNormalsDiff (right, center);
199  r[2]+= squaredNormalsDiff (left, center);
200 
201  r[3] = squaredNormalsDiff (downright, center);
202  r[3]+= squaredNormalsDiff (upleft, center);
203 
204  float d = *(std::min_element (r.begin (), r.end ()));
205 
206  if (d < first_threshold_) continue;
207 
208  std::vector<float> B (4,0);
209  std::vector<float> A (4,0);
210  std::vector<float> sumAB (4,0);
211  B[0] = normalsDiff (upright, up) * normalsDiff (up, center);
212  B[0]+= normalsDiff (downleft, down) * normalsDiff (down, center);
213  B[1] = normalsDiff (right, upright) * normalsDiff (upright, center);
214  B[1]+= normalsDiff (left, downleft) * normalsDiff (downleft, center);
215  B[2] = normalsDiff (downright, right) * normalsDiff (downright, center);
216  B[2]+= normalsDiff (upleft, left) * normalsDiff (upleft, center);
217  B[3] = normalsDiff (down, downright) * normalsDiff (downright, center);
218  B[3]+= normalsDiff (up, upleft) * normalsDiff (upleft, center);
219  A[0] = r[1] - r[0] - B[0] - B[0];
220  A[1] = r[2] - r[1] - B[1] - B[1];
221  A[2] = r[3] - r[2] - B[2] - B[2];
222  A[3] = r[0] - r[3] - B[3] - B[3];
223  sumAB[0] = A[0] + B[0];
224  sumAB[1] = A[1] + B[1];
225  sumAB[2] = A[2] + B[2];
226  sumAB[3] = A[3] + B[3];
227  if ((*std::max_element (B.begin (), B.end ()) < 0) &&
228  (*std::min_element (sumAB.begin (), sumAB.end ()) > 0))
229  {
230  std::vector<float> D (4,0);
231  D[0] = B[0] * B[0] / A[0];
232  D[1] = B[1] * B[1] / A[1];
233  D[2] = B[2] * B[2] / A[2];
234  D[3] = B[3] * B[3] / A[3];
235  response (i,j) = *(std::min (D.begin (), D.end ()));
236  }
237  else
238  response (i,j) = d;
239  }
240  }
241  }
242  // Non maximas suppression
243  pcl::Indices indices = *indices_;
244  std::sort (indices.begin (), indices.end (), [this] (int p1, int p2) { return greaterCornernessAtIndices (p1, p2); });
245 
246  output.clear ();
247  output.reserve (input_->size ());
248 
249  std::vector<bool> occupency_map (indices.size (), false);
250  const int width (input_->width);
251  const int height (input_->height);
252 
253 #if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
254 #pragma omp parallel for \
255  default(none) \
256  shared(indices, occupency_map, output) \
257  num_threads(threads_)
258 #else
259 #pragma omp parallel for \
260  default(none) \
261  shared(height, indices, occupency_map, output, width) \
262  num_threads(threads_)
263 #endif
264  for (int i = 0; i < static_cast<int>(indices.size ()); ++i)
265  {
266  int idx = indices[static_cast<std::size_t>(i)];
267  if (((*response_)[idx] < second_threshold_) || occupency_map[idx])
268  continue;
269 
270  PointOutT p;
271  p.getVector3fMap () = (*input_)[idx].getVector3fMap ();
272  p.intensity = response_->points [idx];
273 
274 #pragma omp critical
275  {
276  output.push_back (p);
277  keypoints_indices_->indices.push_back (idx);
278  }
279 
280  const int x = idx % width;
281  const int y = idx / width;
282  const int u_end = std::min (width, x + half_window_size_);
283  const int v_end = std::min (height, y + half_window_size_);
284  for(int v = std::max (0, y - half_window_size_); v < v_end; ++v)
285  for(int u = std::max (0, x - half_window_size_); u < u_end; ++u)
286  occupency_map[v*width + u] = true;
287  }
288 
289  output.height = 1;
290  output.width = output.size();
291  // we don not change the denseness
292  output.is_dense = true;
293 }
294 
295 } // namespace pcl
296 
297 #define PCL_INSTANTIATE_TrajkovicKeypoint3D(T,U,N) template class PCL_EXPORTS pcl::TrajkovicKeypoint3D<T,U,N>;
298 
299 #endif
300 
pcl
Definition: convolution.h:46
pcl::Normal
A point structure representing normal coordinates and the surface curvature estimate.
Definition: point_types.hpp:812
pcl::isFinite
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
pcl::IntegralImageNormalEstimation
Surface normal estimation on organized data using integral images.
Definition: integral_image_normal.h:65
pcl::TrajkovicKeypoint3D::PointCloudIn
typename Keypoint< PointInT, PointOutT >::PointCloudIn PointCloudIn
Definition: trajkovic_3d.h:59
pcl::TrajkovicKeypoint3D::initCompute
bool initCompute() override
Definition: trajkovic_3d.hpp:48
pcl::PCLBase
PCL base class.
Definition: pcl_base.h:69
pcl::PointCloud< NormalT >
pcl::IntegralImageNormalEstimation::setInputCloud
void setInputCloud(const typename PointCloudIn::ConstPtr &cloud) override
Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
Definition: integral_image_normal.h:232
pcl::IntegralImageNormalEstimation::setNormalEstimationMethod
void setNormalEstimationMethod(NormalEstimationMethod normal_estimation_method)
Set the normal estimation method.
Definition: integral_image_normal.h:214
pcl::TrajkovicKeypoint3D::NormalsPtr
typename Normals::Ptr NormalsPtr
Definition: trajkovic_3d.h:63
pcl::IntegralImageNormalEstimation::setNormalSmoothingSize
void setNormalSmoothingSize(float normal_smoothing_size)
Set the normal smoothing size.
Definition: integral_image_normal.h:190
pcl::TrajkovicKeypoint3D::detectKeypoints
void detectKeypoints(PointCloudOut &output) override
Definition: trajkovic_3d.hpp:98
pcl::Feature::compute
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition: feature.hpp:194
pcl::TrajkovicKeypoint3D::PointCloudOut
typename Keypoint< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: trajkovic_3d.h:60
pcl::PointIndices
Definition: PointIndices.h:11
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:131
pcl::B
@ B
Definition: norms.h:54