Point Cloud Library (PCL)  1.11.1-dev
intensity_gradient.hpp
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40 
41 #pragma once
42 
43 #include <pcl/features/intensity_gradient.h>
44 
45 #include <pcl/common/point_tests.h> // for pcl::isFinite
46 
47 
48 //////////////////////////////////////////////////////////////////////////////////////////////
49 template <typename PointInT, typename PointNT, typename PointOutT, typename IntensitySelectorT> void
51  const pcl::PointCloud <PointInT> &cloud, const pcl::Indices &indices,
52  const Eigen::Vector3f &point, float mean_intensity, const Eigen::Vector3f &normal, Eigen::Vector3f &gradient)
53 {
54  if (indices.size () < 3)
55  {
56  gradient[0] = gradient[1] = gradient[2] = std::numeric_limits<float>::quiet_NaN ();
57  return;
58  }
59 
60  Eigen::Matrix3f A = Eigen::Matrix3f::Zero ();
61  Eigen::Vector3f b = Eigen::Vector3f::Zero ();
62 
63  for (const auto &nn_index : indices)
64  {
65  PointInT p = cloud[nn_index];
66  if (!std::isfinite (p.x) ||
67  !std::isfinite (p.y) ||
68  !std::isfinite (p.z) ||
69  !std::isfinite (intensity_ (p)))
70  continue;
71 
72  p.x -= point[0];
73  p.y -= point[1];
74  p.z -= point[2];
75  intensity_.demean (p, mean_intensity);
76 
77  A (0, 0) += p.x * p.x;
78  A (0, 1) += p.x * p.y;
79  A (0, 2) += p.x * p.z;
80 
81  A (1, 1) += p.y * p.y;
82  A (1, 2) += p.y * p.z;
83 
84  A (2, 2) += p.z * p.z;
85 
86  b[0] += p.x * intensity_ (p);
87  b[1] += p.y * intensity_ (p);
88  b[2] += p.z * intensity_ (p);
89  }
90  // Fill in the lower triangle of A
91  A (1, 0) = A (0, 1);
92  A (2, 0) = A (0, 2);
93  A (2, 1) = A (1, 2);
94 
95 // Eigen::Vector3f x = A.colPivHouseholderQr ().solve (b);
96 
97  Eigen::Vector3f eigen_values;
98  Eigen::Matrix3f eigen_vectors;
99  eigen33 (A, eigen_vectors, eigen_values);
100 
101  b = eigen_vectors.transpose () * b;
102 
103  if ( eigen_values (0) != 0)
104  b (0) /= eigen_values (0);
105  else
106  b (0) = 0;
107 
108  if ( eigen_values (1) != 0)
109  b (1) /= eigen_values (1);
110  else
111  b (1) = 0;
112 
113  if ( eigen_values (2) != 0)
114  b (2) /= eigen_values (2);
115  else
116  b (2) = 0;
117 
118 
119  Eigen::Vector3f x = eigen_vectors * b;
120 
121 // if (A.col (0).squaredNorm () != 0)
122 // x [0] /= A.col (0).squaredNorm ();
123 // b -= x [0] * A.col (0);
124 //
125 //
126 // if (A.col (1).squaredNorm () != 0)
127 // x [1] /= A.col (1).squaredNorm ();
128 // b -= x[1] * A.col (1);
129 //
130 // x [2] = b.dot (A.col (2));
131 // if (A.col (2).squaredNorm () != 0)
132 // x[2] /= A.col (2).squaredNorm ();
133 // // Fit a hyperplane to the data
134 //
135 // std::cout << A << "\n*\n" << bb << "\n=\n" << x << "\nvs.\n" << x2 << "\n\n";
136 // std::cout << A * x << "\nvs.\n" << A * x2 << "\n\n------\n";
137  // Project the gradient vector, x, onto the tangent plane
138  gradient = (Eigen::Matrix3f::Identity () - normal*normal.transpose ()) * x;
139 }
140 
141 //////////////////////////////////////////////////////////////////////////////////////////////
142 template <typename PointInT, typename PointNT, typename PointOutT, typename IntensitySelectorT> void
144 {
145  // Allocate enough space to hold the results
146  // \note This resize is irrelevant for a radiusSearch ().
147  pcl::Indices nn_indices (k_);
148  std::vector<float> nn_dists (k_);
149  output.is_dense = true;
150 
151  // If the data is dense, we don't need to check for NaN
152  if (surface_->is_dense)
153  {
154 #pragma omp parallel for \
155  default(none) \
156  shared(output) \
157  firstprivate(nn_indices, nn_dists) \
158  num_threads(threads_)
159  // Iterating over the entire index vector
160  for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
161  {
162  PointOutT &p_out = output[idx];
163 
164  if (!this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists))
165  {
166  p_out.gradient[0] = p_out.gradient[1] = p_out.gradient[2] = std::numeric_limits<float>::quiet_NaN ();
167  output.is_dense = false;
168  continue;
169  }
170 
171  Eigen::Vector3f centroid;
172  float mean_intensity = 0;
173  // Initialize to 0
174  centroid.setZero ();
175  for (const auto &nn_index : nn_indices)
176  {
177  centroid += (*surface_)[nn_index].getVector3fMap ();
178  mean_intensity += intensity_ ((*surface_)[nn_index]);
179  }
180  centroid /= static_cast<float> (nn_indices.size ());
181  mean_intensity /= static_cast<float> (nn_indices.size ());
182 
183  Eigen::Vector3f normal = Eigen::Vector3f::Map ((*normals_)[(*indices_) [idx]].normal);
184  Eigen::Vector3f gradient;
185  computePointIntensityGradient (*surface_, nn_indices, centroid, mean_intensity, normal, gradient);
186 
187  p_out.gradient[0] = gradient[0];
188  p_out.gradient[1] = gradient[1];
189  p_out.gradient[2] = gradient[2];
190  }
191  }
192  else
193  {
194 #pragma omp parallel for \
195  default(none) \
196  shared(output) \
197  firstprivate(nn_indices, nn_dists) \
198  num_threads(threads_)
199  // Iterating over the entire index vector
200  for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
201  {
202  PointOutT &p_out = output[idx];
203  if (!isFinite ((*surface_) [(*indices_)[idx]]) ||
204  !this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists))
205  {
206  p_out.gradient[0] = p_out.gradient[1] = p_out.gradient[2] = std::numeric_limits<float>::quiet_NaN ();
207  output.is_dense = false;
208  continue;
209  }
210  Eigen::Vector3f centroid;
211  float mean_intensity = 0;
212  // Initialize to 0
213  centroid.setZero ();
214  unsigned cp = 0;
215  for (const auto &nn_index : nn_indices)
216  {
217  // Check if the point is invalid
218  if (!isFinite ((*surface_) [nn_index]))
219  continue;
220 
221  centroid += surface_->points [nn_index].getVector3fMap ();
222  mean_intensity += intensity_ (surface_->points [nn_index]);
223  ++cp;
224  }
225  centroid /= static_cast<float> (cp);
226  mean_intensity /= static_cast<float> (cp);
227  Eigen::Vector3f normal = Eigen::Vector3f::Map ((*normals_)[(*indices_) [idx]].normal);
228  Eigen::Vector3f gradient;
229  computePointIntensityGradient (*surface_, nn_indices, centroid, mean_intensity, normal, gradient);
230 
231  p_out.gradient[0] = gradient[0];
232  p_out.gradient[1] = gradient[1];
233  p_out.gradient[2] = gradient[2];
234  }
235  }
236 }
237 
238 #define PCL_INSTANTIATE_IntensityGradientEstimation(InT,NT,OutT) template class PCL_EXPORTS pcl::IntensityGradientEstimation<InT,NT,OutT>;
239 
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::PointCloud< PointInT >
pcl::eigen33
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition: eigen.hpp:296
pcl::IntensityGradientEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the intensity gradients for a set of points given in <setInputCloud (), setIndices ()> using...
Definition: intensity_gradient.hpp:143
pcl::PointCloud::is_dense
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:397
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
pcl::IntensityGradientEstimation
IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position...
Definition: intensity_gradient.h:56