Point Cloud Library (PCL)  1.11.0
approximate_progressive_morphological_filter.hpp
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38 
39 #ifndef PCL_SEGMENTATION_APPROXIMATE_PROGRESSIVE_MORPHOLOGICAL_FILTER_HPP_
40 #define PCL_SEGMENTATION_APPROXIMATE_PROGRESSIVE_MORPHOLOGICAL_FILTER_HPP_
41 
42 #include <pcl/common/common.h>
43 #include <pcl/common/io.h>
44 #include <pcl/filters/morphological_filter.h>
45 #include <pcl/filters/extract_indices.h>
46 #include <pcl/segmentation/approximate_progressive_morphological_filter.h>
47 #include <pcl/point_cloud.h>
48 #include <pcl/point_types.h>
49 
50 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
51 template <typename PointT>
53  max_window_size_ (33),
54  slope_ (0.7f),
55  max_distance_ (10.0f),
56  initial_distance_ (0.15f),
57  cell_size_ (1.0f),
58  base_ (2.0f),
59  exponential_ (true),
60  threads_ (0)
61 {
62 }
63 
64 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
65 template <typename PointT>
67 {
68 }
69 
70 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
71 template <typename PointT> void
73 {
74  bool segmentation_is_possible = initCompute ();
75  if (!segmentation_is_possible)
76  {
77  deinitCompute ();
78  return;
79  }
80 
81  // Compute the series of window sizes and height thresholds
82  std::vector<float> height_thresholds;
83  std::vector<float> window_sizes;
84  std::vector<int> half_sizes;
85  int iteration = 0;
86  float window_size = 0.0f;
87 
88  while (window_size < max_window_size_)
89  {
90  // Determine the initial window size.
91  int half_size = (exponential_) ? (static_cast<int> (std::pow (static_cast<float> (base_), iteration))) : ((iteration+1) * base_);
92 
93  window_size = 2 * half_size + 1;
94 
95  // Calculate the height threshold to be used in the next iteration.
96  float height_threshold = (iteration == 0) ? (initial_distance_) : (slope_ * (window_size - window_sizes[iteration-1]) * cell_size_ + initial_distance_);
97 
98  // Enforce max distance on height threshold
99  if (height_threshold > max_distance_)
100  height_threshold = max_distance_;
101 
102  half_sizes.push_back (half_size);
103  window_sizes.push_back (window_size);
104  height_thresholds.push_back (height_threshold);
105 
106  iteration++;
107  }
108 
109  // setup grid based on scale and extents
110  Eigen::Vector4f global_max, global_min;
111  pcl::getMinMax3D<PointT> (*input_, global_min, global_max);
112 
113  float xextent = global_max.x () - global_min.x ();
114  float yextent = global_max.y () - global_min.y ();
115 
116  int rows = static_cast<int> (std::floor (yextent / cell_size_) + 1);
117  int cols = static_cast<int> (std::floor (xextent / cell_size_) + 1);
118 
119  Eigen::MatrixXf A (rows, cols);
120  A.setConstant (std::numeric_limits<float>::quiet_NaN ());
121 
122  Eigen::MatrixXf Z (rows, cols);
123  Z.setConstant (std::numeric_limits<float>::quiet_NaN ());
124 
125  Eigen::MatrixXf Zf (rows, cols);
126  Zf.setConstant (std::numeric_limits<float>::quiet_NaN ());
127 
128 #pragma omp parallel for \
129  default(none) \
130  shared(A, global_min) \
131  num_threads(threads_)
132  for (int i = 0; i < (int)input_->points.size (); ++i)
133  {
134  // ...then test for lower points within the cell
135  PointT p = input_->points[i];
136  int row = std::floor((p.y - global_min.y ()) / cell_size_);
137  int col = std::floor((p.x - global_min.x ()) / cell_size_);
138 
139  if (p.z < A (row, col) || std::isnan (A (row, col)))
140  {
141  A (row, col) = p.z;
142  }
143  }
144 
145  // Ground indices are initially limited to those points in the input cloud we
146  // wish to process
147  ground = *indices_;
148 
149  // Progressively filter ground returns using morphological open
150  for (std::size_t i = 0; i < window_sizes.size (); ++i)
151  {
152  PCL_DEBUG (" Iteration %d (height threshold = %f, window size = %f, half size = %d)...",
153  i, height_thresholds[i], window_sizes[i], half_sizes[i]);
154 
155  // Limit filtering to those points currently considered ground returns
157  pcl::copyPointCloud<PointT> (*input_, ground, *cloud);
158 
159  // Apply the morphological opening operation at the current window size.
160 #pragma omp parallel for \
161  default(none) \
162  shared(A, cols, half_sizes, i, rows, Z) \
163  num_threads(threads_)
164  for (int row = 0; row < rows; ++row)
165  {
166  int rs, re;
167  rs = ((row - half_sizes[i]) < 0) ? 0 : row - half_sizes[i];
168  re = ((row + half_sizes[i]) > (rows-1)) ? (rows-1) : row + half_sizes[i];
169 
170  for (int col = 0; col < cols; ++col)
171  {
172  int cs, ce;
173  cs = ((col - half_sizes[i]) < 0) ? 0 : col - half_sizes[i];
174  ce = ((col + half_sizes[i]) > (cols-1)) ? (cols-1) : col + half_sizes[i];
175 
176  float min_coeff = std::numeric_limits<float>::max ();
177 
178  for (int j = rs; j < (re + 1); ++j)
179  {
180  for (int k = cs; k < (ce + 1); ++k)
181  {
182  if (A (j, k) != std::numeric_limits<float>::quiet_NaN ())
183  {
184  if (A (j, k) < min_coeff)
185  min_coeff = A (j, k);
186  }
187  }
188  }
189 
190  if (min_coeff != std::numeric_limits<float>::max ())
191  Z(row, col) = min_coeff;
192  }
193  }
194 
195 #pragma omp parallel for \
196  default(none) \
197  shared(cols, half_sizes, i, rows, Z, Zf) \
198  num_threads(threads_)
199  for (int row = 0; row < rows; ++row)
200  {
201  int rs, re;
202  rs = ((row - half_sizes[i]) < 0) ? 0 : row - half_sizes[i];
203  re = ((row + half_sizes[i]) > (rows-1)) ? (rows-1) : row + half_sizes[i];
204 
205  for (int col = 0; col < cols; ++col)
206  {
207  int cs, ce;
208  cs = ((col - half_sizes[i]) < 0) ? 0 : col - half_sizes[i];
209  ce = ((col + half_sizes[i]) > (cols-1)) ? (cols-1) : col + half_sizes[i];
210 
211  float max_coeff = -std::numeric_limits<float>::max ();
212 
213  for (int j = rs; j < (re + 1); ++j)
214  {
215  for (int k = cs; k < (ce + 1); ++k)
216  {
217  if (Z (j, k) != std::numeric_limits<float>::quiet_NaN ())
218  {
219  if (Z (j, k) > max_coeff)
220  max_coeff = Z (j, k);
221  }
222  }
223  }
224 
225  if (max_coeff != -std::numeric_limits<float>::max ())
226  Zf (row, col) = max_coeff;
227  }
228  }
229 
230  // Find indices of the points whose difference between the source and
231  // filtered point clouds is less than the current height threshold.
232  std::vector<int> pt_indices;
233  for (std::size_t p_idx = 0; p_idx < ground.size (); ++p_idx)
234  {
235  PointT p = cloud->points[p_idx];
236  int erow = static_cast<int> (std::floor ((p.y - global_min.y ()) / cell_size_));
237  int ecol = static_cast<int> (std::floor ((p.x - global_min.x ()) / cell_size_));
238 
239  float diff = p.z - Zf (erow, ecol);
240  if (diff < height_thresholds[i])
241  pt_indices.push_back (ground[p_idx]);
242  }
243 
244  A.swap (Zf);
245 
246  // Ground is now limited to pt_indices
247  ground.swap (pt_indices);
248 
249  PCL_DEBUG ("ground now has %d points\n", ground.size ());
250  }
251 
252  deinitCompute ();
253 }
254 
255 
256 #define PCL_INSTANTIATE_ApproximateProgressiveMorphologicalFilter(T) template class pcl::ApproximateProgressiveMorphologicalFilter<T>;
257 
258 #endif // PCL_SEGMENTATION_APPROXIMATE_PROGRESSIVE_MORPHOLOGICAL_FILTER_HPP_
259 
pcl::ApproximateProgressiveMorphologicalFilter::~ApproximateProgressiveMorphologicalFilter
~ApproximateProgressiveMorphologicalFilter()
Definition: approximate_progressive_morphological_filter.hpp:66
point_types.h
common.h
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: projection_matrix.h:52
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:629
pcl::ApproximateProgressiveMorphologicalFilter::ApproximateProgressiveMorphologicalFilter
ApproximateProgressiveMorphologicalFilter()
Constructor that sets default values for member variables.
Definition: approximate_progressive_morphological_filter.hpp:52
pcl::ApproximateProgressiveMorphologicalFilter::extract
virtual void extract(std::vector< int > &ground)
This method launches the segmentation algorithm and returns indices of points determined to be ground...
Definition: approximate_progressive_morphological_filter.hpp:72
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:428