38 #ifndef PCL_SIFT_KEYPOINT_IMPL_H_ 39 #define PCL_SIFT_KEYPOINT_IMPL_H_ 41 #include <pcl/keypoints/sift_keypoint.h> 42 #include <pcl/common/io.h> 43 #include <pcl/filters/voxel_grid.h> 46 template <
typename Po
intInT,
typename Po
intOutT>
void 49 min_scale_ = min_scale;
50 nr_octaves_ = nr_octaves;
51 nr_scales_per_octave_ = nr_scales_per_octave;
56 template <
typename Po
intInT,
typename Po
intOutT>
void 59 min_contrast_ = min_contrast;
63 template <
typename Po
intInT,
typename Po
intOutT>
bool 68 PCL_ERROR (
"[pcl::%s::initCompute] : Minimum scale (%f) must be strict positive!\n",
69 name_.c_str (), min_scale_);
74 PCL_ERROR (
"[pcl::%s::initCompute] : Number of octaves (%d) must be at least 1!\n",
75 name_.c_str (), nr_octaves_);
78 if (nr_scales_per_octave_ < 1)
80 PCL_ERROR (
"[pcl::%s::initCompute] : Number of scales per octave (%d) must be at least 1!\n",
81 name_.c_str (), nr_scales_per_octave_);
84 if (min_contrast_ < 0)
86 PCL_ERROR (
"[pcl::%s::initCompute] : Minimum contrast (%f) must be non-negative!\n",
87 name_.c_str (), min_contrast_);
97 template <
typename Po
intInT,
typename Po
intOutT>
void 100 if (surface_ && surface_ != input_)
102 PCL_WARN (
"[pcl::%s::detectKeypoints] : ", name_.c_str ());
103 PCL_WARN (
"A search surface has been set by setSearchSurface, but this SIFT keypoint detection algorithm does ");
104 PCL_WARN (
"not support search surfaces other than the input cloud. ");
105 PCL_WARN (
"The cloud provided in setInputCloud is being used instead.\n");
109 scale_idx_ = pcl::getFieldIndex<PointOutT> (output,
"scale", out_fields_);
112 output.points.clear ();
119 float scale = min_scale_;
120 for (
int i_octave = 0; i_octave < nr_octaves_; ++i_octave)
123 const float s = 1.0f * scale;
127 voxel_grid.
filter (*temp);
131 const size_t min_nr_points = 25;
132 if (cloud->points.size () < min_nr_points)
136 tree_->setInputCloud (cloud);
139 detectKeypointsForOctave (*cloud, *tree_, scale, nr_scales_per_octave_, output);
147 output.width =
static_cast<uint32_t
> (output.points.size ());
148 output.header = input_->header;
149 output.sensor_origin_ = input_->sensor_origin_;
150 output.sensor_orientation_ = input_->sensor_orientation_;
155 template <
typename Po
intInT,
typename Po
intOutT>
void 161 std::vector<float> scales (nr_scales_per_octave + 3);
162 for (
int i_scale = 0; i_scale <= nr_scales_per_octave + 2; ++i_scale)
164 scales[i_scale] = base_scale * powf (2.0f, (1.0f * static_cast<float> (i_scale) - 1.0f) / static_cast<float> (nr_scales_per_octave));
166 Eigen::MatrixXf diff_of_gauss;
167 computeScaleSpace (input, tree, scales, diff_of_gauss);
170 std::vector<int> extrema_indices, extrema_scales;
171 findScaleSpaceExtrema (input, tree, diff_of_gauss, extrema_indices, extrema_scales);
173 output.
points.reserve (output.
points.size () + extrema_indices.size ());
175 if (scale_idx_ != -1)
178 for (
size_t i_keypoint = 0; i_keypoint < extrema_indices.size (); ++i_keypoint)
181 const int &keypoint_index = extrema_indices[i_keypoint];
183 keypoint.x = input.
points[keypoint_index].x;
184 keypoint.y = input.
points[keypoint_index].y;
185 keypoint.z = input.
points[keypoint_index].z;
186 memcpy (reinterpret_cast<char*> (&keypoint) + out_fields_[scale_idx_].offset,
187 &scales[extrema_scales[i_keypoint]],
sizeof (
float));
188 output.
points.push_back (keypoint);
194 for (
size_t i_keypoint = 0; i_keypoint < extrema_indices.size (); ++i_keypoint)
197 const int &keypoint_index = extrema_indices[i_keypoint];
199 keypoint.x = input.
points[keypoint_index].x;
200 keypoint.y = input.
points[keypoint_index].y;
201 keypoint.z = input.
points[keypoint_index].z;
203 output.
points.push_back (keypoint);
210 template <
typename Po
intInT,
typename Po
intOutT>
213 Eigen::MatrixXf &diff_of_gauss)
215 diff_of_gauss.resize (input.
size (), scales.size () - 1);
218 const float max_radius = 3.0f * scales.back ();
220 for (
int i_point = 0; i_point < static_cast<int> (input.
size ()); ++i_point)
222 std::vector<int> nn_indices;
223 std::vector<float> nn_dist;
224 tree.
radiusSearch (i_point, max_radius, nn_indices, nn_dist);
230 float filter_response = 0.0f;
231 float previous_filter_response;
232 for (
size_t i_scale = 0; i_scale < scales.size (); ++i_scale)
234 float sigma_sqr = powf (scales[i_scale], 2.0f);
236 float numerator = 0.0f;
237 float denominator = 0.0f;
238 for (
size_t i_neighbor = 0; i_neighbor < nn_indices.size (); ++i_neighbor)
240 const float &value = getFieldValue_ (input.
points[nn_indices[i_neighbor]]);
241 const float &dist_sqr = nn_dist[i_neighbor];
242 if (dist_sqr <= 9*sigma_sqr)
244 float w = expf (-0.5f * dist_sqr / sigma_sqr);
245 numerator += value * w;
250 previous_filter_response = filter_response;
251 filter_response = numerator / denominator;
255 diff_of_gauss (i_point, i_scale - 1) = filter_response - previous_filter_response;
261 template <
typename Po
intInT,
typename Po
intOutT>
void 264 std::vector<int> &extrema_indices, std::vector<int> &extrema_scales)
267 std::vector<int> nn_indices (k);
268 std::vector<float> nn_dist (k);
270 const int nr_scales =
static_cast<int> (diff_of_gauss.cols ());
271 std::vector<float> min_val (nr_scales), max_val (nr_scales);
273 for (
int i_point = 0; i_point < static_cast<int> (input.
size ()); ++i_point)
276 const size_t nr_nn = tree.
nearestKSearch (i_point, k, nn_indices, nn_dist);
282 for (
int i_scale = 0; i_scale < nr_scales; ++i_scale)
284 min_val[i_scale] = std::numeric_limits<float>::max ();
285 max_val[i_scale] = -std::numeric_limits<float>::max ();
287 for (
size_t i_neighbor = 0; i_neighbor < nr_nn; ++i_neighbor)
289 const float &d = diff_of_gauss (nn_indices[i_neighbor], i_scale);
291 min_val[i_scale] = (std::min) (min_val[i_scale], d);
292 max_val[i_scale] = (std::max) (max_val[i_scale], d);
297 for (
int i_scale = 1; i_scale < nr_scales - 1; ++i_scale)
299 const float &val = diff_of_gauss (i_point, i_scale);
302 if (fabs (val) >= min_contrast_)
305 if ((val == min_val[i_scale]) &&
306 (val < min_val[i_scale - 1]) &&
307 (val < min_val[i_scale + 1]))
309 extrema_indices.push_back (i_point);
310 extrema_scales.push_back (i_scale);
313 else if ((val == max_val[i_scale]) &&
314 (val > max_val[i_scale - 1]) &&
315 (val > max_val[i_scale + 1]))
317 extrema_indices.push_back (i_point);
318 extrema_scales.push_back (i_scale);
325 #define PCL_INSTANTIATE_SIFTKeypoint(T,U) template class PCL_EXPORTS pcl::SIFTKeypoint<T,U>; 327 #endif // #ifndef PCL_SIFT_KEYPOINT_IMPL_H_ SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset ...
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
virtual int radiusSearch(const PointT &point, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const =0
Search for all the nearest neighbors of the query point in a given radius.
void filter(PointCloud &output)
Calls the filtering method and returns the filtered dataset in output.
VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data...
void setScales(float min_scale, int nr_octaves, int nr_scales_per_octave)
Specify the range of scales over which to search for keypoints.
void setMinimumContrast(float min_contrast)
Provide a threshold to limit detection of keypoints without sufficient contrast.
PointCloud represents the base class in PCL for storing collections of 3D points. ...
void detectKeypoints(PointCloudOut &output)
Detect the SIFT keypoints for a set of points given in setInputCloud () using the spatial locator in ...
virtual int nearestKSearch(const PointT &point, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances) const =0
Search for the k-nearest neighbors for the given query point.
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
void setLeafSize(const Eigen::Vector4f &leaf_size)
Set the voxel grid leaf size.