template <typename PointInT, typename PointOutT> void
pcl::SIFTKeypoint<PointInT, PointOutT>::detectKeypoints (PointCloudOut &output)
{
if (surface_ && surface_ != input_)
{
PCL_WARN ("[pcl::%s::detectKeypoints] : ", name_.c_str ());
PCL_WARN ("A search surface has been set by setSearchSurface, but this SIFT keypoint detection algorithm does ");
PCL_WARN ("not support search surfaces other than the input cloud. ");
PCL_WARN ("The cloud provided in setInputCloud is being used instead.\n");
}
// Check if the output has a "scale" field
scale_idx_ = pcl::getFieldIndex<PointOutT> (output, "scale", out_fields_);
// Make sure the output cloud is empty
output.points.clear ();
// Create a local copy of the input cloud that will be resized for each octave
boost::shared_ptr<pcl::PointCloud<PointInT> > cloud (new pcl::PointCloud<PointInT> (*input_));
VoxelGrid<PointInT> voxel_grid;
// Search for keypoints at each octave
float scale = min_scale_;
for (int i_octave = 0; i_octave < nr_octaves_; ++i_octave)
{
// Downsample the point cloud
const float s = 1.0f * scale; // note: this can be adjusted
voxel_grid.setLeafSize (s, s, s);
voxel_grid.setInputCloud (cloud);
boost::shared_ptr<pcl::PointCloud<PointInT> > temp (new pcl::PointCloud<PointInT>);
voxel_grid.filter (*temp);
cloud = temp;
// Make sure the downsampled cloud still has enough points
const size_t min_nr_points = 25;
if (cloud->points.size () < min_nr_points)
break;
// Update the KdTree with the downsampled points
tree_->setInputCloud (cloud);
// Detect keypoints for the current scale
detectKeypointsForOctave (*cloud, *tree_, scale, nr_scales_per_octave_, output, *keypoints_indices_);
// Increase the scale by another octave
scale *= 2;
}
// Set final properties
output.height = 1;
output.width = static_cast<uint32_t> (output.points.size ());
output.header = input_->header;
output.sensor_origin_ = input_->sensor_origin_;
output.sensor_orientation_ = input_->sensor_orientation_;
}
template <typename PointInT, typename PointOutT> void
pcl::SIFTKeypoint<PointInT, PointOutT>::detectKeypoints (PointCloudOut &output)
{
// Check for valid inputs
if (min_scale_ == 0 || nr_octaves_ == 0 || nr_scales_per_octave_ == 0)
{
ROS_ERROR ("[pcl::%s::detectKeypoints] : A valid range of scales must be specified by setScales before detecting keypoints!", name_.c_str ());
return;
}
if (surface_ != input_)
{
ROS_WARN ("[pcl::%s::detectKeypoints] : A search surface has be set by setSearchSurface, but this SIFT keypoint detection algorithm does not support search surfaces other than the input cloud. The cloud provided in setInputCloud is being used instead.", name_.c_str ());
}
// Make sure the output cloud is empty
output.points.clear ();
// Create a local copy of the input cloud that will be resized for each octave
boost::shared_ptr<pcl::PointCloud<PointInT> > cloud = input_->makeShared ();
// Search for keypoints at each octave
float scale = min_scale_;
for (int i_octave = 0; i_octave < nr_octaves_; ++i_octave)
{
// Downsample the point cloud
VoxelGrid<PointInT> voxel_grid;
float s = 1.0 * scale; // note: this can be adjusted
voxel_grid.setLeafSize (s, s, s);
voxel_grid.setInputCloud (cloud);
voxel_grid.filter (*cloud);
// Make sure the downsampled cloud still has enough points
const size_t min_nr_points = 25;
if (cloud->points.size () < min_nr_points)
{
return;
}
// Update the KdTree with the downsampled points
tree_->setInputCloud (cloud);
// Detect keypoints for the current scale
detectKeypointsForOctave (*cloud, *tree_, scale, nr_scales_per_octave_, output);
// Increase the scale by another octave
scale *= 2;
}
}
