Point Cloud Library (PCL) 1.14.0
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gpu_seeded_hue_segmentation.hpp
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38
39#ifndef PCL_GPU_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_
40#define PCL_GPU_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_
41
42#include <pcl/gpu/segmentation/gpu_seeded_hue_segmentation.h>
43
44//////////////////////////////////////////////////////////////////////////////////////////////
45void
47 const pcl::gpu::Octree::Ptr& tree,
48 float tolerance,
49 PointIndices& indices_in,
50 PointIndices& indices_out,
51 float delta_hue)
52{
53
54 // Create a bool vector of processed point indices, and initialize it to false
55 // cloud is a DeviceArray<PointType>
56 std::vector<bool> processed(host_cloud_->size(), false);
57
58 const auto max_answers = host_cloud_->size();
59
60 // Process all points in the indices vector
61 for (std::size_t k = 0; k < indices_in.indices.size(); ++k) {
62 int i = indices_in.indices[k];
63 // if we already processed this point continue with the next one
64 if (processed[i])
65 continue;
66 // now we will process this point
67 processed[i] = true;
68
70 p = (*host_cloud_)[i];
71 PointXYZHSV h;
73
74 // Create the query queue on the device, point based not indices
75 pcl::gpu::Octree::Queries queries_device;
76 // Create the query queue on the host
78 // Push the starting point in the vector
79 queries_host.push_back((*host_cloud_)[i]);
80
81 unsigned int found_points = queries_host.size();
82 unsigned int previous_found_points = 0;
83
84 pcl::gpu::NeighborIndices result_device;
85
86 // Host buffer for results
87 std::vector<int> sizes, data;
88
89 // once the area stop growing, stop also iterating.
90 while (previous_found_points < found_points) {
91 // Move queries to GPU
92 queries_device.upload(queries_host);
93 // Execute search
94 tree->radiusSearch(queries_device, tolerance, max_answers, result_device);
95
96 // Store the previously found number of points
97 previous_found_points = found_points;
98
99 // Clear the Host vectors
100 sizes.clear();
101 data.clear();
102
103 // Copy results from GPU to Host
104 result_device.sizes.download(sizes);
105 result_device.data.download(data);
106
107 for (std::size_t qp = 0; qp < sizes.size(); qp++) {
108 for (int qp_r = 0; qp_r < sizes[qp]; qp_r++) {
109 if (processed[data[qp_r + qp * max_answers]])
110 continue;
111
112 PointXYZRGB p_l;
113 p_l = (*host_cloud_)[data[qp_r + qp * max_answers]];
114 PointXYZHSV h_l;
115 PointXYZRGBtoXYZHSV(p_l, h_l);
116
117 if (std::abs(h_l.h - h.h) < delta_hue) {
118 processed[data[qp_r + qp * max_answers]] = true;
119 queries_host.push_back((*host_cloud_)[data[qp_r + qp * max_answers]]);
120 found_points++;
121 }
122 }
123 }
124 }
125 for (std::size_t qp = 0; qp < sizes.size(); qp++) {
126 for (int qp_r = 0; qp_r < sizes[qp]; qp_r++) {
127 indices_out.indices.push_back(data[qp_r + qp * max_answers]);
128 }
129 }
130 }
131 // @todo: do we need to sort here and remove double points?
132}
133
134void
136 PointIndices& indices_out)
137{
138 // Initialize the GPU search tree
139 if (!tree_) {
140 tree_.reset(new pcl::gpu::Octree());
141 ///@todo what do we do if input isn't a PointXYZ cloud?
142 tree_->setCloud(input_);
143 }
144 if (!tree_->isBuild()) {
145 tree_->build();
146 }
147 /*
148 if(tree_->cloud_.size() != host_cloud.size ())
149 {
150 PCL_ERROR("[pcl::gpu::SeededHueSegmentation] size of host cloud and device cloud
151 don't match!\n"); return;
152 }
153 */
154 // Extract the actual clusters
156 host_cloud_, tree_, cluster_tolerance_, indices_in, indices_out, delta_hue_);
157}
158
159#endif // PCL_GPU_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_
void push_back(const PointT &pt)
Insert a new point in the cloud, at the end of the container.
std::size_t size() const
std::vector< PointT, Eigen::aligned_allocator< PointT > > VectorType
shared_ptr< PointCloud< PointT > > Ptr
void upload(const T *host_ptr, std::size_t size)
Uploads data to internal buffer in GPU memory.
void download(T *host_ptr) const
Downloads data from internal buffer to CPU memory.
Octree implementation on GPU.
Definition octree.hpp:59
shared_ptr< Octree > Ptr
Types.
Definition octree.hpp:69
PointCloudHostPtr host_cloud_
the original cloud the Host
CloudDevice input_
the input cloud on the GPU
float delta_hue_
The allowed difference on the hue.
double cluster_tolerance_
The spatial cluster tolerance as a measure in the L2 Euclidean space.
void segment(PointIndices &indices_in, PointIndices &indices_out)
extract clusters of a PointCloud given by <setInputCloud(), setIndices()>
GPUTreePtr tree_
A pointer to the spatial search object.
float4 PointXYZRGB
Definition internal.hpp:60
void seededHueSegmentation(const pcl::PointCloud< pcl::PointXYZRGB >::Ptr &host_cloud_, const pcl::gpu::Octree::Ptr &tree, float tolerance, PointIndices &clusters_in, PointIndices &clusters_out, float delta_hue=0.0)
void PointXYZRGBtoXYZHSV(const PointXYZRGB &in, PointXYZHSV &out)
Convert a XYZRGB point type to a XYZHSV.
DeviceArray< int > sizes
DeviceArray< int > data