Point Cloud Library (PCL) 1.12.0
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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 {
63 int i = indices_in.indices[k];
64 // if we already processed this point continue with the next one
65 if (processed[i])
66 continue;
67 // now we will process this point
68 processed[i] = true;
69
71 p = (*host_cloud_)[i];
72 PointXYZHSV h;
74
75 // Create the query queue on the device, point based not indices
76 pcl::gpu::Octree::Queries queries_device;
77 // Create the query queue on the host
79 // Push the starting point in the vector
80 queries_host.push_back ((*host_cloud_)[i]);
81
82 unsigned int found_points = queries_host.size ();
83 unsigned int previous_found_points = 0;
84
85 pcl::gpu::NeighborIndices result_device;
86
87 // Host buffer for results
88 std::vector<int> sizes, data;
89
90 // once the area stop growing, stop also iterating.
91 while (previous_found_points < found_points)
92 {
93 // Move queries to GPU
94 queries_device.upload(queries_host);
95 // Execute search
96 tree->radiusSearch(queries_device, tolerance, max_answers, result_device);
97
98 // Store the previously found number of points
99 previous_found_points = found_points;
100
101 // Clear the Host vectors
102 sizes.clear (); data.clear ();
103
104 // Copy results from GPU to Host
105 result_device.sizes.download (sizes);
106 result_device.data.download (data);
107
108 for(std::size_t qp = 0; qp < sizes.size (); qp++)
109 {
110 for(int qp_r = 0; qp_r < sizes[qp]; qp_r++)
111 {
112 if(processed[data[qp_r + qp * max_answers]])
113 continue;
114
115 PointXYZRGB p_l;
116 p_l = (*host_cloud_)[data[qp_r + qp * max_answers]];
117 PointXYZHSV h_l;
118 PointXYZRGBtoXYZHSV(p_l, h_l);
119
120 if (std::abs(h_l.h - h.h) < delta_hue)
121 {
122 processed[data[qp_r + qp * max_answers]] = true;
123 queries_host.push_back ((*host_cloud_)[data[qp_r + qp * max_answers]]);
124 found_points++;
125 }
126 }
127 }
128 }
129 for(std::size_t qp = 0; qp < sizes.size (); qp++)
130 {
131 for(int qp_r = 0; qp_r < sizes[qp]; qp_r++)
132 {
133 indices_out.indices.push_back(data[qp_r + qp * max_answers]);
134 }
135 }
136 }
137 // @todo: do we need to sort here and remove double points?
138}
139
140void
142{
143 // Initialize the GPU search tree
144 if (!tree_)
145 {
146 tree_.reset (new pcl::gpu::Octree());
147 ///@todo what do we do if input isn't a PointXYZ cloud?
148 tree_->setCloud(input_);
149 }
150 if (!tree_->isBuild())
151 {
152 tree_->build();
153 }
154/*
155 if(tree_->cloud_.size() != host_cloud.size ())
156 {
157 PCL_ERROR("[pcl::gpu::SeededHueSegmentation] size of host cloud and device cloud don't match!\n");
158 return;
159 }
160*/
161 // Extract the actual clusters
163}
164
165#endif //PCL_GPU_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_
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:58
shared_ptr< Octree > Ptr
Types.
Definition octree.hpp:68
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)
Cluster extraction in 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