[PCL]3 欧式距离分类EuclideanClusterExtraction

Posted 2020-06-09

EuclideanClusterExtraction这个名字起的很奇怪，欧式距离聚类这个该如何理解？欧式距离只是一种距离测度的方法呀！有了一个Cluster在里面，我以为是某一种聚类算法，层次聚类？k-NN聚类？K-Means？还是模糊聚类？感觉很奇怪，看下代码吧。

找一个实例cluster_extraction.cpp的main入口函数。

找到computer函数，该方法中定义了一个pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;对象，接着就是参数赋值。

具体的算法执行在ec.extract (cluster_indices)中。因此进入EuclideanClusterExtraction查看具体的方法。

 1 void compute (const pcl::PCLPointCloud2::ConstPtr &input, std::vector<pcl::PCLPointCloud2::Ptr> &output,
 2          int min, int max, double tolerance)
 3 {
 4   // Convert data to PointCloud<T>
 5   PointCloud<pcl::PointXYZ>::Ptr xyz (new PointCloud<pcl::PointXYZ>);
 6   fromPCLPointCloud2 (*input, *xyz);
 7 
 8   // Estimate
 9   TicToc tt;
10   tt.tic ();
11   
12   print_highlight (stderr, "Computing ");
13 
14   // Creating the KdTree object for the search method of the extraction
15   pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
16   tree->setInputCloud (xyz);
17 
18   std::vector<pcl::PointIndices> cluster_indices;
19   pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
20   ec.setClusterTolerance (tolerance);
21   ec.setMinClusterSize (min);
22   ec.setMaxClusterSize (max);
23   ec.setSearchMethod (tree);
24   ec.setInputCloud (xyz);
25   ec.extract (cluster_indices);
26 
27   print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cluster_indices.size ()); print_info (" clusters]\n");
28 
29   output.reserve (cluster_indices.size ());
30   for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
31   {
32     pcl::ExtractIndices<pcl::PCLPointCloud2> extract;
33     extract.setInputCloud (input);
34     extract.setIndices (boost::make_shared<const pcl::PointIndices> (*it));
35     pcl::PCLPointCloud2::Ptr out (new pcl::PCLPointCloud2);
36     extract.filter (*out);
37     output.push_back (out);
38   }
39 }

可以在pcl_segmentation项目下的extract_clusters.h文件中查看EuclideanClusterExtraction的定义，可知这是一个模板类。

 1 template <typename PointT> 2 class EuclideanClusterExtraction: public PCLBase<PointT> 

实现文件在项目下的extract_clusters.hpp中，（还有一个extract_clusters.cpp文件），查看其中的extract方法：

 1 template <typename PointT> void pcl::EuclideanClusterExtraction<PointT>::extract (std::vector<PointIndices> &clusters)
 2 {
 3   if (!initCompute () || 
 4       (input_ != 0   && input_->points.empty ()) ||
 5       (indices_ != 0 && indices_->empty ()))
 6   {
 7     clusters.clear ();
 8     return;
 9   }
10 
11   // Initialize the spatial locator
12   if (!tree_)
13   {
14     if (input_->isOrganized ())
15       tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
16     else
17       tree_.reset (new pcl::search::KdTree<PointT> (false));
18   }
19 
20   // Send the input dataset to the spatial locator
21   tree_->setInputCloud (input_, indices_);
22   extractEuclideanClusters (*input_, *indices_, tree_, static_cast<float> (cluster_tolerance_), clusters, min_pts_per_cluster_, max_pts_per_cluster_);
23 
24   //tree_->setInputCloud (input_);
25   //extractEuclideanClusters (*input_, tree_, cluster_tolerance_, clusters, min_pts_per_cluster_, max_pts_per_cluster_);
26 
27   // Sort the clusters based on their size (largest one first)
28   std::sort (clusters.rbegin (), clusters.rend (), comparePointClusters);
29 
30   deinitCompute ();
31 }

 注意在extract_clusters.h中定义了四个

1  template <typename PointT> void 
2   extractEuclideanClusters (
3       const PointCloud<PointT> &cloud, const boost::shared_ptr<search::Search<PointT> > &tree, 
4       float tolerance, std::vector<PointIndices> &clusters, 
5       unsigned int min_pts_per_cluster = 1, unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());

1 template <typename PointT> void 
2   extractEuclideanClusters (
3       const PointCloud<PointT> &cloud, const std::vector<int> &indices, 
4       const boost::shared_ptr<search::Search<PointT> > &tree, float tolerance, std::vector<PointIndices> &clusters, 
5       unsigned int min_pts_per_cluster = 1, unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());

1  template <typename PointT, typename Normal> void 
2   extractEuclideanClusters (
3       const PointCloud<PointT> &cloud, const PointCloud<Normal> &normals, 
4       float tolerance, const boost::shared_ptr<KdTree<PointT> > &tree, 
5       std::vector<PointIndices> &clusters, double eps_angle, 
6       unsigned int min_pts_per_cluster = 1, 
7       unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ())

1  template <typename PointT, typename Normal> 
2   void extractEuclideanClusters (
3       const PointCloud<PointT> &cloud, const PointCloud<Normal> &normals, 
4       const std::vector<int> &indices, const boost::shared_ptr<KdTree<PointT> > &tree, 
5       float tolerance, std::vector<PointIndices> &clusters, double eps_angle, 
6       unsigned int min_pts_per_cluster = 1, 
7       unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ())

前两个的方法实现在文件extract_clusters.hpp中，后两个直接在头文件中就以内联函数的形式实现了，两个大同小异。择其中第一个加点注释，发现其实是采用的区域生长算法实现的分割。

 1  template <typename PointT, typename Normal> void 
 2   extractEuclideanClusters (
 3       const PointCloud<PointT> &cloud, const PointCloud<Normal> &normals, 
 4       float tolerance, const boost::shared_ptr<KdTree<PointT> > &tree, 
 5       std::vector<PointIndices> &clusters, double eps_angle, 
 6       unsigned int min_pts_per_cluster = 1, 
 7       unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ())
 8   {
 9     if (tree->getInputCloud ()->points.size () != cloud.points.size ())
10     {
11       PCL_ERROR ("[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n", tree->getInputCloud ()->points.size (), cloud.points.size ());
12       return;
13     }
14     if (cloud.points.size () != normals.points.size ())
15     {
16       PCL_ERROR ("[pcl::extractEuclideanClusters] Number of points in the input point cloud (%lu) different than normals (%lu)!\n", cloud.points.size (), normals.points.size ());
17       return;
18     }
19 
20     // Create a bool vector of processed point indices, and initialize it to false
21     std::vector<bool> processed (cloud.points.size (), false);
22 
23     std::vector<int> nn_indices;
24     std::vector<float> nn_distances;
25     // Process all points in the indices vector
26     for (size_t i = 0; i < cloud.points.size (); ++i)//遍历点云中的每一个点
27     {
28           if (processed[i])//如果该点已经处理则跳过
29             continue;
30 
31           std::vector<unsigned int> seed_queue;//定义一个种子队列
32           int sq_idx = 0;
33           seed_queue.push_back (static_cast<int> (i));//加入一个种子
34 
35           processed[i] = true;
36 
37           while (sq_idx < static_cast<int> (seed_queue.size ()))//遍历每一个种子
38           {
39             // Search for sq_idx
40             if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))//没找到近邻点就继续
41             {
42               sq_idx++;
43               continue;
44             }
45 
46             for (size_t j = 1; j < nn_indices.size (); ++j)             // nn_indices[0] should be sq_idx
47             {
48               if (processed[nn_indices[j]])                         // Has this point been processed before ?种子点的近邻点中如果已经处理就跳出此次循环继续
49                 continue;
50 
51               //processed[nn_indices[j]] = true;
52               // [-1;1]
53               double dot_p = normals.points[i].normal[0] * normals.points[nn_indices[j]].normal[0] +
54                              normals.points[i].normal[1] * normals.points[nn_indices[j]].normal[1] +
55                              normals.points[i].normal[2] * normals.points[nn_indices[j]].normal[2];
56               if ( fabs (acos (dot_p)) < eps_angle ) //根据内积求夹角，法向量都是单位向量，种子点和近邻点的法向量夹角小于阈值，
57               {
58                 processed[nn_indices[j]] = true;
59                 seed_queue.push_back (nn_indices[j]);//将此种子点的临近点作为新的种子点。
60               }
61             }
62 
63             sq_idx++;
64           }
65 
66           // If this queue is satisfactory, add to the clusters
67           if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
68           {
69             pcl::PointIndices r;
70             r.indices.resize (seed_queue.size ());
71             for (size_t j = 0; j < seed_queue.size (); ++j)
72               r.indices[j] = seed_queue[j];
73 
74             // These two lines should not be needed: (can anyone confirm?) -FF
75             std::sort (r.indices.begin (), r.indices.end ());
76             r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
77 
78             r.header = cloud.header;
79             clusters.push_back (r);   // We could avoid a copy by working directly in the vector
80           }
81     }
82   }