使用python-pcl, 也就是python版本的pcl库将bin格式转化为pcd格式,然后将点云进行显示
(C++版本的这里暂时不做说明)
参考python-pcl文件中的cluster_extraction.py
文件中已经添加啦备注, 可作为参考.
# -*- coding: utf-8 -*-# Euclidean Cluster Extraction# /documentation/tutorials/cluster_extraction.php#cluster-extractionimport numpy as npimport pcldef main():# // Read in the cloud data# pcl::PCDReader reader;# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);# reader.read ("table_scene_lms400.pcd", *cloud);# std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*cloud = pcl.load('./table_scene_lms400.pcd')# // Create the filtering object: downsample the dataset using a leaf size of 1cm# pcl::VoxelGrid<pcl::PointXYZ> vg;# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);# vg.setInputCloud (cloud);# vg.setLeafSize (0.01f, 0.01f, 0.01f);# vg.filter (*cloud_filtered);# std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl; //*vg = cloud.make_voxel_grid_filter()vg.set_leaf_size(0.005, 0.005, 0.005)cloud_filtered = vg.filter()# // Create the segmentation object for the planar model and set all the parameters# pcl::SACSegmentation<pcl::PointXYZ> seg;# pcl::PointIndices::Ptr inliers (new pcl::PointIndices);# pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<pcl::PointXYZ> ());# pcl::PCDWriter writer;# seg.setOptimizeCoefficients (true);# seg.setModelType (pcl::SACMODEL_PLANE);# seg.setMethodType (pcl::SAC_RANSAC);# seg.setMaxIterations (100);# seg.setDistanceThreshold (0.02);seg = cloud.make_segmenter()seg.set_optimize_coefficients(True)seg.set_model_type(pcl.SACMODEL_PLANE)seg.set_method_type(pcl.SAC_RANSAC)seg.set_MaxIterations(100)seg.set_distance_threshold(0.005)# int i=0, nr_points = (int) cloud_filtered->points.size ();# while (cloud_filtered->points.size () > 0.3 * nr_points)# {#// Segment the largest planar component from the remaining cloud#seg.setInputCloud (cloud_filtered);#seg.segment (*inliers, *coefficients);#if (inliers->indices.size () == 0)#{# std::cout << "Could not estimate a planar model for the given dataset." << std::endl;# break;#}#// Extract the planar inliers from the input cloud#pcl::ExtractIndices<pcl::PointXYZ> extract;#extract.setInputCloud (cloud_filtered);#extract.setIndices (inliers);#extract.setNegative (false);##// Get the points associated with the planar surface#extract.filter (*cloud_plane);#std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;##// Remove the planar inliers, extract the rest#extract.setNegative (true);#extract.filter (*cloud_f);#*cloud_filtered = *cloud_f;# }i = 0nr_points = cloud_filtered.size# while nr_points > 0.3 * nr_points:## Segment the largest planar component from the remaining cloud#[inliers, coefficients] = seg.segment()## extract = cloud_filtered.extract()## extract = pcl.PointIndices()#cloud_filtered.extract(extract)#extract.set_Indices (inliers)#extract.set_Negative (false)#cloud_plane = extract.filter ()##extract.set_Negative (True)#cloud_f = extract.filter ()#cloud_filtered = cloud_f# Creating the KdTree object for the search method of the extraction# pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);# tree->setInputCloud (cloud_filtered);tree = cloud_filtered.make_kdtree()# tree = cloud_filtered.make_kdtree_flann()# std::vector<pcl::PointIndices> cluster_indices;# pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;# ec.setClusterTolerance (0.02); // 2cm# ec.setMinClusterSize (100);# ec.setMaxClusterSize (25000);# ec.setSearchMethod (tree);# ec.setInputCloud (cloud_filtered);# ec.extract (cluster_indices);ec = cloud_filtered.make_EuclideanClusterExtraction()ec.set_ClusterTolerance(0.02)ec.set_MinClusterSize(100)ec.set_MaxClusterSize(25000)ec.set_SearchMethod(tree)cluster_indices = ec.Extract()print('cluster_indices : ' + str(cluster_indices.count) + " count.")# print('cluster_indices : ' + str(cluster_indices.indices.max_size) + " count.")# int j = 0;# for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)# {#pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);#for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit)# cloud_cluster->points.push_back (cloud_filtered->points[*pit]); //*#cloud_cluster->width = cloud_cluster->points.size ();#cloud_cluster->height = 1;#cloud_cluster->is_dense = true;##std::cout << "PointCloud representing the Cluster: " << cloud_cluster->points.size () << " data points." << std::endl;#std::stringstream ss;#ss << "cloud_cluster_" << j << ".pcd";#writer.write<pcl::PointXYZ> (ss.str (), *cloud_cluster, false); //*#j++;# }#cloud_cluster = pcl.PointCloud() # 首先定义pcl的格式for j, indices in enumerate(cluster_indices):# cloudsize = indicesprint('indices = ' + str(len(indices)))# cloudsize = len(indices)points = np.zeros((len(indices), 3), dtype=np.float32)# 定义一个全为0的矩阵, 形状与点云相似(n, 3)# points = np.zeros((cloudsize, 3), dtype=np.float32)# for indice in range(len(indices)):for i, indice in enumerate(indices):# print('dataNum = ' + str(i) + ', data point[x y z]: ' + str(cloud_filtered[indice][0]) + ' ' + str(cloud_filtered[indice][1]) + ' ' + str(cloud_filtered[indice][2]))# print('PointCloud representing the Cluster: ' + str(cloud_cluster.size) + " data points.")points[i][0] = cloud_filtered[indice][0] # pcl格式的点云送入到刚才定义的(n,3)矩阵中points[i][1] = cloud_filtered[indice][1]points[i][2] = cloud_filtered[indice][2]cloud_cluster.from_array(points) # 将points矩阵转化为pcd的形式ss = "cloud_cluster_" + str(j) + ".pcd" # 定义pcd的文件名pcl.save(cloud_cluster, ss) # 将pcd格式点云文件进行保存if __name__ == "__main__":# import cProfile# cProfile.run('main()', sort='time')main()
举个例子:
例子中,将bin格式的点云转化为pcd格式,然后使用pyqtgraph进行显示
# -*- coding: utf-8 -*-import osimport sysif hasattr(sys, 'frozen'):os.environ['PATH'] = sys._MEIPASS + ";" + os.environ['PATH']from PyQt5 import QtWidgetsfrom PyQt5.QtCore import *from PyQt5.QtWidgets import *# import initExamplefrom pyqtgraph.Qt import QtCore, QtGuiimport pyqtgraph.opengl as gl# from kitti import show_lidar_with_boxesimport numpy as npimport csvimport pandas as pdimport socketimport glob# from datetime import datetime, timedeltaimport structimport timeimport tracebackfrom multiprocessing import Process, Queue, Poolimport threadingimport loggingimport logging.configfrom decimal import Decimalimport mathimport datetimeimport pcldef inte_to_rgb(pc_inte):minimum, maximum = np.min(pc_inte), np.max(pc_inte)try:if minimum == maximum:ratio = 2 * (pc_inte + minimum) / (maximum + minimum)b = (np.maximum((1 - ratio), 0))r = (np.maximum((ratio - 1), 0))g = 1 - b - relse:ratio = 2 * (pc_inte - minimum) / (maximum - minimum)b = (np.maximum((1 - ratio), 0))r = (np.maximum((ratio - 1), 0))g = 1 - b - rexcept:ratio = 2 * (pc_inte + minimum) / (maximum + minimum)b = (np.maximum((1 - ratio), 0))r = (np.maximum((ratio - 1), 0))g = 1 - b - rreturn np.stack([r, g, b, np.ones_like(r)]).transpose()if __name__ == '__main__':app = QtWidgets.QApplication(sys.argv)w = gl.GLViewWidget()w.setBackgroundColor('w') # 设置背景为白色，点云显示为其他颜色w.opts['distance'] = 20w.setWindowTitle('pyqtgraph example: GLScatterPlotItem')# g = gl.GLGridItem() # 显示网格# w.addItem(g)coord = gl.GLAxisItem() #显示坐标轴coord.setSize(1,1,1)ｗ.addItem(coord)save_bin_name = 0path = "000090.bin"points = np.fromfile(path, dtype=np.float32).reshape(-1, 4)[:,:3]print("当前点云大小为：", points.shape)cloud_cluster = pcl.PointCloud() # 首先定义pcl的格式cloud_cluster.from_array(points) # 将points矩阵转化为pcd的形式pcl.save(cloud_cluster, "000000.pcd") # 将pcd格式点云文件进行保存# # print("new_name.shape", new_name.shape)# # 将分割后的点云进行保存,保存为bin格式, 然后使用pointnet进行分类# pointcloud_name_path = os.getcwd() + "/bin2pcd/"# pointcloud_name = str(save_bin_name).zfill(6) + ".pcd"# pointcloud_name_new = os.path.join(pointcloud_name_path, pointcloud_name)# points = points.astype(np.float32)# points.tofile(pointcloud_name_new)# save_bin_name = save_bin_name + 1# sp2 = gl.GLScatterPlotItem(pos=points, color=(1,1,1,1), size=2) #不带有任何颜色的白点sp2 = gl.GLScatterPlotItem(pos=None, color=None, size=2) # 带有颜色的点phase = 0.w.addItem(sp2)if points.shape[1] == 4:print("global_value.points_var.shape[1]", points.shape[1])pc_inte = points[:, 3]pc_color = inte_to_rgb(pc_inte)sp2.setData(pos=points[:, 0:3], color=pc_color)sp2.setGLOptions('translucent') # 设置背景为白色，点云显示为其他颜色elif points.shape[1] == 3:print("global_value.points_var2.shape[1]", points.shape[1])sp2.setData(pos=points[:, 0:3], color=(0,0,0,1))sp2.setGLOptions('translucent') # 设置背景为白色，点云显示为其他颜色w.show()sys.exit(app.exec_())
