ModelNet40点云数据集预处理

ModelNet40 中加入自己的数据集
ModelNet40 ：http://modelnet.cs.princeton.edu/#
含有40个内别的CAD三维模型，是评价点云深度学习模型进行语意分割、实例分割和分类的标准数据集
代码功能：对自己的点云目标进行标准化融合到ModleNet40中
主要步骤：

中心化：将点云中心移动到坐标原点；
尺度缩放：将所有点的坐标的绝对值限制在1以内；
采样：将点云采样到固定大小点数；
输出保存文件的格式；

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 25 21:53:03 2019@author: sgl
"""
import os
import sys
import numpy as np
import h5py
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)def getDataFiles(list_filename):return [line.rstrip() for line in open(list_filename)]def loadDataFile(path):data = np.loadtxt(path)num = data.shape[0]point_xyz = data[:,0:3]point_normal = data[:,3:6]point_rgb = data[:,6:9]# label just a example, should be repalced the real.# modlenet40 is 0-39, so the label can be 40 and 41label = np.ones((num,1), dtype=int)+39return point_xyz, labeldef change_scale(data):#centre xyz_min = np.min(data[:,0:3],axis=0)xyz_max = np.max(data[:,0:3],axis=0)xyz_move = xyz_min+(xyz_max-xyz_min)/2data[:,0:3] = data[:,0:3]-xyz_move#scalescale = np.max(data[:,0:3])data[:,0:3] = data[:,0:3]/scalereturn datadef sample_data(data, num_sample):""" data is in N x ...we want to keep num_samplexC of them.if N > num_sample, we will randomly keep num_sample of them.if N < num_sample, we will randomly duplicate samples."""N = data.shape[0]if (N == num_sample):return data, range(N)elif (N > num_sample):sample = np.random.choice(N, num_sample)return data[sample, ...], sampleelse:sample = np.random.choice(N, num_sample-N)dup_data = data[sample, ...]return np.concatenate([data, dup_data], 0), list(range(N))+list(sample)if __name__ == "__main__":DATA_FILES =getDataFiles(os.path.join(BASE_DIR, 'file_path.txt'))num_sample = 4096DATA_ALL = []for fn in range(len(DATA_FILES)):current_data, current_label = loadDataFile(DATA_FILES[fn])change_data = change_scale(current_data)data_sample,index = sample_data(change_data, num_sample)data_label = np.hstack((data_sample,current_label[index]))DATA_ALL.append(data_label)output = np.vstack(DATA_ALL)output = output.reshape(-1,num_sample,4)# train and test number, save dataif not os.path.exists('plant_train.h5'):with h5py.File('plant_train.h5') as f:f['data'] = output[0:7,0:3]f['labels'] = output[0:8,4]if not os.path.exists('plant_test.h5'):with h5py.File('plant_test.h5') as f:f['data'] = output[7:9,0:3]f['labels'] = output[7:9,4]

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 25 16:42:25 2019@author: sgl
"""
import os
import sys
import numpy as np
import h5py
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)def sample_data(data, num_sample):""" data is in N x ...we want to keep num_samplexC of them.if N > num_sample, we will randomly keep num_sample of them.if N < num_sample, we will randomly duplicate samples."""N = data.shape[0]if (N == num_sample):
#        return data, range(N)return dataelif (N > num_sample):sample = np.random.choice(N, num_sample)
#        return data[sample, ...], samplereturn data[sample, ...]else:sample = np.random.choice(N, num_sample-N)dup_data = data[sample, ...]
#        return np.concatenate([data, dup_data], 0), list(range(N))+list(sample)return np.concatenate([data, dup_data], 0)def creat_pcd_rgba(data,path):#write pcd file
#    path = os.path.join(BASE_DIR, 'out_put_'+str(1)+ '.pcd')if os.path.exists(path):os.remove(path)Output_Data = open(path, 'a')# headersOutput_Data.write('# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z rgba\nSIZE 4 4 4 4\nTYPE F F F U\nCOUNT 1 1 1 1')string = '\nWIDTH ' + str(data.shape[0])Output_Data.write(string)Output_Data.write('\nHEIGHT 1\nVIEWPOINT 0 0 0 1 0 0 0')string = '\nPOINTS ' + str(data.shape[0])Output_Data.write(string)Output_Data.write('\nDATA ascii')# pack RGBfor j in range(data.shape[0]):string = ('\n' + str(data[j,0]) + ' ' + str(data[j,1]) + ' ' +str(data[j,2]) + ' ' + str(int(data[j,3])))Output_Data.write(string)Output_Data.close()def creat_pcd_rgb(data,path):if os.path.exists(path):os.remove(path)Output_Data = open(path, 'a')# headersOutput_Data.write('# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z rgba\nSIZE 4 4 4 4\nTYPE F F F U\nCOUNT 1 1 1 1')string = '\nWIDTH ' + str(data.shape[0])Output_Data.write(string)Output_Data.write('\nHEIGHT 1\nVIEWPOINT 0 0 0 1 0 0 0')string = '\nPOINTS ' + str(data.shape[0])Output_Data.write(string)Output_Data.write('\nDATA ascii')# pack RGBfor j in range(data.shape[0]):R=data[j,3]G=data[j,4]B=data[j,5]value = (int(R) << 16 | int(G) << 8 | int(B))string = ('\n' + str(data[j,0]) + ' ' + str(data[j,1]) + ' ' +str(data[j,2]) + ' ' + str(value))Output_Data.write(string)Output_Data.close()def creat_txt(data,path):np.savetxt(path,data)data = np.loadtxt("/media/sgl/数据(混合硬盘)/pcl_ubuntu/pcl_test/plant.txt")
#centre
xyz_min = np.min(data[:,0:3],axis=0)
xyz_max = np.max(data[:,0:3],axis=0)
xyz_move = xyz_min+(xyz_max-xyz_min)/2
data[:,0:3] = data[:,0:3]-xyz_move
#scale
scale = np.max(data[:,0:3])
data[:,0:3] = data[:,0:3]/scale
#sample
data = sample_data(data,4096)
creat_pcd_rgba(data,'/media/sgl/数据(混合硬盘)/PointNet/basisnet/out_put_1.pcd')
creat_txt(data,'/media/sgl/数据(混合硬盘)/PointNet/basisnet/out_put_1.txt')

TXT生成PCD文件（Python实现）
代码功能：将保存点云信息的TXT文件转化为pcl可读的pcd文件。

'''
This code is transform .txt to the .pcd.
and this is the original point cloud
2019.5.23
'''import os
import sys
import numpy as npBASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(os.path.dirname(BASE_DIR))
sys.path.append(BASE_DIR)
sys.path.append(ROOT_DIR)
ROOT_DIR = os.path.join(ROOT_DIR, 'pointnet(enhence_11)/sem_seg/log11')colour_map = (255,0,0,0,255,0,0,0,255,156,102,31,255,192,203,255,0,255,0,255,255,255,255,0,51,161,201,128,42,42,48,128,20,160,32,240,255,128,0)def creat_real_pcd(input_path, output_path):# loda dataFull_Data = np.loadtxt(input_path)# creat output fileif os.path.exists(output_path):os.remove(output_path)Output_Data = open(output_path, 'a')# headersOutput_Data.write('# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z rgba\nSIZE 4 4 4 4\nTYPE F F F U\nCOUNT 1 1 1 1')string = '\nWIDTH ' + str(Full_Data.shape[0])Output_Data.write(string)Output_Data.write('\nHEIGHT 1\nVIEWPOINT 0 0 0 1 0 0 0')string = '\nPOINTS ' + str(Full_Data.shape[0])Output_Data.write(string)Output_Data.write('\nDATA ascii')# pack RGBfor j in range(Full_Data.shape[0]):R=Full_Data[j,3]G=Full_Data[j,4]B=Full_Data[j,5]value = (int(R) << 16 | int(G) << 8 | int(B))string = ('\n' + str(Full_Data[j,0]) + ' ' + str(Full_Data[j, 1]) + ' ' +str(Full_Data[j, 2]) + ' ' + str(value))Output_Data.write(string)Output_Data.close()def creat_pred_pcd(input_path, output_path):# loda dataFull_Data = np.loadtxt(input_path)# creat output fileif os.path.exists(output_path):os.remove(output_path)Output_Data = open(output_path, 'a')# headersOutput_Data.write('# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z rgba\nSIZE 4 4 4 4\nTYPE F F F U\nCOUNT 1 1 1 1')string = '\nWIDTH ' + str(Full_Data.shape[0])Output_Data.write(string)Output_Data.write('\nHEIGHT 1\nVIEWPOINT 0 0 0 1 0 0 0')string = '\nPOINTS ' + str(Full_Data.shape[0])Output_Data.write(string)Output_Data.write('\nDATA ascii')# pack RGBfor j in range(Full_Data.shape[0]):index = Full_Data[j,7]R=colour_map[int(index)]G=colour_map[int(index+1)]B=colour_map[int(index+2)]value = (int(R) << 16 | int(G) << 8 | int(B))string = ('\n' + str(Full_Data[j,0]) + ' ' + str(Full_Data[j, 1]) + ' ' +str(Full_Data[j, 2]) + ' ' + str(value))Output_Data.write(string)Output_Data.close()def creat_gt_pcd(input_data_path, input_label_path, output_path):# loda dataFull_Data = np.loadtxt(input_data_path)Label_Data = np.loadtxt(input_label_path)# creat output fileif os.path.exists(output_path):os.remove(output_path)Output_Data = open(output_path, 'a')# headersOutput_Data.write('# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z rgba\nSIZE 4 4 4 4\nTYPE F F F U\nCOUNT 1 1 1 1')string = '\nWIDTH ' + str(Full_Data.shape[0])Output_Data.write(string)Output_Data.write('\nHEIGHT 1\nVIEWPOINT 0 0 0 1 0 0 0')string = '\nPOINTS ' + str(Full_Data.shape[0])Output_Data.write(string)Output_Data.write('\nDATA ascii')# pack RGBfor j in range(Full_Data.shape[0]):index = Label_Data[j]R=colour_map[int(index)]G=colour_map[int(index+1)]B=colour_map[int(index+2)]value = (int(R) << 16 | int(G) << 8 | int(B))string = ('\n' + str(Full_Data[j,0]) + ' ' + str(Full_Data[j, 1]) + ' ' +str(Full_Data[j, 2]) + ' ' + str(value))Output_Data.write(string)Output_Data.close()def creat_err_pcd(input_data_path, input_label_path, output_path):# loda dataFull_Data = np.loadtxt(input_data_path)Label_Data = np.loadtxt(input_label_path)# creat output fileif os.path.exists(output_path):os.remove(output_path)Output_Data = open(output_path, 'a')# headersOutput_Data.write('# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z rgba\nSIZE 4 4 4 4\nTYPE F F F U\nCOUNT 1 1 1 1')string = '\nWIDTH ' + str(Full_Data.shape[0])Output_Data.write(string)Output_Data.write('\nHEIGHT 1\nVIEWPOINT 0 0 0 1 0 0 0')string = '\nPOINTS ' + str(Full_Data.shape[0])Output_Data.write(string)Output_Data.write('\nDATA ascii')# pack RGBfor j in range(Full_Data.shape[0]):index = int(Label_Data[j])index1 = int(Full_Data[j,7])if (index != index1):R=int(255)G=int(0)B=int(0)else:R=int(234)G=int(234)B=int(234)value = (int(R) << 16 | int(G) << 8 | int(B))string = ('\n' + str(Full_Data[j,0]) + ' ' + str(Full_Data[j, 1]) + ' ' +str(Full_Data[j, 2]) + ' ' + str(value))Output_Data.write(string)Output_Data.close()if __name__=='__main__': OUTPUT_PATH_LIST = [os.path.join(ROOT_DIR,line.rstrip()) for line in open(os.path.join(ROOT_DIR, 'output_filelist.txt'))]for i in range(len(OUTPUT_PATH_LIST)):print('Processing: %d/%d'%(i,len(OUTPUT_PATH_LIST)))input_data_path = OUTPUT_PATH_LIST[i]input_label_path = (OUTPUT_PATH_LIST[i])[:-8] + 'gt.txt'output_real_path = os.path.join(ROOT_DIR,'PCD_file/epoch_80', os.path.basename(OUTPUT_PATH_LIST[i])[:-8] + 'real.pcd')output_pred_path = os.path.join(ROOT_DIR,'PCD_file/epoch_80', os.path.basename(OUTPUT_PATH_LIST[i])[:-8] + 'pred.pcd')output_gt_path = os.path.join(ROOT_DIR,'PCD_file/epoch_80', os.path.basename(OUTPUT_PATH_LIST[i])[:-8] + 'gt.pcd')output_err_path = os.path.join(ROOT_DIR,'PCD_file/epoch_80', os.path.basename(OUTPUT_PATH_LIST[i])[:-8] + 'err.pcd')creat_real_pcd(input_data_path, output_real_path)creat_pred_pcd(input_data_path, output_pred_path)creat_gt_pcd(input_data_path, input_label_path, output_gt_path)creat_err_pcd(input_data_path, input_label_path, output_err_path)

循环可视化文件夹下的所有PCD文件
程序功能：显示文件夹下的所有PCD文件（包含有获取文件夹下所有文件名）

#include <pcl/point_cloud.h>
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>
#include <pcl/octree/octree.h>
#include <pcl/features/integral_image_normal.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/point_types.h>
#include <pcl/features/normal_3d.h>
#include <iostream>
#include <vector>
#include <ctime>
#include <pcl/io/ply_io.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>
#include <pcl/io/vtk_lib_io.h>
#include <pcl/io/vtk_io.h>
#include <vtkPolyData.h>
#include <vtkSmartPointer.h>
#include <io.h>
#include<vector>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/filters/conditional_removal.h>
#include <pcl/filters/filter.h>
#include <pcl/common/impl/io.hpp>
#include <pcl/filters/passthrough.h>
#include<pcl\filters\radius_outlier_removal.h>
#include <pcl/filters/voxel_grid.h>using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;
using namespace std;void getFiles(string path, vector<string>& files)
{//文件句柄  long   hFile = 0;//文件信息  struct _finddata_t fileinfo;string p;if ((hFile = _findfirst(p.assign(path).append("\\*").c_str(), &fileinfo)) != -1){do{//如果是目录,迭代之  //如果不是,加入列表  if ((fileinfo.attrib &  _A_SUBDIR)){if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)getFiles(p.assign(path).append("\\").append(fileinfo.name), files);}else{files.push_back(p.assign(path).append("\\").append(fileinfo.name));}} while (_findnext(hFile, &fileinfo) == 0);_findclose(hFile);}
}int main()
{vector<string> files;char * filePath = "F:\\S3DIS\\pointnet(enhence_pcd_result)\\find";获取该路径下的所有文件  getFiles(filePath, files);char str[30];int size = files.size();for (int i = 0; i < size; i++){cout << files[i].c_str() << endl;pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGBA>);pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_filtered_1(new pcl::PointCloud<pcl::PointXYZRGBA>);//string path = files[i].c_str;pcl::io::loadPCDFile(files[i], *cloud);// 植物在xyz大方向上的杂点去除pcl::PassThrough<pcl::PointXYZRGBA> pass;pass.setInputCloud(cloud);pass.setFilterFieldName("y");pass.setFilterLimits(0.0, 2.5);    // 调节该处参数即可 0 是上 0.75是下 这个是铜钱草的高度设置 2018-9-5 15:28:03pass.filter(*cloud_filtered_1);//pass.setInputCloud(cloud_filtered_1);//pass.setFilterFieldName("x");//pass.setFilterLimits(-18, -3);//pass.filter(*cloud_filtered_12);//创建PCL的可视化窗口  2016-8-6 12:54:27pcl::visualization::PCLVisualizer viewer("PCL Viewer");//设置框内背景颜色，1.0相当于255。  2016-8-1 16:40:15viewer.setBackgroundColor(1.0, 1.0, 1.0);//将新点云呈现在窗口中viewer.addPointCloud(cloud_filtered_1, "PCL Viewer");viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 6, "PCL Viewer");//viewer.addCoordinateSystem(1);//将新点云呈现在窗口中  2016-8-1 17:23:28while (!viewer.wasStopped()){viewer.spinOnce();}}return 0;}

pcd2txt

"""
Created on Tue Oct 29 22:43:59 2019@author: sgl
"""
import os
from os import listdir, pathpath_str = '/media/david/pcd'  # your directory path
txts = [f for f in listdir(path_str)if f.endswith('.pcd') and path.isfile(path.join(path_str, f))]for txt in txts:with open(os.path.join(path_str, txt), 'r') as f:lines = f.readlines()with open(os.path.join(path_str,os.path.splitext(txt)[0]+".txt"), 'w') as f:f.write(''.join(lines[11:]))

参考链接：
https://blog.csdn.net/SGL_LGS/article/details/101382997
https://blog.csdn.net/SGL_LGS/article/details/105890437

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