python_kitti_激光点云变换到图像平面并保存成int16灰度图

男娘i 2023-01-11 09:06 349阅读 0赞

**目录**

说明:

输入:

输出:

坐标系:

变换公式:

代码:

结果:

参考:

--------------------

# 说明: #

*  将kitti数据集中 雷达点云图像投影到camera图像平面,
 *  并生成 深度图的灰度图(灰度值=深度x256 保存成int16位图像(kitti中 depth benchmark的做法))

## 输入: ##

*  P\_rect\_02: camera02相机内参
 *  **R\_rect\_00****:** 3x3 纠正**旋转矩阵**（使图像平面共面）(kitti特有的)
 *  Tr\_velo\_to\_cam: 激光雷达到camera00的变换矩阵

## 输出: ##

*  投影图
 *  深度图的灰度图

## 坐标系: ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NoeWpoeXAxMQ_size_16_color_FFFFFF_t_70][]

## 变换公式: ##

以下等式说明了如何使用齐次坐标在相机0的图像平面上将空间中的3D激光雷达点X投影到2D像素点Y（使用Kitti自述文件中的表示法）：

![20210126170746609.png][]

*  `RT_velo_to_cam * x` ：是将Velodyne坐标中的点x投影到编号为0的相机（参考相机）坐标系中
 *  `R_rect00 *RT_velo_to_cam * x` ：是将Velodyne坐标中的点x投影到编号为0的相机（参考相机）坐标系中, 再以参考相机0为基础进行图像共面对齐修正(这是使用KITTI数据集的进行3D投影的必要操作)
 *  `P_rect_00 * R_rect00 *RT_velo_to_cam * x` ：是将Velodyne坐标中的点x投影到编号为0的相机（参考相机）坐标系中, 再进行图像共面对齐修正, 然后投影到相机0的像素坐标系中. 如果将P\_rect\_00改成P\_rect\_2, 也就是从参考相机0投影到相机2的像素坐标系中(其他相机相对与相机0有偏移b(i)).
 *  原始论文:[ Vision meets Robotics: The KITTI Dataset][Vision meets Robotics_ The KITTI Dataset]

# 代码: #

# -*- coding: utf-8 -*-
    
    #  数据来源: calib_cam_to_cam.txt 
    #  下载链接: http://www.cvlibs.net/datasets/kitti/raw_data.php?type=road  >  2011_10_03_drive_0047  >  [calibration]
    # R_rect_00: 9.999454e-01 7.259129e-03 -7.519551e-03 -7.292213e-03 9.999638e-01 -4.381729e-03 7.487471e-03 4.436324e-03 9.999621e-01
    ## P_rect_00: 7.188560e+02 0.000000e+00 6.071928e+02 0.000000e+00 0.000000e+00 7.188560e+02 1.852157e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00
    # ...
    ## R_rect_02: 9.999191e-01 1.228161e-02 -3.316013e-03 -1.228209e-02 9.999246e-01 -1.245511e-04 3.314233e-03 1.652686e-04 9.999945e-01
    # P_rect_02: 7.188560e+02 0.000000e+00 6.071928e+02 4.538225e+01 0.000000e+00 7.188560e+02 1.852157e+02 -1.130887e-01 0.000000e+00 0.000000e+00 1.000000e+00 3.779761e-03
    
    #  数据来源: calib_velo_to_cam.txt
    #  下载链接: http://www.cvlibs.net/datasets/kitti/raw_data.php?type=road  >  2011_10_03_drive_0047  >  [calibration]
    #   calib_time: 15-Mar-2012 11:45:23
    #   R: 7.967514e-03 -9.999679e-01 -8.462264e-04 -2.771053e-03 8.241710e-04 -9.999958e-01 9.999644e-01 7.969825e-03 -2.764397e-03
    #   T: -1.377769e-02 -5.542117e-02 -2.918589e-01
    
    
    # # png bin来源
    # data_odometry_color/dataset/sequences/00/image_2
    # data_odometry_velodyne/dataset/sequences/00/velodyne
    
    import sys
    import matplotlib.pyplot as plt
    import matplotlib.image as mpimg
    import numpy as np
    import utils
    from PIL import Image
    import math
    
    
    #-----------------------------------相机02内参矩阵-----------------------------------
    P_rect_02 = np.array( [ 7.188560000000e+02, 0.000000000000e+00, 6.071928000000e+02, 4.538225000000e+01, 
                                                    0.000000000000e+00,7.188560000000e+02, 1.852157000000e+02, -1.130887000000e-01,
                                                    0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 3.779761000000e-03]).reshape((3,4))
    
    R_rect_00 = np.array( [ 9.999454e-01, 7.259129e-03, -7.519551e-03,
                                                    -7.292213e-03, 9.999638e-01, -4.381729e-03, 
                                                    7.487471e-03, 4.436324e-03, 9.999621e-01]).reshape((3,3))
    
    # R_rect_02 = np.array( [ 9.999191e-01, 1.228161e-02 -3,.316013e-03,
    #                                                 -1.228209e-02, 9.999246e-01, -1.245511e-04, 
    #                                                 3.314233e-03, 1.652686e-04, 9.999945e-01]).reshape((3,3))
    
    
    #velo激光雷达 到 相机00(此处已知条件重点注意) 的变换矩阵
    Tr_velo_to_cam = np.array( [    7.967514e-03, -9.999679e-01, -8.462264e-04, -1.377769e-02,
                                                                    -2.771053e-03, 8.241710e-04, -9.999958e-01, -5.542117e-02,
                                                                    9.999644e-01, 7.969825e-03, -2.764397e-03, -2.918589e-01]).reshape((3,4))    
    
    
    #-----------------------------------数据文件位置---------------------------------------
    velo_files = "./data/00_velodyne/000005.bin"
    rgbimg = "./data/00_image_02/000005.png"
    resultImg = "./data/result_merge.png"
    
    data = {}
    data['P_rect_20'] = P_rect_02
    # Compute the velodyne to rectified camera coordinate transforms
    data['T_cam0_velo'] = Tr_velo_to_cam
    data['T_cam0_velo'] = np.vstack([data['T_cam0_velo'], [0, 0, 0, 1]])
    
    # pattern1:
    R_rect_00 = np.insert(R_rect_00,3,values=[0,0,0],axis=0)
    R_rect_00 = np.insert(R_rect_00,3,values=[0,0,0,1],axis=1)
    data['T_cam2_velo'] = R_rect_00.dot(data['T_cam0_velo']) #雷达 到 相机02的变换矩阵
    print(data['T_cam2_velo'])
    
    pointCloud = utils.load_velo_scan(velo_files)   #读取lidar原始数据
    points = pointCloud[:, 0:3]                                           # 获取 lidar xyz (front, left, up)
    points_homo = np.insert(points,3,1,axis=1).T    # 齐次化,并转置(一列表示一个点(x,y,z,1), 多少列就有多少个点)
    points_homo = np.delete(points_homo,np.where(points_homo[0,:]<0),axis=1) #以列为基准, 删除深度x=0的点
    
    proj_lidar = data['P_rect_20'].dot( data['T_cam2_velo'] ).dot(points_homo)  #相机坐标系3D点=相机02内参*雷达到激光的变换矩阵*雷达3D点
    cam = np.delete(proj_lidar,np.where(proj_lidar[2,:]<0),axis=1)  #以列为基准, 删除投影图像点中深度z<0(在投影图像后方)的点 #3xN
    cam[:2,:] /= cam[2,:]   # 等价写法 cam[:2] /= cam[2] # 前两行元素分布除以第三行元素(归一化到相机坐标系z=1平面)(x=x/z, y =y/z)
    
    
    # -----------------------------------将激光投影点绘制到图像平面:绘制原图------------------------------------
    png = mpimg.imread(rgbimg)
    IMG_H,IMG_W,_ = png.shape
    plt.figure(figsize=((IMG_W)/72.0,(IMG_H)/72.0),dpi=72.0, tight_layout=True)
    # restrict canvas in range
    plt.axis([0,IMG_W,IMG_H,0])
    # plt.axis('off') 
    plt.imshow(png) #在画布上画出原图
    
    # filter point out of canvas
    u,v,z = cam
    u_out = np.logical_or(u<0, u>IMG_W)
    v_out = np.logical_or(v<0, v>IMG_H)
    outlier = np.logical_or(u_out, v_out)
    cam = np.delete(cam,np.where(outlier),axis=1)
    # generate color map from depth
    u,v,z = cam
    
    # 将激光投影点绘制到图像平面:绘制激光深度散点图
    plt.scatter([u],[v],c=[z],cmap='rainbow_r',alpha=0.5,s=1)
    plt.title("projection")
    plt.savefig(resultImg,bbox_inches='tight')
    plt.show()  #在画布上画出散点雷达深度投影
    
    
    #-----------------------------------单独保存深度图像成灰度图像---------------------------------------------------
    image_array = np.zeros((IMG_H, IMG_W), dtype=np.int16)
    for i in range(cam.shape[1]):
        x = int(round(u[i]))
        y = int(round(v[i]))
    
        # x = math.ceil(u[i]) #向上取整
        # y = math.ceil(v[i])
    
        depth =  int(z[i]*256)
        if 0<x<image_array.shape[1] and 0<y<image_array.shape[0]:
            image_array[y,x] = depth
    
    image_pil = Image.fromarray(image_array, 'I;16')   
    image_pil.save("result_16.png")
    
    
    
    print("done")

# 结果: #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NoeWpoeXAxMQ_size_16_color_FFFFFF_t_70 1][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NoeWpoeXAxMQ_size_16_color_FFFFFF_t_70 2][]

# 参考: #

*  [自动驾驶视觉融合-相机校准与激光点云投影][-]
 *  [KITTI激光雷达点云解析与图像反投影][KITTI]
 *  [https://github.com/azureology/kitti-velo2cam][https_github.com_azureology_kitti-velo2cam]
 *  python3.6 > sit-packages>**pykitti**>odometry.py

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[20210126170746609.png]: /images/20221119/4ce0117207f8497392f1a1402cdb1b1b.png
[Vision meets Robotics_ The KITTI Dataset]: https://www.researchgate.net/publication/258140919_Vision_meets_robotics_the_KITTI_dataset
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NoeWpoeXAxMQ_size_16_color_FFFFFF_t_70 1]: /images/20221119/0aa16a8c710d44f391ea0aac553798c5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NoeWpoeXAxMQ_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20210126170558422.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NoeWpoeXAxMQ==,size_16,color_FFFFFF,t_70
[-]: https://zhuanlan.zhihu.com/p/136263753
[KITTI]: https://www.cnblogs.com/azureology/p/14004131.html
[https_github.com_azureology_kitti-velo2cam]: https://github.com/azureology/kitti-velo2cam