一、参考资料
kitti2bag代码仓库
二、KITTI数据集之tracking数据集
ROS1结合自动驾驶数据集Kitti开发教程(七)下载图像标注资料并读取显示
1. tracking数据集简介
tracking
tracking任务分为三种类型，分别是Multi-Object Tracking（多目标跟踪）、Multi-Object Tracking and Segmentation（多目标跟踪和分割）和Segmenting and Tracking Every Pixel（分割和像素跟踪）。本文以Multi-Object Tracking（多目标跟踪）为例，展开介绍。
2. 下载label标注数据
label标注数据下载：Download training labels of tracking data set (9 MB)
label标注数据其目录结构如下所示：
├── training
│ └── label_02
│ ├── 0000.txt
│ ├── 0001.txt
│ ├── 0002.txt
│ ├── 0003.txt
│ ├── 0004.txt
│ ├── 0005.txt
│ ├── 0006.txt
│ ├── 0007.txt
│ ├── 0008.txt
│ ├── 0009.txt
│ ├── 0010.txt
│ ├── 0011.txt
│ ├── 0012.txt
│ ├── 0013.txt
│ ├── 0014.txt
│ ├── 0015.txt
│ ├── 0016.txt
│ ├── 0017.txt
│ ├── 0018.txt
│ ├── 0019.txt
│ └── 0020.txt
label标注数据以xxxx.txt 命名，以0000.txt为例，标注格式如下所示：
0 -1 DontCare -1 -1 -10.000000 219.310000 188.490000 245.500000 218.560000 -1000.000000 -1000.000000 -1000.000000 -10.000000 -1.000000 -1.000000 -1.000000
0 -1 DontCare -1 -1 -10.000000 47.560000 195.280000 115.480000 221.480000 -1000.000000 -1000.000000 -1000.000000 -10.000000 -1.000000 -1.000000 -1.000000
0 0 Van 0 0 -1.793451 296.744956 161.752147 455.226042 292.372804 2.000000 1.823255 4.433886 -4.552284 1.858523 13.410495 -2.115488
0 1 Cyclist 0 0 -1.936993 737.619499 161.531951 931.112229 374.000000 1.739063 0.824591 1.785241 1.640400 1.675660 5.776261 -1.675458
0 2 Pedestrian 0 0 -2.523309 1106.137292 166.576807 1204.470628 323.876144 1.714062 0.767881 0.972283 6.301919 1.652419 8.455685 -1.900245
1 -1 DontCare -1 -1 -10.000000 228.120000 183.030000 258.830000 217.340000 -1000.000000 -1000.000000 -1000.000000 -10.000000 -1.000000 -1.000000 -1.000000
1 -1 DontCare -1 -1 -10.000000 59.210000 191.300000 137.370000 227.430000 -1000.000000 -1000.000000 -1000.000000 -10.000000 -1.000000 -1.000000 -1.000000
...
...
...
3. 解析label标注数据
关于label标注数据的字段解释，请参阅：https://github.com/pratikac/kitti/blob/master/readme.tracking.txt。
kitti数据集原始数据集被分为Road，City，Residential， Campus 和 Person。对于3D物体检测，label细分为car，van，truck，pedestrian，pedestrian(sitting)， cyclist， tram 以及 misc 组成。注意，DontCarelabel 表示该区域没有被标注，比如目标物体距离激光雷达太远。为了防止在评估过程中（主要计算precision），将本来是目标物体但是因为某些原因而没有标注的区域统计为假阳性（false positives），评估脚本会自动忽略DontCare区域的预测结果。
4. 读取label标注数据
使用jupyter notebook 工具，读取label标注数据。
# 安装jupyter
pip install jupyter
# 启动jupyter
jupyter notebook
import sys
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
import cv2
import pandas as pd
import numpy as np
import os
LABEL_NAME = ["frame", "track id", "type", "truncated", "occluded", "alpha", "bbox_left", "bbox_top", "bbox_right", "bbox_bottom", "dimensions_height", "dimensions_width", "dimensions_length", "location_x", "location_y", "location_z", "rotation_y"]
BASE_PATH = "/media/yoyo/U/2011_10_03/2011_10_03_drive_0027_sync"
data = pd.read_csv(os.path.join(BASE_PATH, "training/label_02/0000.txt"), header=None, sep=" ")
# 修改列名
data.columns = LABEL_NAME
data
三、加载kitti数据集在rivz中显示
1. 准备KITTI数据集
下载并解压KITTI数据集的 RawData数据。
2011_10_03_drive_0027_sync.zip
2011_10_03_calib.zip
文件目录结构
.
├── 2011_10_03
│ ├── 2011_10_03_drive_0027_sync
│ │ ├── image_00
│ │ ├── image_01
│ │ ├── image_02
│ │ ├── image_03
│ │ ├── oxts
│ │ └── velodyne_points
│ ├── calib_cam_to_cam.txt
│ ├── calib_imu_to_velo.txt
│ └── calib_velo_to_cam.txt
2. KITTI转bag文件
使用kitti2bag将KITTI数据集转换成ROS的bag文件。
2.1 安装依赖
安装kitti2bag之前，需要安装ROS。缺什么就安装什么。
pip install -U numpy
pip install opencv-python
pip install pycryptodomex
pip install gnupg
2.2 安装kitti2bag
# 安装pykitti
pip install pykitti
# 安装kitti2bag
pip install kitti2bag
kitti2bag安装成功
yoyo@yoyo:~$ kitti2bag
usage: kitti2bag [-h] [-t DATE] [-r DRIVE]
[-s {00,01,02,03,04,05,06,07,08,09,10,11,12,13,14,15,16,17,18,19,20,21}]
{raw_synced,odom_color,odom_gray} [dir]
kitti2bag: error: too few arguments
2.3 转换bag文件
# 转换文件
kitti2bag -t 2011_10_03 -r 0027 raw_synced
yoyo@yoyo:/media/yoyo/U$ kitti2bag -t 2011_10_03 -r 0027 raw_synced
Exporting static transformations
Exporting time dependent transformations
Exporting IMU
Exporting camera 0
100% (4544 of 4544) |#####################| Elapsed Time: 0:00:34 Time: 0:00:34
Exporting camera 1
100% (4544 of 4544) |#####################| Elapsed Time: 0:00:38 Time: 0:00:38
Exporting camera 2
100% (4544 of 4544) |#####################| Elapsed Time: 0:01:29 Time: 0:01:29
Exporting camera 3
100% (4544 of 4544) |#####################| Elapsed Time: 0:01:29 Time: 0:01:29
Exporting velodyne data
100% (4544 of 4544) |#####################| Elapsed Time: 0:08:31 Time: 0:08:31
## OVERVIEW ##
path: kitti_2011_10_03_drive_0027_synced.bag
version: 2.0
duration: 7:50s (470s)
start: Oct 03 2011 12:55:34.00 (1317617735.00)
end: Oct 03 2011 13:03:25.83 (1317618205.83)
size: 24.0 GB
messages: 63616
compression: none [18184/18184 chunks]
types: geometry_msgs/TwistStamped [98d34b0043a2093cf9d9345ab6eef12e]
sensor_msgs/CameraInfo [c9a58c1b0b154e0e6da7578cb991d214]
sensor_msgs/Image [060021388200f6f0f447d0fcd9c64743]
sensor_msgs/Imu [6a62c6daae103f4ff57a132d6f95cec2]
sensor_msgs/NavSatFix [2d3a8cd499b9b4a0249fb98fd05cfa48]
sensor_msgs/PointCloud2 [1158d486dd51d683ce2f1be655c3c181]
tf2_msgs/TFMessage [94810edda583a504dfda3829e70d7eec]
topics: /kitti/camera_color_left/camera_info 4544 msgs : sensor_msgs/CameraInfo
/kitti/camera_color_left/image_raw 4544 msgs : sensor_msgs/Image
/kitti/camera_color_right/camera_info 4544 msgs : sensor_msgs/CameraInfo
/kitti/camera_color_right/image_raw 4544 msgs : sensor_msgs/Image
/kitti/camera_gray_left/camera_info 4544 msgs : sensor_msgs/CameraInfo
/kitti/camera_gray_left/image_raw 4544 msgs : sensor_msgs/Image
/kitti/camera_gray_right/camera_info 4544 msgs : sensor_msgs/CameraInfo
/kitti/camera_gray_right/image_raw 4544 msgs : sensor_msgs/Image
/kitti/oxts/gps/fix 4544 msgs : sensor_msgs/NavSatFix
/kitti/oxts/gps/vel 4544 msgs : geometry_msgs/TwistStamped
/kitti/oxts/imu 4544 msgs : sensor_msgs/Imu
/kitti/velo/pointcloud 4544 msgs : sensor_msgs/PointCloud2
/tf 4544 msgs : tf2_msgs/TFMessage
/tf_static 4544 msgs : tf2_msgs/TFMessage
执行结束之后，生成文件 kitti_2011_10_03_drive_0027_synced.bag。
3. rviz可视化bag文件
使用rviz可视化bag文件。
# 打开新终端，启动roscore
roscore
# 打开新终端，启动rviz
rosrun rviz rviz
yoyo@yoyo:~/catkin_ws$ rosrun rviz rviz
[ INFO] [1690354820.980297976]: rviz version 1.12.17
[ INFO] [1690354820.980329927]: compiled against Qt version 5.5.1
[ INFO] [1690354820.980337661]: compiled against OGRE version 1.9.0 (Ghadamon)
[ INFO] [1690354821.111348690]: Stereo is NOT SUPPORTED
[ INFO] [1690354821.111407271]: OpenGl version: 3 (GLSL 1.3).
4. 播放bag文件
rosbag play kitti_2011_10_03_drive_0027_synced.bag
5. 查看bag
三、FAQ
Q：ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type
ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type
解决python3.5无法导入cv2.so的问题
(demo) yoyo@yoyo:~$ python
Python 3.9.6 (default, Aug 18 2021, 19:38:01)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pykitti
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/yoyo/miniconda3/envs/demo/lib/python3.9/site-packages/pykitti/__init__.py", line 5, in <module>
from pykitti.tracking import tracking
File "/home/yoyo/miniconda3/envs/demo/lib/python3.9/site-packages/pykitti/tracking.py", line 12, in <module>
import cv2
ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type
>>>
KeyboardInterrupt
错误原因：
安装ROS时，ROS的环境变量覆盖了Anaconda的环境变量
ROS默认的PYTHONPATH为python2.7
具体参见 /opt/ros/kinetic/_setup_util.py文件中的PYTHONPATH
默认使用 /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so
方法一（亲测有效）
退出Anaconda虚拟环境，在python2.7环境中安装kitti2bag
方法二
在python文件中，清除ROS的环境变量。
import sys
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
(demo) yoyo@yoyo:~$ python
Python 3.9.6 (default, Aug 18 2021, 19:38:01)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys
>>> sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
>>> import cv2
>>>
