WADDA¶

Wind’s Autonomous Driving Development Art

This repository provides some handy tools and useful libraries

Tools	Description
pcd visualizer	view point cloud files
gif generator	generate gif from image or point cloud
data collection	record data via ros
voc2coco	convert standard voc format dataset to coco format
ros visualizer	convert the common message format of ros to marker for visualization

Libs	Description
pypcd	libraries for working with point clouds

Install¶

pip3 install wadda

The following is a brief introduction to its simple usage. For more advanced usage, please refer to its documentation.

Simple Usage¶

wadda [function_name] [path]

pcd visualizer¶

path : pcd file or the folder containing the pcd file
tricks：If viewing a folder containing pcd files, use the “space” and “z” keys to control viewing the next and previous frames, and the “q” key to exit

wadda pcd . # view pcd file or pcd folder

data collection¶

path：Specify the path where you want the data to be stored
pro：For advanced usage, please refer to the doc

wadda dc . # By filtering the default ros message type, and then store the data

gif generator¶

All folders under this path will be traversed. If a folder contains images or point cloud files, a gif will be generated with the name of the folder and stored in its parent directory

path：Want to traverse the root path of the generated gif

wadda gif . # generator gif from specify path

voc2coco¶

path：The path where the standard voc format data set is located

path
├── Annotations
├── coco
├── ImageSets
├── JPEGImages
└── labels.txt

labels.txt ：The labels.txt file must be included, and its content is the name of the category, which is used to map from class name to label id when converting to coco
```
class_name_0
class_name_1
class_name_2
...
```

wadda v2c . # convert voc to coco

ros visualizer¶

pro：For advanced usage, please refer to the doc

wadda ros # start ros visualizer for converting ros msg

pypcd¶

from wadda import pypcd
# parse ros pointcloud2 data
pc = pypcd.PointCloud.from_msg(data)
x = pc.pc_data['x']
y = pc.pc_data['y']
z = pc.pc_data['z']

# parse pcd format file
pc = pypcd.PointCloud.from_path('foo.pcd')
x = pc.pc_data['x']
y = pc.pc_data['y']
z = pc.pc_data['z']