This package provides basic tools for working with the PedX dataset [1] in Python.
The dataset is available from the webpage (http://pedx.io/downloads/). You can download the entire dataset by running the script download_data.py
. The data will be organized as in the following directory tree.
pedx/
├── pedx/
├── data/
│ ├── images/
│ │ ├── 20171130T2000/
│ │ ├── 20171207T2024/
│ │ └── 20171212T2030/
│ │ ├── ylw79D0/
│ │ ├── red707B/
│ │ ├── blu79CF/
│ │ └── grn43E3/
│ │ └── 20171212T2030_grn43E3_0001234.jpg
│ ├── pointclouds/
│ │ ├── 20171130T2000/
│ │ ├── 20171207T2024/
│ │ └── 20171212T2030/
│ │ └── 20171212T2030_0001234.ply
│ ├── labels/
│ | ├── 2d/
│ | │ ├── 20171130T2000/
│ | │ ├── 20171207T2024/
│ | │ └── 20171212T2030/
│ | └── 3d/
│ | ├── smpl/
│ | │ ├── 20171130T2000/
│ | │ ├── 20171207T2024/
│ | | └── 20171212T2030/
│ | └── segment/
│ | ├── 20171130T2000/
│ | ├── 20171207T2024/
│ | └── 20171212T2030/
│ ├── calib/
│ │ ├── calib_cam_to_cam_blu79CF-grn43E3.txt
│ │ ├── calib_cam_to_cam_blu79CF-red707B.txt
│ │ ├── calib_cam_to_range_blu79CF.txt
│ │ └── calib_cam_to_range_ylw79D0.txt
│ └── timestamps/
│ ├── timestamps-images-20171130T2000.txt
│ ├── timestamps-images-20171207T2024.txt
│ ├── timestamps-images-20171212T2030.txt
│ ├── timestamps-pointclouds-20171130T2000.txt
│ ├── timestamps-pointclouds-20171207T2024.txt
│ └── timestamps-pointclouds-20171212T2030.txt
├── demo.py
└── README.md
data
contains the rectified images, point clouds, calibrated parameters and frame metadata.- All the manual/automatic annotations are in
data/labels
. 2D/3D annotations are provided in an instance-level. - We provide 3 video sequences captured at different 4-way stop intersections on different dates.
- Capture dates:
20171130T2000
,20171207T2024
,20171212T2030
- Capture dates:
- The cameras are color-coded for our convenience.
- Cameras:
ylw79D0
,red707B
,blu79CF
,grn43E3
- Stereo pairs:
ylw79D0-red707B
,blu79CF-grn43E3
(left-right camera)
- Cameras:
- We provide a simple Python demo script:
demo.py
.pedx
provides Python helper functions to load and visualize the data. We have tested the script with the Python packages listed inrequirements.txt
.
Email: [email protected]
[1] Kim, Wonhui, et al. "Pedx: Benchmark dataset for metric 3d pose estimation of pedestrians in complex urban intersections." IEEE Robotics and Automation Letters (2019). http://pedx.io/