Python tools for working with PedX dataset.
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.data/labels
. 2D/3D annotations are provided in an instance-level.20171130T2000
, 20171207T2024
, 20171212T2030
ylw79D0
, red707B
, blu79CF
, grn43E3
ylw79D0-red707B
, blu79CF-grn43E3
(left-right camera)demo.py
. pedx
provides Python helper functions to load and visualize the data. We have tested the script with the Python packages listed in requirements.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/