Awesome socc_plotter created by AdityaNG
Project description
socc_plotter
Semantic Occupancy 3D Plotter. This is the plotter made by the SOccDPT project to create fancy 3D visuals. You can use this for your own AV or Robotics visualization!
Install it from PyPI
pip install socc_plotter
Usage
The socc_plotter works on a callback mechanism since the GUI must be run on the main thread.
from socc_plotter.plotter import Plotter
import time
def callback(plot: Plotter):
time.sleep(1)
print("in callback")
graph_region = plot.graph_region
points = np.array([[1, 0, 0]])
colors = np.array([[1, 1, 1]])
graph_region.setData(pos=points, color=colors)
plotter = Plotter(
callback=callback,
)
plotter.start()
NuScenes Demo
Start by downloading the NuScenes mini datset
mkdir -p data/nuscenes/
cd data/nuscenes/
wget -c https://www.nuscenes.org/data/v1.0-mini.tgz
tar -xf v1.0-mini.tgz
Install a few dependencies for the demo
pip install nuscenes-devkit==1.1.10
pip install transformers torch torchvision timm accelerate general_navigation
Run the demo
$ python -m socc_plotter
#or
$ socc_plotter
Development
Read the CONTRIBUTING.md file.
Cite
Cite our work if you find it useful
@article{NG2024SOccDPT,
title={SOccDPT: 3D Semantic Occupancy from Dense Prediction Transformers trained under memory constraints},
author={NG, Aditya},
journal={Advances in Artificial Intelligence and Machine Learning},
volume={ISSN: 2582-9793, Source Id: 21101164612},
year={2024},
url={https://www.oajaiml.com/}
}
TODO
- Demo
- RGB Frame
- Depth perception
- Semantic segmentation
- NuScenes Calibration
- NuScenes Vehicle trajectory
- Semantic Occupancy Grid
- Ensure demo dependencies are seperate from the module
- Demo is to prompt the user to install dependencies
- Demo is to auto download NuScenes and unarchive it
- Test Cases
- PiPy deployment
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