ONNX-CoEx-Stereo-Depth-estimation

Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

Stereo depth estimation on the cones images from the Middlebury dataset (https://vision.middlebury.edu/stereo/data/scenes2003/)

Requirements

• OpenCV, imread-from-url, onnx and onnxruntime. Also, pafy and youtube-dl are required for youtube video inference.

Installation

pip install -r requirements.txt
pip install pafy youtube-dl

ONNX model

The original models were converted to different formats (including .onnx) by PINTO0309, download the models from his repository and save them into the models folder.

Original Pytorch model

The Pytorch pretrained model was taken from the original repository.

Examples

• Image inference:

python imageDepthEstimation.py 

• Video inference:

python videoDepthEstimation.py

• DrivingStereo dataset inference:

python drivingStereoTest.py

Inference video Example

References:

• CoEx model: https://github.com/antabangun/coex
• PINTO0309's model zoo: https://github.com/PINTO0309/PINTO_model_zoo
• PINTO0309's model conversion tool: https://github.com/PINTO0309/openvino2tensorflow
• DrivingStereo dataset: https://drivingstereo-dataset.github.io/
• Original paper: Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation Authors: Antyanta Bangunharcana, Jae Won Cho, Seokju Lee, In So Kweon, Kyung-Soo Kim, Soohyun Kim IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021