Dive into the world of computer vision with this comprehensive Python project that brings stereo matching to life. Designed for enthusiasts and professionals alike, this project leverages cutting-edge algorithms to generate precise depth maps from stereo image pairs. Whether you're exploring autonomous systems, 3D reconstruction, or just expanding your knowledge, this project offers a hands-on approach to mastering stereo vision.
This code was tested with Python 3.8.3.
git clone https://github.com/june65/StereoMatchingPro
pip install -r requirements.txt
Middlebury Computer Vision Pages provide datasets and benchmarks for computer vision research, including stereo vision, optical flow, and multi-view stereo. Download datasets at link here.
We demonstrate the precision of our stereo matching algorithms using the benchmark Tsukuba dataset.
python main.py --costmethod AD --rgbexpand True
python main.py --costmethod SD --rgbexpand True
python main.py --costmethod SAD --costwindow 3
python test.py --costmethod SAD --costwindow 33
python main.py --costmethod SSD --costwindow 3
python test.py --costmethod SSD --costwindow 33
python main.py --costmethod ASW --costwindow 33
python test.py --costmethod ASW --costwindow 51
python main.py --costmethod SGM
python main.py --costmethod AD --graphcut True
python main.py --costmethod SAD --costwindow 3 --lrcheck True
python main.py --costmethod ASW --costwindow 27 --treefilter True --lrcheck True
python main.py --costmethod ASW --costwindow 33 --midfilter 5
ASW + Left Right Consistency Check + Tree Filtering + Weighted Median Filter
python main.py --costmethod ASW --costwindow 33 --lrcheck True --treefilter True --midfilter 5
Stereo Matching With Fusing Adaptive Support Weights
Stereo Processing by Semi-Global Matching and Mutual Information