This repository contains an Python wrapper of MAGSAC. Daniel Barath, Jana Noskova and Jiri Matas; MAGSAC: Marginalizing sample consensus, CVPR 2019
https://github.com/danini/magsac -- main repo.
If you use the algorithm, please cite
@inproceedings{barath2019magsac,
author = {Barath, Daniel and Matas, Jiri and Noskova, Jana},
title = {MAGSAC: marginalizing sample consensus},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2019},
}
@inproceedings{barath2019magsacplusplus,
author = {Barath, Daniel and Noskova, Jana and Ivashechkin, Maksym and Matas, Jiri},
title = {MAGSAC++, a fast, reliable and accurate robust estimator},
booktitle = {arXiv preprint:1912.05909},
year = {2019},
}
If you use it for fundamental matrix estimation with DEGENSAC turned on, please cite
@inproceedings{Chum2005,
author = {Chum, Ondrej and Werner, Tomas and Matas, Jiri},
title = {Two-View Geometry Estimation Unaffected by a Dominant Plane},
booktitle = {CVPR},
year = {2005},
}
Magsac is the state of the art according to the recent study Yin et.al."Image Matching across Wide Baselines: From Paper to Practice", 2020.
To build and install python_cpp_example
, clone or download this repository and then, from within the repository, run:
python3 ./setup.py install
or
pip3 install .
import pymagsac
H, mask = pymagsac.findHomography(src_pts, dst_pts, 3.0)
F, mask = pymagsac.findFundamentalMatrix(src_pts, dst_pts, 3.0)
See also this notebook
- Python 3
- CMake 2.8.12 or higher
- OpenCV 3.4
- A modern compiler with C++14 support
This wrapper part is based on great Benjamin Jack python_cpp_example
.