The Graph-Cut RANSAC algorithm proposed in paper: Daniel Barath and Jiri Matas; Graph-Cut RANSAC, Conference on Computer Vision and Pattern Recognition, 2018. It is available at http://openaccess.thecvf.com/content_cvpr_2018/papers/Barath_Graph-Cut_RANSAC_CVPR_2018_paper.pdf

To build and install C++ only GraphCutRANSAC, clone or download this repository and then build the project by CMAKE.

$ git clone https://github.com/danini/graph-cut-ransac
$ cd build
$ cmake ..
$ make

python3 ./setup.py install

or

pip3 install -e .

To build the sample project showing examples of fundamental matrix, homography and essential matrix fitting, set variable CREATE_SAMPLE_PROJECT = ON when creating the project in CMAKE. Then

$ cd build
$ ./SampleProject

• Eigen 3.0 or higher
• CMake 2.8.12 or higher
• OpenCV 3.0 or higher
• A modern compiler with C++17 support

import pygcransac
h1, w1 = img1.shape
h2, w2 = img2.shape
H, mask = pygcransac.findHomography(src_pts, dst_pts, h1, w1, h2, w2, 3.0)
F, mask = pygcransac.findFundamentalMatrix(src_pts, dst_pts, h1, w1, h2, w2, 3.0)

See also this notebook

• Python 3
• CMake 2.8.12 or higher
• OpenCV 3.4
• A modern compiler with C++11 support

When using the algorithm, please cite

@inproceedings{GCRansac2018,
	author = {Barath, Daniel and Matas, Jiri},
	title = {Graph-cut {RANSAC}},
	booktitle = {Conference on Computer Vision and Pattern Recognition},
	year = {2018},
}

If you use it together with DEGENSAC or PROSAC sampling, please cite

@inproceedings{Degensac2005,
	author = {Chum, Ondrej and Werner, Tomas and Matas, Jiri},
	title = {Two-View Geometry Estimation Unaffected by a Dominant Plane},
	booktitle = {Conference on Computer Vision and Pattern Recognition},
	year = {2005},
}

@inproceedings{PROSAC2005,
	author = {Chum, Ondrej and Matas, Jiri},
	title = {Matching with {PROSAC}-progressive sample consensus},
	booktitle = {Conference on Computer Vision and Pattern Recognition},
	year = {2005},
}

The Python wrapper part is based on the great Benjamin Jack python_cpp_example.