liao961028 / graph-cut-ransac

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

Graph-Cut RANSAC

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

Installation C++

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

Install Python package and compile C++

python3 ./setup.py install

or

pip3 install -e .

Example project

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

Requirements

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

Example of usage in python

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

Requirements

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

Acknowledgements

When using the algorithm, please cite Barath, Daniel, and Matas, Jiří. "Graph-cut RANSAC." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. Python wrapper part is based on great Benjamin Jack python_cpp_example.