This repository contains an Python wrapper of RANSAC for homography and fundamental matrix estimation from sparse correspondences. It implements LO-RANSAC and DEGENSAC.

It was originally located in https://github.com/ducha-aiki/pyransac, but was renamed to avoid conflict with already existing pyransac in pypi from other author.

Vanilla pydegensac implementation is marginally better than OpenCV one and with degeneracy-check enabled (DEGENSAC) it is the state of the art, according to the recent study Yin et.al."Image Matching across Wide Baselines: From Paper to Practice", 2020.

For homography, pydegensac is worse than newest OpenCV MAGSAC++ (cv2.USAC_MAGSAC), but better than OpenCV vanilla RANSAC, according to recent Barath et al. A Large Scale Homography Benchmark, CVPR2023

To build and install pydegensac, you can use pip from Windows, macOS and Linux:

pip install pydegensac

Or clone or download this repository and then, from within the repository, run:

python3 ./setup.py install

or

pip3 install .

To check if everything works, run the following:

cd examples
python -utt simple-example.py

You should see the following output:

Running homography estimation
cv2 found 40 inliers
OpenCV runtime 0.02355  sec
pydegensac found 78 inliers
pydegensac runtime 0.00320  sec
H =  [[ 5.59934334e-03 -2.36037104e-03 -2.78369679e+01]
 [ 4.86321171e-02 -1.24542142e-01 -1.00600649e+01]
 [ 1.95536148e-04  9.43300063e-06 -1.76685691e-01]]
Running fundamental matrix estimation
cv2 found 32 inliers
OpenCV runtime 0.67554  sec
pydegensac found 44 inliers
pydegensac 0.04702  sec
F =  [[-7.35044984e-04 -2.72572333e-03  1.38155992e+00]
 [ 1.43946998e-03  2.33120834e-05 -7.88961637e-01]
 [-3.35556093e-01  1.00000000e+00 -1.78675406e+02]]

1. Compiling pydegensac without a system-wide install.

python3 ./setup.py build

2. Compiling on Mac OS X computer Use GCC instead of Clang. The most recent version on my machine (installed via brew) is gcc-8. Try this:

CC=gcc-8 python3 ./setup.py build

3. Compiling on Ubuntu 18.04 You need LAPACK and a few other libraries and I always forget those specific package names. Take a look at my pydegensac Dockerfile to see the exact packages you need to apt install on an Ubuntu 18.04 system (https://github.com/quantombone/pydegensac-dockerfile/blob/master/Dockerfile)

FROM ubuntu:18.04

RUN apt-get clean
RUN apt-get update
RUN apt-get install -qy \
    git python3 python3-setuptools python3-dev
RUN apt-get install -y cmake libblas-dev liblapack-dev gfortran
RUN apt-get install -y g++ gcc

RUN git clone https://github.com/ducha-aiki/pydegensac.git
WORKDIR pydegensac
RUN python3 ./setup.py build

CMD cp -R /pydegensac/build/lib.linux-x86_64-3.6/pydegensac /target_directory

https://github.com/quantombone/pydegensac-dockerfile

import pydegensac
H, mask = pydegensac.findHomography(src_pts, dst_pts, 3.0)
F, mask = pydegensac.findFundamentalMatrix(src_pts, dst_pts, 3.0)

See also this notebook with simple example

And this notebook with detailed explanation of possible options

• Python 3
• CMake 2.8.12 or higher
• LAPACK,
• BLAS (OpenBLAS, MKL, Atlas, ...)
• A modern compiler with C++11 support

Please cite us if you use this code:

@InProceedings{Chum2003,
author="Chum, Ond{\v{r}}ej and Matas, Ji{\v{r}}{\'i} and Kittler, Josef",
title="Locally Optimized RANSAC",
booktitle="Pattern Recognition",
year="2003",
}

@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},
}

@article{Mishkin2015MODS,
      title = "MODS: Fast and robust method for two-view matching ",
      journal = "Computer Vision and Image Understanding ",
      year = "2015",
      issn = "1077-3142",
      doi = "http://dx.doi.org/10.1016/j.cviu.2015.08.005",
      url = "http://www.sciencedirect.com/science/article/pii/S1077314215001800",
      author = "Dmytro Mishkin and Jiri Matas and Michal Perdoch"
}

This wrapper part is based on great Benjamin Jack python_cpp_example.