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 pydegensacOr clone or download this repository and then, from within the repository, run:
python3 ./setup.py installor
pip3 install .To check if everything works, run the following:
cd examples
python -utt simple-example.pyYou 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]]
- Compiling pydegensac without a system-wide install.
python3 ./setup.py build- 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- 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:18.04RUN 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++ gccRUN 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.