pydegensac

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.

Performance

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.

Installation

To build and install pydegensac, you can use pip:

pip install pydegensac

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

python3 ./setup.py install

or

pip3 install .

Building hints from Tomasz Malisiewicz

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

update system

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

download and build pydegensac

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

copy built assets into target directory (which will be a -v volume)

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

dockerfile

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

Example of usage

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

Requirements

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

Citation

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"
}

Acknowledgements

This wrapper part is based on great Benjamin Jack python_cpp_example.