$\color{red}{\textnormal{Image\ Matching\ WebUI}}$
Identify matching points between two images

Description

This simple tool efficiently matches image pairs using multiple famous image matching algorithms. The tool features a Graphical User Interface (GUI) designed using gradio. You can effortlessly select two images and a matching algorithm and obtain a precise matching result. Note: the images source can be either local images or webcam images.

Try it on

Here is a demo of the tool:

https://github.com/Vincentqyw/image-matching-webui/assets/18531182/263534692-c3484d1b-cc00-4fdc-9b31-e5b7af07ecd9

The tool currently supports various popular image matching algorithms, namely:

• [x] XFeat, CVPR 2024
• [x] RoMa, CVPR 2024
• [x] DeDoDe, 3DV 2024
• [ ] Mickey, CVPR 2024
• [ ] GIM, ICLR 2024
• [x] LightGlue, ICCV 2023
• [x] DarkFeat, AAAI 2023
• [ ] ASTR, CVPR 2023
• [ ] SEM, CVPR 2023
• [ ] DeepLSD, CVPR 2023
• [x] GlueStick, ICCV 2023
• [ ] ConvMatch, AAAI 2023
• [x] LoFTR, CVPR 2021
• [x] SOLD2, CVPR 2021
• [ ] LineTR, RA-L 2021
• [x] DKM, CVPR 2023
• [ ] NCMNet, CVPR 2023
• [x] TopicFM, AAAI 2023
• [x] AspanFormer, ECCV 2022
• [x] LANet, ACCV 2022
• [ ] LISRD, ECCV 2022
• [ ] REKD, CVPR 2022
• [x] ALIKE, TMM 2022
• [x] RoRD, IROS 2021
• [x] SGMNet, ICCV 2021
• [x] SuperPoint, CVPRW 2018
• [x] SuperGlue, CVPR 2020
• [x] D2Net, CVPR 2019
• [x] R2D2, NeurIPS 2019
• [x] DISK, NeurIPS 2020
• [ ] Key.Net, ICCV 2019
• [ ] OANet, ICCV 2019
• [x] SOSNet, CVPR 2019
• [x] HardNet, NeurIPS 2017
• [x] SIFT, IJCV 2004

How to use

HuggingFace / Lightning AI

Just try it on

or deploy it locally following the instructions below.

Requirements

git clone --recursive https://github.com/Vincentqyw/image-matching-webui.git
cd image-matching-webui
conda env create -f environment.yaml
conda activate imw

or using docker:

docker pull vincentqin/image-matching-webui:latest
docker run -it -p 7860:7860 vincentqin/image-matching-webui:latest python app.py --server_name "0.0.0.0" --server_port=7860

Run demo

python3 ./app.py

then open http://localhost:7860 in your browser.

Add your own feature / matcher

I provide an example to add local feature in hloc/extractors/example.py. Then add feature settings in confs in file hloc/extract_features.py. Last step is adding some settings to matcher_zoo in file common/config.yaml.

Contributions welcome!

External contributions are very much welcome. Please follow the PEP8 style guidelines using a linter like flake8 (reformat using command python -m black .). This is a non-exhaustive list of features that might be valuable additions:

• [x] add webcam support
• [x] add line feature matching algorithms
• [x] example to add a new feature extractor / matcher
• [x] ransac to filter outliers
• [ ] add rotation images options before matching
• [ ] support export matches to colmap (#issue 6)
• [ ] add config file to set default parameters
• [ ] dynamically load models and reduce GPU overload

Adding local features / matchers as submodules is very easy. For example, to add the GlueStick:

git submodule add https://github.com/cvg/GlueStick.git third_party/GlueStick

If remote submodule repositories are updated, don't forget to pull submodules with git submodule update --remote, if you only want to update one submodule, use git submodule update --remote third_party/GlueStick.

Contributors

Resources

• Image Matching: Local Features & Beyond
• Long-term Visual Localization

Acknowledgement

This code is built based on Hierarchical-Localization. We express our gratitude to the authors for their valuable source code.