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:
imw.mp4
The tool currently supports various popular image matching algorithms, namely:
- MINIMA, ARXIV 2024
- XoFTR, CVPR 2024
- EfficientLoFTR, CVPR 2024
- MASt3R, CVPR 2024
- DUSt3R, CVPR 2024
- OmniGlue, CVPR 2024
- XFeat, CVPR 2024
- RoMa, CVPR 2024
- DeDoDe, 3DV 2024
- Mickey, CVPR 2024
- GIM, ICLR 2024
- ALIKED, ICCV 2023
- LightGlue, ICCV 2023
- DarkFeat, AAAI 2023
- SFD2, CVPR 2023
- IMP, CVPR 2023
- ASTR, CVPR 2023
- SEM, CVPR 2023
- DeepLSD, CVPR 2023
- GlueStick, ICCV 2023
- ConvMatch, AAAI 2023
- LoFTR, CVPR 2021
- SOLD2, CVPR 2021
- LineTR, RA-L 2021
- DKM, CVPR 2023
- NCMNet, CVPR 2023
- TopicFM, AAAI 2023
- AspanFormer, ECCV 2022
- LANet, ACCV 2022
- LISRD, ECCV 2022
- REKD, CVPR 2022
- CoTR, ICCV 2021
- ALIKE, TMM 2022
- RoRD, IROS 2021
- SGMNet, ICCV 2021
- SuperPoint, CVPRW 2018
- SuperGlue, CVPR 2020
- D2Net, CVPR 2019
- R2D2, NeurIPS 2019
- DISK, NeurIPS 2020
- Key.Net, ICCV 2019
- OANet, ICCV 2019
- SOSNet, CVPR 2019
- HardNet, NeurIPS 2017
- SIFT, IJCV 2004
Just try it on 
or deploy it locally following the instructions below.
Update: now support install from pip, just run:
pip install imcuigit clone --recursive https://github.com/Vincentqyw/image-matching-webui.git
cd image-matching-webui
conda env create -f environment.yaml
conda activate imw
pip install -e .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=7860Deploy to Railway, setting up a Custom Start Command in Deploy section:
python -m imcui.api.serverpython app.py --config ./config/config.yamlthen open http://localhost:7860 in your browser.
I provide an example to add local feature in imcui/hloc/extractors/example.py. Then add feature settings in confs in file imcui/hloc/extract_features.py. Last step is adding some settings to matcher_zoo in file imcui/ui/config.yaml.
IMCUI hosts all models on Huggingface. You can upload your model to Huggingface and add it to the Realcat/imcui_checkpoints repository.
External contributions are very much welcome. Please follow the PEP8 style guidelines using a linter like flake8. This is a non-exhaustive list of features that might be valuable additions:
- support pip install command
- add CPU CI
- add webcam support
- add line feature matching algorithms
- example to add a new feature extractor / matcher
- 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 imcui/third_party/GlueStickIf remote submodule repositories are updated, don't forget to pull submodules with:
git submodule update --init --recursive # init and download
git submodule update --remote # updateif you only want to update one submodule, use git submodule update --remote imcui/third_party/GlueStick.
To format code before committing, run:
pre-commit run -a # Auto-checks and fixesThis code is built based on Hierarchical-Localization. We express our gratitude to the authors for their valuable source code.
![@pre-commit-ci[bot]](https://avatars.githubusercontent.com/in/68672?s=64&v=4){kind=small}