metadata

title: Image Matching Webui
emoji: 🤗
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 4.28.3
app_file: app.py
pinned: true
license: apache-2.0

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:

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

DUSt3R, CVPR 2024
OmniGlue, CVPR 2024
XFeat, CVPR 2024
RoMa, CVPR 2024
DeDoDe, 3DV 2024
Mickey, CVPR 2024
GIM, ICLR 2024
DUSt3R, arXiv 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

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 model_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:

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 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.

Resources

Acknowledgement

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

Image Matching WebUIIdentify matching points between two images