This is a web interface for the Segment Anything.
Environment Require: Python >= 3.8.13, Node >= 18.15.0 (LTS), CUDA or MPS(optional)
- Fowllow the instructions in the Segment Anything and CLIP to install SAM and CLIP. And prepare webui environment:
# e.g. for Segment Anything
pip install git+https://github.com/facebookresearch/segment-anything.git
pip install opencv-python pycocotools matplotlib onnxruntime onnx
mkdir model
# download the model to `model/`
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth -O model/sam_vit_b_01ec64.pth
# https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth
# https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
# e.g. for CLIP
pip install torch torchvision
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git
# python server as backend
pip3 install torch numpy 'uvicorn[standard]' fastapi pydantic python-multipart Pillow click
# or
cd scripts && pip3 install -r requirements.txt
# webui frontend
npm i- run the server and webui on different terminals:
python3 scripts/server.py # webui backend
npm run dev # interactive webui frontendOr
docker compose upChange the .env.local file to change the server address.
The model server can be run on a remote GUI server, and the webui can be run on a local machine.
The API in server.py is Pure Function. Though it is slow (Encoding Image Each Request), it is easy to deploy and maintain.
- Segment Anything | Meta AI
- facebookresearch/segment-anything: The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
MIT