petebankhead/samapi

Segment Anything Model API

Segment Anything Models (SAM) API

A web API for SAM implemented with FastAPI.

This is a part of the following paper. Please cite it when you use this project. You will also cite the original SAM paper.

• Sugawara, K. Training deep learning models for cell image segmentation with sparse annotations. bioRxiv 2023. doi:10.1101/2023.06.13.544786

Install

Create a conda environment.

conda create -n samapi -y python=3.11
conda activate samapi

If you're using a computer with CUDA-compatible GPU, install cudatoolkit.

conda install -y cudatoolkit=11.8

If you're using a computer with CUDA-compatible GPU on Windows, install torch with GPU-support with the following command.

# Windows with CUDA-compatible GPU only
python -m pip install torch --index-url https://download.pytorch.org/whl/cu118

Install samapi and its dependencies.

python -m pip install git+https://github.com/ksugar/samapi.git

If you are using WSL2, LD_LIBRARY_PATH will need to be updated as follows.

export LD_LIBRARY_PATH=/usr/lib/wsl/lib:$LD_LIBRARY_PATH

Usage

Launch a server

uvicorn samapi.main:app

The command above will launch a server at http://localhost:8000.

INFO:     Started server process [21258]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

For more information, see uvicorn documentation.

Request body

class SAMBody(BaseModel):
    type: Optional[ModelType] = ModelType.vit_h
    bbox: Tuple[int, int, int, int] = Field(example=(0, 0, 0, 0))
    b64img: str

key	value
type	One of vit_h, vit_l, or vit_b
bbox	Coordinate of a bbox (x1, y1, x2, y2)
b64img	Base64-encoded image data

Response body

The response body contains a list of GeoJSON Feature objects.

Supporting other formats is a future work.

Updates

• v0.2.0: Support for MPS backend (MacOS) by @petebankhead

Citation

Please cite my paper on bioRxiv.

@article {Sugawara2023.06.13.544786,
	author = {Ko Sugawara},
	title = {Training deep learning models for cell image segmentation with sparse annotations},
	elocation-id = {2023.06.13.544786},
	year = {2023},
	doi = {10.1101/2023.06.13.544786},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Deep learning is becoming more prominent in cell image analysis. However, collecting the annotated data required to train efficient deep-learning models remains a major obstacle. I demonstrate that functional performance can be achieved even with sparsely annotated data. Furthermore, I show that the selection of sparse cell annotations significantly impacts performance. I modified Cellpose and StarDist to enable training with sparsely annotated data and evaluated them in conjunction with ELEPHANT, a cell tracking algorithm that internally uses U-Net based cell segmentation. These results illustrate that sparse annotation is a generally effective strategy in deep learning-based cell image segmentation. Finally, I demonstrate that with the help of the Segment Anything Model (SAM), it is feasible to build an effective deep learning model of cell image segmentation from scratch just in a few minutes.Competing Interest StatementKS is employed part-time by LPIXEL Inc.},
	URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.13.544786},
	eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.13.544786.full.pdf},
	journal = {bioRxiv}
}