Metadata-Version: 2.1
Name: x_segment_anything
Version: 0.0.6
Home-page: https://github.com/Jordan-Pierce/xSAM
Author: Jordan Pierce
Author-email: jordan.pierce@noaa.gov
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: torchvision
Provides-Extra: all
Requires-Dist: matplotlib; extra == "all"
Requires-Dist: pycocotools; extra == "all"
Requires-Dist: opencv-python; extra == "all"
Requires-Dist: onnx; extra == "all"
Requires-Dist: onnxruntime; extra == "all"
Provides-Extra: dev
Requires-Dist: flake8; extra == "dev"
Requires-Dist: isort; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: mypy; extra == "dev"

# xSAM

Segment Anything Model (SAM) variants including:
- RepViT-SAM 
- EdgeSAM
- MobileSAM
- SAM (original) 

combined in the same API (to make life easier).

For more information on different SAM variants, please see the following:
- [_On Efficient Variants of Segment Anything Model: A Survey_](https://arxiv.org/html/2410.04960v1)

## Installation

The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. 
Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision 
dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

Install xSAM:

```bash
pip install x-segment-anything
```

## Getting Started
The SAM models can be loaded in the following ways:

```python
from x_segment_anything import sam_model_registry, SamPredictor

model_type = "repvit"
model_type = "edge_sam"
model_type = "vit_t"
model_type = "vit_b"
model_type = "vit_l"
model_type = "vit_h"

sam_checkpoint = "path_to_checkpoints/model_x_weights.pt"

device = "cuda" if torch.cuda.is_available() else "cpu"

x_sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
x_sam.to(device=device)
x_sam.eval()

predictor = SamPredictor(x_sam)
predictor.set_image(<your_image>)
masks, _, _ = predictor.predict(<input_prompts>)
```

or generate masks for an entire image:

```python
from x_segment_anything import SamAutomaticMaskGenerator

mask_generator = SamAutomaticMaskGenerator(x_sam)
masks = mask_generator.generate(<your_image>)
```

## Model Checkpoints
For convenience, The following model checkpoints are available in the `sam_model_urls` dictionary and can be downloaded 
in python:

```python
import requests
from x_segment_anything.build_sam import sam_model_urls

def download_asset(asset_url, asset_path):
    response = requests.get(asset_url)
    with open(asset_path, 'wb') as f:
        f.write(response.content)

model_path = "repvit.pt"
model_path = "edge_sam.pt"
model_path = "edge_sam_3x.pt"
model_path = "vit_t.pt"
model_path = "vit_b.pt"
model_path = "vit_l.pt"
model_path = "vit_h.pt"

model = model_path.split(".")[0]
model_url = sam_model_urls[model]

download_asset(model_url, model_path)

```

### Model Checkpoint URLs:
- [repvit](https://huggingface.co/spaces/jameslahm/repvit-sam/resolve/main/repvit_sam.pt)
- [edge_sam](https://huggingface.co/spaces/chongzhou/EdgeSAM/resolve/main/weights/edge_sam.pth)
- [edge_sam_3x](https://huggingface.co/spaces/chongzhou/EdgeSAM/resolve/main/weights/edge_sam_3x.pth)
- [vit_t](https://huggingface.co/spaces/dhkim2810/MobileSAM/resolve/main/mobile_sam.pt)
- [vit_b](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)
- [vit_l](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)
- [vit_h](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)

### Acknowledgements:
- [RepViT-SAM](https://github.com/THU-MIG/RepViT/tree/main)
- [EdgeSAM](https://github.com/chongzhou96/EdgeSAM)
- [MobileSAM](https://github.com/ChaoningZhang/MobileSAM)
- [SAM](https://github.com/facebookresearch/segment-anything)

--- 
## Disclaimer

This repository is a scientific product and is not official communication of the National 
Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA 
GitHub project code is provided on an 'as is' basis and the user assumes responsibility for its 
use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from 
the use of this GitHub project will be governed by all applicable Federal law. Any reference to 
specific commercial products, processes, or services by service mark, trademark, manufacturer, or 
otherwise, does not constitute or imply their endorsement, recommendation or favoring by the 
Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC 
bureau, shall not be used in any manner to imply endorsement of any commercial product or activity 
by DOC or the United States Government.


## License 

Software code created by U.S. Government employees is not subject to copyright in the United States 
(17 U.S.C. Â§105). The United States/Department of Commerce reserve all rights to seek and obtain 
copyright protection in countries other than the United States for Software authored in its 
entirety by the Department of Commerce. To this end, the Department of Commerce hereby grants to 
Recipient a royalty-free, nonexclusive license to use, copy, and create derivative works of the 
Software outside of the United States.
