Metadata-Version: 2.1
Name: NPSAM
Version: 0.2
Summary: NP-SAM is an easy-to-use segmentation and analysis tool for nanoparticles and more.
Home-page: https://gitlab.au.dk/disorder/np-sam
Author: Torben Villadsen & Rasmus Larsen
Author-email: torben-v@hotmail.com
License: Apache License
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: torch (==2.1.1)
Requires-Dist: torchvision (==0.16.1)
Requires-Dist: numba (==0.57.0)
Requires-Dist: numpy (==1.24.3)
Requires-Dist: opencv-python (==4.8.0.74)
Requires-Dist: pandas (==2.0.1)
Requires-Dist: matplotlib (==3.7.1)
Requires-Dist: scikit-image (==0.20.0)
Requires-Dist: ipympl (==0.9.3)
Requires-Dist: PyQt5 (==5.15.9)
Requires-Dist: Pillow (>=7.1.2)
Requires-Dist: ultralytics (==8.0.120)
Requires-Dist: jupyterlab (==4.0.7)

# NP-SAM

## Introduction

In this project we propose an easily implementable workflow for a fast, accurate and seamless experience of segmentation of nanoparticles.

The project's experience could be significantly enhanced with the presence of a CUDA-compatible device; alternatively, Google Colab can be utilized if such a device is not accessible. For a quick access to the program and a CUDA-GPU try our Google Colab notebook.

### Get started
In the working directory and NPSAM environment execute `jupyter lab` in the terminal. This will launch jupyterlab. 

## Citation
```
@article{NPSAM,
   author = {Larsen, Rasmus and Villadsen, Torben L. and Mathiesen, Jette K. and Jensen, Kirsten M. Ø and Bøjesen, Espen D.},
   title = {NP-SAM: Implementing the Segment Anything Model for Easy Nanoparticle Segmentation in Electron Microscopy Images},
   journal = {ChemRxiv},
   DOI = {10.26434/chemrxiv-2023-k73qz-v2},
   year = {2023},
   type = {Journal Article}
}
```

## Acknowledgment
This repo benefits from Meta's [Segment Anything](https://github.com/facebookresearch/segment-anything) and [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM). Thanks for their great work.


