npsam 3.2.0

NP-SAM is an easy-to-use segmentation and analysis tool for nanoparticles and more.

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 can 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.

Installation

Windows Application

Download one of the .zip files below and extract the content. Afterwards, simply run the .exe file.

NP-SAM for PCs with CUDA compatible graphics card (2.5 GB) here: NP-SAM with CUDA

NP-SAM for PCs without CUDA compatible graphics card (500 MB) here: NP-SAM without CUDA

Python package

Step 0 (Optional): Create a new conda environment called npsam (line 1) and activate it (line 2). This prevents interference with other previously installed Python packages.

conda create -n npsam python=3.10
conda activate npsam

Step 1: Install Pytorch. If you use Windows and have a CUDA compatible graphics card, you can use the following line of code:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Otherwise, install the CPU-only version with:

pip3 install torch torchvision torchaudio

For other operating systems, please refer to PyTorch's website. NP-SAM has been tested with Pytorch 2.1.2 and CUDA 11.8.

Step 2: Install NP-SAM:

pip install npsam

Step 3 (Optional): Make a static link to the npsam ipykernel for easy access to the npsam environment from JupyterLab. This way you don't have to activate the npsam environment every time you want to run NP-SAM.

python -m ipykernel install --user --name npsam --display-name npsam

Get started

In the npsam environment execute jupyter lab in the terminal. This will launch JupyterLab. Try out one of the example notebooks from our GitLab.

Citation

@article{NPSAM,
   author = {Rohde, 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 and FastSAM. Thanks for their great work.