https://github.com/byrkbrk/prompting-for-segmentation

Image segmentation using FastSAM and (negative and positive) prompts in PyTorch
https://github.com/byrkbrk/prompting-for-segmentation

fastsam prompts pytorch

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Image segmentation using FastSAM and (negative and positive) prompts in PyTorch

• Host: GitHub
• URL: https://github.com/byrkbrk/prompting-for-segmentation
• Owner: byrkbrk
• Created: 2024-05-09T05:39:42.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-21T09:10:58.000Z (7 months ago)
• Last Synced: 2024-05-21T13:01:20.691Z (7 months ago)
• Topics: fastsam, prompts, pytorch
• Language: Python
• Homepage:
• Size: 9.19 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Prompting for Segmentation

## Introduction

We implement a module for segmenting images using (points and bounding box) prompts, in PyTorch. As a model, we use pretrained [FastSAM](https://arxiv.org/abs/2306.12156) provided by [ultralytics](https://docs.ultralytics.com/models/fast-sam/). 

## Setting Up the Environment

1. Install [Conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html), if not already installed.

2. Clone the repository

    ~~~

    git clone https://github.com/byrkbrk/prompting-for-segmentation.git

    ~~~

3. Change the directory:

    ~~~

    cd prompting-for-segmentation

    ~~~

4. For macos, run:

    ~~~

    conda env create -f prompting-for-sam_macos.yaml

    ~~~

    For linux or windows, run:

    ~~~

    conda env create -f prompting-for-sam_linux.yaml

    ~~~

5. Activate the environment:

    ~~~

    conda activate prompting-for-sam

    ~~~

## Prompts

Check it out how to use:

~~~

python3 segment.py -h

~~~

Output:

~~~

Segment given image

positional arguments:

  image_name            Name of the image that be processed

  point_or_bbox_prompts

                        List of point prompts in (height, width) or bbox prompts in (x_min, y_min, x_max, y_max)

options:

  -h, --help            show this help message and exit

  --label_prompts LABEL_PROMPTS [LABEL_PROMPTS ...]

                        List of labels of prompts. Use the values 0 and 1 for negative and positive prompts, respectively

  --image_size IMAGE_SIZE [IMAGE_SIZE ...]

                        Size (height, width) to which the image be transformed

  --checkpoint_name {FastSAM-x.pt,FastSAM-s.pt}

                        Name of the pretrained model for FastSAM

  --device DEVICE       Name of the device on which the model be run

~~~

### Point prompts

#### Using single prompt

~~~

python3 segment.py dogs.jpg 400,400 --label_prompts 1 --image_size 1024 1024

~~~



  

  

  



#### Using multiple prompts

~~~

python3 segment.py dogs.jpg 400,400 700,400 --label_prompts 0 1 --image_size 1024 1024

~~~



  

  

  



### Bounding box prompts

#### Using single prompt

~~~

python3 segment.py dogs.jpg 625,625,700,700 --label_prompts 1 --image_size 1024 1024

~~~



  

  

  



#### Using multiple prompts

~~~

python3 segment.py dogs.jpg 500,200,800,900 510,210,810,610 --label_prompts 1 0 --image_size 1024 1024

~~~