[Semantic Segmentation] An introduction to the labeling tool ISAT with segment anything

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https://blog.csdn.net/weixin_39190382?type=blog

0. Preface

Extremely fast segmentation and labeling tool
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1. Text

1.1 Installation

Create a virtual environment

conda create -n ISAT_with_segment_anything python==3.8
conda activate ISAT_with_segment_anything

install segment anything

git clone https://github.com/facebookresearch/segment-anything.git
cd segment-anything
pip install -e .
cd ..

Install ISAT_with_segment_anything

git clone https://github.com/yatengLG/ISAT_with_segment_anything.git
cd ISAT_with_segment_anything
pip install -r requirements.txt

Download the pre-trained model
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https://github.com/yatengLG/ISAT_with_segment_anything
https://github.com/yatengLG/ISAT_with_segment_anything

1.2 run

python main.py

1.3 Operating instructions

1.3.1 Basic description

  • Left button: select the area of ​​interest
  • Right click: deselect (area not of interest)
  • E: end annotation, select category
  • Drag and drop the vertices of the polygon to fine-tune the annotation.
  • Adjust the occlusion relationship between targets through the height of the target layer (when there are overlapping areas between multiple targets)

1.3.2 Common shortcut keys

  • q: Enable model annotation mode
  • c: start normal annotation mode
  • e: end annotation, select category
  • s: save
  • d: next picture
  • a: previous picture

1.4 Precautions

  • Only supports opening pictures under the file, does not support pictures in recursive subfolders
  • If multiple folders are labeled in batches, you need to close the program and run it again. If the current folder is marked, do not close the program, open (load) a new file, if the picture names in the two folders are duplicated, confusion may occur
  • For poorly labeled images, you need to use the regular labeling mode

reference

[1] [https://github.com/yatengLG/ISAT_with_segment_anything

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