README.md

Vvannot - Video Object Annotation Tool

Vvannot is an open-source video object tracking annotation tool.

Main Page

Result Example

Usage Method

1. Web Platform : Streamlit
2. Human Detection : Yolov8
3. Human Tracking : DeepSort
4. Human Segmentation : SAM2 basemodel

Installation Requirements

1. Navigate to the vvannot directory:

   cd vvannot

2. Create a new Conda environment:

   conda create -n {env_name} python=3.8

3. Activate the environment:

   conda activate {env_name}

4. Install dependencies:

   pip install -r requirements.txt

Running the Application

To start the Streamlit app, use the following command:

streamlit run app/main.py

Important Directories:

• Upload Directory: pages
• Save Directory: annotations/{video_name}.mp4
• Weight Directory: weights/{weight}.pth
• Annotation Directory: app/annotations/{video_name}.json

Usage Guide

1. Register your account
2. Upload a video
3. Annotate objects using bounding boxes

File Structure

vvannot/
│
├── app/
│   ├── annotations/
│   ├── main.py
│   ├── utils/
│   └── ...
├── img/
│   ├── mainpage.png
│   └── demo_example.gif
├── pages/
├── weights/
├── requirements.txt
└── README.md

• app/: Contains the main application files including annotations and utility scripts.
• img/: Stores images and demo GIFs for the README.
• pages/: Directory for uploading video files.
• weights/: Stores model weight files (.pth).
• requirements.txt: Lists the required Python packages.
• README.md: Project documentation file.

Contribution Guidelines

Feel free to submit issues, requests, or pull requests to contribute to Vvannot.

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