TattooTrace

Introduction

TattooTrace integrates advanced machine learning models like YOLOv5 and CLIP to analyze and identify tattoos from images and videos. Leveraging the robust backend of Immich, this tool aims to support efforts to combat and understand human trafficking dynamics.

Installation Instructions

Requirements

• Docker Desktop
• npm
• Makefile (for UNIX-like operating systems)

Setting Up the YOLOv5 (Localization)

1. Clone the YOLOv5 Repository

   git clone https://github.com/ultralytics/yolov5
   cd yolov5
   

2. Install Dependencies

   pip install -r requirements.txt
   

3. Download Pre-trained Weights Download the YOLOv5s model weights Link

4. Inference

   • To perform inference on images:

   cd 
   python detect.py --source /path/to/images --weights best.pt --conf 0.25
   

   • To perform inference on video:

   python detect.py --source /path/to/video.mp4 --weights best.pt --conf 0.25
   

5. Output

   cd yolov5 Tattoo Recognition/detected/exp6
   

Setting Up the CLIP (Recognition & Analysis)

1. Frontend Setup

   cd clip/frontend/clip-app
   npm install
   npm start
   

   This will start the frontend application at http://localhost:3000.

2. Backend Setup

   cd clip/backend/
   python app.py
   

Setting Up the Immich-Based Application

1. Environment Setup Ensure Docker Desktop is installed and running on your system. Navigate to the main Immich directory:

   cd immich
   

2. Configuration Modify docker/.env to set the UPLOAD_LOCATION environment variable.

3. Model Weights Download the model weights and place them in:

   /immich/machine-learning/app/models/weight/
   

   • Download link for weight file: [https://drive.google.com/file/d/14VpsSrTkOxp0MTzN7uaVJp8uAZ167l-z/view?usp=sharing]

4. Build and Run From the Immich directory, execute:

   make dev
   

   Access the development instance at http://localhost:3000.

Features

Current Functionality (YOLOv5 & CLIP)

• Tattoo Detection: Utilizes YOLOv5 to detect tattoos with a configurable minimum confidence score.
• Tattoo Recognition: Using the Clip model to recognize and analyze a given tattoo with its meaning.
• Tattoo Clustering: To cluster similar tattoos based on the CLIP output from the given prompt.
• User Interaction: Simple user interface to trigger tattoo detection.

Current Functionality for Immich-Based Application

• The user can press the button to trigger the tattoo detection function.
• Use YOLOv5 to detect tattoos in the photos.
• Adjust the setting for the minimum confidence score for the model.

Data Collection

• NTU Dataset: Contains 32,145 images of tattoos without labels.
• Labeled Tattoo Images: Collected 1,000 images with bounding box labels.

Model Information

YOLOv5

• Utilized for tattoo detection with bounding box regression.
• Model trained on labelled tattoo dataset.
• YOLOv5 GitHub Repository

CLIP (Contrastive Language-Image Pre-Training)

• Used for recognizing and interpreting the context and significance of tattoos.
• Demonstrates impressive zero-shot performance.
• CLIP Model Information

Future Enhancements

• Expand Object Detection: Plan to upgrade from YOLOv5 to State-of-the-art model for enhanced accuracy and speed.
• CLIP Integration: Improve tattoo recognition and contextual understanding by integrating CLIP into the Immich framework.
• Enhanced Clustering Algorithms: Develop sophisticated algorithms to categorize and analyze tattoos more effectively.
• Video Timestamps Display: Implement functionality to display video timestamps post-clustering analysis.

Contributing

Contributions are welcome! Please refer to the CONTRIBUTING.md for guidelines on how to contribute effectively to this project.