🛡️ Violence Detection App using OpenCV + CLIP

Welcome to the Violence Detection App — a lightweight, AI-powered system that monitors video streams and detects violent or harmful activities in real-time using OpenAI's CLIP, OpenCV, and PyTorch.

Ideal for surveillance, content moderation, and safety automation.

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🚀 Features

• 🎥 Real-time video stream analysis using webcam or video file
• 🔍 Violence/harm detection using zero-shot CLIP model
• 📸 Automatic frame capture upon detection
• 📂 Easily configurable detection sensitivity via settings.yaml
• ⚙️ Modular and extensible architecture

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🧠 How It Works

The app uses OpenAI’s CLIP model to match visual frames against text descriptions like "a photo of violence" or "a photo of normal activity".
It calculates the similarity between the image and text features and selects the most likely label.

A confidence threshold is used to determine whether a frame should be flagged.

✅ If the confidence is below the threshold → considered normal
⚠️ If the confidence is greater than or equal to the threshold → considered violent

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🖼️ Examples

✅ No violence detected — everything is running smoothly:

⚠️ Violence detected — immediate action recommended:

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⚙️ Configuration

The app behavior is fully controlled through settings.yaml.

Example configuration:

model-settings:
  model-name: ViT-B/32
  prediction-threshold: 0.24  # 📌 Adjust sensitivity here

📌 Lower values make the model more sensitive (may increase false positives)  
📌 Higher values reduce sensitivity (may miss subtle incidents)

label-settings:
  labels:
    - violence
    - no violence detected
  default-label: no violence detected
``yaml

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🛠️ Requirements

• Python 3.7 or higher
• OpenCV (cv2)
• PyYAML
• NumPy
• TensorFlow or PyTorch (based on the model used)

Install all dependencies using:

pip install -r requirements.txt

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▶️ Running the App

To run the application:

python app.py