Skip to content

Latest commit

 

History

History
146 lines (105 loc) · 3.49 KB

README.md

File metadata and controls

146 lines (105 loc) · 3.49 KB

Danger Detection with Computer Vision and ML

License Python 3.8+ Code Style

A real-time threat detection system using computer vision and machine learning to identify potentially dangerous objects (firearms and fire) and provide instant email notifications with visual evidence.

Made with ❤️, for Safety 🦺

🎯 Features

  • Real-time detection of guns, rifles, and fire in video streams
  • Automated email notifications with captured images
  • Easy-to-use interface for security monitoring
  • Configurable alert thresholds and notification settings

🌟 System Requirements

  • Python 3.8+
  • OpenCV 4.5+
  • CUDA support (optional, for GPU acceleration)
  • Webcam or USB camera
  • Minimum 4GB RAM
  • x86_64 architecture
  • Git (for cloning)

🚀 Quick Start

Installation

  1. Clone the repository:
git clone https://github.com/phanindra-max/danger-detection-with-ml.git
cd danger-detection-with-ml
  1. Create and activate a virtual environment:
python -m venv venv
venv\Scripts\activate  # Windows
  1. Install dependencies:
pip install -r requirements.txt

Your requirements.txt should contain:

opencv-python>=4.5.0
numpy>=1.19.0
argparse>=1.4.0

Required Files Setup

  1. Download YOLOv3 weights and config:
# Download weights
curl -L https://pjreddie.com/media/files/yolov3.weights -o yolov3.weights

# Download config
curl -L https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg -o yolov3.cfg
  1. Create obj.names file: Create a new file named obj.names with the following content:
gun
rifle
fire

Email Configuration

  1. Setup Gmail Authentication:

    • Enable 2-Factor Authentication in your Gmail account
    • Generate an App Password:
      1. Go to Google Account → Security → App Passwords
      2. Select "Mail" and "Windows Computer"
      3. Copy the 16-digit password

  2. Update Email Settings:

# In main.py, update these variables:
mailfrom = "<[email protected]>"
gmailpass = "<your-16-digit-app-password>"
mailto = "<[email protected]>"

💻 Usage

Command Line Options

python main.py [--webcam True/False] [--image True/False] [--image_path PATH] [--verbose True/False]

Options:

  • --webcam: Enable webcam mode
  • --image: Enable image mode
  • --image_path: Path to input image (default: "use-this.jpg")
  • --verbose: Enable verbose output

Basic Usage

python main.py

When prompted, enter 'Y' to start webcam detection.

📊 Performance

  • Average processing speed: 25 FPS on CPU
  • GPU acceleration available with CUDA support
  • Accuracy depends on image quality and lighting conditions

🤝 Contributing

We welcome contributions! Please see Contributing Guidelines.

🌈 Community & Support

🙏 Acknowledgments

  • YOLOv3 team for the detection model
  • OpenCV community for vision tools

📈 Roadmap

  • Add support for more object types
  • Implement real-time API (Abandoned due to monetary constraints)
  • Improve detection accuracy
  • Add cloud deployment options

Made with ❤️ for Safety 🦺