This project is a comprehensive People or person count system designed to monitor occupancy levels.

• Dataset: Utilizes a dataset of Yolov7.
• Model: Trained using YOLOv7, optimized with NVIDIA DGX A100 for accelerated processing.
• Alert System: Integrated with Gmail API via Google Cloud to notify users of non-compliance.
• Real-time Detection: Capable of detecting PPE in real-time from video feeds.
• Containerization: The entire project is containerized using Docker and deployed on an NVIDIA DGX A100.
• Frontend: A minimalist Flask frontend for easy monitoring of detection processes.

• Docker
• NVIDIA Docker Toolkit
• Python 3.8+
• Flask
• YOLOv7 dependencies
• Google Cloud credentials for Gmail API

git clone https://github.com/Harsha-108/PPE-Violation-Detection.git
cd PPE-Violation-Detection

pip install -r requirements.txt

python app.py

• Real-time Detection
• Connect your video feed to the system.
• Access the Flask frontend at http://localhost:5000 to monitor detection in real-time.

• Ensure your Google Cloud credentials are properly set up to enable email notifications for non-compliance. The system will automatically send alerts to designated users.

• Roboflow for the dataset curation tools.
• NVIDIA for providing the DGX A100.
• Google Cloud for the Gmail API.