SRISTI_assignment_1

Coffee Shop People Count Maintenance

This project aims to maintain the count of people entering and exiting a coffee shop using YOLO object detection.

• To adhere to COVID-19 safety measures, the coffee shop enforces a maximum capacity of four customers inside the premises.
• This restriction aims to maintain adequate social distancing among patrons and prevent overcrowding.
• Once the count of customers inside reaches four, entry to the shop is prohibited until the number decreases below the threshold.
• Implementing this limit prioritizes the health and safety of both customers and staff, reducing the risk of virus transmission.
• The measure reflects the coffee shop's commitment to responsible management and compliance with public health guidelines during the pandemic.

Usage

• Clone the repository.
• Ensure you have the necessary dependencies installed (OpenCV, Pandas, NumPy, Ultralytics YOLO).
• Run the main.py script.
• Ensure the peoplecount1.mp4 file is present in the directory.

Features

• Counts the number of people entering and exiting the coffee shop.
• Displays the count on the video frame.
• Draws regions of interest (ROI) for entry and exit.
• Supports tracking of people using the Tracker class.
• Dataset is just one required video.
• for individual videos we assign the required "region of interest" manually.

Contributing

Contributions are welcome! If you'd like to contribute to the project, please open an issue to discuss your ideas or submit a pull request.

Credits

• The implementation of this project was inspired by the YouTube video (https://www.youtube.com/watch?v=tbscP_d11Zw) and (https://www.youtube.com/watch?v=m9fH9OWn8YM)
• Special thanks to "freedomwebtech" and "computer vision engineer" for providing valuable insights and guidance in the video.
• The YOLO model used in this project is from the Ultralytics YOLO repository: Ultralytics YOLO.