WEBCAM PULSE DETECTOR
This project is a Python application designed to detect a person's pulse using a webcam. The application captures video frames from a connected camera, isolates the forehead region, and analyzes the green-light intensity over time to estimate the pulse rate.
Key features include:
Camera Integration: Connects to a webcam to capture video frames.
Face and Forehead Detection: Uses image processing to find the face and isolate the forehead.
Pulse Estimation: Analyzes the green-light intensity in the forehead region to estimate the pulse.
Data Communication: Optionally sends pulse data via serial port or UDP.
GUI and Controls: Provides a graphical interface to display the processed video and pulse data, with keyboard controls for various functions.
In the time of emergencies it detects the Pulse rate In your Mobile app or a websites it can be easily detected
Key objectives include:
Utilizing image processing techniques to detect faces and isolate the forehead region.
Collecting green-light intensity data from the forehead area to estimate the pulse rate.
Providing real-time data display of the raw signal and power spectral density (PSD) of the pulse data.
Allowing user interaction through keyboard controls to toggle features like face detection, data display, camera selection, and data export to CSV.
The main components of the code are:
Initialization: Sets up the camera, serial, and UDP communication based on user arguments.
Main Loop: Captures frames, processes them to detect the pulse, and handles user inputs.
Data Display: Plots the raw signal and power spectral density (PSD) of the pulse data.
Key Handlers: Maps keystrokes to specific functions like toggling the camera, displaying plots, and writing data to CSV.
The application is designed to work with a connected camera (not an IP camera or MJPEG stream) and provides a graphical interface for users to interact with the pulse detection process. The project leverages OpenCV for image processing and Python libraries like NumPy for data manipulation.
Overall, the goal is to create a user-friendly tool that can accurately estimate a person's pulse rate in real-time using a webcam feed and provide visual feedback on the detected pulse data.
