I created a program using OpenCV, which allows a user to control the volume of their computer or laptop through hand gestures. By using hand gestures, the user can adjust the volume of the system without needing to use the physical volume control buttons or keyboard shortcuts. The OpenCV library uses computer vision techniques to detect and track the user's hand movements, which are then mapped to a range of system volume levels. This allows the user to easily adjust the volume of their system in a hands-free manner.
In addition to OpenCV, I also utilized the MediaPipe and NumPy libraries in this project. The MediaPipe library was used for hand detection and tracking. Specifically, I used the mp.solutions.hands.Hands() class to create a hand detection model, and then used the process() method to detect hands in each frame of the video stream. Once a hand was detected, I used the landmark_detection module of the library to extract the hand landmarks (keypoints) and calculate the distance between the thumb and index finger.
NumPy was used to map the distance between the thumb and index finger to a range of volume levels. Specifically, I used the numpy.interp() function to map the distance to a range of volume levels between 0 and 100. This mapping allowed the hand gestures to control the system volume in a more intuitive and natural way. Overall, the combination of OpenCV, MediaPipe, and NumPy allowed for a robust and effective system for the creation of this project.
- Source Codehttps://www.jerwellsavella/github.com/
- BlogGesture Volume Control using Python
- Tech & LibrariesPython, OpenCV, MediaPipe, NumPy