This project utilizes MediaPipe, a cross-platform framework by Google, to perform real-time hand feature detection. The program captures live video from the webcam, detects hand landmarks, and visualizes the detected hand landmarks with the connections between the landmarks.
To run this project, you need the following dependencies:
- Python 3.x
- OpenCV (
opencv-python) - MediaPipe (
mediapipe)
You can install these dependencies using the following pip commands:
pip install opencv-python
pip install mediapipe- Webcam Feed: The program uses
cv2.VideoCapture(0)to capture the live video feed from your webcam. - Hand Detection: The MediaPipe Hands module processes each frame to detect hand landmarks.
- Landmarks Visualization: If hands are detected, the program draws landmarks on the hands and visualizes the connections between those landmarks in real-time using the
mp.solutions.drawing_utils. - Exit: To exit the program, simply press the Esc key.
.
├── hand_feature_detection.py # Python script for hand feature detection using MediaPipe
└── README.md # Project description
- Ensure that you have the required dependencies installed.
- Run the Python script:
python hand_feature_detection.py- The webcam feed will open, and you should see landmarks drawn on your hand as you move it in front of the camera.
- Press Esc to close the window and stop the detection.
- Mediapipe Hands Module: This is the core of the hand feature detection, where
mp_hands.Hands()is used to detect and track hands. - Landmark Detection:
hands.process(rgb_frame)detects hand landmarks in the provided frame. These landmarks are represented as 3D coordinates (x, y, z). - Drawing Landmarks:
mp_draw.draw_landmarks()is used to draw the detected landmarks and their connections in real-time on the frame. - Exit Condition: The program listens for the Esc key to exit the loop and release the webcam.