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By Kevin McAleer, 2 Minutes
Hand detection is the computer vision technique of identifying and locating hands in images or videos. Recognizing hands can serve as a basis for gesture recognition, touchless control systems, and more. In this lesson, we’ll utilize CVZone to perform hand detection in real time.
Capture video from the camera:
As with the face detection lesson, we’ll be using the OpenCV library to capture live video feed from the camera. If you’ve followed the face detection lesson, this step should be familiar.
import cv2 cap = cv2.VideoCapture(0)
Use CVZone for hand detection:
hand_detector = cvzone.HandDetector(detectionCon=0.8) while True: success, img = cap.read() img, list_hands = hand_detector.findHands(img) cv2.imshow("Hand Detection", img) if cv2.waitKey(1) & 0xFF == ord('q'): break
Here’s a breakdown of the code:
0.8
hand_detector.findHands(img)
cv2.imshow()
Gesture Recognition: Build upon hand detection to recognize specific hand gestures which can be used to control applications, games, or devices without physical contact.
Customize Visuals: Play with the visual feedback. Adjust the color, size, and annotations displayed around detected hands.
Landmark Detection: Many hand detection systems also provide hand landmarks – specific points on a hand, like fingertip or wrist. Explore how these can be used for more detailed analysis or interaction.
Performance Tweaks: If running on a Raspberry Pi, consider optimizations such as frame resizing or adjusting detection parameters to ensure real-time performance.
Through this lesson, you’ll have the foundational knowledge needed to explore more complex applications of hand detection. The sky’s the limit when it comes to building upon these basics!
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