2915828adf
- Interactive mission selector with metadata-driven design - 5 educational missions (basics + advanced) - AI assistant roles (Deepbit, Bugsy, Schnippsi, Tobi) - SnakeCam gesture recognition system - Token tracking utilities - CLAUDE.md documentation - .gitignore for logs and secrets
40 lines
1.3 KiB
Python
Executable File
40 lines
1.3 KiB
Python
Executable File
import cv2
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import numpy as np
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def detect_hand_gesture(frame):
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x, y, w, h = 100, 100, 150, 150
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roi = frame[y:y+h, x:x+w]
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hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
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# einfache Hautfarbe (hell bis leicht gebräunt)
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lower_skin = np.array([0, 30, 60], dtype=np.uint8)
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upper_skin = np.array([20, 150, 255], dtype=np.uint8)
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mask = cv2.inRange(hsv, lower_skin, upper_skin)
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mask = cv2.GaussianBlur(mask, (5, 5), 0)
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contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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gesture = "none"
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if contours:
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contour = max(contours, key=cv2.contourArea)
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area = cv2.contourArea(contour)
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if area > 2500:
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hull = cv2.convexHull(contour, returnPoints=False)
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if hull is not None and len(hull) > 3:
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defects = cv2.convexityDefects(contour, hull)
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if defects is not None:
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defect_count = defects.shape[0]
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ratio = float(w) / h
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print(f"[debug] Defekte: {defect_count}, Fläche: {int(area)}, Ratio: {ratio:.2f}")
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# 🌊 einfache Regel für Winken
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if 3 <= defect_count <= 10 and 2000 < area < 15000 and 1.3 < ratio < 2.3:
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gesture = "wave"
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return gesture, (x, y, w, h)
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