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
36 lines
1.2 KiB
Plaintext
Executable File
36 lines
1.2 KiB
Plaintext
Executable File
import cv2
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import numpy as np
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# --- Handgestenerkennung: Einfacher Hautfarbfilter + Konturanalyse ---
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def detect_hand_gesture(frame):
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# Region of Interest (z. B. linke obere Ecke) definieren
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roi = frame[20:120, 20:120]
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# Konvertiere in HSV-Farbraum
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hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
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# Hautfarb-Bereich (kann je nach Licht angepasst werden)
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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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# Maske für Hautfarbe erzeugen
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mask = cv2.inRange(hsv, lower_skin, upper_skin)
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# Konturen finden
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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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# Wenn große Kontur gefunden → einfache „Winken“-Simulation
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if contours:
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largest_contour = max(contours, key=cv2.contourArea)
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if cv2.contourArea(largest_contour) > 1000:
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gesture = "wave"
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# ROI im Hauptframe markieren
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cv2.rectangle(frame, (20, 20), (120, 120), (0, 255, 0), 2)
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cv2.putText(frame, f"Gesture: {gesture}", (20, 160),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
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return gesture, frame
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