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
51 lines
1.8 KiB
Plaintext
Executable File
51 lines
1.8 KiB
Plaintext
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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height, width, _ = frame.shape
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# Dynamische ROI: Mitte des Bildes, zentriert
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roi_w, roi_h = 200, 200
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x_start = width // 2 - roi_w // 2
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y_start = height // 2 - roi_h // 2
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roi = frame[y_start:y_start + roi_h, x_start:x_start + roi_w]
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# Zeichne das ROI-Fenster im Originalbild
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cv2.rectangle(frame, (x_start, y_start), (x_start + roi_w, y_start + roi_h), (0, 255, 0), 2)
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# Konvertiere zu HSV-Farbraum für bessere Hauterkennung
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hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
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# Hautfarbmaske (getestet für mittlere Hauttöne)
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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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# Glättung und Konturen finden
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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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if contours:
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# Größte Kontur annehmen
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max_contour = max(contours, key=cv2.contourArea)
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area = cv2.contourArea(max_contour)
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if area > 2000:
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# Konvexitätsdefekte (Finger) berechnen
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hull = cv2.convexHull(max_contour, returnPoints=False)
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if hull is not None and len(hull) > 3:
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defects = cv2.convexityDefects(max_contour, hull)
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finger_count = 0
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if defects is not None:
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for i in range(defects.shape[0]):
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s, e, f, d = defects[i, 0]
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if d > 10000:
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finger_count += 1
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if finger_count >= 3:
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return "wave", roi
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else:
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return "fist", roi
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return "none", roi
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