94 lines
3.7 KiB
Python
94 lines
3.7 KiB
Python
import cv2
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import mediapipe as mp
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import json, time, math, numpy as np
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from pythonosc import udp_client
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# Verbindung zum Spiel herstellen
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client = udp_client.SimpleUDPClient("127.0.0.1", 5005)
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# Hilfsfunktion zur Kalibrierung (wie zuvor)
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def map_to_screen(x, y, calib_points, screen_size=(800, 600)):
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pts_src = np.array(calib_points, dtype=np.float32)
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pts_dst = np.array([
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[0, 0],
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[screen_size[0], 0],
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[screen_size[0], screen_size[1]],
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[0, screen_size[1]]
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], dtype=np.float32)
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M = cv2.getPerspectiveTransform(pts_src, pts_dst)
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p = np.array([[[x, y]]], dtype=np.float32)
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mapped = cv2.perspectiveTransform(p, M)[0][0]
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return int(mapped[0]), int(mapped[1])
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def run_gesture_input(touch_cam_index=0, gesture_cam_index=1, screen_size=(800, 600)):
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mp_hands = mp.solutions.hands
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mp_draw = mp.solutions.drawing_utils
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hands_touch = mp_hands.Hands(max_num_hands=1, min_detection_confidence=0.6)
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hands_gesture = mp_hands.Hands(max_num_hands=2, min_detection_confidence=0.6)
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# Kalibrierung laden
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try:
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calib_points = json.load(open("calibration.json"))
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print("📄 Kalibrierung geladen:", calib_points)
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except:
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print("⚠️ Keine calibration.json – zuerst calibrate_touch.py ausführen!")
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return
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cam_touch = cv2.VideoCapture(touch_cam_index)
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cam_gesture = cv2.VideoCapture(gesture_cam_index)
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prev_clap_time = 0
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clap_cooldown = 1.5
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while True:
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ok1, frame_touch = cam_touch.read()
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ok2, frame_gest = cam_gesture.read()
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if not ok1 or not ok2:
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print("⚠️ Kamera nicht verfügbar"); break
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frame_touch = cv2.flip(frame_touch, 1)
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frame_gest = cv2.flip(frame_gest, 1)
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# --- Touch-Erkennung ---
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rgb_t = cv2.cvtColor(frame_touch, cv2.COLOR_BGR2RGB)
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res_t = hands_touch.process(rgb_t)
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h, w, _ = frame_touch.shape
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if res_t.multi_hand_landmarks:
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lm = res_t.multi_hand_landmarks[0]
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mp_draw.draw_landmarks(frame_touch, lm, mp_hands.HAND_CONNECTIONS)
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fx = int(lm.landmark[8].x * w)
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fy = int(lm.landmark[8].y * h)
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sx, sy = map_to_screen(fx, fy, calib_points, screen_size)
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if lm.landmark[8].y > 0.8:
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client.send_message("/touch", [sx, sy])
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cv2.putText(frame_touch, f"Touch ({sx},{sy})", (40, 60),
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cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
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# --- Clap-Erkennung ---
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rgb_g = cv2.cvtColor(frame_gest, cv2.COLOR_BGR2RGB)
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res_g = hands_gesture.process(rgb_g)
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gh, gw, _ = frame_gest.shape
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if res_g.multi_hand_landmarks and len(res_g.multi_hand_landmarks) == 2:
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h1, h2 = res_g.multi_hand_landmarks
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x1 = np.mean([p.x for p in h1.landmark]) * gw
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y1 = np.mean([p.y for p in h1.landmark]) * gh
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x2 = np.mean([p.x for p in h2.landmark]) * gw
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y2 = np.mean([p.y for p in h2.landmark]) * gh
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dist = math.hypot(x2 - x1, y2 - y1)
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if dist < 100 and (time.time() - prev_clap_time) > clap_cooldown:
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client.send_message("/clap", 1)
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prev_clap_time = time.time()
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cv2.putText(frame_gest, "👏", (int(gw / 2) - 20, 80),
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cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 255), 3)
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cv2.imshow("Touch-Cam", frame_touch)
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cv2.imshow("Gesture-Cam", frame_gest)
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if cv2.waitKey(5) & 0xFF == 27:
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break
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cam_touch.release()
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cam_gesture.release()
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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run_gesture_input()
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