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import warnings
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import threading #导入多线程模块
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import cv2
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import mediapipe as mp
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import numpy as np
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from tensorflow.keras.models import load_model
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# 禁用特定警告
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warnings.filterwarnings("ignore", category=UserWarning, message='SymbolDatabase.GetPrototype() is deprecated')
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# 初始化 MediaPipe 和 OpenCV
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hands = None
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mp_draw = mp.solutions.drawing_utils
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cap = None
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keep_running = False
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# 加载手势识别模型
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model_path = 'D:/hand/hand_gesture_model.h5'
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model = load_model(model_path)
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# 定义手势类别
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gesture_classes = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09']
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def start_recognition(callback=None):
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global keep_running, cap, hands
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if cap is None or not cap.isOpened():
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cap = cv2.VideoCapture(0)
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if hands is None:
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hands = mp.solutions.hands.Hands(static_image_mode=False, max_num_hands=2,
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model_complexity=1, min_detection_confidence=0.5,
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min_tracking_confidence=0.5)
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keep_running = True
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threading.Thread(target=run_recognition, args=(callback,)).start()
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def run_recognition(callback=None):
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global keep_running
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last_gesture = None
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while keep_running and cap.isOpened():
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ret, img = cap.read()
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if not ret:
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break
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img = cv2.flip(img, 1)
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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results = hands.process(img_rgb)
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total_raised_fingers = 0
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if results.multi_hand_landmarks:
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for handLms in results.multi_hand_landmarks:
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mp_draw.draw_landmarks(img, handLms, mp.solutions.hands.HAND_CONNECTIONS)
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_, raised_fingers = detect_gesture_and_fingers(handLms)
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total_raised_fingers += raised_fingers
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cv2.putText(img, f'Total Raised Fingers: {total_raised_fingers}', (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2, cv2.LINE_AA,)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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if callback:
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callback(img)
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stop_recognition()
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#停止识别
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def stop_recognition():
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global keep_running, cap
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keep_running = False
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if cap is not None and cap.isOpened():
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cap.release()
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cap = None
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cv2.destroyAllWindows()
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#释放摄像头资源
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def release_camera():
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global cap
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if cap is not None and cap.isOpened():
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cap.release()
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cap = None
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def detect_gesture_and_fingers(hand_landmarks):
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# 手势识别
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gesture_image = get_hand_image(hand_landmarks)
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gesture = predict_gesture(gesture_image)
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# 手指竖起数量检测
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raised_fingers = count_raised_fingers(hand_landmarks)
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return gesture, raised_fingers
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def get_hand_image(hand_landmarks):
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# 提取手部区域图像
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# 示例实现，请根据你的需要进行调整
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img = np.zeros((150, 150, 3), dtype=np.uint8) # 示例图像
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return img
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def predict_gesture(img):
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img = cv2.resize(img, (150, 150))
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img_array = np.expand_dims(img, axis=0) / 255.0
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predictions = model.predict(img_array)
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predicted_class = gesture_classes[np.argmax(predictions)]
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return predicted_class
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def count_raised_fingers(hand_landmarks):
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fingers_status = [0, 0, 0, 0, 0]
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# 拇指
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thumb_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_TIP]
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thumb_ip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_IP]
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thumb_mcp = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_MCP]
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thumb_cmc = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.THUMB_CMC]
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# 计算拇指的角度
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angle_thumb = calculate_angle(thumb_cmc, thumb_mcp, thumb_tip)
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if angle_thumb > 160: # 如果拇指的角度大于160度，认为拇指竖起
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fingers_status[0] = 1
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# 其他手指
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for i, finger_tip_id in enumerate([mp.solutions.hands.HandLandmark.INDEX_FINGER_TIP,
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mp.solutions.hands.HandLandmark.MIDDLE_FINGER_TIP,
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mp.solutions.hands.HandLandmark.RING_FINGER_TIP,
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mp.solutions.hands.HandLandmark.PINKY_TIP]):
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finger_tip = hand_landmarks.landmark[finger_tip_id]
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finger_pip = hand_landmarks.landmark[finger_tip_id - 2]
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finger_mcp = hand_landmarks.landmark[finger_tip_id - 3]
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# 计算手指的角度
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angle_finger = calculate_angle(finger_mcp, finger_pip, finger_tip)
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if angle_finger > 160: # 如果手指的角度大于160度，认为手指竖起
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fingers_status[i + 1] = 1
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return sum(fingers_status)
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def calculate_angle(point1, point2, point3):
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# 计算三个点之间的角度
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angle = np.arctan2(point3.y - point2.y, point3.x - point2.x) - np.arctan2(point1.y - point2.y, point1.x - point2.x)
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angle = np.abs(angle)
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if angle > np.pi:
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angle = 2 * np.pi - angle
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return angle * 180 / np.pi
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