编写程序使用Mediapipe 和opencv实现手指数量的识别

下面是使用Mediapipe和OpenCV实现手指数量识别的Python代码：

import cv2
import mediapipe as mp

# 初始化mediapipe手部模型
mp_hands = mp.solutions.hands
hands = mp_hands.Hands()

# 初始化计数器和状态
count = 0
tipIds = [4, 8, 12, 16, 20]
hand_status = ""

# 打开摄像头
cap = cv2.VideoCapture(0)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        print("Ignoring empty camera frame.")
        continue

    # 将图像从BGR转换为RGB
    image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # 检测手
    results = hands.process(image)

    # 获取手部landmarks
    landmarks = []
    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            for lm in hand_landmarks.landmark:
                landmarks.append(lm)

    # 计算手指数量
    fingers = []
    if len(landmarks) > 0:
        for id in tipIds:
            x = landmarks[id].x
            y = landmarks[id].y
            if landmarks[id - 2].x > x:
                fingers.append(1)
            else:
                fingers.append(0)
        count = fingers.count(1)

    # 更新手部状态
    if count == 0:
        hand_status = "Closed"
    elif count == 5:
        hand_status = "Open"
    else:
        hand_status = "Unknown"

    # 将手指数量和状态显示在图像上
    cv2.putText(frame, f'Fingers: {count}', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
    cv2.putText(frame, f'Status: {hand_status}', (10, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)

    # 显示图像
    cv2.imshow("Hand Gesture Recognition", frame)

    # 按下q键退出程序
    if cv2.waitKey(5) & 0xFF == ord('q'):
        break

# 释放资源
cap.release()
cv2.destroyAllWindows()

这段代码使用了Mediapipe和OpenCV库，通过摄像头获取图像，并调用Mediapipe的手部模型检测手部landmarks，然后根据手指的终点和起点的位置来计算手指数量，最后将手指数量和手部状态显示在图像上。用户可以在程序运行过程中通过摄像头展示手势，程序会实时计算出手指的数量和手部状态，并在图像上进行显示。