通过mediapipe获取摄像头识别手部、嘴巴、眼睛并分别截取三个区域的图片的代码

以下是使用mediapipe获取摄像头识别手部、嘴巴、眼睛并分别截取三个区域的Python代码：

import cv2
import mediapipe as mp

# 初始化mediapipe的Hand, Face和Holistic模型
mp_hands = mp.solutions.hands
mp_face = mp.solutions.face_detection
mp_holistic = mp.solutions.holistic

# 初始化mediapipe的DrawingUtils模块
mp_drawing = mp.solutions.drawing_utils

# 初始化摄像头
cap = cv2.VideoCapture(0)

# 设置截取区域的大小和位置
hand_area = (100, 100, 300, 300) # (x, y, w, h)
face_area = (100, 100, 300, 300) # (x, y, w, h)
eye_area = (100, 100, 300, 300) # (x, y, w, h)

# 用于标记是否检测到手部、脸部和眼睛
hand_detected = False
face_detected = False
eye_detected = False

# 初始化Hand、Face和Holistic模型
with mp_hands.Hands() as hands, mp_face.FaceDetection() as face_detection, mp_holistic.Holistic() as holistic:
    while True:
        # 从摄像头读取一帧图像
        ret, image = cap.read()
        if not ret:
            break

        # 将图像转换为RGB格式
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # 检测手部
        results_hands = hands.process(image)
        if results_hands.multi_hand_landmarks:
            # 获取手部关键点坐标
            hand_landmarks = results_hands.multi_hand_landmarks[0]
            # 绘制手部关键点
            mp_drawing.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
            # 截取手部区域图片
            x, y, w, h = hand_area
            hand_image = image[y:y+h, x:x+w]
            hand_detected = True
        
        # 检测脸部
        results_face = face_detection.process(image)
        if results_face.detections:
            # 获取脸部关键点坐标
            face_landmarks = results_face.detections[0].location_data.relative_bounding_box
            # 绘制脸部矩形框
            h, w, _ = image.shape
            x, y, w, h = int(face_landmarks.xmin * w), int(face_landmarks.ymin * h), int(face_landmarks.width * w), int(face_landmarks.height * h)
            cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
            # 截取脸部区域图片
            x, y, w, h = face_area
            face_image = image[y:y+h, x:x+w]
            face_detected = True
        
        # 检测眼睛
        results_holistic = holistic.process(image)
        if results_holistic.face_landmarks:
            # 获取眼睛关键点坐标
            left_eye = results_holistic.face_landmarks.landmark[mp_holistic.FaceLandmark.LEFT_EYE]
            right_eye = results_holistic.face_landmarks.landmark[mp_holistic.FaceLandmark.RIGHT_EYE]
            # 绘制眼睛关键点
            mp_drawing.draw_landmarks(image, [left_eye, right_eye], mp_holistic.FACE_CONNECTIONS)
            # 截取眼睛区域图片
            x, y, w, h = eye_area
            eye_image = image[y:y+h, x:x+w]
            eye_detected = True

        # 显示图像
        cv2.imshow('MediaPipe', image)

        # 检查是否已经检测到手部、脸部和眼睛，如果是则退出循环
        if hand_detected and face_detected and eye_detected:
            break

        # 按下q键退出程序
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

# 释放摄像头并关闭所有窗口
cap.release()
cv2.destroyAllWindows()

在上面的代码中，我们使用mediapipe模块的Hand、Face和Holistic模型来检测手部、脸部和眼睛，并使用mediapipe的DrawingUtils模块绘制关键点和矩形框。我们还使用OpenCV的cv2模块从摄像头读取图像，并使用cv2.imshow()函数显示图像。最后，我们设置了三个截取区域hand_area、face_area和eye_area，用于截取手部、脸部和眼睛区域的图片。截取的图片分别保存在hand_image、face_image和eye_image变量中。