mediapipe简单手势判断

Mediapipe是一个Google开源的跨平台机器学习框架，可以用于构建各种视觉和媒体处理应用程序。Mediapipe中提供了手部关键点检测模型，可以用于手势识别。具体实现步骤如下：

1. 使用Mediapipe中的HandLandmark模型检测手部关键点。
2. 根据手部关键点的位置计算手指的弯曲角度。
3. 根据手指的弯曲角度判断手势类型。

以下是一个简单的手势判断代码示例：

import cv2
import mediapipe as mp
import math

mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands

# 初始化HandLandmark模型
hands = mp_hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5)

# 定义手势类型
GESTURES = {
    0: 'Fist',
    1: 'Five',
    2: 'Ok',
    3: 'Peace',
    4: 'Thumbs up'
}

# 计算两个点之间的距离
def distance(p1, p2):
    return math.sqrt((p1.x - p2.x) ** 2 + (p1.y - p2.y) ** 2)
# 计算三个点之间的夹角
def angle(p1, p2, p3):
    a = distance(p2, p3)
    b = distance(p1, p2)
    c = distance(p1, p3)
    return math.degrees(math.acos((a ** 2 + b ** 2 - c ** 2) / (2 * a * b)))

cap = cv2.VideoCapture(0)

while cap.isOpened():
    success, image = cap.read()
    if not success:
        break

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

    # 检测手部关键点
    results = hands.process(image)

    # 绘制手部关键点
    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            mp_drawing.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS)

            # 计算手指的弯曲角度
            thumb_angle = angle(hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_TIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_IP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_MCP])
            index_angle = angle(hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_PIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_MCP])
            middle_angle = angle(hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_TIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_PIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_MCP])
            ring_angle = angle(hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_TIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_PIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_MCP])
            pinky_angle = angle(hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_TIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_PIP],
                                 hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_MCP])

            # 判断手势类型
            if thumb_angle < 90 and index_angle < 90 and middle_angle < 90 and ring_angle < 90 and pinky_angle < 90:
                gesture = GESTURES[0]
            elif thumb_angle > 90 and index_angle > 90 and middle_angle > 90 and ring_angle > 90 and pinky_angle > 90:
                gesture = GESTURES[1]
            elif thumb_angle < 90 and index_angle < 90 and middle_angle < 90 and ring_angle < 90 and pinky_angle > 90:
                gesture = GESTURES[2]
            elif thumb_angle > 90 and index_angle < 90 and middle_angle < 90 and ring_angle > 90 and pinky_angle > 90:
                gesture = GESTURES[3]
            elif thumb_angle > 90 and index_angle > 90 and middle_angle < 90 and ring_angle < 90 and pinky_angle < 90:
                gesture = GESTURES[4]
            else:
                gesture = 'Unknown'

            # 在图像上显示手势类型
            cv2.putText(image, gesture, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    # 显示图像
    cv2.imshow('Hand Gesture Recognition', image)
    if cv2.waitKey(5) & 0xFF == 27:
        break

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