参考视频：https://www.youtube.com/watch?v=NZde8Xt78Iw

完整代码见：github仓库

前期准备

1、安装opencv-python和mediapipe库（参考[[安装类]]）

手部关节21点标号说明

1、读取摄像头

import cv2  
import mediapipe as mp  
import time  

cap = cv2.VideoCapture(0)  

while True:  
    success, img = cap.read()  

    cv2.imshow("Image",img)  
    cv2.waitKey(1)

2、检测手部并将手部的21个点表现出来

import cv2  
import mediapipe as mp  
import time  

# 打开默认摄像头  
cap = cv2.VideoCapture(0)  

# 创建一个手的实体类来存储手部信息  
mpHands = mp.solutions.hands  
hands = mpHands.Hands()  

# 创建一个用于绘制手部关键点和连接线的实体类  
mpDraw = mp.solutions.drawing_utils  

while True:  
    # 从摄像头捕获一帧图像  
    success, img = cap.read()  

    # 将捕获的图像从BGR颜色空间转换为RGB颜色空间  
    imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  

    # 处理图像以检测手部信息  
    results = hands.process(imgRGB)  

    # 检查是否检测到了手部关键点  
    if results.multi_hand_landmarks:  
        # 遍历每个检测到的手  
        for handLms in results.multi_hand_landmarks:  
            # 在图像上绘制手部关键点和连接线  
            mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)  

    # 在窗口中显示图像  
    cv2.imshow("Image", img)  

    # 按键等待1毫秒，允许窗口保持打开状态  
    cv2.waitKey(1)

3、添加帧率显示

# 初始化参数
pTime = 0  
cTime = 0
...
...
...
# 当前时间
cTime = time.time()  
# 计算fps
fps = 1 / (cTime-pTime)  
# 存储当前时间
pTime = cTime  
# 将fps显示在img上
cv2.putText(img,str(int(fps)),(10,70),cv2.FONT_HERSHEY_PLAIN,3, 
        (255,0,255),3)

4、获取每个点的坐标

# 获得手部21点的id和其对应的坐标  
for id, lm in enumerate(handLms.landmark):  
    # print(id,lm)  
    h, w, c = img.shape  
    cx, cy = int(lm.x*w), int(lm.y*h)  
    print(id, cx, cy)  
    if id ==0:  
        cv2.circle(img, (cx,cy), 25, (255,0,255), cv2.FILLED)

完整版代码

import cv2  
import mediapipe as mp  
import time  

# 打开默认摄像头  
cap = cv2.VideoCapture(0)  

# 创建一个手的实体类来存储手部信息  
mpHands = mp.solutions.hands  
hands = mpHands.Hands()  

# 创建一个用于绘制手部关键点和连接线的实体类  
mpDraw = mp.solutions.drawing_utils  

pTime = 0  
cTime = 0  

while True:  
    # 从摄像头捕获一帧图像  
    success, img = cap.read()  

    # 将捕获的图像从BGR颜色空间转换为RGB颜色空间  
    imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  

    # 处理图像以检测手部信息  
    results = hands.process(imgRGB)  

    # 检查是否检测到了手部关键点  
    if results.multi_hand_landmarks:  
        # 遍历每个检测到的手  
        for handLms in results.multi_hand_landmarks:  
            # 获得手部21点的id和其对应的坐标  
            for id, lm in enumerate(handLms.landmark):  
                # print(id,lm)  
                h, w, c = img.shape  
                cx, cy = int(lm.x*w), int(lm.y*h)  
                print(id, cx, cy)  
                if id ==0:  
                    cv2.circle(img, (cx,cy), 25, (255,0,255), cv2.FILLED)  

            # 在图像上绘制手部关键点和连接线  
            mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)  

    cTime = time.time()  
    fps = 1 / (cTime-pTime)  
    pTime = cTime  

    cv2.putText(img,str(int(fps)),(10,70),cv2.FONT_HERSHEY_PLAIN,3,  
            (255,0,255),3)  

    # 在窗口中显示图像  
    cv2.imshow("Image", img)  

    # 按键等待1毫秒，允许窗口保持打开状态  
    cv2.waitKey(1)

完整版代码(模块化)

将v1版本封装成实体类和函数

import cv2  
import mediapipe as mp  
import time  

class handDetector():  
    def __init__(self, mode=False, maxHands = 2, detectionCon=0.5, trackCon=0.5):  
        self.mode = mode  
        self.maxHands = maxHands  
        self.detectionCon = detectionCon  
        self.trackCon = trackCon  
        # 创建一个手的实体类来存储手部信息  
        self.mpHands = mp.solutions.hands  
        self.hands = self.mpHands.Hands(self.mode, self.maxHands, 1, self.detectionCon, self.trackCon)  
        # 创建一个用于绘制手部关键点和连接线的实体类  
        self.mpDraw = mp.solutions.drawing_utils  

    def findHands(self, img, draw = True):  
        # 将捕获的图像从BGR颜色空间转换为RGB颜色空间  
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  
        # 处理图像以检测手部信息  
        self.results = self.hands.process(imgRGB)  

        # 检查是否检测到了手部关键点  
        if self.results.multi_hand_landmarks:  
            # 遍历每个检测到的手  
            for handLms in self.results.multi_hand_landmarks:  
                if draw:  
                    # 在图像上绘制手部关键点和连接线  
                    self.mpDraw.draw_landmarks(img, handLms,  
                                               self.mpHands.HAND_CONNECTIONS)  
        return img  

    def findPosition(self, img, handNo=0, draw=True):  

        lmList = []  

        # 检查是否检测到了手部关键点  
        if self.results.multi_hand_landmarks:  
            # 存储对应的手，此处默认handNo=0  
            myHand = self.results.multi_hand_landmarks[handNo]  
            # 获得手部21点的id和其对应的坐标  
            for id, lm in enumerate(myHand.landmark):  
                h, w, c = img.shape  
                cx, cy = int(lm.x * w), int(lm.y * h)  
                # 将参数添加入lmList  
                lmList.append([id, cx, cy])  
                if draw:  
                    cv2.circle(img, (cx, cy), 10, (255, 0, 255), cv2.FILLED)  

        return lmList  

def main():  
    pTime = 0  
    cTime = 0  
    # 打开默认摄像头  
    cap = cv2.VideoCapture(0)  
    # 生成实体类  
    detector = handDetector()  

    while True:  
        # 从摄像头捕获一帧图像  
        success, img = cap.read()  
        # 调用实体类的相关函数  
        img = detector.findHands(img)  
        lmList = detector.findPosition(img)  
        if len(lmList) != 0:  
            print(lmList[4])  

        cTime = time.time()  
        fps = 1 / (cTime - pTime)  
        pTime = cTime  

        cv2.putText(img, str(int(fps)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3,  
                    (255, 0, 255), 3)  

        # 在窗口中显示图像  
        cv2.imshow("Image", img)  

        # 按键等待1毫秒，允许窗口保持打开状态  
        cv2.waitKey(1)  

if __name__ == "__main__":  
    main()