手势远程操控鼠标

一个手势控制鼠标小项目，可以拿来当课设

完整代码如下
AiVirtualMouse.py

import cv2
import HandTrackingModule as htm
import autopy
import numpy as np
import time

##############################
wCam, hCam = 1080, 720
frameR = 100
smoothening = 5
##############################
cap = cv2.VideoCapture(0)  # 若使用笔记本自带摄像头则编号为0  若使用外接摄像头 则更改为1或其他编号
cap.set(3, wCam)
cap.set(4, hCam)
pTime = 0
plocX, plocY = 0, 0
clocX, clocY = 0, 0

detector = htm.handDetector()
wScr, hScr = autopy.screen.size()
# print(wScr, hScr)

while True:
    success, img = cap.read()
    # 1. 检测手部 得到手指关键点坐标
    img = detector.findHands(img)
    cv2.rectangle(img, (frameR, frameR), (wCam - frameR, hCam - frameR), (0, 255, 0), 2,  cv2.FONT_HERSHEY_PLAIN)
    lmList = detector.findPosition(img, draw=False)

    # 2. 判断食指和中指是否伸出
    if len(lmList) != 0:
        x1, y1 = lmList[8][1:]
        x2, y2 = lmList[12][1:]
        fingers = detector.fingersUp()

        # 3. 若只有食指伸出 则进入移动模式
        if fingers[1] and fingers[2] == False:
            # 4. 坐标转换： 将食指在窗口坐标转换为鼠标在桌面的坐标
            # 鼠标坐标
            x3 = np.interp(x1, (frameR, wCam - frameR), (0, wScr))
            y3 = np.interp(y1, (frameR, hCam - frameR), (0, hScr))

            # smoothening values
            clocX = plocX + (x3 - plocX) / smoothening
            clocY = plocY + (y3 - plocY) / smoothening

            autopy.mouse.move(wScr - clocX, clocY)
            cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
            plocX, plocY = clocX, clocY

        # 5. 若是食指和中指都伸出 则检测指头距离 距离够短则对应鼠标点击
        if fingers[1] and fingers[2]:
            length, img, pointInfo = detector.findDistance(8, 12, img)
            if length < 40:
                cv2.circle(img, (pointInfo[4], pointInfo[5]),
                           15, (0, 255, 0), cv2.FILLED)
                autopy.mouse.click()

    cTime = time.time()
    fps = 1 / (cTime - pTime)
    pTime = cTime
    cv2.putText(img, f'fps:{int(fps)}', [15, 25],
                cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 255), 2)
    cv2.imshow("Image", img)
    cv2.waitKey(1)

HandTrackingModule.py

import cv2
import mediapipe as mp
import time
import math

class handDetector():
    def __init__(self, mode=False, maxHands=2, detectionCon=0.8, trackCon=0.8):
        self.mode = mode
        self.maxHands = maxHands

        self.detectionCon = detectionCon
        self.trackCon = trackCon

        self.mpHands = mp.solutions.hands
        self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.detectionCon, self.trackCon)
        self.mpDraw = mp.solutions.drawing_utils
        self.tipIds = [4, 8, 12, 16, 20]

    def findHands(self, img, draw=True):
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        self.results = self.hands.process(imgRGB)

        print(self.results.multi_handedness)  # 获取检测结果中的左右手标签并打印

        if self.results.multi_hand_landmarks:
            for handLms in self.results.multi_hand_landmarks:
                if draw:
                    self.mpDraw.draw_landmarks(img, handLms, self.mpHands.HAND_CONNECTIONS)
        return img

    def findPosition(self, img, draw=True):
        self.lmList = []
        if self.results.multi_hand_landmarks:
            for handLms in self.results.multi_hand_landmarks:
                for id, lm in enumerate(handLms.landmark):
                    h, w, c = img.shape
                    cx, cy = int(lm.x * w), int(lm.y * h)
                    # print(id, cx, cy)
                    self.lmList.append([id, cx, cy])
                    if draw:
                        cv2.circle(img, (cx, cy), 12, (255, 0, 255), cv2.FILLED)
        return self.lmList

    def fingersUp(self):
        fingers = []
        # 大拇指
        if self.lmList[self.tipIds[0]][1] > self.lmList[self.tipIds[0] - 1][1]:
            fingers.append(1)
        else:
            fingers.append(0)

        # 其余手指
        for id in range(1, 5):
            if self.lmList[self.tipIds[id]][2] < self.lmList[self.tipIds[id] - 2][2]:
                fingers.append(1)
            else:
                fingers.append(0)

        # totalFingers = fingers.count(1)
        return fingers

    def findDistance(self, p1, p2, img, draw=True, r=15, t=3):
        x1, y1 = self.lmList[p1][1:]
        x2, y2 = self.lmList[p2][1:]
        cx, cy = (x1 + x2) // 2, (y1 + y2) // 2

        if draw:
            cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), t)
            cv2.circle(img, (x1, y1), r, (255, 0, 255), cv2.FILLED)
            cv2.circle(img, (x2, y2), r, (255, 0, 255), cv2.FILLED)
            cv2.circle(img, (cx, cy), r, (0, 0, 255), cv2.FILLED)
            length = math.hypot(x2 - x1, y2 - y1)

        return length, img, [x1, y1, x2, y2, cx, cy]


def main():
    pTime = 0
    cTime = 0
    cap = cv2.VideoCapture(0)
    detector = handDetector()
    while True:
        success, img = cap.read()
        img = detector.findHands(img)        # 检测手势并画上骨架信息

        lmList = detector.findPosition(img)  # 获取得到坐标点的列表
        if len(lmList) != 0:
            print(lmList[4])

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

        cv2.putText(img, 'fps:' + str(int(fps)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 255), 3)
        cv2.imshow('Image', img)
        cv2.waitKey(1)


if __name__ == "__main__":
    main()