计算机视觉项目:基于Opencv实现手势控制电脑音量_volume.setmastervolumelevel

环境配置

开发环境：Pycharm

主要软件包：Opencv-python,Mediapipe

源码

音量控制模块

import cv2
import HandTrackingModule as htm
import numpy as np
import time
import math
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
devices = AudioUtilities.GetSpeakers()
interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)
volume = cast(interface, POINTER(IAudioEndpointVolume))
volumeRange = volume.GetVolumeRange()  # (-63.5, 0.0, 0.03125)
minVol = volumeRange[0]
maxVol = volumeRange[1]
 
#############################
wCam, hCam = 640, 480
#############################
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
detector = htm.handDetector()
 
while True:
    success, img = cap.read()
    img = detector.findHands(img)
    lmList = detector.findPosition(img, draw=False)
    if len(lmList) != 0:
        # print(lmList[4], lmList[8])
        x1, y1, x2, y2 = lmList[4][1], lmList[4][2], lmList[8][1], lmList[8][2]
        xc, yc = (x2 + x1) // 2, (y2 + y1) // 2
        cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
        cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)
        cv2.circle(img, (xc, yc), 15, (255, 0, 255), cv2.FILLED)
        cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
 
        length = math.hypot(x2 - x1, y2 -y1) # 15--200
        if length < 25:
            cv2.circle(img, (xc, yc), 15, (0, 255, 0), cv2.FILLED)
        # print(length)   # 25--230
        # 下面实现长度到音量的转换
        vol = np.interp(length, [15, 200], [minVol, maxVol])
        volume.SetMasterVolumeLevel(vol, None)
        volBar = np.interp(length, [15, 200], [350, 150])
        volPer = np.interp(length, [15, 200], [0, 100])
 
        cv2.rectangle(img, (20, 150), (50, 350), (255, 0, 255), 2)
        cv2.rectangle(img, (20, int(volBar)), (50, 350), (255, 0, 255), cv2.FILLED)
        cv2.putText(img, f'{int(volPer)}%', (10, 380), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 255), 2)
 
    cTime = time.time()
    fps = 1 / (cTime - pTime)
    pTime = cTime
    cv2.putText(img, f'fps: {int(fps)}', (10, 40), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)
 
    cv2.imshow("Image", img)
    cv2.waitKey(1)

手势跟踪模块
 

import cv2
import mediapipe as mp
import time
import math
 
class handDetector():
    def __init__(self, mode=False, maxHands=2, model_complexity=1,detectionCon=0.8, trackCon=0.8):
        self.mode = mode
        self.maxHands = maxHands
        self.detectionCon = detectionCon
        self.trackCon = trackCon
        self.model_complexity = model_complexity
        self.mpHands = mp.solutions.hands
        self.hands = self.mpHands.Hands(self.mode, self.maxHands,self.model_complexity, 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()

最终演示效果