python pcm音量调大 python 控制音量

    本文基于前面的手部跟踪功能做一个手势音量控制功能，代码用到了前面手部跟踪封装的HandDetector.这篇文章在这里：

      使用了pycaw来做音量控制，pacaw的安装直接使用pip install pycaw即可。

        代码如下：

import cv2 as cv
import math
import mediapipe as mp
import time
from ctypes import cast,POINTER
from comtypes import CLSCTX_ALL
#使用pycaw来控制音量，pip install pycaw
from pycaw.pycaw import AudioUtilities,IAudioEndpointVolume

class HandDetector():
    def __init__(self, mode=False,
                 maxNumHands=2,
                 modelComplexity=1,
                 minDetectionConfidence=0.5,
                 minTrackingConfidence=0.5):
        self.mode = mode
        self.maxNumHands = maxNumHands
        self.modelComplexity = modelComplexity
        self.minDetectionConfidence = minDetectionConfidence
        self.minTrackingConfidence = minTrackingConfidence
        #创建mediapipe的solutions.hands对象
        self.mpHands = mp.solutions.hands
        self.handsDetector = self.mpHands.Hands(self.mode, self.maxNumHands, self.modelComplexity, self.minDetectionConfidence, self.minTrackingConfidence)
        #创建mediapipe的绘画工具
        self.mpDrawUtils = mp.solutions.drawing_utils

    def findHands(self, img, drawOnImage=True):
        #mediapipe手部检测器需要输入图像格式为RGB
        #cv默认的格式是BGR，需要转换
        imgRGB = cv.cvtColor(img, cv.COLOR_BGR2RGB)
        #调用手部检测器的process方法进行检测
        self.results = self.handsDetector.process(imgRGB)
        #print(results.multi_hand_landmarks)
    
        #如果multi_hand_landmarks有值表示检测到了手
        if self.results.multi_hand_landmarks:
            #遍历每一只手的landmarks
            for handLandmarks in self.results.multi_hand_landmarks:
                if drawOnImage:
                    self.mpDrawUtils.draw_landmarks(img, handLandmarks, self.mpHands.HAND_CONNECTIONS)
        return img;

    #从结果中查询某只手的landmark list
    def findHandPositions(self, img, handID=0, drawOnImage=True):
        landmarkList = []
        if self.results.multi_hand_landmarks:
            handLandmarks = self.results.multi_hand_landmarks[handID]
            for id,landmark in enumerate(handLandmarks.landmark):
                #处理每一个landmark,将landmark里的X,Y（比例）转换为帧数据的XY坐标
                h,w,c = img.shape
                centerX,centerY = int(landmark.x * w), int(landmark.y * h)
                landmarkList.append([id, centerX, centerY])
                if (drawOnImage):
                    #将landmark绘制成圆
                    cv.circle(img, (centerX,centerY), 8, (0,255,0))
        return landmarkList

def DisplayFPS(img, preTime):
    curTime = time.time()
    if (curTime - preTime == 0):
        return curTime;
    fps = 1 / (curTime - preTime)
    cv.putText(img, "FPS:" + str(int(fps)), (10,70), cv.FONT_HERSHEY_PLAIN,
              3, (0,255,0), 3)
    return curTime

def AudioEndpointGet():
    devices = AudioUtilities.GetSpeakers()
    interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)
    volume = cast(interface, POINTER(IAudioEndpointVolume))
    range = volume.GetVolumeRange()
    return volume,range

def AudioVolumeLevelSet(volume, range, value):
    if volume:
        if (value < range[0]) or (value > range[1]):
            return
        volume.SetMasterVolumeLevel(value, None)

def main():
    video = cv.VideoCapture('../../SampleVideos/handVolumeControl.mp4')
    #FPS显示
    preTime = 0
    handDetector = HandDetector(minDetectionConfidence=0.7)
    volume,volumeRange = AudioEndpointGet()
    print(volumeRange)
    #AudioVolumeLevelSet(volume, volumeRange, volumeRange[0])
    minFingerDistance = 1000
    maxFingerDistance = 0
    
    while True:
        ret,frame = video.read()
        if ret == False:
            break;
        frame = handDetector.findHands(frame)
        hand0Landmarks = handDetector.findHandPositions(frame)
        if (len(hand0Landmarks) != 0):
            #print(hand0Landmarks[4], hand0Landmarks[8])
            #取出大拇指(4)和食指(8)的指尖的点对应的坐标
            thumbX,thumbY = hand0Landmarks[4][1], hand0Landmarks[4][2]
            indexFingerX,indexFingerY = hand0Landmarks[8][1],hand0Landmarks[8][2]
            #计算两个指尖的点指尖的中点
            cx,cy = (thumbX + indexFingerX) / 2, (thumbY + indexFingerY) / 2
            #用实心圆突出显示出这两个个点
            cv.circle(frame, (thumbX,thumbY), 18, (90,220,180), cv.FILLED)
            cv.circle(frame, (indexFingerX,indexFingerY), 18, (0,120,255), cv.FILLED)
            
            #绘制两个点形成的直线
            cv.line(frame, (thumbX,thumbY), (indexFingerX,indexFingerY), (255,60,60), 3)
            #计算食指和拇指指尖的距离
            distance = math.hypot(thumbX - indexFingerX, thumbY - indexFingerY)
            #测试两指指尖最小和最大距离，改进方案可以是用摄像头做实时校准后再进行控制
            #本案例中直接获取视频里的最小和最大距离直接用作判断（我拍的视频里范围是30 - 425之间）
            if distance < minFingerDistance:
                minFingerDistance = distance
            if distance > maxFingerDistance:
                maxFingerDistance = distance
            #print(distance)
            if distance < 40:
                #两个指尖的中点显示为绿色，音量设置为最小值
                cv.circle(frame, (int(cx),int(cy)), 18, (0,255,0), cv.FILLED)
                AudioVolumeLevelSet(volume, volumeRange, volumeRange[0])
            else:
                cv.circle(frame, (int(cx),int(cy)), 18, (0,0,255), cv.FILLED)
                #这里为了方便直接使用425（本视频最大值）做比例换算
                #我本机的volumeRange是-63.5 到 0， 步长0.5
                value = volumeRange[0] * (1 - (distance / 425))
                print(value)
                AudioVolumeLevelSet(volume, volumeRange, value)
            
        preTime = DisplayFPS(frame, preTime)
        cv.imshow('Real Time Hand Detection', frame)
        if cv.waitKey(30) & 0xFF == ord('q'):
            break;
    print("Min & Max distance between thumb and index finger tips: ", minFingerDistance, maxFingerDistance)
    video.release()
    cv.destroyAllWindows()

if __name__ == "__main__":
    main()

        效果可以参考我的B站视频：