應用執行個體 | 通過手勢控制系統音量大小實作步驟(附源碼)

導讀

本文主要介紹使用Python和MediaPipe實作通過手勢控制系統音量大小的應用。

效果示範

視訊實時識别效果示範：

實作思路與步驟

思路：通過識别大拇指和食指指尖的距離來調整系統音量的大小。

具體步驟：

(1)提取手部骨架和關鍵點：基于Google開源項目Mediapipe實作，具體步驟與介紹請檢視下面文章↓↓↓：

更穩定的手勢識别方法--基于手部骨架與關鍵點檢測

看完上面文章，你可以實作提取手部骨架和關鍵點效果如下：

同時你還會了解每個關鍵點的編号如下圖所示：

(2)計算大拇指與食指指尖距離：如上圖所示，即計算關鍵點4與8之間的距離；

(3)左邊顯示部分實作；FPS計算顯示、矩形框顯示模拟音量大小變化(矩形高度範圍與手指距離範圍映射對應)、百分比顯示；

(4)控制系統音量大小：手指距離範圍與音量大小範圍映射對應，調整系統音量使用pycaw子產品(pip install pycaw)中的函數。

完整代碼如下：

import cv2
import math
import time
import mediapipe as mp
from os import listdir
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
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))
volRange = volume.GetVolumeRange()
minVol = volRange[0]
maxVol = volRange[1]
print(minVol, maxVol)




def calAngle(pt1, pt2, pt3):
  #print('---------')
  a = math.sqrt(math.pow(pt2[1]-pt1[1],2)+math.pow(pt2[0]-pt1[0],2))
  b = math.sqrt(math.pow(pt3[1]-pt2[1],2)+math.pow(pt3[0]-pt2[0],2))
  c = math.sqrt(math.pow(pt1[1]-pt3[1],2)+math.pow(pt1[0]-pt3[0],2))
  #print(a,b,c)
  angle = math.acos((a*a + b*b -c*c)/(2*a*b))*180/math.pi
  #print(angle)
  return angle




def Normalize_landmarks(image, hand_landmarks):
  new_landmarks = []
  for i in range(0,len(hand_landmarks.landmark)):
    float_x = hand_landmarks.landmark[i].x
    float_y = hand_landmarks.landmark[i].y
    width = image.shape[1]
    height = image.shape[0]
    pt = mp_drawing._normalized_to_pixel_coordinates(float_x,float_y,width,height)
    new_landmarks.append(pt)
  return new_landmarks




def Draw_hand_points(image, normalized_hand_landmarks):
  cv2.circle(image,normalized_hand_landmarks[4],12,(255,0,255),-1, cv2.LINE_AA)
  cv2.circle(image,normalized_hand_landmarks[8],12,(255,0,255),-1, cv2.LINE_AA)
  cv2.line(image, normalized_hand_landmarks[4], normalized_hand_landmarks[8],(255,0,255),3)
  x1, y1 = normalized_hand_landmarks[4][0], normalized_hand_landmarks[4][1]
  x2, y2 = normalized_hand_landmarks[8][0], normalized_hand_landmarks[8][1]
  cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
  length = math.hypot(x2 - x1, y2 - y1)
  if length < 100:
      cv2.circle(image, (cx, cy),12,(0,255,0),cv2.FILLED)
  else:
      cv2.circle(image, (cx, cy),12,(255,0,255),cv2.FILLED)
  return image,length


pTime = 0     


hands = mp_hands.Hands(
    min_detection_confidence=0.5, min_tracking_confidence=0.5)
cap = cv2.VideoCapture(0)
while cap.isOpened():
  success, image = cap.read()
  if not success:
    print("camera frame is empty!")
    continue
  
  image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
  image.flags.writeable = False
  results = hands.process(image)
   
  image.flags.writeable = True
  image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
  if results.multi_hand_landmarks:
    for hand_landmarks in results.multi_hand_landmarks:
      mp_drawing.draw_landmarks(
          image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
      
      normalized_landmarks = Normalize_landmarks(image, hand_landmarks)
      
      try:
          image,length = Draw_hand_points(image, normalized_landmarks)
          #print(length) #20~300
          cv2.rectangle(image, (50, 150), (85, 350), (255, 0, 0), 3)
          if length > 200:
              length = 200
              
          # Hand range 0 - 210
          # Volume Range -65 - 0
          vol = int((200-length)/200*(minVol))
          #print(vol)
          volume.SetMasterVolumeLevel(vol, None)
          
          cv2.rectangle(image, (50, int(350-length)), (85, 350), (255, 0, 0), cv2.FILLED)
          percent = int(length / 200.0 * 100)
          #print(percent)
          if percent > 100:
              percent = 100
          strRate = str(percent) + '%'
          cv2.putText(image,strRate,(40,410),cv2.FONT_HERSHEY_COMPLEX,1.2,(255,0,0),2)
          cTime = time.time()
          fps = 1 / (cTime - pTime)
          pTime = cTime
          cv2.putText(image, f'FPS: {int(fps)}',(20, 40),cv2.FONT_HERSHEY_COMPLEX,1.2,(255,0,0),2)
      except:
          pass
       
  cv2.imshow('result', image)
  if cv2.waitKey(5) & 0xFF == 27:
    break
cv2.destroyAllWindows()
hands.close()
cap.release()      

結尾語

(1) 代碼隻是完成對應功能，大家可以根據自己的需要來優化，方法僅供參考；

(2) 大拇指與食指指尖的距離計算依賴于手距離攝像頭的距離，請根據實際情況調整。