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all.py
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all.py
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import math
import time
from ctypes import cast, POINTER
import autopy
import cv2
import numpy as np
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
import HandTrackingModule as htm
from utils.util import compute_distance
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 = 1280, 720
pt1, pt2 = (100, 100), (1100, 500) # 虚拟鼠标的移动范围，左上坐标pt1，右下坐标pt2
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
stable_thres = 40 # 判断为稳定触发的时间
stop_thres = 15 # 判断为停滞的移动距离
stop_time = [0, 0]
stop_time1 = [-50]
old_lmList = None
functionflag1 = False
detector = htm.handDetector()
wScr, hScr = autopy.screen.size()
print(wScr, hScr)
def voiceControl(img, lmList, old_lmList, functionflag1):
draw = True
functionflag = False
if len(lmList):
# 下面实现长度到音量的转换
# 判断食指是否稳定
stop_length = compute_distance(lmList[8][1], lmList[8][2], old_lmList[8][1], old_lmList[8][2])
fingers = detector.fingersUp()
if fingers[0] and fingers[1]:
functionflag = True
if stop_length < stop_thres or functionflag1:
if stop_time[1] < stable_thres:
stop_time[1] += 1
fill_cnt = stop_time[1] / stable_thres * 360
cv2.circle(img, (lmList[8][1], lmList[8][2]), 15, (0, 255, 0), cv2.FILLED)
if 0 < fill_cnt < 360:
cv2.ellipse(img, (lmList[8][1], lmList[8][2]), (30, 30), 0, 0, fill_cnt, (255, 255, 0),
2)
draw = True
# 进入功能调节音量
else:
if draw:
cv2.ellipse(img, (lmList[8][1], lmList[8][2]), (30, 30), 0, 0, fill_cnt, (0, 150, 255),
4)
functionflag1 = True
old_lmList = lmList
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)
# 下面实现长度到音量的转换
# np.interp为插值函数，简而言之，看line_len的值在[15，200]中所占比例，然后去[min_volume,max_volume]中线性寻找相应的值，作为返回值
vol = np.interp(length, [15, 200], [minVol, maxVol])
# 用之前得到的vol值设置电脑音量
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)
if stop_length < stop_thres - 5:
if stop_time1[0] < stable_thres:
stop_time1[0] += 1
fill_cnt = stop_time1[0] / stable_thres * 360
cv2.circle(img, (lmList[8][1], lmList[8][2]), 15, (0, 255, 0), cv2.FILLED)
if 0 < fill_cnt < 360:
cv2.ellipse(img, (lmList[8][1], lmList[8][2]), (30, 30), 0, 0, fill_cnt, (0, 255, 0),
2)
if fill_cnt == 360:
draw = False
stop_time1[0] = -50
stop_time[1] = -50
functionflag1 = False
functionflag = True
else:
stop_time1[0] = 0
else:
stop_time[1] = 0
else:
stop_time[1] = 0
old_lmList = lmList
return img, functionflag, old_lmList, functionflag1
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)
if old_lmList is None and len(lmList):
old_lmList = lmList
img, functionflag, old_lmList, functionflag1 = voiceControl(img, lmList, old_lmList, functionflag1)
# 2. 判断食指和中指是否伸出
if len(lmList) != 0 and functionflag == False:
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))
x3 = np.interp(x1, (pt1[0], pt2[0]), (0, wScr))
y3 = np.interp(y1, (pt1[1], pt2[1]), (0, hScr))
# smoothening values
clocX = plocX + (x3 - plocX) / smoothening
clocY = plocY + (y3 - plocY) / smoothening
# autopy.mouse.move(x3, y3) # 给出鼠标移动位置坐标
autopy.mouse.move(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 < 60:
if stop_time[0] < stable_thres:
stop_time[0] += 1
fill_cnt = stop_time[0] / stable_thres * 360
cv2.circle(img, ((pointInfo[4], pointInfo[5])), 15, (0, 255, 0), cv2.FILLED)
if 0 < fill_cnt < 360:
cv2.ellipse(img, ((pointInfo[4], pointInfo[5])), (30, 30), 0, 0, fill_cnt, (255, 255, 0),
2)
# 进入功能开始调节音量
else:
stop_time[0] = 0
autopy.mouse.click()
else:
stop_time[0] = 0
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)
if cv2.waitKey(1) & 0xFF == ord('q'):
break