{"id":336100,"date":"2022-07-24T15:00:24","date_gmt":"2022-07-24T15:00:24","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=336100"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=336100","title":{"rendered":"\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u044c\u044e \u0437\u0432\u0443\u043a\u0430 \u0436\u0435\u0441\u0442\u0430\u043c\u0438 \u043d\u0430 Python<\/span>"},"content":{"rendered":"
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\u0412 \u0434\u0430\u043d\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u0445\u043e\u0447\u0443 \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u0430\u0442\u044c \u043f\u0440\u043e \u043e\u0434\u0438\u043d \u043f\u0440\u043e\u0435\u043a\u0442 \u0438\u0437 \u043a\u0443\u0440\u0441\u0430 \u043f\u043e OpenCV Python. \u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c \u043f\u043e\u043b\u043d\u044b\u0439 \u043a\u043e\u0434 \u043c\u043e\u0436\u043d\u043e \u043d\u0430 Github<\/a>.<\/p>\n

\u0418\u0434\u0435\u044f \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e\u0431\u044b \u043c\u0435\u043d\u044f\u0442\u044c \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u044c \u0437\u0432\u0443\u043a\u0430 \u043d\u0430 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u0435 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u0436\u0435\u0441\u0442\u043e\u0432. \u0413\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u044c \u0431\u0443\u0434\u0435\u0442 \u0440\u0435\u0433\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435\u043c \u043c\u0435\u0436\u0434\u0443 \u0443\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u043c \u0438 \u0431\u043e\u043b\u044c\u0448\u0438\u043c \u043f\u0430\u043b\u044c\u0446\u0430\u043c\u0438, \u0430 \u0441\u0438\u0433\u043d\u0430\u043b\u043e\u043c \u0434\u043b\u044f \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u044f \u0431\u0443\u0434\u0435\u0442 \u0441\u043e\u0433\u043d\u0443\u0442\u044b\u0439 \u043c\u0438\u0437\u0438\u043d\u0435\u0446.<\/p>\n

\u041f\u0435\u0440\u0432\u044b\u043c \u0434\u0435\u043b\u043e\u043c \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u043c \u0432\u0441\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438:<\/p>\n

pip install mediapipe pip install opencv-python pip install numpy pip install pycaw<\/code><\/pre>\n
\u0421\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u0444\u0430\u0439\u043b \u0438 \u043d\u0430\u0437\u043e\u0432\u0435\u043c \u0435\u0433\u043e `HandTrackingModule.py`.<\/p>\n
import cv2 import mediapipe as mp import time import math  class handDetector(): def __init__(self, mode=False, maxHands=2, modelComplexity=1, detectionCon=0.5, trackCon=0.5): self.mode = mode self.maxHands = maxHands self.modelComplexity = modelComplexity self.detectionCon = detectionCon self.trackCon = trackCon  self.mpHands = mp.solutions.hands self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.modelComplexity, 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)  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, handNo=0, draw=True): xList = [] yList = [] bbox = [] self.lmList = [] if self.results.multi_hand_landmarks: myHand = self.results.multi_hand_landmarks[handNo] for id, lm in enumerate(myHand.landmark): #print(id, lm) h, w, c = img.shape cx, cy = int(lm.x*w), int(lm.y*h) xList.append(cx) yList.append(cy) self.lmList.append([id, cx, cy]) if draw: cv2.circle(img, (cx, cy), 5, (255,0,255), cv2.FILLED) xmin, xmax = min(xList), max(xList) ymin, ymax = min(yList), max(yList) bbox = xmin, ymin, xmax, ymax  if draw: cv2.rectangle(img, (bbox[0]-20, bbox[1]-20), (bbox[2]+20, bbox[3]+20), (0, 255, 0), 2) return self.lmList, bbox  def findDistance(self, p1, p2, img, draw=True): x1, y1 = self.lmList[p1][1], self.lmList[p1][2] x2, y2 = self.lmList[p2][1], self.lmList[p2][2] cx, cy = (x1+x2)\/\/2, (y1+y2)\/\/2  if draw: cv2.circle(img, (x1,y1), 15, (255,0,255), cv2.FILLED) cv2.circle(img, (x2,y2), 15, (255,0,255), cv2.FILLED) cv2.line(img, (x1,y1), (x2,y2), (255,0,255), 3) cv2.circle(img, (cx,cy), 15, (255,0,255), cv2.FILLED)  length = math.hypot(x2-x1, y2-y1) return length, img, [x1, y1, x2, y2, cx, cy]  def fingersUp(self): fingers = []  # Thumb if self.lmList[self.tipIds[0]][1] < self.lmList[self.tipIds[0]-1][1]: fingers.append(1) else: fingers.append(0)  # 4 Fingers 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) return fingers <\/code><\/pre>\n
\u0415\u0441\u043b\u0438 \u0432\u043d\u0438\u043c\u0430\u0442\u0435\u043b\u044c\u043d\u043e \u0438\u0437\u0443\u0447\u0438\u0442\u044c \u043a\u043b\u0430\u0441\u0441, \u043e\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0439 \u0432\u044b\u0448\u0435, \u0442\u043e \u043d\u0438\u0447\u0435\u0433\u043e \u0441\u043b\u043e\u0436\u043d\u043e\u0433\u043e \u0442\u0430\u043c \u043d\u0435\u0442. \u0414\u0430\u043d\u043d\u044b\u0439 \u043a\u043b\u0430\u0441\u0441 \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d \u0438 \u0432 \u0434\u0440\u0443\u0433\u0438\u0445 \u0432\u0430\u0448\u0438\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u0445, \u043a\u0430\u043a \u0448\u0430\u0431\u043b\u043e\u043d\u043d\u044b\u0439.<\/p>\n
\u0422\u0435\u043f\u0435\u0440\u044c \u043f\u0440\u0438\u0441\u0442\u0443\u043f\u0438\u043c \u043a \u0441\u0430\u043c\u043e\u043c\u0443 \u043a\u043e\u0434\u0443 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b.<\/p>\n
\u0418\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u0443\u0435\u043c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438:<\/p>\n
import cv2 import time import numpy as np import HandTrackingModule as htm import math from ctypes import cast, POINTER from comtypes import CLSCTX_ALL from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume<\/code><\/pre>\n
\u041d\u0430\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0435\u043c \u043a\u0430\u043c\u0435\u0440\u0443:<\/p>\n
wCam, hCam = 1280, 720 # \u0440\u0430\u0437\u043c\u0435\u0440 \u043e\u043a\u043d\u0430  cap = cv2.VideoCapture(0) cap.set(3, wCam) cap.set(4, hCam) pTime = 0<\/code><\/pre>\n
\u041f\u0440\u0438 \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0438 \u043a\u0430\u043c\u0435\u0440\u044b \u043c\u043e\u0433\u0443\u0442 \u0432\u043e\u0437\u043d\u0438\u043a\u043d\u0443\u0442\u044c \u043e\u0448\u0438\u0431\u043a\u0438, \u043f\u043e\u043c\u0435\u043d\u044f\u0439\u0442\u0435 0<\/code> \u0438\u0437 `cap = cv2.VideoCapture(0)` \u043d\u0430 1<\/code> \u0438\u043b\u0438 2<\/code>.<\/p>\n
\u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043c \u0433\u043b\u043e\u0431\u0430\u043b\u044c\u043d\u044b\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435:<\/p>\n
detector = htm.handDetector(detectionCon=0.7, maxHands=1)  devices = AudioUtilities.GetSpeakers() interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None) volume = cast(interface, POINTER(IAudioEndpointVolume)) volRange = volume.GetVolumeRange() volume.SetMasterVolumeLevel(0, None) minVol = volRange[0] maxVol = volRange[1] vol = 0 volBar = 400 volPer = 0 area = 0 colorVol = (255, 0, 0)<\/code><\/pre>\n
\u0417\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u044b\u0439 \u0446\u0438\u043a\u043b \u0438 \u043f\u0440\u043e\u0431\u0443\u0435\u043c \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0441 \u043a\u0430\u043c\u0435\u0440\u044b.<\/p>\n
_, img = cap.read()<\/code><\/pre>\n
\u041d\u0430\u0439\u0434\u0435\u043c \u043d\u0430\u0448\u0443 \u0440\u0443\u043a\u0443 \u0432 \u043a\u0430\u0434\u0440\u0435 \u0438 \u0432\u044b\u0434\u0435\u043b\u0438\u043c \u0435\u0435:<\/p>\n
img = detector.findHands(img) lmList, bbox = detector.findPosition(img, draw=True)<\/code><\/pre>\n
\u0414\u043b\u044f \u0442\u0435\u0445, \u043a\u043e\u043c\u0443 \u043d\u0435 \u043d\u0443\u0436\u043d\u043e \u0432\u044b\u0434\u0435\u043b\u044f\u0442\u044c \u0440\u0443\u043a\u0443, \u043f\u043e\u043c\u0435\u043d\u044f\u0439\u0442\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 draw=True<\/code> \u043d\u0430 False<\/code>. <\/p>\n
\u0415\u0441\u043b\u0438 \u0441\u043f\u0438\u0441\u043e\u043a lmList<\/code> \u043d\u0435 \u043f\u0443\u0441\u0442\u043e\u0439, \u0442\u043e \u043c\u043e\u0436\u0435\u043c \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0442\u044c \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u043c\u0435\u0436\u0434\u0443 \u0443\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u043c \u0438 \u0431\u043e\u043b\u044c\u0448\u0438\u043c \u043f\u0430\u043b\u044c\u0446\u0430\u043c\u0438. \u0414\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u0432 \u043e\u043f\u0438\u0441\u0430\u043d\u043d\u043e\u043c \u0432\u044b\u0448\u0435 \u043a\u043b\u0430\u0441\u0441\u0435 handDetector<\/code> \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d \u043c\u0435\u0442\u043e\u0434 findDistance<\/code>.<\/p>\n
length, img, lineInfo = detector.findDistance(4, 8, img)<\/code><\/pre>\n
\u0415\u0441\u043b\u0438 \u043e\u0441\u0442\u0430\u0432\u0438\u043c \u0442\u0430\u043a, \u0442\u043e \u0441\u043c\u0435\u043d\u0430 \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u0438 \u0437\u0432\u0443\u043a\u0430 \u0431\u0443\u0434\u0435\u0442 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u043d\u0435\u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043d\u0443\u0436\u043d\u043e \u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u044b\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b:<\/p>\n
# Convert Volume volBar = np.interp(length, [50,200], [400, 150]) volPer = np.interp(length, [50,200], [0, 100])<\/code><\/pre>\n
\u041f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u043c \u044d\u0442\u0430\u043f\u043e\u043c \u043d\u0443\u0436\u043d\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0441\u043e\u0433\u043d\u0443\u0442 \u043b\u0438 \u043c\u0438\u0437\u0438\u043d\u0435\u0446, \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0434\u0438\u0442\u044c \u0441\u043c\u0435\u043d\u0443 \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u0438 \u0437\u0432\u0443\u043a\u0430.<\/p>\n
\u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0432\u0441\u0435\u0445 \u043f\u0430\u043b\u044c\u0446\u0435\u0432 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:<\/p>\n
fingers = detector.fingersUp()<\/code><\/pre>\n
\u0415\u0441\u043b\u0438 \u043c\u0438\u0437\u0438\u043d\u0435\u0446 \u0441\u043e\u0433\u043d\u0443\u0442, \u0442\u043e \u043c\u0435\u043d\u044f\u0435\u043c \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u044c:<\/p>\n
if not fingers[4]: volume.SetMasterVolumeLevelScalar(volPer\/100, None)<\/code><\/pre>\n
\u0418 \u0434\u043e\u043b\u0433\u043e\u0436\u0434\u0430\u043d\u043d\u044b\u0439 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442:<\/p>\n
<\/figcaption><\/figure>\n
\u041f\u043e\u043b\u043d\u044b\u0439 \u043a\u043e\u0434 \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 \u043d\u0430 \u043c\u043e\u0435\u043c Github. \u042d\u0442\u043e \u043c\u043e\u044f \u043f\u0435\u0440\u0432\u0430\u044f \u0441\u0442\u0430\u0442\u044c\u044f \u0438 \u043e\u0434\u0438\u043d \u0438\u0437 \u043f\u0435\u0440\u0432\u044b\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u0432 \u043d\u0430 OpenCV, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043d\u0435 \u0441\u0443\u0434\u0438\u0442\u0435 \u0441\u0442\u0440\u043e\u0433\u043e =)<\/p>\n<\/p>\n<\/div>\n<\/div>\n<\/div>\n
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 \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438  https:\/\/habr.com\/ru\/post\/678638\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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\u0412 \u0434\u0430\u043d\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u0445\u043e\u0447\u0443 \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u0430\u0442\u044c \u043f\u0440\u043e \u043e\u0434\u0438\u043d \u043f\u0440\u043e\u0435\u043a\u0442 \u0438\u0437 \u043a\u0443\u0440\u0441\u0430 \u043f\u043e OpenCV Python. \u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c \u043f\u043e\u043b\u043d\u044b\u0439 \u043a\u043e\u0434 \u043c\u043e\u0436\u043d\u043e \u043d\u0430 Github<\/a>.<\/p>\n
\u0418\u0434\u0435\u044f \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e\u0431\u044b \u043c\u0435\u043d\u044f\u0442\u044c \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u044c \u0437\u0432\u0443\u043a\u0430 \u043d\u0430 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u0435 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u0436\u0435\u0441\u0442\u043e\u0432. \u0413\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u044c \u0431\u0443\u0434\u0435\u0442 \u0440\u0435\u0433\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435\u043c \u043c\u0435\u0436\u0434\u0443 \u0443\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u043c \u0438 \u0431\u043e\u043b\u044c\u0448\u0438\u043c \u043f\u0430\u043b\u044c\u0446\u0430\u043c\u0438, \u0430 \u0441\u0438\u0433\u043d\u0430\u043b\u043e\u043c \u0434\u043b\u044f \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u044f \u0431\u0443\u0434\u0435\u0442 \u0441\u043e\u0433\u043d\u0443\u0442\u044b\u0439 \u043c\u0438\u0437\u0438\u043d\u0435\u0446.<\/p>\n
\u041f\u0435\u0440\u0432\u044b\u043c \u0434\u0435\u043b\u043e\u043c \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u043c \u0432\u0441\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438:<\/p>\n
pip install mediapipe pip install opencv-python pip install numpy pip install pycaw<\/code><\/pre>\n
\u0421\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u0444\u0430\u0439\u043b \u0438 \u043d\u0430\u0437\u043e\u0432\u0435\u043c \u0435\u0433\u043e `HandTrackingModule.py`.<\/p>\n
import cv2 import mediapipe as mp import time import math  class handDetector(): def __init__(self, mode=False, maxHands=2, modelComplexity=1, detectionCon=0.5, trackCon=0.5): self.mode = mode self.maxHands = maxHands self.modelComplexity = modelComplexity self.detectionCon = detectionCon self.trackCon = trackCon  self.mpHands = mp.solutions.hands self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.modelComplexity, 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)  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, handNo=0, draw=True): xList = [] yList = [] bbox = [] self.lmList = [] if self.results.multi_hand_landmarks: myHand = self.results.multi_hand_landmarks[handNo] for id, lm in enumerate(myHand.landmark): #print(id, lm) h, w, c = img.shape cx, cy = int(lm.x*w), int(lm.y*h) xList.append(cx) yList.append(cy) self.lmList.append([id, cx, cy]) if draw: cv2.circle(img, (cx, cy), 5, (255,0,255), cv2.FILLED) xmin, xmax = min(xList), max(xList) ymin, ymax = min(yList), max(yList) bbox = xmin, ymin, xmax, ymax  if draw: cv2.rectangle(img, (bbox[0]-20, bbox[1]-20), (bbox[2]+20, bbox[3]+20), (0, 255, 0), 2) return self.lmList, bbox  def findDistance(self, p1, p2, img, draw=True): x1, y1 = self.lmList[p1][1], self.lmList[p1][2] x2, y2 = self.lmList[p2][1], self.lmList[p2][2] cx, cy = (x1+x2)\/\/2, (y1+y2)\/\/2  if draw: cv2.circle(img, (x1,y1), 15, (255,0,255), cv2.FILLED) cv2.circle(img, (x2,y2), 15, (255,0,255), cv2.FILLED) cv2.line(img, (x1,y1), (x2,y2), (255,0,255), 3) cv2.circle(img, (cx,cy), 15, (255,0,255), cv2.FILLED)  length = math.hypot(x2-x1, y2-y1) return length, img, [x1, y1, x2, y2, cx, cy]  def fingersUp(self): fingers = []  # Thumb if self.lmList[self.tipIds[0]][1] < self.lmList[self.tipIds[0]-1][1]: fingers.append(1) else: fingers.append(0)  # 4 Fingers 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) return fingers <\/code><\/pre>\n
\u0415\u0441\u043b\u0438 \u0432\u043d\u0438\u043c\u0430\u0442\u0435\u043b\u044c\u043d\u043e \u0438\u0437\u0443\u0447\u0438\u0442\u044c \u043a\u043b\u0430\u0441\u0441, \u043e\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0439 \u0432\u044b\u0448\u0435, \u0442\u043e \u043d\u0438\u0447\u0435\u0433\u043e \u0441\u043b\u043e\u0436\u043d\u043e\u0433\u043e \u0442\u0430\u043c \u043d\u0435\u0442. \u0414\u0430\u043d\u043d\u044b\u0439 \u043a\u043b\u0430\u0441\u0441 \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d \u0438 \u0432 \u0434\u0440\u0443\u0433\u0438\u0445 \u0432\u0430\u0448\u0438\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u0445, \u043a\u0430\u043a \u0448\u0430\u0431\u043b\u043e\u043d\u043d\u044b\u0439.<\/p>\n
\u0422\u0435\u043f\u0435\u0440\u044c \u043f\u0440\u0438\u0441\u0442\u0443\u043f\u0438\u043c \u043a \u0441\u0430\u043c\u043e\u043c\u0443 \u043a\u043e\u0434\u0443 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b.<\/p>\n
\u0418\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u0443\u0435\u043c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438:<\/p>\n
import cv2 import time import numpy as np import HandTrackingModule as htm import math from ctypes import cast, POINTER from comtypes import CLSCTX_ALL from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume<\/code><\/pre>\n
\u041d\u0430\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0435\u043c \u043a\u0430\u043c\u0435\u0440\u0443:<\/p>\n
wCam, hCam = 1280, 720 # \u0440\u0430\u0437\u043c\u0435\u0440 \u043e\u043a\u043d\u0430  cap = cv2.VideoCapture(0) cap.set(3, wCam) cap.set(4, hCam) pTime = 0<\/code><\/pre>\n
\u041f\u0440\u0438 \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0438 \u043a\u0430\u043c\u0435\u0440\u044b \u043c\u043e\u0433\u0443\u0442 \u0432\u043e\u0437\u043d\u0438\u043a\u043d\u0443\u0442\u044c \u043e\u0448\u0438\u0431\u043a\u0438, \u043f\u043e\u043c\u0435\u043d\u044f\u0439\u0442\u0435 0<\/code> \u0438\u0437 `cap = cv2.VideoCapture(0)` \u043d\u0430 1<\/code> \u0438\u043b\u0438 2<\/code>.<\/p>\n
\u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043c \u0433\u043b\u043e\u0431\u0430\u043b\u044c\u043d\u044b\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435:<\/p>\n
detector = htm.handDetector(detectionCon=0.7, maxHands=1)  devices = AudioUtilities.GetSpeakers() interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None) volume = cast(interface, POINTER(IAudioEndpointVolume)) volRange = volume.GetVolumeRange() volume.SetMasterVolumeLevel(0, None) minVol = volRange[0] maxVol = volRange[1] vol = 0 volBar = 400 volPer = 0 area = 0 colorVol = (255, 0, 0)<\/code><\/pre>\n
\u0417\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u044b\u0439 \u0446\u0438\u043a\u043b \u0438 \u043f\u0440\u043e\u0431\u0443\u0435\u043c \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0441 \u043a\u0430\u043c\u0435\u0440\u044b.<\/p>\n
_, img = cap.read()<\/code><\/pre>\n
\u041d\u0430\u0439\u0434\u0435\u043c \u043d\u0430\u0448\u0443 \u0440\u0443\u043a\u0443 \u0432 \u043a\u0430\u0434\u0440\u0435 \u0438 \u0432\u044b\u0434\u0435\u043b\u0438\u043c \u0435\u0435:<\/p>\n
img = detector.findHands(img) lmList, bbox = detector.findPosition(img, draw=True)<\/code><\/pre>\n
\u0414\u043b\u044f \u0442\u0435\u0445, \u043a\u043e\u043c\u0443 \u043d\u0435 \u043d\u0443\u0436\u043d\u043e \u0432\u044b\u0434\u0435\u043b\u044f\u0442\u044c \u0440\u0443\u043a\u0443, \u043f\u043e\u043c\u0435\u043d\u044f\u0439\u0442\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 draw=True<\/code> \u043d\u0430 False<\/code>. <\/p>\n
\u0415\u0441\u043b\u0438 \u0441\u043f\u0438\u0441\u043e\u043a lmList<\/code> \u043d\u0435 \u043f\u0443\u0441\u0442\u043e\u0439, \u0442\u043e \u043c\u043e\u0436\u0435\u043c \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0442\u044c \u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u043c\u0435\u0436\u0434\u0443 \u0443\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u043c \u0438 \u0431\u043e\u043b\u044c\u0448\u0438\u043c \u043f\u0430\u043b\u044c\u0446\u0430\u043c\u0438. \u0414\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u0432 \u043e\u043f\u0438\u0441\u0430\u043d\u043d\u043e\u043c \u0432\u044b\u0448\u0435 \u043a\u043b\u0430\u0441\u0441\u0435 handDetector<\/code> \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d \u043c\u0435\u0442\u043e\u0434 findDistance<\/code>.<\/p>\n
length, img, lineInfo = detector.findDistance(4, 8, img)<\/code><\/pre>\n
\u0415\u0441\u043b\u0438 \u043e\u0441\u0442\u0430\u0432\u0438\u043c \u0442\u0430\u043a, \u0442\u043e \u0441\u043c\u0435\u043d\u0430 \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u0438 \u0437\u0432\u0443\u043a\u0430 \u0431\u0443\u0434\u0435\u0442 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u043d\u0435\u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043d\u0443\u0436\u043d\u043e \u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u044b\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b:<\/p>\n
# Convert Volume volBar = np.interp(length, [50,200], [400, 150]) volPer = np.interp(length, [50,200], [0, 100])<\/code><\/pre>\n
\u041f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u043c \u044d\u0442\u0430\u043f\u043e\u043c \u043d\u0443\u0436\u043d\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0441\u043e\u0433\u043d\u0443\u0442 \u043b\u0438 \u043c\u0438\u0437\u0438\u043d\u0435\u0446, \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0434\u0438\u0442\u044c \u0441\u043c\u0435\u043d\u0443 \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u0438 \u0437\u0432\u0443\u043a\u0430.<\/p>\n
\u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0432\u0441\u0435\u0445 \u043f\u0430\u043b\u044c\u0446\u0435\u0432 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:<\/p>\n
fingers = detector.fingersUp()<\/code><\/pre>\n
\u0415\u0441\u043b\u0438 \u043c\u0438\u0437\u0438\u043d\u0435\u0446 \u0441\u043e\u0433\u043d\u0443\u0442, \u0442\u043e \u043c\u0435\u043d\u044f\u0435\u043c \u0433\u0440\u043e\u043c\u043a\u043e\u0441\u0442\u044c:<\/p>\n
if not fingers[4]: volume.SetMasterVolumeLevelScalar(volPer\/100, None)<\/code><\/pre>\n
\u0418 \u0434\u043e\u043b\u0433\u043e\u0436\u0434\u0430\u043d\u043d\u044b\u0439 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442:<\/p>\n
<\/figcaption><\/figure>\n
\u041f\u043e\u043b\u043d\u044b\u0439 \u043a\u043e\u0434 \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 \u043d\u0430 \u043c\u043e\u0435\u043c Github. \u042d\u0442\u043e \u043c\u043e\u044f \u043f\u0435\u0440\u0432\u0430\u044f \u0441\u0442\u0430\u0442\u044c\u044f \u0438 \u043e\u0434\u0438\u043d \u0438\u0437 \u043f\u0435\u0440\u0432\u044b\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u0432 \u043d\u0430 OpenCV, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043d\u0435 \u0441\u0443\u0434\u0438\u0442\u0435 \u0441\u0442\u0440\u043e\u0433\u043e =)<\/p>\n<\/p>\n<\/div>\n<\/div>\n<\/div>\n
<\/div>\n<\/div>\n
  
 \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438  https:\/\/habr.com\/ru\/post\/678638\/<\/a>
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