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Author: SrinadhVura

V/vol-handcontroller/HTrack.py

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+"""
+This file uses mediapipe - a framework by google to detect the landmarks of hand (21)
+in the image captured from webcam using openCV
+The functionality wrapped as handDetector class containing methods drawHands() and getPositions()
+drawHands() --> To draw land marks of detected hands
+getPositions() --> To return the found landmarks as a list
+"""
+import cv2
+import mediapipe as mp
+import time
+
+class handDetector():
+    def __init__(self, mode=False, maxHands=2, detectionCon=0.5, trackCon=0.5):
+        self.mode = mode
+        self.maxHands = maxHands
+        self.detectionCon = detectionCon
+        self.trackCon = trackCon
+        self.mpDraw = mp.solutions.drawing_utils
+        self.hands = mp.solutions.hands.Hands(static_image_mode=self.mode,max_num_hands= self.maxHands,min_detection_confidence=self.detectionCon,min_tracking_confidence= self.trackCon)
+    def drawHands(self, img, draw=True):
+        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        self.res = self.hands.process(imgRGB)
+        if self.res.multi_hand_landmarks:
+            for handLms in self.res.multi_hand_landmarks:
+                if draw:
+                    self.mpDraw.draw_landmarks(img, handLms,mp.solutions.hands.HAND_CONNECTIONS)
+        return img
+
+    def getPositions(self, img, handId=0):
+        self.xList=[]
+        self.yList=[]
+        self.lmList = []
+        if self.res.multi_hand_landmarks:
+            decHand = self.res.multi_hand_landmarks[handId]
+            for index, lmark in enumerate(decHand.landmark):
+                h, w, c = img.shape
+                cx, cy = int(lmark.x * w), int(lmark.y * h)
+                self.xList.append(cx)
+                self.yList.append(cy)
+                self.lmList.append([index, cx, cy])
+        return self.lmList
+
+def main():
+    prevTime = 0
+    currTime = 0
+    cap = cv2.VideoCapture(0)
+    detector = handDetector()
+    while True:
+        _, img = cap.read()
+        img = detector.drawHands(img)
+        lms= detector.getPositions(img)
+        print(lms)
+        currTime = time.time()
+        fps = 1 / (cTime - pTime)
+        prevTime = cTime
+        cv2.putText(img, str(int(fps)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3,(51, 0, 255), 3)
+        cv2.imshow("Image", img)
+        cv2.waitKey(1)
+
+if __name__ == "__main__":
+    main()

V/vol-handcontroller/VolCon.py

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+""" I have used pycaw by Andre Miras at https://github.com/AndreMiras/pycaw 
+    To control audio. It is an open-source module which can be installed by --> pip install pycaw  
+"""
+import math
+import cv2
+import time
+import numpy as np
+import HTrack as ht
+from comtypes import CLSCTX_ALL
+from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
+
+height = 720 # height of webwindow
+width = 1080 # width of webwindow
+cap = cv2.VideoCapture(0) # using default system web cam 0
+cap.set(3, width) # 3 is a index specifying width
+cap.set(4, height) # 4 is a index specifying height
+prevTime = 0
+detector = ht.handDetector(detectionCon=0.7) # we need to detect hand more precisely
+devices = AudioUtilities.GetSpeakers() # instantiating system speakers as objects
+interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None) # activating the interface to control volume
+volume = interface.QueryInterface(IAudioEndpointVolume)
+rangeVol=volume.GetVolumeRange() # volume range for my system is found to be -96 to 0
+volLeast=rangeVol[0]
+volHigh=rangeVol[1] 
+vol=volLeast #initialising volume to least volume
+volBar=400 #initialising volume bar
+while True:
+    success, img = cap.read() # reading the image
+    img=detector.drawHands(img) # detecting the hands
+    lmList=detector.getPositions(img) # finding landmarks of detected hands
+    if len(lmList)!=0:
+        # print(lmList[8],lmList[12])
+        x1,y1=lmList[8][1],lmList[8][2]
+        x2,y2=lmList[12][1],lmList[12][2]
+        cx,cy=(x1+x2)//2,(y1+y2)//2
+        cv2.circle(img,(x1,y1),10,(170,153,255),cv2.FILLED) # drawing circle around index finger
+        cv2.circle(img,(x2,y2),10,(170,153,255),cv2.FILLED) # drawing circle around middle finger
+        cv2.circle(img,(cx,cy),10,(170,153,255),cv2.FILLED) # drawing circle around center point of both fingers
+        cv2.line(img,(x1,y1),(x2,y2),(25,102,180),2)
+        distance=math.dist([x1,y1],[x2,y2])
+        # print(distance) # range of distance found to be 25 and 150
+        vol=np.interp(distance,[25,150],[volLeast,volHigh]) # To interpolate distance between index and middle fingers to the volume of system
+        volBar=np.interp(distance,[25,150],[400,100]) # To interpolate distance between index and middle fingers to the volume bar length
+        volume.SetMasterVolumeLevel(vol, None)
+        print(int(distance),vol)
+        if distance<25:
+            cv2.circle(img,(cx,cy),10,(0,0,120),cv2.FILLED) # indicating volume is 0 bby changing color of center circle
+    #drawing volume bar
+    cv2.rectangle(img,(80,100),(60,400),(230,153,0),2) # outer rectangle
+    cv2.rectangle(img,(80,int(volBar)),(60,400),(150,153,0),cv2.FILLED) # actual volume 
+    cv2.putText(img,"Volume",(40,420),cv2.FONT_HERSHEY_SIMPLEX,0.5,(149,100,149),2) 
+
+    currTime = time.time()
+    fps = 1/(currTime - prevTime) # using time to get timestamp of previous iteration and current iteration to know the frames per second
+    prevTime = currTime
+    cv2.putText(img, str(int(fps)), (30, 40), cv2.FONT_HERSHEY_PLAIN, 2, (255, 255, 0), 3) # writing FPS on screen
+    cv2.imshow("Image", img)
+    cv2.waitKey(1)
+