Creating 2 Python Files for each project
HandTrackingModule.py |
FingerCounting.py |
HandTrackingModule.py |
AIVirtualPainting.py |
HandTrackingModule.py |
HandVirtualMouse.py |
HandTrackingModule.py |
HandVolumeControl.py |
For each project, it is important to create a separate python file that comprises Class and the functions associated with that respective project.
Defining Hand Class and it's Methods
import cv2
import mediapipe as mp
import time
import math
import numpy as np
class handDetector():
def __init__(self, mode=False, maxHands=2, detectionCon=0, trackCon=0):
self.mode = mode
self.maxHands = maxHands
self.detectionCon = detectionCon
self.trackCon = trackCon
self.mpHands = mp.solutions.hands
self.hands = self.mpHands.Hands(self.mode, self.maxHands, 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)
# print(results.multi_hand_landmarks)
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)
# print(id, cx, 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, (xmin - 20, ymin - 20), (xmax + 20, ymax + 20), (0, 255, 0), 2)
return self.lmList, bbox
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)
# 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)
# totalFingers = fingers.count(1)
return fingers
def findDistance(self, p1, p2, img, draw=True,r=15, t=3):
x1, y1 = self.lmList[p1][1:]
x2, y2 = self.lmList[p2][1:]
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
if draw:
cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), t)
cv2.circle(img, (x1, y1), r, (255, 0, 255), cv2.FILLED)
cv2.circle(img, (x2, y2), r, (255, 0, 255), cv2.FILLED)
cv2.circle(img, (cx, cy), r, (0, 0, 255), cv2.FILLED)
length = math.hypot(x2 - x1, y2 - y1)
return length, img, [x1, y1, x2, y2, cx, cy]
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This Class represents the Hand and the methods represent the various actions associated with those hands and finger moments in order to capture the actions.
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HandVirtualPaint File
import cv2 as cv
import numpy as np
import time
import os
import HandTrackingModule as htm
brushThickness = 25
eraserThickness = 100
folderPath = "/home/meghal/Personal/Konverge_AI/Training/Advance Computer Visions/AI Virtual Painter/Header"
myList = os.listdir(folderPath)
myList.sort()
print(myList)
overlayList = []
for imPath in myList:
image = cv.imread(f"{folderPath}/{imPath}")
overlayList.append(image)
#overlayList.sort()
print(len(overlayList))
header = overlayList[0]
drawColor = (255, 0, 255)
cap = cv.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)
detector = htm.handDetector(detectionCon=0,maxHands=1)
xp, yp = 0, 0
vidCanvas = np.zeros((480, 640, 3), np.uint8)
while True:
# 1. Import image
success, vid = cap.read()
vid = cv.flip(vid, 1)
#print(vid.shape)
# 2. Find Hand Landmarks
vid = detector.findHands(vid)
lmList = detector.findPosition(vid, draw=False)
if len(lmList) != 0:
print(lmList)
# tip of index and middle fingers
x1, y1 = lmList[8][1:]
x2, y2 = lmList[12][1:]
# 3. Check which fingers are up
fingers = detector.fingersUp()
# print(fingers)
# 4. If Selection Mode – Two finger are up
if fingers[1] and fingers[2]:
# xp, yp = 0, 0
#cv.rectangle(vid, (x1, y1-25), (x2, y2+25), (255, 0, 255), cv.FILLED)
print("Selection Mode")
# Checking for the click
if y1 < 100:
if 50 < x1 < 150:
header = overlayList[0]
drawColor = (255, 0, 255)
elif 150 < x1 < 200:
header = overlayList[1]
drawColor = (255, 0, 0)
elif 240 < x1 < 390:
header = overlayList[2]
drawColor = (0, 255, 0)
elif 490 < x1 < 640:
header = overlayList[3]
drawColor = (0, 0, 0)
cv.rectangle(vid, (x1, y1 - 25), (x2, y2 + 25), drawColor, cv.FILLED)
# 5. If Drawing Mode – Index finger is up
if fingers[1] and fingers[2] == False:
cv.circle(vid, (x1, y1), 15, drawColor, cv.FILLED)
print("Drawing Mode")
if xp == 0 and yp == 0:
xp, yp = x1, y1
#cv.line(vid, (xp, yp), (x1, y1), drawColor, brushThickness)
if drawColor == (0, 0, 0):
cv.line(vid, (xp, yp), (x1, y1), drawColor, eraserThickness)
cv.line(vidCanvas, (xp, yp), (x1, y1), drawColor, eraserThickness)
else:
cv.line(vid, (xp, yp), (x1, y1), drawColor, brushThickness)
cv.line(vidCanvas, (xp, yp), (x1, y1), drawColor, brushThickness)
xp, yp = x1, y1
# # Clear Canvas when all fingers are up
# if all (x >= 1 for x in fingers):
# vidCanvas = np.zeros((720, 1280, 3), np.uint8)
vidGray = cv.cvtColor(vidCanvas, cv.COLOR_BGR2GRAY)
_, vidInv = cv.threshold(vidGray, 50, 255, cv.THRESH_BINARY_INV)
vidInv = cv.cvtColor(vidInv,cv.COLOR_GRAY2BGR)
vid = cv.bitwise_and(vid,vidInv)
vid = cv.bitwise_or(vid,vidCanvas)
# Setting the header image
vid[0:100, 0:640] = header
#vid = cv.addWeighted(vid,0.5,vidCanvas,0.5,0)
cv.imshow("Video", vid)
#cv.imshow("Video Canvas", vidCanvas)
#print(vidCanvas.shape)
# cv.imshow("Video Inv", vidInv)
# print(vidInv.shape)
cv.waitKey(1)
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This Code represents the logic of how to detect hand moments i.e. Selection / Drawing.
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AI Virtual Mouse
import cv2
import numpy as np
import HandTrackingModule as htm
import time
import autopy
wCam, hCam = 640, 480
frameR = 100 # Frame Reduction
smoothening = 7
pTime = 0
plocX, plocY = 0, 0
clocX, clocY = 0, 0
cap = cv2.VideoCapture(0)
cap.set(3, wCam)
cap.set(4, hCam)
detector = htm.handDetector(maxHands=1)
wScr, hScr = autopy.screen.size()
# print(wScr, hScr)
while True:
# 1. Find hand Landmarks
success, img = cap.read()
img = detector.findHands(img)
lmList, bbox = detector.findPosition(img)
# 2. Get the tip of the index and middle fingers
if len(lmList) != 0:
x1, y1 = lmList[8][1:]
x2, y2 = lmList[12][1:]
# print(x1, y1, x2, y2)
# 3. Check which fingers are up
fingers = detector.fingersUp()
# print(fingers)
cv2.rectangle(img, (frameR, frameR), (wCam - frameR, hCam - frameR), (255, 0, 255), 2)
# 4. Only Index Finger : Moving Mode
if fingers[1] == 1 and fingers[2] == 0:
# 5. Convert Coordinates
x3 = np.interp(x1, (frameR, wCam - frameR), (0, wScr))
y3 = np.interp(y1, (frameR, hCam - frameR), (0, hScr))
print(x3)
# 6. Smoothen Values
clocX = plocX + (x3 - plocX) / smoothening
clocY = plocY + (y3 - plocY) / smoothening
# 7. Move Mouse
autopy.mouse.move(wScr - clocX, clocY)
cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
plocX, plocY = clocX, clocY
# 8. Both Index and middle fingers are up : Clicking Mode
if fingers[1] == 1 and fingers[2] == 1:
# 9. Find distance between fingers
length, img, lineInfo = detector.findDistance(8, 12, img)
print(length)
# 10. Click mouse if distance short
if length < 40:
cv2.circle(img, (lineInfo[4], lineInfo[5]),
15, (0, 255, 0), cv2.FILLED)
autopy.mouse.click()
# 11. Frame Rate
cTime = time.time()
fps = 1 / (cTime - pTime)
pTime = cTime
cv2.putText(img, str(int(fps)), (20, 50), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3)
# 12. Display
cv2.imshow("Image", img)
cv2.waitKey(1)
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It is the same as Hand Virtual Painting Tool.
Virtual Volume Control
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
# Defining the Width and Height of the Video to be displayed
wCam, hCam = 640, 480
cap = cv2.VideoCapture(0)
cap.set(3, wCam)
cap.set(4, hCam)
pTime = 0
detector = htm.handDetector(detectionCon=0.7)
devices = AudioUtilities.GetSpeakers()
interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)
volume = cast(interface, POINTER(IAudioEndpointVolume))
# volume.GetMute()
# volume.GetMasterVolumeLevel()
volRange = volume.GetVolumeRange()
minVol = volRange[0]
maxVol = volRange[1]
vol = 0
volBar = 400
volPer = 0
while True:
success, img = cap.read()
img = detector.findHands(img)
lmList = detector.findPosition(img, draw=False)
if len(lmList) != 0:
# print(lmList[4], lmList[8])
x1, y1 = lmList[4][1], lmList[4][2]
x2, y2 = lmList[8][1], lmList[8][2]
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
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)
# Hand range 50 - 300
# Volume Range -65 - 0
vol = np.interp(length, [50, 300], [minVol, maxVol])
volBar = np.interp(length, [50, 300], [400, 150])
volPer = np.interp(length, [50, 300], [0, 100])
print(int(length), vol)
volume.SetMasterVolumeLevel(vol, None)
if length < 50:
cv2.circle(img, (cx, cy), 15, (0, 255, 0), cv2.FILLED)
cv2.rectangle(img, (50, 150), (85, 400), (255, 0, 0), 3)
cv2.rectangle(img, (50, int(volBar)), (85, 400), (255, 0, 0), cv2.FILLED)
cv2.putText(img, f'{int(volPer)} %', (40, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 3)
cTime = time.time()
fps = 1 / (cTime - pTime)
pTime = cTime
cv2.putText(img, f'FPS: {int(fps)}', (40, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 3)
cv2.imshow("Img", img)
cv2.waitKey(1)
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It captures the tips and the moments of 2 finger's tips to control the volume.
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