Recognizing gestures of hand and controlling the mouse. Use the hand_detection.py file to detect your hand and hand_detection_tracking.py to control mouse with your palm.
3 | 4 | NOTE: Change the bgr value range according to your skin color, provide a range i.e. skin color appears different when exposed to different amount of light 5 | 6 |
8 |
9 | ### Support Me
10 | If you liked this Repository, then please leave a star on this repository. It motivates me to contribute more in such Open Source projects in the future.
11 | ### Happy Coding =)
12 |
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/ezgif-4-1135f6c536.gif:
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https://raw.githubusercontent.com/sudonitin/Hand_detection_tracking_opencv-/87ba12c1a1e6eeeee1e7d7cdf118f197b6e30344/ezgif-4-1135f6c536.gif
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/hand_detection1.py:
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1 | import cv2
2 | import numpy as np
3 | from sklearn.metrics import pairwise
4 |
5 | cap = cv2.VideoCapture(0)
6 | kernelOpen = np.ones((5,5))#if jiggers are present other than yellow area
7 | kernelClose = np.ones((20,20)) #if jiggers are present in yellow area
8 |
9 | #HSV color range which should be detected
10 | lb = np.array([20,100,100])
11 | ub = np.array([120,255,255])
12 |
13 | while True:
14 | ret, frame = cap.read()
15 | flipped = cv2.flip(frame, 1)
16 | flipped = cv2.resize(flipped,(500,400))
17 |
18 | #use HSV of yellow to detect only yellow color
19 | imgSeg = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
20 | imgSegFlipped = cv2.flip(imgSeg, 1)
21 | imgSegFlipped = cv2.resize(imgSegFlipped,(500,400))
22 |
23 | #masking and filtering all shades of yellow
24 | mask = cv2.inRange(imgSegFlipped, lb, ub)
25 | mask = cv2.resize(mask,(500,400))
26 |
27 | #apply morphology to avoid jiggers
28 | maskOpen = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernelOpen)
29 | maskOpen = cv2.resize(maskOpen,(500,400))
30 | maskClose = cv2.morphologyEx(maskOpen, cv2.MORPH_CLOSE, kernelClose)
31 | maskClose = cv2.resize(maskClose,(500,400))
32 |
33 | final = maskClose
34 | _, conts, h = cv2.findContours(maskClose,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
35 |
36 |
37 | if(len(conts)!=0): #draws the contours of that object which has the highest
38 | b = max(conts, key=cv2.contourArea)
39 | west = tuple(b[b[:, :, 0].argmin()][0]) #gives the co-ordinate of the left extreme of contour
40 | east = tuple(b[b[:, :, 0].argmax()][0]) #above for east i.e right
41 | north = tuple(b[b[:, :, 1].argmin()][0])
42 | south = tuple(b[b[:, :, 1].argmax()][0])
43 | centre_x = (west[0]+east[0])/2
44 | centre_y = (north[0]+south[0])/2
45 |
46 | cv2.drawContours(flipped, b, -1, (0,255,0), 3)
47 | cv2.circle(flipped, west, 6, (0,0,255), -1)
48 | cv2.circle(flipped, east, 6, (0,0,255), -1)
49 | cv2.circle(flipped, north, 6, (0,0,255), -1)
50 | cv2.circle(flipped, south, 6, (0,0,255), -1)
51 | cv2.circle(flipped, (int(centre_x),int(centre_y)), 6, (255,0,0), -1)#plots centre of the area
52 |
53 | cv2.imshow('video', flipped)
54 | #cv2.imshow('mask', mask)
55 | #cv2.imshow('mask open', maskOpen)
56 | #cv2.imshow('mask close', maskClose)
57 | if cv2.waitKey(1) & 0xFF == ord(' '):#exiting
58 | break
59 |
60 | cap.release()
61 | cv2.destroyAllWindows()
62 |
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/hand_detection_tracking.py:
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1 | import cv2
2 | import numpy as np
3 | import wx
4 | from pynput.mouse import Button, Controller
5 |
6 | mouse = Controller()
7 |
8 | app = wx.App(False)
9 | (screenx,screeny) = wx.GetDisplaySize()
10 | (capturex,capturey) = (700,500)#captures this size frame
11 |
12 |
13 | cap = cv2.VideoCapture(0)
14 | cap.set(3,capturex)
15 | cap.set(4,capturey)
16 |
17 | kernelOpen = np.ones((5,5))#if noise are present other than yellow area
18 | kernelClose = np.ones((20,20)) #if noise are present in yellow area
19 |
20 | #HSV color range which should be detected
21 | lb = np.array([20,100,100])
22 | ub = np.array([120,255,255])
23 |
24 | cd = 0
25 |
26 | while True:
27 | ret, frame = cap.read()
28 |
29 | #use HSV of yellow to detect only yellow color
30 | imgSeg = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
31 | #imgSegFlipped = cv2.resize(imgSegFlipped,(500,400))
32 |
33 | #masking and filtering all shades of yellow
34 | mask = cv2.inRange(imgSeg, lb, ub)
35 | #mask = cv2.resize(mask,(500,400))
36 |
37 | #apply morphology to avoid noise
38 | maskOpen = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernelOpen)
39 | #maskOpen = cv2.resize(maskOpen,(500,400))
40 | maskClose = cv2.morphologyEx(maskOpen, cv2.MORPH_CLOSE, kernelClose)
41 | #maskClose = cv2.resize(maskClose,(500,400))
42 |
43 | final = maskClose
44 | _, conts, h = cv2.findContours(maskClose,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
45 |
46 |
47 | if(len(conts)!=0): #draws the contours of that object which has the highest
48 | b = max(conts, key=cv2.contourArea)
49 | west = tuple(b[b[:, :, 0].argmin()][0]) #gives the co-ordinate of the left extreme of contour
50 | east = tuple(b[b[:, :, 0].argmax()][0]) #above for east i.e right
51 | north = tuple(b[b[:, :, 1].argmin()][0])
52 | south = tuple(b[b[:, :, 1].argmax()][0])
53 | centre_x = (west[0]+east[0])/2
54 | centre_y = (north[0]+south[0])/2
55 |
56 | cv2.drawContours(frame, b, -1, (0,255,0), 3)
57 | cv2.circle(frame, west, 6, (0,0,255), -1)
58 | cv2.circle(frame, east, 6, (0,0,255), -1)
59 | cv2.circle(frame, north, 6, (0,0,255), -1)
60 | cv2.circle(frame, south, 6, (0,0,255), -1)
61 | cv2.circle(frame, (int(centre_x),int(centre_y)), 6, (255,0,0), -1)#plots centre of the area
62 |
63 | bint = int(cv2.contourArea(b))
64 |
65 | if (bint in range(8000,18000)): #hand is open
66 | mouse.release(Button.left)
67 | cv2.circle(frame, (int(centre_x),int(centre_y)), 6, (255,0,0), -1)#plots centre of the area
68 | mouse.position = (screenx-(centre_x*screenx/capturex),screeny-(centre_y*screeny/capturey))
69 |
70 | elif(bint in range(2000,7000)): #hand is closed
71 | cv2.circle(frame, (int(centre_x),int(centre_y)), 10, (255,255,255), -1)#plots centre of the area
72 | mouse.position = (screenx-(centre_x*screenx/capturex), screeny-(centre_y*screeny/capturey))
73 | mouse.press(Button.left)
74 |
75 |
76 | cv2.imshow('video', frame)
77 | if cv2.waitKey(1) & 0xFF == ord(' '):#exiting
78 | break
79 |
80 | cap.release()
81 | cv2.destroyAllWindows()
82 |
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