├── README.md
└── InvisibleCloak.py


/README.md:
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 1 | # The-Invisible-Cloak
 2 | An OpenCV project which uses an image processing technique called Color detection and segmentation to make yourself invisible live in the webcam with the help of asingle-color cloth(Here I'm using a red cloth). 
 3 | 
 4 | Technologies Used: Python 3.9-> OpenCV library, NumPy library
 5 | 
 6 | BASIC IDEA behind the project:
 7 | 
 8 | 1.Capture and store the background frame. 
 9 | 
10 | 2. Detecting the red colored cloth using color detection
11 | algorithm. 
12 | 
13 | 3. Segmenting out the red colored cloth by generating a mask. 
14 | 
15 | 4. Generating the final augmented output to create the magical
16 |    effect.
17 | 


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/InvisibleCloak.py:
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 1 | import cv2
 2 | import numpy as np
 3 | import time
 4 | 
 5 | cap = cv2.VideoCapture(0)
 6 | time.sleep(3)
 7 | background=0
 8 | 
 9 | for i in range(30):
10 |     ret,background = cap.read()
11 | 
12 | background = np.flip(background,axis=1)
13 | 
14 | while(cap.isOpened()):
15 |     ret, img = cap.read()
16 | 
17 |     # Flip the image 
18 |     img = np.flip(img, axis = 1)
19 | 
20 |     # Now Converting  the image to HSV color space.
21 |     hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
22 |     blurred = cv2.GaussianBlur(hsv, (35, 35), 0)
23 | 
24 |     # Defining lower range for red color detection.
25 |     lower = np.array([0,120,70])
26 |     upper = np.array([10,255,255])
27 |     mask1 = cv2.inRange(hsv, lower, upper)
28 | 
29 |     # Defining upper range for red color detection
30 |     lower_red = np.array([170,120,70])
31 |     upper_red = np.array([180,255,255])
32 |     mask2 = cv2.inRange(hsv, lower_red, upper_red)
33 | 
34 |     # Addition of the two masks to generate the final mask.
35 |     mask = mask1 + mask2
36 |     mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, np.ones((5,5), np.uint8))
37 | 
38 |     # Replacing pixels corresponding to cloak with the background pixels.
39 |     img[np.where(mask == 255)] = background[np.where(mask == 255)]
40 |     cv2.imshow('Display',img)
41 |     k = cv2.waitKey(10)
42 |     if k == 27:
43 |         break


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