invisible cloak.py

import cv2 
import numpy as np 
import time 
  
# replace the red pixels ( or undesired area ) with 
# background pixels to generate the invisibility feature. 
  
## 1. Hue: This channel encodes color information. Hue can be 
# thought of an angle where 0 degree corresponds to the red color,  
# 120 degrees corresponds to the green color, and 240 degrees  
# corresponds to the blue color. 
  
## 2. Saturation: This channel encodes the intensity/purity of color. 
# For example, pink is less saturated than red. 
  
## 3. Value: This channel encodes the brightness of color. 
# Shading and gloss components of an image appear in this  
# channel reading the videocapture video   
  
# in order to check the cv2 version 
print(cv2.__version__)    
  
# taking video.mp4 as input. 
# Make your path according to your needs  
capture_video = cv2.VideoCapture(0) 
     
# give the camera to warm up 
time.sleep(1)  
count = 0 
background = 0 
  
# capturing the background in range of 60 
# you should have video that have some seconds 
# dedicated to background frame so that it  
# could easily save the background image 
for i in range(60): 
    return_val, background = capture_video.read() 
    if return_val == False : 
        continue 

background = np.flip(background, axis = 1) # flipping of the frame  
  
# we are reading from video  
while (capture_video.isOpened()): 
    return_val, img = capture_video.read() 
    if not return_val : 
        break 
    count = count + 1
    img = np.flip(img, axis = 1) 
  
    # convert the image - BGR to HSV 
    # as we focused on detection of red color  
  
    # converting BGR to HSV for better  
    # detection or you can convert it to gray 
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)  
  
    #-------------------------------------BLOCK----------------------------# 
    # ranges should be carefully chosen 
    # setting the lower and upper range for mask1 
    lower_red = np.array([100, 40, 40])        
    upper_red = np.array([100, 255, 255]) 
    mask1 = cv2.inRange(hsv, lower_red, upper_red) 
    # setting the lower and upper range for mask2  
    lower_red = np.array([155, 40, 40]) 
    upper_red = np.array([180, 255, 255]) 
    mask2 = cv2.inRange(hsv, lower_red, upper_red) 
    #----------------------------------------------------------------------# 
  
    # the above block of code could be replaced with 
    # some other code depending upon the color of your cloth  
    mask1 = mask1 + mask2 
  
    # Refining the mask corresponding to the detected red color 
    mask1 = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, np.ones((3, 3),np.uint8), iterations = 2) 
    mask1 = cv2.dilate(mask1, np.ones((3, 3), np.uint8), iterations = 1) 
    mask2 = cv2.bitwise_not(mask1) 
  
    # Generating the final output 
    res1 = cv2.bitwise_and(background, background, mask = mask1) 
    res2 = cv2.bitwise_and(img, img, mask = mask2) 
    final_output = cv2.addWeighted(res1, 1, res2, 1, 0) 
  
    cv2.imshow("INVISIBLE MAN", final_output) 
    
    k = cv2.waitKey(1) & 0xFF
	# 'Q' to exit
    if k == ord("q"):
	    break