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Commit 43a859c

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Merge pull request #577 from Cartikx3/main
Invisible Clock using Python #417 
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# Invisibility Cloak
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# Aim
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Hello EVERYONE,lets do some magic using computer vision. I hope you all know about ‘invisible cloak’, this will make you invisible. We will see how we can do the same magic trick with the help of computer vision. I will code with python and use the opencv and numpy library.
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# Purpose
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Our purpose is to become Invisible using a cloak with the help of various Python libraries.
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# Setup instructions
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1. Install the latest python 3.5 and above and once you finish installing it .
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2. Add opencv library.
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3. Add Numpy library.
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# Detailed explanation
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We wll learn how to create our own ‘Invisibility Cloak’ using simple computer vision techniques in OpenCV. Here we have written this code in Python because it provides exhaustive and sufficient library to build this program.
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Here, we will create this magical experience using an image processing technique called Color detection and segmentation. In order to run this code, you need an mp4 video named "video.mp4". You must have a cloth of same color and no other color should be visible into that cloth. We are taking the red cloth. If you are taking some other cloth, the code will remain the same but with minute changes.
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firstly the program when you run it it will capture the background image and then. When you put a red colored colored cloth in front of camera the program will detect the colored cloak using color detection and segmentation algorithm . And the program will generate a mask to determine the region in the frame corresponding to the detected color(red in this case). Program will refine this mask and then use it for segmenting out the cloth from the frame. Program will replace the pixels value of the detected red color region with corresponding pixel values of the static background.
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# Compilation Steps
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1. Capture and store the background frame
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2. Detect the defined color using color detection and segmentation algorithm.
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3. Segment out the defined colored part by generating a mask.
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4. Generate the final augmented output to create a magical effect.
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# Output
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![](Media/output.gif)
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# Author(s):-
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[Kartik Srivardhan](http://github.com/Cartikx3)
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import cv2
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import time
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import numpy as np
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## Preparation for writing the ouput video
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fourcc = cv2.VideoWriter_fourcc(*'XVID')
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out = cv2.VideoWriter('output.avi',fourcc,20.0, (640,480))
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##video capturing from the webcam
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cap = cv2.VideoCapture(0)
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## Allow the system to sleep for 3 seconds before the webcam starts
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time.sleep(3)
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count = 0
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background = 0
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## Capture the background in range of 60
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for i in range(60):
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ret,background = cap.read()
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background = np.flip(background,axis=1)
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## Read every frame from the webcam, until the camera is open
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while(cap.isOpened()):
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ret, img = cap.read()
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if not ret:
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break
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count+=1
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img = np.flip(img,axis=1)
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## Convert the color space from BGR to HSV
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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## Generate masks to detect red color
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lower_red = np.array([0,120,50])
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upper_red = np.array([10,255,255])
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mask1 = cv2.inRange(hsv,lower_red,upper_red)
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lower_red = np.array([170,120,70])
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upper_red = np.array([180,255,255])
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mask2 = cv2.inRange(hsv,lower_red,upper_red)
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mask1 = mask1+mask2
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## Open and Dilate the mask image
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mask1 = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, np.ones((3,3),np.uint8))
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mask1 = cv2.morphologyEx(mask1, cv2.MORPH_DILATE, np.ones((3,3),np.uint8))
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## Create an inverted mask to segment out the red color from the frame
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mask2 = cv2.bitwise_not(mask1)
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## Segment the red color part out of the frame using bitwise and with the inverted mask
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res1 = cv2.bitwise_and(img,img,mask=mask2)
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## Create image showing static background frame pixels only for the masked region
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res2 = cv2.bitwise_and(background, background, mask = mask1)
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## Generating the final output
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finalOutput = cv2.addWeighted(res1,1,res2,1,0)
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out.write(finalOutput)
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cv2.imshow("magic",finalOutput)
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cv2.waitKey(1)
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cap.release()
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out.release()
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cv2.destroyAllWindows()
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This projects requires latest Python version (i.e 3.5 and above) that has PIP built in and the libraries that are imported are :-
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1. openCV Library.
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2. Numpy Library.
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3. time Module.

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