I am trying to replace a segmented part of an image with it's unsegmented part with OpenCV in Python. The pictures will make you understand what I mean.
The following picture is the first one, before segmentation :
This is the picture after segmentation :
This is the third picture, after doing what I'm talking about :
How can I do this ? Thanks in advance for your help !
This is actually pretty easy. All you have to do is take your picture after segmentation, and multiply it by a mask where any pixel in the mask that is 0 becomes 1, and anything else becomes 0.
This will essentially blacken all of the pixels with the exception of the pixels within the mask that are 1. By multiplying each of the pixels in your image by the mask, you would effectively produce what you have shown in the figure, but the background is black. All you would have to do now is figure out which locations in your mask are white and set the corresponding locations in your output image to white. In other words:
import cv2
# Load in your original image
originalImg = cv2.imread('Inu8B.jpg',0)
# Load in your mask
mask = cv2.imread('2XAwj.jpg', 0)
# Get rid of quantization artifacts
mask[mask < 128] = 0
mask[mask > 128] = 1
# Create output image
outputImg = originalImg * (mask == 0)
outputImg[mask == 1] = 255
# Display image
cv2.imshow('Output Image', outputImg)
cv2.waitKey(0)
cv2.destroyAllWindows()
Take note that I downloaded the images from your post and loaded them from my computer. Also, your mask has some quantization artifacts due to JPEG, and so I thresholded at intensity 128 to ensure that your image consists of either 0s or 1s.
This is the output I get:
Hope this helps!
Basically, you have a segmentation mask and an image. All you need to do is copy the pixels in the image corresponding to the pixels in the label mask. Generally, the mask dimensions and the image dimensions are the same (if not, you need to resize your mask to the image dimensions). Also, the segmentation pixels corresponding to a particular mask would have the same integer value (1,2,3 etc and background pixels would have a value of 0). So, find out which pixel co-ordinates have a value corresponding to the mask value and use those co-ordinates to find out the intensity values in the image. If you know the syntax of how to access a pixel co-ordinate, read an image in the programming environment you are using and follow the aforementioned procedure, you should be able to do it.
