The mask I am creating is clipping the image I am trying to paste over it

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I am trying to paste an image(noise) on top of a background image(back_eq). The problem is that when applying the mask (mask = np.uint8(alpha/255) the mask gets clipped clipped mask this is the original shape i am trying to paste the white shape should get onto the background (but black) so the result is this clipped result

The problem fixes when instead of normalizing with 255 we use a value smaller s.a 245 or 240 (mask = np.uint8(alpha/240)) The problem is that this is a correct normalization. Any suggestion on how to fix the mask with a correct normalization being mandatory?

import numpy as np
import cv2
import matplotlib.pyplot as plt

noise = cv2.imread("3_noisy.jpg")
noise = cv2.resize(noise,(300,300), interpolation = cv2.INTER_LINEAR)
alpha = cv2.imread("3_alpha.jpg")
alpha = cv2.resize(alpha,(300,300), interpolation = cv2.INTER_LINEAR)
back_eq = cv2.imread('Results/back_eq.jpg')

back_eq_crop = cv2.imread('Results/back_eq_crop.jpg')
im_3_tone = cv2.imread('Results/im_3_tone.jpg')

final = back_eq.copy()
back_eq_h, back_eq_w, _ = back_eq.shape
noisy_h, noisy_w,_ = noise.shape


l1 = back_eq_h//2 - noisy_h//2
l2 = back_eq_h//2 + noisy_h//2
l3 = back_eq_w//2 - noisy_w//2
l4 = back_eq_w//2 + noisy_w//2
print(alpha.shape)

# normalizing the values
mask = np.uint8(alpha/255)
# masking back_eq_crop
masked_back_eq_crop = cv2.multiply(back_eq_crop,(1-mask))
cv2.imshow('as',masked_back_eq_crop)
cv2.waitKey(0)
cv2.destroyAllWindows()



# creating the masked region
mask_to_add = cv2.multiply(im_3_tone, mask)
cv2.imshow('as',mask_to_add)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Combining
masked_image = cv2.add(masked_back_eq_crop, mask_to_add)
final[l1:l2, l3:l4] = masked_image
cv2.imshow('aa',masked_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

plt.figure()
plt.imshow(final[:, :, ::-1]);plt.axis("off");plt.title("Final Image")
plt.show()

retval=cv2.imwrite("Results/Final Image.jpg", final)
computer-vision opencv numpy python
1 Answers

To use binary mask threshold of 255, you have to have a properly prepared image - preferably already binary. Because 255 means only pure white (#FFFFFF) will stay white. Even the lightest gray will become black.

And in your case, well... the image has antialiasing (edges are softened), and you're doing scaling in the code. But moreover, your white is not pure white. There's a hole in the result.

To show it, instead of just talking:

I loaded your mask in GIMP,I loaded your mask pic in gimp, got the 'select by colour' tool, disabled antialiasing and turned threshold to 0 - everything so that only pure white FFFFFF gets selected, the same as your code. enter image description here

Aaaand we see the holes. The tail is pixely already, same with hair... the hole in the face is there... Hole's colour is #FEFEFE - 254, making it black with threshold of 255.

The best threshold in such (pseudo) "black-and-white" is actually near the middle (128). Because antialiasing makes the edges be blackish-gray or whiteish-gray - no middle grays, so middle gray separates the two groups nicely. And your "visually white but not pure white" (+similar blacks) get into those groups as well. Even if you believe to have only pure black and pure white in your image - if you load it as colour or grayscale, you have 0 and 255 values anyways, so 128 will work. (I don't have access to my old code right now, but I believe I kept my thresholds around 200 when I played with images?)

tl;dr:

  • Threshold 255 only makes #FFFFFF white, it's never good
  • your picture has a lot of "visually white but not #FFFFFF white" pixels
  • there's nothing bad in using lower threshold, even around middle of the range for pseudo black-and-white
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