How to merge two RGBA images

Question

I'm trying to merge two RGBA images (with a shape of (h,w,4)), taking into account their alpha channels.
Example :

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What I've tried

I tried to do this using opencv for that, but I getting some strange pixels on the output image.

Images Used:

and

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

image1 = cv2.imread("image1.png", cv2.IMREAD_UNCHANGED)
image2 = cv2.imread("image2.png", cv2.IMREAD_UNCHANGED)

mask1 = image1[:,:,3]
mask2 = image2[:,:,3]
mask2_inv = cv2.bitwise_not(mask2)

mask2_bgra = cv2.cvtColor(mask2, cv2.COLOR_GRAY2BGRA)
mask2_inv_bgra = cv2.cvtColor(mask2_inv, cv2.COLOR_GRAY2BGRA)

# output = image2*mask2_bgra + image1
output = cv2.bitwise_or(cv2.bitwise_and(image2, mask2_bgra), cv2.bitwise_and(image1, mask2_inv_bgra))
output[:,:,3] = cv2.bitwise_or(mask1, mask2)
plt.figure(figsize=(12,12))
plt.imshow(cv2.cvtColor(output, cv2.COLOR_BGRA2RGBA))
plt.axis('off')

Output :

So what I figured out is that I'm getting those weird pixels because I used cv2.bitwise_and function (Which btw works perfectly with binary alpha channels). I tried using different approaches

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Question

Is there an approach to do this (While keeping the output image as an 8bit image).

Answer 1

I was able to obtain the expected result in 2 stages.

# Read both images preserving the alpha channel
hh1 = cv2.imread(r'C:\Users\524316\Desktop\Stack\house.png', cv2.IMREAD_UNCHANGED)  
hh2 = cv2.imread(r'C:\Users\524316\Desktop\Stack\memo.png', cv2.IMREAD_UNCHANGED)   

# store the alpha channels only
m1 = hh1[:,:,3]
m2 = hh2[:,:,3]

# invert the alpha channel and obtain 3-channel mask of float data type
m1i = cv2.bitwise_not(m1)
alpha1i = cv2.cvtColor(m1i, cv2.COLOR_GRAY2BGRA)/255.0

m2i = cv2.bitwise_not(m2)
alpha2i = cv2.cvtColor(m2i, cv2.COLOR_GRAY2BGRA)/255.0

# Perform blending and limit pixel values to 0-255 (convert to 8-bit)
b1i = cv2.convertScaleAbs(hh2*(1-alpha2i) + hh1*alpha2i)

Note: In the b=above the we are using only the inverse alpha channel of the memo image

But I guess this is not the expected result. So moving on ....

# Finding common ground between both the inverted alpha channels
mul = cv2.multiply(alpha1i,alpha2i)

# converting to 8-bit
mulint = cv2.normalize(mul, dst=None, alpha=0, beta=255,norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)

# again create 3-channel mask of float data type
alpha = cv2.cvtColor(mulint[:,:,2], cv2.COLOR_GRAY2BGRA)/255.0

# perform blending using previous output and multiplied result
final = cv2.convertScaleAbs(b1i*(1-alpha) + mulint*alpha)

Sorry for the weird variable names. I would request you to analyze the result in each line. I hope this is the expected output.