How to merge two images base transparent python

Question

I need to join two images where the base image has a transparent background, I already tried to do it using

image 01

image and a photo I need to put her in the second in the blank space second image

expected result and this

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

dim = (425, 425)

apple = mpimg.imread('image.png')
apple = cv2.resize(apple, dim)

banana = mpimg.imread('image2.png')
banana = cv2.resize(banana, dim)


_ = plt.imshow(apple)
_ = plt.show()

_ = plt.imshow(banana)
_ = plt.show()

list_images = [apple, banana]

def blend(list_images): # Blend images equally.

    equal_fraction = 1.0 / (len(list_images))

    output = np.zeros_like(list_images[0])

    for img in list_images:
        output = output + img * equal_fraction

    output = output.astype(np.uint8)
    _ = plt.imshow(output)
    return output


output = blend(list_images)

_ = plt.imshow(output)

Answer 1

You can easily make use of your alpha (transparent) channel for this purpose. Unfortunately, when I tried reading your frame image using cv2.IMREAD_UNCHANGED , it didn't have the appropriate alpha channel. According to your result, the region outside the rounded corners is in white.

So using your frame image I created the alpha channel and used it the following.

# Reading images
tr = cv2.imread('frame.png')
la = cv2.imread('sunset.jpg')

# resizing
dim = (425, 425)
tr = cv2.resize(tr, dim)
la = cv2.resize(la, dim)

# Finding the largest contour (white region) from the first channel in frame
th = cv2.threshold(tr[:,:,0],127,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
contours, hierarchy = cv2.findContours(th, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
c = max(contours, key = cv2.contourArea)

# Draw the largest contour onto a mask
black = np.zeros((tr.shape[0], tr.shape[1]), np.uint8)
mask = cv2.drawContours(black,[c],0,255, -1)

Mask image: we want the sunset image to be present in the white region

# Create 3-channel mask of float datatype
alpha = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)/255.0

# Perform blending and limit pixel values to 0-255
blended = cv2.convertScaleAbs(tr*(1-alpha) + la*alpha)

Answer 2

Here is the minimalistic code needed to apply the mask:

import cv2

img1 = cv2.imread("img1.jpg")
img2 = cv2.imread("img2.png", cv2.IMREAD_GRAYSCALE)

img1 = cv2.resize(img1, img2.shape[1::-1])
img1[img2 < 128] = 0

cv2.imshow("Image", img1)
cv2.waitKey(0)

Input images img1.jpg and img2.png :

Output: