How to morph two grid-like images seamlessly?

I have two images that consist of colored squares with different grid step (10x10 and 12x12).

What I want is to make the first image to be smoothly transformed into the second one.

When I use a plain image overlay with cv2.addWeighted() function, the result (left) is not good because of the intersected grid spaces. I suppose it would be better to shift remaining grid cells to the borders and clear out the rest (right).

Is there any algorithm to deal with this task?

Thanks.

Solution

You can interpolate each pixel individually between different images.

import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt

np.random.seed(200)

num_images = 2
images = np.random.rand(num_images, 8,8)

for index, im in enumerate(images):
    print(f'Images {index}')
    fig = plt.imshow(im)
    plt.show()

Interpolating these images:


n_frames = 4
x_array = np.linspace(0, 1, int(n_frames))


def interpolate_images(frame):
    intermediate_image = np.zeros((1, *images.shape[1:]))
    for lay in range(images.shape[1]):
        for lat in range(images.shape[2]):
            tck = interpolate.splrep(np.linspace(0, 1, images.shape[0]), images[:, lay, lat], k = 1)
            intermediate_image[:, lay, lat] = interpolate.splev(x_array[frame], tck)
    return intermediate_image


for frame in range(n_frames):
    im = interpolate_images(int(frame))
    fig = plt.imshow(im[0])
    plt.show()