How do I get the image color tones of an image and set it to another one?

I have these two images and want to make Ashley Benson in Mona Lisa's image colors.

CodePudding user response：

I don't think there could be a "filter" to do it.

Using classical computer vision you could make Fast Fourier Transform of each image, and than replace low-frequency components of Ashley Benson image with Mona Lisa's. But in this case you can only change the color domain of the image. Code example here:

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

lisa = cv2.imread(r"path/to/monalisa")
ashley = cv2.imread(r"path/to/ashley")

def domain_adoptation(src, trg, freq):

    """
    Parameters: 
    src - source image, which style has to be changed
    trg - target image, which low-frequency domain will be adopted
    freq - number of frequencies to be used

    Returns:
    result - np.array based on srs image (shape and high frequencies) 
         with low frequencies of the target image
    """

    result = np.zeros((src.shape[0],src.shape[1],src.shape[2]))

    for i in range(src.shape[2]):
        trg_fft = np.fft.fft2(trg[:,:,i])
        src_fft = np.fft.fft2(src[:,:,i])

        trg_fft_shift = np.fft.fftshift(trg_fft)
        src_fft_shift = np.fft.fftshift(src_fft)

        src_fft_shift[src.shape[0]//2-freq:src.shape[0]//2 freq,
                         src.shape[1]//2-freq:src.shape[1]//2 freq] = \
            trg_fft_shift[trg.shape[0]//2-freq:trg.shape[0]//2 freq,
                           trg.shape[1]//2-freq:trg.shape[1]//2 freq]

        src_ifft_shift = np.fft.ifftshift(src_fft_shift)

        result[:,:,i] = np.fft.ifft2(src_ifft_shift)
        result[:,:,i] = np.abs(result[:,:,i])

    result = np.float32(result)
    result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
    result = cv2.normalize(result,None,0,1,cv2.NORM_MINMAX)

    return result

image = domain_adoptation(src=ashley,trg=lisa,freq=1)
plt.imshow(a)

And there's a GIF:

If you want to have better results, you can take a look at the quite old deep learning method called "

Of course there are many modern state-of-the-art approaches of style transfer, see here.