This script performs the linear color transfer step that the Scale Control feature in leongatys/NeuralImageSynthesis performs.

linear-color-transfer.py

#This script performs the linear color transfer step that 
#leongatys/NeuralImageSynthesis' Scale Control code performs.
#https://github.com/leongatys/NeuralImageSynthesis/blob/master/ExampleNotebooks/ScaleControl.ipynb
import scipy
import h5py
import skimage
import os
from skimage import io,transform,img_as_float
from skimage.io import imread,imsave
from collections import OrderedDict
#from PIL import Image, ImageFilter
import numpy as np
from numpy import eye 
import decimal
#import click

target_img = imread('fig4_style2.jpg').astype(float)/256
source_img = imread('fig4_content.jpg').astype(float)/256

def match_color(target_img, source_img, mode='pca', eps=1e-5):
    '''
    Matches the colour distribution of the target image to that of the source image
    using a linear transform.
    Images are expected to be of form (w,h,c) and float in [0,1].
    Modes are chol, pca or sym for different choices of basis.
    '''
    mu_t = target_img.mean(0).mean(0)
    t = target_img - mu_t
    t = t.transpose(2,0,1).reshape(3,-1)
    Ct = t.dot(t.T) / t.shape[1] + eps * eye(t.shape[0])
    mu_s = source_img.mean(0).mean(0)
    s = source_img - mu_s
    s = s.transpose(2,0,1).reshape(3,-1)
    Cs = s.dot(s.T) / s.shape[1] + eps * eye(s.shape[0])
    if mode == 'chol':
        chol_t = np.linalg.cholesky(Ct)
        chol_s = np.linalg.cholesky(Cs)
        ts = chol_s.dot(np.linalg.inv(chol_t)).dot(t)
    if mode == 'pca':
        eva_t, eve_t = np.linalg.eigh(Ct)
        Qt = eve_t.dot(np.sqrt(np.diag(eva_t))).dot(eve_t.T)
        eva_s, eve_s = np.linalg.eigh(Cs)
        Qs = eve_s.dot(np.sqrt(np.diag(eva_s))).dot(eve_s.T)
        ts = Qs.dot(np.linalg.inv(Qt)).dot(t)
    if mode == 'sym':
        eva_t, eve_t = np.linalg.eigh(Ct)
        Qt = eve_t.dot(np.sqrt(np.diag(eva_t))).dot(eve_t.T)
        Qt_Cs_Qt = Qt.dot(Cs).dot(Qt)
        eva_QtCsQt, eve_QtCsQt = np.linalg.eigh(Qt_Cs_Qt)
        QtCsQt = eve_QtCsQt.dot(np.sqrt(np.diag(eva_QtCsQt))).dot(eve_QtCsQt.T)
        ts = np.linalg.inv(Qt).dot(QtCsQt).dot(np.linalg.inv(Qt)).dot(t)
    matched_img = ts.reshape(*target_img.transpose(2,0,1).shape).transpose(1,2,0)
    matched_img += mu_s
    matched_img[matched_img>1] = 1
    matched_img[matched_img<0] = 0
    return matched_img

output_img = match_color(target_img, source_img)
imsave('result_pca.png', output_img)