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| # 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 | |
| # Standalone script by github.com/htoyryla, and github.com/ProGamerGov | |
| #based on this: https://github.com/ProGamerGov/Neural-Tools | |
| import numpy as np | |
| # import argparse | |
| import imageio | |
| from skimage import io,transform,img_as_float | |
| from skimage.io import imread,imsave | |
| from PIL import Image | |
| from numpy import eye | |
| import cv2 | |
| # parser = argparse.ArgumentParser() | |
| # parser.add_argument('-t', '--target_image', type=str, help="The image you are transfering color to. Ex: target.png", required=True) | |
| # parser.add_argument('-s', '--source_image', type=str, help="The image you are transfering color from. Ex: source.png", required=True) | |
| # parser.add_argument('-o', '--output_image', default='output.png', help="The name of your output image. Ex: output.png", type=str) | |
| # parser.add_argument('-m', '--mode', default='pca', help="The color transfer mode. Options are pca, chol, or sym.", type=str) | |
| # parser.add_argument('-e', '--eps', default='1e-5', help="Your epsilon value in scientific notation or normal notation. Ex: 1e-5 or 0.00001", type=float) | |
| # parser.parse_args() | |
| # args = parser.parse_args() | |
| Image.MAX_IMAGE_PIXELS = 1000000000 # Support gigapixel images | |
| # def main(): | |
| # target_img = imageio.v2.imread(args.target_image, pilmode="RGB").astype(float)/256 | |
| # source_img = imageio.v2.imread(args.source_image, pilmode="RGB").astype(float)/256 | |
| # output_img = match_color(target_img, source_img, mode=args.mode, eps=args.eps) | |
| # output_img = (output_img * 255).astype(np.uint8) | |
| # imsave(args.output_image, output_img) | |
| # # imsave(args.output_image, output_img) | |
| 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 | |
| # if __name__ == "__main__": | |
| # main() | |
| def create_target_image(hex_colors): | |
| # Convert hex colors to RGB | |
| rgb_colors = [tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) for hex_color in hex_colors] | |
| # Create an image with the RGB colors | |
| target_image = np.zeros((100, len(rgb_colors), 3), dtype=np.uint8) | |
| for i, rgb_color in enumerate(rgb_colors): | |
| target_image[:, i] = rgb_color | |
| return target_image | |
| def recolor(image_np, colors): | |
| colors = [color.replace('#', '') for color in colors] | |
| palette = create_target_image(colors) | |
| # No need to convert to BGR and then read it again | |
| target_img = image_np.astype(float)/256 | |
| source_img = palette.astype(float)/256 | |
| output_img = match_color(target_img, source_img) | |
| output_img = (output_img * 255).astype(np.uint8) | |
| imsave('result.jpg', output_img) | |
| return output_img |