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| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| def color_transfer(source, target): | |
| source = cv2.cvtColor(source, cv2.COLOR_BGR2Lab).astype("float32") | |
| target = cv2.cvtColor(target, cv2.COLOR_BGR2Lab).astype("float32") | |
| lMeanSrc, aMeanSrc, bMeanSrc = (source[..., i].mean() for i in range(3)) | |
| lStdSrc, aStdSrc, bStdSrc = (source[..., i].std() for i in range(3)) | |
| lMeanTar, aMeanTar, bMeanTar = (target[..., i].mean() for i in range(3)) | |
| lStdTar, aStdTar, bStdTar = (target[..., i].std() for i in range(3)) | |
| (l, a, b) = cv2.split(source) | |
| l -= lMeanSrc | |
| a -= aMeanSrc | |
| b -= bMeanSrc | |
| l = (lStdTar / lStdSrc) * l | |
| a = (aStdTar / aStdSrc) * a | |
| b = (bStdTar / bStdSrc) * b | |
| l += lMeanTar | |
| a += aMeanTar | |
| b += bMeanTar | |
| l = np.clip(l, 0, 255) | |
| a = np.clip(a, 0, 255) | |
| b = np.clip(b, 0, 255) | |
| transfer = cv2.merge([l, a, b]) | |
| transfer = cv2.cvtColor(transfer.astype("uint8"), cv2.COLOR_Lab2BGR) | |
| return transfer | |
| def hex_to_rgb(hex_val): | |
| hex_val = hex_val.lstrip('#') | |
| return tuple(int(hex_val[i:i+2], 16) for i in (0, 2, 4)) | |
| def create_color_palette(colors, palette_width=800, palette_height=200): | |
| pixels = [] | |
| n_colors = len(colors) | |
| for i in range(n_colors): | |
| color = hex_to_rgb(colors[i]) | |
| for j in range(palette_width//n_colors * palette_height): | |
| pixels.append(color) | |
| img = Image.new('RGB', (palette_height, palette_width)) | |
| img.putdata(pixels) | |
| return img | |
| def recolor_statistical(source, colors): | |
| pallete_img = create_color_palette(colors) | |
| palette_bgr = cv2.cvtColor(np.array(pallete_img), cv2.COLOR_RGB2BGR) | |
| recolored = color_transfer(source, palette_bgr) | |
| # Adjust bilateral filtering parameters | |
| diameter = 15 | |
| sigma_color = 30 | |
| sigma_space = 20 | |
| smoothed = cv2.bilateralFilter(recolored, diameter, sigma_color, sigma_space) | |
| # Save image at maximum quality | |
| filename = "result.jpg" | |
| cv2.imwrite(filename, smoothed, [cv2.IMWRITE_JPEG_QUALITY, 100]) | |
| return filename | |
| # Usage | |
| # source_img = cv2.imread("path_to_your_source_image.jpg") | |
| # hexcolors = ["#db5a1e", "#555115", "#9a690e", "#1f3a19", "#da8007", | |
| # "#9a0633", "#b70406", "#d01b4b", "#e20b0f", "#f7515d"] | |
| # result_path = recolor_statistical(source_img, hexcolors) | |
| def recolor(source, colors): | |
| # pallete_img = create_color_palette(colors) | |
| # palette_bgr = cv2.cvtColor(np.array(pallete_img), cv2.COLOR_RGB2BGR) | |
| # recolored = optimal_transport_color_transfer(source, palette_bgr) | |
| # smooth = True#test with true for different results. | |
| # if smooth: | |
| # # Apply bilateral filtering | |
| # diameter = 10 # diameter of each pixel neighborhood, adjust based on your image size | |
| # sigma_color = 25 # larger value means colors farther to each other will mix together | |
| # sigma_space = 15 # larger values means farther pixels will influence each other if their colors are close enough | |
| # smoothed = cv2.bilateralFilter(recolored, diameter, sigma_color, sigma_space) | |
| # recoloredFile = cv2.imwrite("result.jpg", smoothed, [cv2.IMWRITE_JPEG_QUALITY, 100]) | |
| # return recoloredFile | |
| # else: | |
| # recoloredFile = cv2.imwrite("result.jpg", recolored) | |
| # return recoloredFile | |
| source = source.astype(np.uint8) | |
| source = cv2.cvtColor(source, cv2.COLOR_RGB2BGR) | |
| source_img = source | |
| hexcolors = ["#db5a1e", "#555115", "#9a690e", "#1f3a19", "#da8007", | |
| "#9a0633", "#b70406", "#d01b4b", "#e20b0f", "#f7515d"] | |
| result_path = recolor_statistical(source_img, colors) | |