import numpy as np import cv2 from PIL import Image from skimage.color import rgb2lab from skimage.color import lab2rgb from sklearn.cluster import KMeans def count_high_freq_colors(image): im = image.getcolors(maxcolors=1024 * 1024) sorted_colors = sorted(im, key=lambda x: x[0], reverse=True) freqs = [c[0] for c in sorted_colors] mean_freq = sum(freqs) / len(freqs) high_freq_colors = [c for c in sorted_colors if c[0] > max(2, mean_freq * 1.25)] return high_freq_colors def get_high_freq_colors(image, similarity_threshold=30): image_copy = image.copy() high_freq_colors = count_high_freq_colors(image) # Check for similar colors and replace the lower frequency color with the higher frequency color in the image for i, (freq1, color1) in enumerate(high_freq_colors): for j, (freq2, color2) in enumerate(high_freq_colors): if (color_distance(color1, color2) < similarity_threshold) or ( color_distance(color1, opaque_color_on_white(color2, 0.5)) < 5): if (freq2 > freq1): replace_color(image_copy, color1, color2) high_freq_colors = count_high_freq_colors(image_copy) print(high_freq_colors) return [high_freq_colors, image_copy] def color_quantization(image, color_frequency_list): # Convert the color frequency list to a set of unique colors unique_colors = set([color for _, color in color_frequency_list]) # Create a mask for the image with True where the color is in the unique colors set mask = np.any(np.all(image.reshape(-1, 1, 3) == np.array(list(unique_colors)), axis=2), axis=1).reshape( image.shape[:2]) # Create a new image with all pixels set to white new_image = np.full_like(image, 255) # Copy the pixels from the original image that have a color in the color frequency list new_image[mask] = image[mask] return new_image def create_binary_matrix(img_arr, target_color): # Create mask of pixels with target color mask = np.all(img_arr == target_color, axis=-1) # Convert mask to binary matrix binary_matrix = mask.astype(int) from datetime import datetime binary_file_name = f'mask-{datetime.now().timestamp()}.png' cv2.imwrite(binary_file_name, binary_matrix * 255) # binary_matrix = torch.from_numpy(binary_matrix).unsqueeze(0).unsqueeze(0) return binary_file_name def color_distance(color1, color2): return sum((a - b) ** 2 for a, b in zip(color1, color2)) ** 0.5 def replace_color(image, old_color, new_color): pixels = image.load() width, height = image.size for x in range(width): for y in range(height): if pixels[x, y] == old_color: pixels[x, y] = new_color def opaque_color_on_white(color, a): r, g, b = color opaque_red = int((1 - a) * 255 + a * r) opaque_green = int((1 - a) * 255 + a * g) opaque_blue = int((1 - a) * 255 + a * b) return (opaque_red, opaque_green, opaque_blue)