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Morandi Palette: A palette with 18 colors inspired by Giorgio Morandi
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| import os | |
| import numpy as np | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| from collections import defaultdict | |
| from gurobipy import Model, GRB | |
| from PIL import Image | |
| colors = [ | |
| '#686789', '#B77F70', '#E5E2B9', '#BEB1A8', '#A79A89', '#8A95A9', | |
| '#ECCED0', '#7D7465', '#E8D3C0', '#7A8A71', '#789798', '#B57C82', | |
| '#9FABB9', '#B0B1B6', '#99857E', '#88878D', '#91A0A5', '#9AA690' | |
| ] | |
| def hex_to_rgb(value): | |
| """Convert a hex color to an RGB tuple.""" | |
| value = value.lstrip("#") | |
| return tuple(int(value[i : i + 2], 16) for i in (0, 2, 4)) | |
| def rgb_to_hex(rgb): | |
| return "#{:02x}{:02x}{:02x}".format(rgb[0], rgb[1], rgb[2]) | |
| def euclidean_distance(color1, color2): | |
| """Calculate the Euclidean distance between two RGB colors.""" | |
| return int(sum((c1 - c2) ** 2 for c1, c2 in zip(color1, color2))) | |
| def find_next_color(palette, remaining_colors): | |
| """Find the color from remaining_colors that maximizes the average distance to the current palette.""" | |
| max_avg_distance = 0 | |
| next_color = None | |
| for color in remaining_colors: | |
| print(color, palette) | |
| avg_distance = sum( | |
| euclidean_distance(hex_to_rgb(color), hex_to_rgb(p)) for p in palette | |
| ) / len(palette) | |
| if avg_distance > max_avg_distance: | |
| max_avg_distance = avg_distance | |
| next_color = color | |
| return next_color | |
| def sort_colors(colors): | |
| """This is only to demonstrate the algorithm to get the ordered colors.""" | |
| current_palette = colors[:6] | |
| remaining_colors = [color for color in colors if color not in current_palette] | |
| for _ in range(len(remaining_colors)): | |
| next_color = find_next_color(current_palette, remaining_colors) | |
| current_palette.append(next_color) | |
| remaining_colors.remove(next_color) | |
| return current_palette | |
| def get_colors(dir: str, num: int = 18, delta: int = 1500): | |
| """Get a list of colors from the Morandi palette.""" | |
| if dir is None: | |
| return colors[:num] | |
| color_counts = defaultdict(int) | |
| for filename in os.listdir(dir): | |
| if filename.endswith(".jpg") or filename.endswith(".png"): | |
| filepath = os.path.join(dir, filename) | |
| print(f"Processing {filepath}") | |
| with Image.open(filepath) as img: | |
| img = img.resize((100, 100)) | |
| img = img.convert("RGB") | |
| color_sig = img.getcolors(img.size[0] * img.size[1]) | |
| for count, color in color_sig: | |
| matched = False | |
| for existing_color in color_counts: | |
| distance = euclidean_distance(existing_color, color) | |
| if distance < delta: | |
| color_counts[existing_color] += count | |
| matched = True | |
| break | |
| if not matched: | |
| color_counts[color] = count | |
| top_colors = sorted(color_counts.items(), key=lambda x: x[1], reverse=True)[:num] | |
| top_colors_hex = [(rgb_to_hex(color), count) for color, count in top_colors] | |
| return [color for color, _ in top_colors_hex] | |
| def group_colors(colors, num_per_group): | |
| """Group colors into groups of fixed size such that the sum of the distances between each pair of colors in the same group is maximized.""" | |
| # Create a new model | |
| m = Model("color_grouping") | |
| # Create variables | |
| x = [ | |
| [m.addVar(vtype=GRB.BINARY, name=f"{i}-{j}") for j in range(len(num_per_group))] | |
| for i in range(len(colors)) | |
| ] | |
| # Set objective | |
| objective = 0 | |
| for i in range(len(colors)): | |
| for j in range(i + 1, len(colors)): | |
| for k in range(len(num_per_group)): | |
| objective += ( | |
| x[i][k] | |
| * x[j][k] | |
| * euclidean_distance(hex_to_rgb(colors[i]), hex_to_rgb(colors[j])) | |
| ) | |
| m.setObjective(objective, GRB.MAXIMIZE) | |
| m.setParam("TimeLimit", 5 * 60) | |
| # Add constraints | |
| for i in range(len(colors)): | |
| m.addConstr(sum(x[i]) <= 1) # each color can only be at most in one group | |
| for j in range(len(num_per_group)): | |
| m.addConstr( | |
| sum(x[i][j] for i in range(len(colors))) == num_per_group[j] | |
| ) # each group has a fixed number of colors | |
| # Optimize model | |
| m.optimize() | |
| # Check if a solution exists | |
| if m.status in [GRB.OPTIMAL, GRB.SUBOPTIMAL, GRB.TIME_LIMIT]: | |
| ( | |
| print("Warning: suboptimal solution found") | |
| if m.status == GRB.SUBOPTIMAL | |
| else None | |
| ) | |
| result = [[] for _ in range(len(num_per_group))] | |
| for i in range(len(colors)): | |
| for k in range(len(num_per_group)): | |
| if x[i][k].x == 1: | |
| result[k].append(colors[i]) | |
| return result | |
| else: | |
| raise Exception("No solution found") | |
| if __name__ == "__main__": | |
| ## Example 1: getting ordered K-colors | |
| colors = sort_colors(get_colors(None, 18)) | |
| sns.palplot(colors) | |
| plt.title("Giorgio Morandi's palette") | |
| plt.show() | |
| ## Example 2: getting grouped K-colors | |
| groups = group_colors(colors, [2, 2, 2, 1, 2, 2, 3, 2, 1]) | |
| print("Grouped colors:", groups) | |
| count, n = 0, len(groups) | |
| fig, axes = plt.subplots(1, n, figsize=(15, 2)) | |
| for ax, group in zip(axes, groups): | |
| ax.imshow( | |
| [[np.array(hex_to_rgb(color)) / 255.0 for color in group]], aspect="auto" | |
| ) | |
| ax.set_title(f"Group-{count}") | |
| ax.axis("off") | |
| count += 1 | |
| plt.show() |
Author
ChuanyuXue
commented
Aug 7, 2023
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