import numpy as np
from PIL import Image
import colorsys
# Image size: 4096 x 4096 = 16,777,216 pixels
WIDTH = HEIGHT = 4096
CENTER_X, CENTER_Y = WIDTH // 2, HEIGHT // 2
# Generate all RGB colors
rgb_colors = np.array([[r, g, b] for r in range(256)
for g in range(256)
for b in range(256)], dtype=np.uint8)
# Convert RGB to HSB (HSV), normalized to 0–1 range
def rgb_to_hsv(rgb):
return np.array([colorsys.rgb_to_hsv(r/255, g/255, b/255) for r, g, b in rgb])
# Convert HSB (HSV) back to RGB 0–255
def hsv_to_rgb(hsv):
return np.array([colorsys.hsv_to_rgb(h, s, v) for h, s, v in hsv]) * 255
# Convert all RGB to HSV to enable sorting
hsv_colors = rgb_to_hsv(rgb_colors)
# Assign image coordinates to each color using radial HSB mapping
# Hue: angle around center
# Saturation: distance from center
# Brightness: vertical axis (top = bright, bottom = dark)
coords = []
for y in range(HEIGHT):
for x in range(WIDTH):
dx = x - CENTER_X
dy = y - CENTER_Y
r = np.hypot(dx, dy) / (WIDTH / 2) # radius as saturation
theta = (np.arctan2(dy, dx) + np.pi) / (2 * np.pi) # hue from angle (0–1)
v = 1.0 - (y / HEIGHT) # brightness vertically
coords.append((theta, r, v, x, y))
# Sort all RGB colors by their HSV mapping to the image layout
# Then place each color exactly once
coords.sort() # sorts by hue, then sat, then bright
# Sort the HSV list the same way to match coordinates
hsv_sorted_idx = np.lexsort((hsv_colors[:, 2], hsv_colors[:, 1], hsv_colors[:, 0]))
rgb_sorted = rgb_colors[hsv_sorted_idx]
# Create image
image = Image.new("RGB", (WIDTH, HEIGHT))
pixels = image.load()
for (h, s, v, x, y), color in zip(coords, rgb_sorted):
pixels[x, y] = tuple(color)
# Save image (lossless)
image.save("radial_hsb_rgb_visualization.png", "PNG")
