Revisit and revise dithered rainbow rasters.

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from PIL import Image

# Fixed palette of seven RGB colors in #RRGGBB string format
palette = [
    "#dfddce",
    "#81857d",
    "#b3a367",
    "#a7695d",
    "#895377",
    "#52657f",
    "#697e7a",
]

def hex_to_rgb(hex_color):
    # Convert the #RRGGBB string to RGB tuple (R, G, B)
    hex_color = hex_color.lstrip("#")
    return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))

def find_closest_color(rgb):
    # Calculate the squared distance between the given RGB color and each color in the palette
    return min(palette, key=lambda c: sum((a - b) ** 2 for a, b in zip(rgb, hex_to_rgb(c))))

def floyd_steinberg_dither(image):
    width, height = image.size
    for y in range(height):
        for x in range(width):
            old_pixel = image.getpixel((x, y))
            new_pixel = hex_to_rgb(find_closest_color(old_pixel))
            image.putpixel((x, y), new_pixel)

            # Calculate the quantization error
            quant_error = tuple(a - b for a, b in zip(old_pixel, new_pixel))

            # Distribute the error to neighboring pixels
            for dx, dy in [(1, 0), (-1, 1), (0, 1), (1, 1)]:
                if 0 <= x + dx < width and 0 <= y + dy < height:
                    neighbor_pixel = image.getpixel((x + dx, y + dy))
                    updated_neighbor = tuple(c + e * 3 // 16 for c, e in zip(neighbor_pixel, quant_error))
                    image.putpixel((x + dx, y + dy), updated_neighbor)

if __name__ == "__main__":
    input_image_path = "cherries.jpg"  # Replace with the path to your input image
    output_image_path = "cherries.png"  # Replace with the desired output path

    image = Image.open(input_image_path)
    floyd_steinberg_dither(image)
    image.save(output_image_path)