ii-pix/convert.py

"""Image converter to Apple II Double Hi-Res format."""

import argparse
import array
import os.path
import time
import collections
import random

import colour
from PIL import Image
import numpy as np
from pyclustering.cluster.kmedians import kmedians
from pyclustering.cluster.kmeans import kmeans
from pyclustering.utils.metric import distance_metric, type_metric
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer

import dither as dither_pyx
import dither_pattern
import image as image_py
import palette as palette_py
import screen as screen_py


# TODO:
# - support LR/DLR
# - support HGR

def cluster_palette(image: Image):
    # line_to_palette = {}

    # shuffle_lines = liprint(st(range(200))
    # random.shuffle(shuffle_lines)
    # for idx, line in enumerate(shuffle_lines):
    #    line_to_palette[line] = idx % 16

    # for line in range(200):
    #     if line % 3 == 0:
    #         line_to_palette[line] = int(line / (200 / 16))
    #     elif line % 3 == 1:
    #         line_to_palette[line] = np.clip(int(line / (200 / 16)) + 1, 0, 15)
    #     else:
    #         line_to_palette[line] = np.clip(int(line / (200 / 16)) + 2, 0, 15)

    # for line in range(200):
    #     if line % 3 == 0:
    #         line_to_palette[line] = int(line / (200 / 16))
    #     elif line % 3 == 1:
    #         line_to_palette[line] = np.clip(int(line / (200 / 16)) + 1, 0, 15)
    #     else:
    #         line_to_palette[line] = np.clip(int(line / (200 / 16)) + 2, 0, 15)

    colours_rgb = np.asarray(image).reshape((-1, 3))
    with colour.utilities.suppress_warnings(colour_usage_warnings=True):
        colours_cam = colour.convert(colours_rgb, "RGB",
                                     "CAM16UCS").astype(np.float32)
    palettes_rgb = np.empty((16, 16, 3), dtype=np.float32)
    palettes_cam = np.empty((16, 16, 3), dtype=np.float32)
    for palette_idx in range(16):
        print("Fitting palette %d" % palette_idx)
        p_lower = max(palette_idx - 2, 0)
        p_upper = min(palette_idx + 2, 16)
        palette_pixels = colours_cam[
                         int(p_lower * (200 / 16)) * 320:int(p_upper * (
                                 200 / 16)) * 320, :]

        # kmeans = KMeans(n_clusters=16, max_iter=10000)
        # kmeans.fit_predict(palette_pixels)
        # palettes_cam[palette_idx] = kmeans.cluster_centers_

        # fixed_centroids = None
        # print(np.array(line_colours), fixed_centroids)
        # palettes_cam[palette_idx] = dither_pyx.k_means_with_fixed_centroids(
        #    16, palette_pixels, fixed_centroids=fixed_centroids,
        #    tolerance=1e-6)

        best_wce = 1e9
        best_medians = None
        for i in range(500):
            # print(i)
            initial_centers = kmeans_plusplus_initializer(
                palette_pixels, 16).initialize()
            kmedians_instance = kmedians(
                palette_pixels, initial_centers, tolerance=0.1, itermax=100,
                metric=distance_metric(type_metric.MANHATTAN))
            kmedians_instance.process()
            if kmedians_instance.get_total_wce() < best_wce:
                best_wce = kmedians_instance.get_total_wce()
                print(i, best_wce)
                best_medians = kmedians_instance
        print("Best %f" % best_wce)
        palettes_cam[palette_idx, :, :] = np.array(
            best_medians.get_medians()).astype(np.float32)

        # palette_colours = collections.defaultdict(list)
        # for line in range(200):
        #     palette = line_to_palette[line]
        #     palette_colours[palette].extend(
        #         colours_cam[line * 320:(line + 1) * 320])

        # For each line grouping, find big palette entries with minimal total
        # distance

        # palette_cam = None
        # for palette_idx in range(16):
        #     line_colours = palette_colours[palette_idx]
        #     #if palette_idx < 15:
        #     #    line_colours += palette_colours[palette_idx + 1]
        #     # if palette_idx < 14:
        #     #     line_colours += palette_colours[palette_idx + 2]
        #     # if palette_idx > 0:
        #     #     fixed_centroids = palette_cam[:8, :]
        #     # else:
        #     fixed_centroids = None
        #     # print(np.array(line_colours), fixed_centroids)
        #     palette_cam = dither_pyx.k_means_with_fixed_centroids(16, np.array(
        #         line_colours), fixed_centroids=fixed_centroids, tolerance=1e-6)

        # kmeans = KMeans(n_clusters=16, max_iter=10000)
        # kmeans.fit_predict(line_colours)
        # palette_cam = kmeans.cluster_centers_

        with colour.utilities.suppress_warnings(colour_usage_warnings=True):
            palette_rgb = colour.convert(palettes_cam[palette_idx], "CAM16UCS",
                                         "RGB")
            # SHR colour palette only uses 4-bit values
            palette_rgb = np.round(palette_rgb * 15) / 15
            palettes_rgb[palette_idx, :, :] = palette_rgb.astype(np.float32)
    # print(palettes_rgb)

    # For each line, pick the palette with lowest total distance
    # best_palette = 15
    # for line in range(200):
    #     line_pixels = colours_cam[line*320:(line+1)*320]
    #     best_palette = dither_pyx.best_palette_for_line(
    #         line_pixels, palettes_cam, best_palette)
    #     line_to_palette[line] = best_palette
    #     print(line, line_to_palette[line])
    return palettes_cam, palettes_rgb


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("input", type=str, help="Input image file to process.")
    parser.add_argument("output", type=str, help="Output file for converted "
                                                 "Apple II image.")
    parser.add_argument(
        "--lookahead", type=int, default=8,
        help=("How many pixels to look ahead to compensate for NTSC colour "
              "artifacts (default: 8)"))
    parser.add_argument(
        '--dither', type=str, choices=list(dither_pattern.PATTERNS.keys()),
        default=dither_pattern.DEFAULT_PATTERN,
        help="Error distribution pattern to apply when dithering (default: "
             + dither_pattern.DEFAULT_PATTERN + ")")
    parser.add_argument(
        '--show-input', action=argparse.BooleanOptionalAction, default=False,
        help="Whether to show the input image before conversion.")
    parser.add_argument(
        '--show-output', action=argparse.BooleanOptionalAction, default=True,
        help="Whether to show the output image after conversion.")
    parser.add_argument(
        '--palette', type=str, choices=list(set(palette_py.PALETTES.keys())),
        default=palette_py.DEFAULT_PALETTE,
        help='RGB colour palette to dither to.  "ntsc" blends colours over 8 '
             'pixels and gives better image quality on targets that '
             'use/emulate NTSC, but can be substantially slower.  Other '
             'palettes determine colours based on 4 pixel sequences '
             '(default: ' + palette_py.DEFAULT_PALETTE + ")")
    parser.add_argument(
        '--show-palette', type=str, choices=list(palette_py.PALETTES.keys()),
        help="RGB colour palette to use when --show_output (default: "
             "value of --palette)")
    parser.add_argument(
        '--verbose', action=argparse.BooleanOptionalAction,
        default=False, help="Show progress during conversion")
    parser.add_argument(
        '--gamma_correct', type=float, default=2.4,
        help='Gamma-correct image by this value (default: 2.4)'
    )
    args = parser.parse_args()
    if args.lookahead < 1:
        parser.error('--lookahead must be at least 1')

    # palette = palette_py.PALETTES[args.palette]()
    screen = screen_py.SHR320Screen()

    # Conversion matrix from RGB to CAM16UCS colour values.  Indexed by
    # 24-bit RGB value
    rgb_to_cam16 = np.load("data/rgb_to_cam16ucs.npy")

    # Open and resize source image
    image = image_py.open(args.input)
    if args.show_input:
        image_py.resize(image, screen.X_RES, screen.Y_RES,
                        srgb_output=False).show()
    rgb = np.array(
        image_py.resize(image, screen.X_RES, screen.Y_RES,
                        gamma=args.gamma_correct, srgb_output=True)).astype(
        np.float32) / 255

    palettes_cam, palettes_rgb = cluster_palette(rgb)
    # print(palette_rgb)
    # screen.set_palette(0, (image_py.linear_to_srgb_array(palette_rgb) *
    #                        15).astype(np.uint8))
    for i in range(16):
        screen.set_palette(i, (np.round(palettes_rgb[i, :, :] * 15)).astype(
            np.uint8))

    for penalty in [1,2,3,4,5,6,7,8,9,10,1e9]:
        output_4bit, line_to_palette = dither_pyx.dither_shr(
            rgb, palettes_cam, palettes_rgb, rgb_to_cam16, float(penalty))
        screen.set_pixels(output_4bit)
        output_rgb = np.zeros((200, 320, 3), dtype=np.uint8)
        for i in range(200):
            screen.line_palette[i] = line_to_palette[i]
            output_rgb[i, :, :] = (
                    palettes_rgb[line_to_palette[i]][
                        output_4bit[i, :]] * 255).astype(np.uint8)
        output_srgb = image_py.linear_to_srgb(output_rgb).astype(np.uint8)

        # dither = dither_pattern.PATTERNS[args.dither]()
        # bitmap = dither_pyx.dither_image(
        #     screen, rgb, dither, args.lookahead, args.verbose, rgb_to_cam16)

        # Show output image by rendering in target palette
        # output_palette_name = args.show_palette or args.palette
        # output_palette = palette_py.PALETTES[output_palette_name]()
        # output_screen = screen_py.DHGRScreen(output_palette)
        # if output_palette_name == "ntsc":
        #     output_srgb = output_screen.bitmap_to_image_ntsc(bitmap)
        # else:
        #     output_srgb = image_py.linear_to_srgb(
        #         output_screen.bitmap_to_image_rgb(bitmap)).astype(np.uint8)
        out_image = image_py.resize(
            Image.fromarray(output_srgb), screen.X_RES, screen.Y_RES,
            srgb_output=False)  # XXX true

        if args.show_output:
            out_image.show()

    # Save Double hi-res image
    outfile = os.path.join(os.path.splitext(args.output)[0] + "-preview.png")
    out_image.save(outfile, "PNG")
    screen.pack()
    # with open(args.output, "wb") as f:
    #     f.write(bytes(screen.aux))
    #     f.write(bytes(screen.main))
    with open(args.output, "wb") as f:
        f.write(bytes(screen.memory))


if __name__ == "__main__":
    main()