"""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()