mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-06-14 14:29:29 +00:00
5111696d5c
depend on the width of the palette sampling. Note the potential issue that since we are clustering in CAM space but then quantizing a (much coarser) 4-bit RGB value we could end up picking multiple centroids that will be represented by the same RGB value. This doesn't seem to be a major issue though (e.g. 3-4 lost colours per typical image)
246 lines
10 KiB
Python
246 lines
10 KiB
Python
"""Image converter to Apple II Double Hi-Res format."""
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import argparse
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import os.path
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from PIL import Image
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import colour
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import numpy as np
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import pygame
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from sklearn import cluster
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import dither as dither_pyx
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import dither_pattern
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import image as image_py
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import palette as palette_py
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import screen as screen_py
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# TODO:
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# - support LR/DLR
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# - support HGR
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class ClusterPalette:
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def __init__(self, image: Image):
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self._colours_cam = self._image_colours_cam(image)
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self._best_palette_distances = [1e9] * 16
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self._palettes_cam = np.empty((16, 16, 3), dtype=np.float32)
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self._palettes_rgb = np.empty((16, 16, 3), dtype=np.float32)
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def _image_colours_cam(self, image: Image):
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colours_rgb = np.asarray(image).reshape((-1, 3))
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with colour.utilities.suppress_warnings(colour_usage_warnings=True):
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colours_cam = colour.convert(colours_rgb, "RGB",
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"CAM16UCS").astype(np.float32)
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return colours_cam
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def _fit_global_palette(self):
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"""Compute a 16-colour palette for the entire image to use as
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starting point for the sub-palettes. This should help when the image
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has large blocks of colour since the sub-palettes will tend to pick the
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same colours."""
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clusters = cluster.MiniBatchKMeans(n_clusters=16, max_iter=10000)
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clusters.fit_predict(self._colours_cam)
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return clusters.cluster_centers_
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def iterate(self):
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self._global_palette = self._fit_global_palette()
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for palette_idx in range(16):
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palette_band_width = 3
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p_lower = max(palette_idx + 0.5 - (palette_band_width / 2), 0)
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p_upper = min(palette_idx + 0.5 + (palette_band_width / 2), 16)
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# TODO: dynamically tune palette cuts
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palette_pixels = self._colours_cam[
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int(p_lower * (200 / 16)) * 320:int(p_upper * (
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200 / 16)) * 320, :]
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# TODO: clustering should be aware of the fact that we will
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# down-quantize to a 4-bit RGB value afterwards. i.e. we should
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# not pick multiple centroids that will quantize to the same RGB
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# value since we'll "waste" a palette entry. This doesn't seem to
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# be a major issue in practise though, and fixing it would require
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# implementing our own (optimized) k-means.
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best_wce = self._best_palette_distances[palette_idx]
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# TODO: tune tolerance
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clusters = cluster.MiniBatchKMeans(
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n_clusters=16, max_iter=10000, init=self._global_palette,
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n_init=1)
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clusters.fit_predict(palette_pixels)
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if clusters.inertia_ < best_wce:
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self._palettes_cam[palette_idx, :, :] = np.array(
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clusters.cluster_centers_).astype(np.float32)
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best_wce = clusters.inertia_
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self._best_palette_distances[palette_idx] = best_wce
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# Suppress divide by zero warning,
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# https://github.com/colour-science/colour/issues/900
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with colour.utilities.suppress_warnings(python_warnings=True):
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palette_rgb = colour.convert(
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self._palettes_cam[palette_idx], "CAM16UCS", "RGB")
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# SHR colour palette only uses 4-bit values
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palette_rgb = np.round(palette_rgb * 15) / 15
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self._palettes_rgb[palette_idx, :, :] = palette_rgb.astype(
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np.float32)
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return self._palettes_cam, self._palettes_rgb
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("input", type=str, help="Input image file to process.")
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parser.add_argument("output", type=str, help="Output file for converted "
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"Apple II image.")
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parser.add_argument(
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"--lookahead", type=int, default=8,
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help=("How many pixels to look ahead to compensate for NTSC colour "
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"artifacts (default: 8)"))
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parser.add_argument(
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'--dither', type=str, choices=list(dither_pattern.PATTERNS.keys()),
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default=dither_pattern.DEFAULT_PATTERN,
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help="Error distribution pattern to apply when dithering (default: "
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+ dither_pattern.DEFAULT_PATTERN + ")")
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parser.add_argument(
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'--show-input', action=argparse.BooleanOptionalAction, default=False,
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help="Whether to show the input image before conversion.")
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parser.add_argument(
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'--show-output', action=argparse.BooleanOptionalAction, default=True,
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help="Whether to show the output image after conversion.")
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parser.add_argument(
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'--palette', type=str, choices=list(set(palette_py.PALETTES.keys())),
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default=palette_py.DEFAULT_PALETTE,
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help='RGB colour palette to dither to. "ntsc" blends colours over 8 '
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'pixels and gives better image quality on targets that '
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'use/emulate NTSC, but can be substantially slower. Other '
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'palettes determine colours based on 4 pixel sequences '
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'(default: ' + palette_py.DEFAULT_PALETTE + ")")
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parser.add_argument(
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'--show-palette', type=str, choices=list(palette_py.PALETTES.keys()),
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help="RGB colour palette to use when --show_output (default: "
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"value of --palette)")
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parser.add_argument(
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'--verbose', action=argparse.BooleanOptionalAction,
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default=False, help="Show progress during conversion")
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parser.add_argument(
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'--gamma_correct', type=float, default=2.4,
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help='Gamma-correct image by this value (default: 2.4)'
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)
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args = parser.parse_args()
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if args.lookahead < 1:
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parser.error('--lookahead must be at least 1')
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# palette = palette_py.PALETTES[args.palette]()
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screen = screen_py.SHR320Screen()
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# Conversion matrix from RGB to CAM16UCS colour values. Indexed by
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# 24-bit RGB value
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rgb_to_cam16 = np.load("data/rgb_to_cam16ucs.npy")
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# Open and resize source image
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image = image_py.open(args.input)
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if args.show_input:
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image_py.resize(image, screen.X_RES, screen.Y_RES,
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srgb_output=False).show()
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rgb = np.array(
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image_py.resize(image, screen.X_RES, screen.Y_RES,
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gamma=args.gamma_correct)).astype(np.float32) / 255
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iigs_palette = np.empty((16, 16, 3), dtype=np.uint8)
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# TODO: flags
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penalty = 1e9 # 0 # 1e9
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iterations = 50 # 0
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pygame.init()
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# TODO: for some reason I need to execute this twice - the first time
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# the window is created and immediately destroyed
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_ = pygame.display.set_mode((640, 400))
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canvas = pygame.display.set_mode((640, 400))
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canvas.fill((0, 0, 0))
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pygame.display.flip()
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total_image_error = 1e9
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cluster_palette = ClusterPalette(rgb)
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image_generation = 0
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for iteration in range(iterations):
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# TODO: clean this up - e.g. pass in an acceptance lambda to iterate()
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old_best_palette_distances = cluster_palette._best_palette_distances
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old_palettes_cam = cluster_palette._palettes_cam
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old_palettes_rgb = cluster_palette._palettes_rgb
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new_palettes_cam, new_palettes_rgb = cluster_palette.iterate()
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output_4bit, line_to_palette, new_total_image_error = \
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dither_pyx.dither_shr(
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rgb, new_palettes_cam, new_palettes_rgb, rgb_to_cam16,
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float(penalty)
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)
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if new_total_image_error < total_image_error:
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if total_image_error < 1e9:
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print("Improved quality +%f%% (%f)" % (
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(1 - new_total_image_error / total_image_error) * 100,
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new_total_image_error))
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total_image_error = new_total_image_error
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palettes_rgb = new_palettes_rgb
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else:
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cluster_palette._palettes_cam = old_palettes_cam
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cluster_palette._palettes_rgb = old_palettes_rgb
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cluster_palette._best_palette_distances = old_best_palette_distances
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continue
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image_generation += 1
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for i in range(16):
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iigs_palette[i, :, :] = (
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np.round(image_py.linear_to_srgb(
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palettes_rgb[i, :, :] * 255) / 255 * 15)).astype(np.uint8)
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screen.set_palette(i, iigs_palette[i, :, :])
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screen.set_pixels(output_4bit)
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output_rgb = np.empty((200, 320, 3), dtype=np.uint8)
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for i in range(200):
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screen.line_palette[i] = line_to_palette[i]
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output_rgb[i, :, :] = (
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palettes_rgb[line_to_palette[i]][
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output_4bit[i, :]] * 255).astype(np.uint8)
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output_srgb = image_py.linear_to_srgb(output_rgb).astype(np.uint8)
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# dither = dither_pattern.PATTERNS[args.dither]()
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# bitmap = dither_pyx.dither_image(
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# screen, rgb, dither, args.lookahead, args.verbose, rgb_to_cam16)
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# Show output image by rendering in target palette
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# output_palette_name = args.show_palette or args.palette
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# output_palette = palette_py.PALETTES[output_palette_name]()
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# output_screen = screen_py.DHGRScreen(output_palette)
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# if output_palette_name == "ntsc":
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# output_srgb = output_screen.bitmap_to_image_ntsc(bitmap)
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# else:
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# output_srgb = image_py.linear_to_srgb(
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# output_screen.bitmap_to_image_rgb(bitmap)).astype(np.uint8)
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out_image = image_py.resize(
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Image.fromarray(output_srgb), screen.X_RES * 2, screen.Y_RES * 2,
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srgb_output=True)
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if args.show_output:
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surface = pygame.surfarray.make_surface(np.asarray(
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out_image).transpose((1, 0, 2)))
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canvas.blit(surface, (0, 0))
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pygame.display.flip()
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unique_colours = np.unique(iigs_palette.reshape(-1, 3), axis=0).shape[0]
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print("%d unique colours" % unique_colours)
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# Save Double hi-res image
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outfile = os.path.join(os.path.splitext(args.output)[0] + "-preview.png")
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out_image.save(outfile, "PNG")
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screen.pack()
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# with open(args.output, "wb") as f:
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# f.write(bytes(screen.aux))
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# f.write(bytes(screen.main))
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with open(args.output, "wb") as f:
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f.write(bytes(screen.memory))
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if __name__ == "__main__":
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main()
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