mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-11-19 23:32:18 +00:00
0009ce8913
all palettes. This will be useful for Total Replay which does an animation effect when displaying the image (first set palettes, then transition in pixels) - this requires us to go back to computing k-means ourself instead of using sklearn, since it can't keep some centroids fixed - try to be more careful about //gs RGB values, which are in the Rec.601 colour space. This isn't quite right yet - the issue seems to be that since we dither in linear RGB space but quantize in the nonlinear space, small differences may lead to a +/- 1 in the 4-bit //gs RGB value, which is quite noticeable. Instead we need to be clustering and/or dithering with awareness of the quantized palette space.
346 lines
15 KiB
Python
346 lines
15 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 typing import Tuple, List
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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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from sklearn import cluster
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from os import environ
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environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
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import pygame
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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__(
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self, image: Image, reserved_colours=0):
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self._colours_cam = self._image_colours_cam(image)
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self._reserved_colours = reserved_colours
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self._errors = [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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self._global_palette = 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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labels = clusters.labels_
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frequency_order = [
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k for k, v in sorted(
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# List of (palette idx, frequency count)
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list(zip(*np.unique(labels, return_counts=True))),
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key=lambda kv: kv[1], reverse=True)]
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return clusters.cluster_centers_[frequency_order]
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def propose_palettes(self) -> Tuple[np.ndarray, np.ndarray, List[float]]:
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"""Attempt to find new palettes that locally improve image quality.
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Re-fit a set of 16 palettes from (overlapping) line ranges of the
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source image, using k-means clustering in CAM16-UCS colour space.
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We maintain the total image error for the pixels on which the 16
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palettes are clustered. A new palette that increases this local
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image error is rejected.
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New palettes that reduce local error cannot be applied immediately
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though, because they may cause an increase in *global* image error
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when dithering. i.e. they would reduce the overall image quality.
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The current (locally) best palettes are returned and can be applied
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using accept_palettes().
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"""
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new_errors = list(self._errors)
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new_palettes_cam = np.copy(self._palettes_cam)
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new_palettes_rgb = np.copy(self._palettes_rgb)
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# Compute a new 16-colour global palette for the entire image,
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# used as the starting center positions for k-means clustering of the
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# individual palettes
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self._global_palette = self._fit_global_palette()
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dynamic_colours = 16 - self._reserved_colours
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# The 16 palettes are striped across consecutive (overlapping) line
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# ranges. The basic unit is 200/16 = 12.5 lines, but we extend the
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# line range to cover a multiple of this so that the palette ranges
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# overlap. Since nearby lines tend to have similar colours, this has
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# the effect of smoothing out the colour transitions across palettes.
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palette_band_width = 3
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for palette_idx in range(16):
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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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# 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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# TODO: tune tolerance
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# clusters = cluster.MiniBatchKMeans(
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# n_clusters=16, max_iter=10000,
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# init=self._global_palette,
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# n_init=1)
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# clusters.fit_predict(palette_pixels)
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#
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# palette_error = clusters.inertia_
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clusters, palette_error = dither_pyx.k_means_with_fixed_centroids(
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n_clusters=16, n_fixed=self._reserved_colours,
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samples=palette_pixels, initial_centroids=self._global_palette,
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max_iterations=1000, tolerance=1e-4
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)
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if (palette_error >= self._errors[palette_idx] and not
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self._reserved_colours):
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# Not a local improvement to the existing palette, so ignore it.
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# We can't take this shortcut when we're reserving colours
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# because it would break the invariant that all palettes must
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# share colours.
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continue
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new_palettes_cam[palette_idx, :, :] = np.array(
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# clusters.cluster_centers_).astype(np.float32)
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clusters).astype(np.float32)
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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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new_palettes_cam[palette_idx, :, :], "CAM16UCS", "RGB")
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palette_rgb_rec601 = np.clip(image_py.srgb_to_linear(
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colour.YCbCr_to_RGB(
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colour.RGB_to_YCbCr(
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image_py.linear_to_srgb(palette_rgb * 255) / 255,
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K=colour.WEIGHTS_YCBCR['ITU-R BT.709']),
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K=colour.WEIGHTS_YCBCR['ITU-R BT.601']) * 255) / 255, 0, 1)
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# palette_rgb = np.clip(
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# image_py.srgb_to_linear(
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# colour.YCbCr_to_RGB(
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# colour.RGB_to_YCbCr(
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# image_py.linear_to_srgb(
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# palette_rgb[:, :] * 255) / 255,
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# K=colour.WEIGHTS_YCBCR['ITU-R BT.709']),
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# K=colour.WEIGHTS_YCBCR[
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# 'ITU-R BT.601']) * 255) / 255,
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# 0, 1)
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new_palettes_rgb[palette_idx, :, :] = palette_rgb # palette_rgb_rec601
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new_errors[palette_idx] = palette_error
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return new_palettes_cam, new_palettes_rgb, new_errors
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def accept_palettes(
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self, new_palettes_cam: np.ndarray,
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new_palettes_rgb: np.ndarray, new_errors: List[float]):
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self._palettes_cam = np.copy(new_palettes_cam)
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self._palettes_rgb = np.copy(new_palettes_rgb)
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self._errors = list(new_errors)
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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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# TODO: flags
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penalty = 1e9
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iterations = 10 # 50
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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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iterations_since_improvement = 0
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palettes_iigs = np.empty((16, 16, 3), dtype=np.uint8)
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cluster_palette = ClusterPalette(rgb, reserved_colours=1)
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while iterations_since_improvement < iterations:
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new_palettes_cam, new_palettes_rgb, new_palette_errors = (
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cluster_palette.propose_palettes())
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# Recompute image with proposed palettes and check whether it has
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# lower total image error than our previous best.
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new_output_4bit, new_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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if new_total_image_error >= total_image_error:
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iterations_since_improvement += 1
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continue
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# We found a globally better set of palettes
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iterations_since_improvement = 0
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cluster_palette.accept_palettes(
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new_palettes_cam, new_palettes_rgb, new_palette_errors)
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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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output_4bit = new_output_4bit
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line_to_palette = new_line_to_palette
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total_image_error = new_total_image_error
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palettes_rgb = new_palettes_rgb
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# Recompute 4-bit //gs RGB palettes
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palette_rgb_rec601 = np.clip(
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colour.YCbCr_to_RGB(
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colour.RGB_to_YCbCr(
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image_py.linear_to_srgb(palettes_rgb * 255) / 255,
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K=colour.WEIGHTS_YCBCR['ITU-R BT.709']),
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K=colour.WEIGHTS_YCBCR['ITU-R BT.601']), 0, 1)
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palettes_iigs = np.round(palette_rgb_rec601 * 15).astype(np.uint8)
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for i in range(16):
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screen.set_palette(i, palettes_iigs[i, :, :])
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# Recompute current screen RGB image
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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]][output_4bit[i, :]] * 255
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).astype(
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# np.round(palettes_rgb[line_to_palette[i]][
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# output_4bit[i, :]] * 15) / 15 * 255).astype(
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np.uint8)
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output_srgb_rec709 = np.clip(colour.YCbCr_to_RGB(
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colour.RGB_to_YCbCr(
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image_py.linear_to_srgb(output_rgb) / 255,
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K=colour.WEIGHTS_YCBCR['ITU-R BT.601']),
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K=colour.WEIGHTS_YCBCR['ITU-R BT.709']), 0, 1) * 255
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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(
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np.asarray(out_image).transpose((1, 0, 2))) # flip y/x axes
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canvas.blit(surface, (0, 0))
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pygame.display.flip()
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print((palettes_rgb * 255).astype(np.uint8))
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unique_colours = np.unique(palettes_iigs.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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