mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-11-19 23:32:18 +00:00
442 lines
18 KiB
Python
442 lines
18 KiB
Python
"""Image converter to Apple II Double Hi-Res format."""
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import argparse
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from collections import defaultdict
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import os.path
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import random
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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, rgb12_iigs_to_cam16ucs, rgb24_to_cam16ucs,
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fixed_colours=0):
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# Source image in 24-bit linear RGB colour space
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self._image_rgb = image
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# Source image in CAM16UCS colour space
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self._colours_cam = self._image_colours_cam(image)
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# How many image colours to fix identically across all 16 SHR
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# palettes. These are taken to be the most prevalent colours from
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# _global_palette.
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self._fixed_colours = fixed_colours
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# We fit a 16-colour palette against the entire image which is used
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# as starting values for fitting the reserved colours in the 16 SHR
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# palettes.
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self._global_palette = np.empty((16, 3), dtype=np.uint8)
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# 16 SHR palettes each of 16 colours, in CAM16UCS colour space
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self._palettes_cam = np.empty((16, 16, 3), dtype=np.float32)
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# 16 SHR palettes each of 16 colours, in //gs 4-bit RGB colour space
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self._palettes_rgb = np.empty((16, 16, 3), dtype=np.uint8)
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# defaultdict(list) mapping palette index to lines using this palette
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self._palette_lines = self._init_palette_lines()
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# Conversion matrix from 12-bit //gs RGB colour space to CAM16UCS
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# colour space
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self._rgb12_iigs_to_cam16ucs = rgb12_iigs_to_cam16ucs
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# Conversion matrix from 24-bit linear RGB colour space to CAM16UCS
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# colour space
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self._rgb24_to_cam16ucs = rgb24_to_cam16ucs
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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 _init_palette_lines(self, init_random = False):
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palette_lines = defaultdict(list)
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if init_random:
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lines = list(range(200))
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random.shuffle(lines)
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idx = 0
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while lines:
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palette_lines[idx].append(lines.pop())
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idx += 1
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else:
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palette_splits = self._equal_palette_splits()
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for i, lh in enumerate(palette_splits):
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l, h = lh
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palette_lines[i].extend(list(range(l, h)))
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return palette_lines
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def _equal_palette_splits(self, palette_height=35):
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# The 16 palettes are striped across consecutive (overlapping) line
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# ranges. 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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# If we want to overlap 16 palettes in 200 lines, where each palette
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# has height H and overlaps the previous one by L lines, then the
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# boundaries are at lines:
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# (0, H), (H-L, 2H-L), (2H-2L, 3H-2L), ..., (15H-15L, 16H - 15L)
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# i.e. 16H - 15L = 200, so for a given palette height H we need to
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# overlap by:
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# L = (16H - 200)/15
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palette_overlap = (16 * palette_height - 200) / 15
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palette_ranges = []
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for palette_idx in range(16):
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palette_lower = palette_idx * (palette_height - palette_overlap)
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palette_upper = palette_lower + palette_height
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palette_ranges.append((int(np.round(palette_lower)),
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int(np.round(palette_upper))))
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return palette_ranges
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def _dither_image(self, palettes_cam, penalty):
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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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palettes_linear_rgb = colour.convert(
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palettes_cam, "CAM16UCS", "RGB").astype(np.float32)
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output_4bit, line_to_palette, total_image_error, palette_line_errors = \
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dither_pyx.dither_shr(
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self._image_rgb, palettes_cam, palettes_linear_rgb,
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self._rgb24_to_cam16ucs, float(penalty))
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# Update map of palettes to image lines for which the palette was the
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# best match
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palette_lines = defaultdict(list)
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for line, palette in enumerate(line_to_palette):
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palette_lines[palette].append(line)
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self._palette_lines = palette_lines
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self._palette_line_errors = palette_line_errors
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return (output_4bit, line_to_palette, palettes_linear_rgb,
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total_image_error)
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def iterate(self, penalty: float, max_inner_iterations: int,
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max_outer_iterations: int):
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total_image_error = 1e9
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outer_iterations_since_improvement = 0
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while outer_iterations_since_improvement < max_outer_iterations:
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print("New iteration")
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inner_iterations_since_improvement = 0
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self._palette_lines = self._init_palette_lines()
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while inner_iterations_since_improvement < max_inner_iterations:
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# print("Iterations %d" % inner_iterations_since_improvement)
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new_palettes_cam, new_palettes_rgb12_iigs = (
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self._fit_shr_palettes())
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# Recompute image with proposed palettes and check whether it
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# has lower total image error than our previous best.
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(output_4bit, line_to_palette, palettes_linear_rgb,
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new_total_image_error) = self._dither_image(
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new_palettes_cam, penalty)
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# TODO: check for duplicate palettes and unused colours
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# within a palette
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self._reassign_unused_palettes(line_to_palette)
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if new_total_image_error >= total_image_error:
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inner_iterations_since_improvement += 1
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continue
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# We found a globally better set of palettes, so restart the
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# clocks
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inner_iterations_since_improvement = 0
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outer_iterations_since_improvement = -1
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total_image_error = new_total_image_error
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self._palettes_cam = new_palettes_cam
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self._palettes_rgb = new_palettes_rgb12_iigs
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yield (new_total_image_error, output_4bit, line_to_palette,
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new_palettes_rgb12_iigs, palettes_linear_rgb)
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outer_iterations_since_improvement += 1
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def _fit_shr_palettes(self) -> Tuple[np.ndarray, np.ndarray]:
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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_palettes_cam = np.empty_like(self._palettes_cam)
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new_palettes_rgb12_iigs = np.empty_like(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._fit_global_palette()
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for palette_idx in range(16):
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palette_pixels = (
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self._colours_cam[
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self._palette_lines[palette_idx], :, :].reshape(-1, 3))
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# Fix reserved colours from the global palette and pick unique
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# random colours from the sample points for the remaining initial
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# centroids. This tends to increase the number of colours in the
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# resulting image, and improves quality.
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initial_centroids = self._global_palette
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pixels_rgb_iigs = dither_pyx.convert_cam16ucs_to_rgb12_iigs(
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palette_pixels)
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seen_colours = set()
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for i in range(self._fixed_colours):
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seen_colours.add(tuple(initial_centroids[i, :]))
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for i in range(self._fixed_colours, 16):
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choice = np.random.randint(0, pixels_rgb_iigs.shape[
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0])
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new_colour = pixels_rgb_iigs[choice, :]
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if tuple(new_colour) in seen_colours:
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continue
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seen_colours.add(tuple(new_colour))
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initial_centroids[i, :] = new_colour
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palettes_rgb12_iigs, palette_error = \
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dither_pyx.k_means_with_fixed_centroids(
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n_clusters=16, n_fixed=self._fixed_colours,
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samples=palette_pixels,
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initial_centroids=initial_centroids,
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max_iterations=1000, tolerance=0.05,
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rgb12_iigs_to_cam16ucs=self._rgb12_iigs_to_cam16ucs
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)
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for i in range(16):
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new_palettes_cam[palette_idx, i, :] = (
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np.array(dither_pyx.convert_rgb12_iigs_to_cam(
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self._rgb12_iigs_to_cam16ucs, palettes_rgb12_iigs[
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i]), dtype=np.float32))
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new_palettes_rgb12_iigs[palette_idx, :, :] = palettes_rgb12_iigs
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self._palettes_accepted = False
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return new_palettes_cam, new_palettes_rgb12_iigs
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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.reshape(-1, 3))
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# Dict of {palette idx : frequency count}
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palette_freq = {idx: 0 for idx in range(16)}
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for idx, freq in zip(*np.unique(clusters.labels_, return_counts=True)):
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palette_freq[idx] = freq
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frequency_order = [
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k for k, v in sorted(
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list(palette_freq.items()), key=lambda kv: kv[1], reverse=True)]
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self._global_palette = (
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dither_pyx.convert_cam16ucs_to_rgb12_iigs(
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clusters.cluster_centers_[frequency_order].astype(
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np.float32)))
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def _reassign_unused_palettes(self, new_line_to_palette):
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palettes_used = [False] * 16
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for palette in new_line_to_palette:
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palettes_used[palette] = True
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best_palette_lines = [v for k, v in sorted(list(zip(
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self._palette_line_errors, range(200))))]
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for palette_idx, palette_used in enumerate(palettes_used):
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if palette_used:
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continue
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# print("Reassigning palette %d" % palette_idx)
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# TODO: also remove from old entry
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worst_line = best_palette_lines.pop()
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self._palette_lines[palette_idx] = [worst_line]
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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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parser.add_argument(
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'--fixed_colours', type=int, default=0,
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help='How many colours to fix as identical across all 16 SHR palettes'
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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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rgb24_to_cam16ucs = np.load("data/rgb24_to_cam16ucs.npy")
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rgb12_iigs_to_cam16ucs = np.load("data/rgb12_iigs_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 = 1 # 1e18 # TODO: is this needed any more?
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inner_iterations = 10
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outer_iterations = 20
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if args.show_output:
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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 = None
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cluster_palette = ClusterPalette(
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rgb, fixed_colours=args.fixed_colours,
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rgb12_iigs_to_cam16ucs=rgb12_iigs_to_cam16ucs,
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rgb24_to_cam16ucs=rgb24_to_cam16ucs)
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seq = 0
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for (new_total_image_error, output_4bit, line_to_palette,
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palettes_rgb12_iigs, palettes_linear_rgb) in cluster_palette.iterate(
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penalty, inner_iterations, outer_iterations):
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if total_image_error is not None:
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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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for i in range(16):
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screen.set_palette(i, palettes_rgb12_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_linear_rgb[line_to_palette[i]][
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output_4bit[i, :]] * 255
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).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, rgb24_to_cam16ucs)
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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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unique_colours = np.unique(
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palettes_rgb12_iigs.reshape(-1, 3), axis=0).shape[0]
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print("%d unique colours" % unique_colours)
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seq += 1
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# Save Double hi-res image
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outfile = os.path.join(
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os.path.splitext(args.output)[0] + "-%d-preview.png" % seq)
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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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