Apple-2-SW
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ii-pix
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- Fill any palettes that have fewer than 16 unique entries after 

clustering, using the most frequent pixel colours that are not yet
  in the palette

- Reassign any palettes that are duplicated after clustering
This commit is contained in:
kris 2021-11-25 13:14:22 +00:00
parent ad50ed103d
commit fc35387360
1 changed files with 61 additions and 24 deletions
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85
convert.py
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@ -179,9 +179,9 @@ class ClusterPalette:
new_total_image_error) = self._dither_image(
new_palettes_cam, penalty)
# TODO: check for duplicate palettes and unused colours
# within a palette
self._reassign_unused_palettes(line_to_palette)
# TODO: check for unused colours within a palette
self._reassign_unused_palettes(
line_to_palette, new_palettes_rgb12_iigs)
if new_total_image_error >= total_image_error:
inner_iterations_since_improvement += 1
@ -230,46 +230,43 @@ class ClusterPalette:
self._colours_cam[
self._palette_lines[palette_idx], :, :].reshape(-1, 3))
# Fix reserved colours from the global palette and pick unique
# random colours from the sample points for the remaining initial
# centroids. This tends to increase the number of colours in the
# resulting image, and improves quality.
# Fix reserved colours from the global palette.
initial_centroids = self._global_palette
pixels_rgb_iigs = dither_pyx.convert_cam16ucs_to_rgb12_iigs(
palette_pixels)
seen_colours = set()
for i in range(self._fixed_colours):
seen_colours.add(tuple(initial_centroids[i, :]))
# Pick unique random colours from the sample points for the
# remaining initial centroids.
for i in range(self._fixed_colours, 16):
choice = np.random.randint(0, pixels_rgb_iigs.shape[
0])
choice = np.random.randint(0, pixels_rgb_iigs.shape[0])
new_colour = pixels_rgb_iigs[choice, :]
if tuple(new_colour) in seen_colours:
continue
seen_colours.add(tuple(new_colour))
initial_centroids[i, :] = new_colour
# If there are any single colours in our source //gs RGB
# pixels that represent more than fixed_colour_fraction_threshold
# of the pixels, then fix these colours for the palette instead of
# clustering them. This reduces artifacting on blocks of
# colour.
# If there are any single colours in our source //gs RGB pixels that
# represent more than fixed_colour_fraction_threshold of the total,
# then fix these colours for the palette instead of clustering
# them. This reduces artifacting on blocks of colour.
fixed_colour_fraction_threshold = 0.1
most_frequent_colours = sorted(list(zip(
*np.unique(pixels_rgb_iigs, return_counts=True, axis=0))),
key=lambda kv: kv[1], reverse=True)
fixed_colours = self._fixed_colours
for colour, freq in sorted(list(zip(
*np.unique(dither_pyx.convert_cam16ucs_to_rgb12_iigs(
palette_pixels), return_counts=True, axis=0))),
key=lambda kv: kv[1], reverse=True):
for colour, freq in most_frequent_colours:
if freq < (palette_pixels.shape[0] *
fixed_colour_fraction_threshold):
break
# print(colour, freq)
if tuple(colour) not in seen_colours:
seen_colours.add(tuple(colour))
initial_centroids[fixed_colours, :] = colour
fixed_colours += 1
palettes_rgb12_iigs, palette_error = \
palette_rgb12_iigs, palette_error = \
dither_pyx.k_means_with_fixed_centroids(
n_clusters=16, n_fixed=fixed_colours,
samples=palette_pixels,
@ -277,14 +274,19 @@ class ClusterPalette:
max_iterations=1000, tolerance=0.05,
rgb12_iigs_to_cam16ucs=self._rgb12_iigs_to_cam16ucs
)
# If the k-means clustering returned fewer than 16 unique colours,
# fill out the remainder with the most common pixels colours that
# have not yet been used.
palette_rgb12_iigs = self._fill_short_palette(
palette_rgb12_iigs, most_frequent_colours)
for i in range(16):
new_palettes_cam[palette_idx, i, :] = (
np.array(dither_pyx.convert_rgb12_iigs_to_cam(
self._rgb12_iigs_to_cam16ucs, palettes_rgb12_iigs[
self._rgb12_iigs_to_cam16ucs, palette_rgb12_iigs[
i]), dtype=np.float32))
new_palettes_rgb12_iigs[palette_idx, :, :] = palettes_rgb12_iigs
new_palettes_rgb12_iigs[palette_idx, :, :] = palette_rgb12_iigs
self._palettes_accepted = False
return new_palettes_cam, new_palettes_rgb12_iigs
@ -312,13 +314,48 @@ class ClusterPalette:
clusters.cluster_centers_[frequency_order].astype(
np.float32)))
def _reassign_unused_palettes(self, new_line_to_palette):
def _fill_short_palette(self, palette_iigs_rgb, most_frequent_colours):
"""Fill out the palette to 16 unique entries."""
palette_set = set()
for palette_entry in palette_iigs_rgb:
palette_set.add(tuple(palette_entry))
if len(palette_set) == 16:
return palette_iigs_rgb
# Add most frequent image colours that are not yet in the palette
for colour, freq in most_frequent_colours:
if tuple(colour) in palette_set:
continue
palette_set.add(tuple(colour))
# print("Added freq %d" % freq)
if len(palette_set) == 16:
break
# We couldn't find any more unique colours, fill out with random ones.
while len(palette_set) < 16:
palette_set.add(
tuple(np.random.randint(0, 16, size=3, dtype=np.uint8)))
return np.array(tuple(palette_set), dtype=np.uint8)
def _reassign_unused_palettes(self, line_to_palette, palettes_iigs_rgb):
palettes_used = [False] * 16
for palette in new_line_to_palette:
for palette in line_to_palette:
palettes_used[palette] = True
best_palette_lines = [v for k, v in sorted(list(zip(
self._palette_line_errors, range(200))))]
all_palettes = set()
for palette_idx, palette_iigs_rgb in enumerate(palettes_iigs_rgb):
palette_set = set()
for palette_entry in palette_iigs_rgb:
palette_set.add(tuple(palette_entry))
palette_set = frozenset(palette_set)
if palette_set in all_palettes:
print("Duplicate palette", palette_idx, palette_set)
palettes_used[palette_idx] = False
for palette_idx, palette_used in enumerate(palettes_used):
if palette_used:
continue