Reorder and tidy
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kris
parent
c78f731cd7
commit
0b985a66b9
191
convert.py
191
convert.py
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@ -61,7 +61,7 @@ class ClusterPalette:
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# List of line ranges used to train the 16 SHR palettes
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# [(lower_0, upper_0), ...]
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self._palette_splits = self._palette_splits()
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self._palette_splits = self._equal_palette_splits()
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# Whether the previous iteration of proposed palettes was accepted
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self._palettes_accepted = False
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@ -123,29 +123,7 @@ class ClusterPalette:
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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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# 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 _palette_splits(self, palette_height=35):
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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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@ -168,77 +146,6 @@ class ClusterPalette:
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int(np.round(palette_upper))))
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return palette_ranges
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def _apply_palette_delta(
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self, palette_to_mutate, palette_lower_delta, palette_upper_delta):
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old_lower, old_upper = self._palette_splits[palette_to_mutate]
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new_lower = old_lower + palette_lower_delta
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new_upper = old_upper + palette_upper_delta
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new_lower = np.clip(new_lower, 0, np.clip(new_upper, 1, 200) - 1)
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new_upper = np.clip(new_upper, new_lower + 1, 200)
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assert new_lower >= 0, new_upper - 1
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self._palette_splits[palette_to_mutate] = (new_lower, new_upper)
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self._palette_mutate_idx = palette_to_mutate
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self._palette_mutate_delta = (palette_lower_delta, palette_upper_delta)
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def _mutate_palette_splits(self):
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if self._palettes_accepted:
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# Last time was good, keep going
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self._apply_palette_delta(self._palette_mutate_idx,
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self._palette_mutate_delta[0],
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self._palette_mutate_delta[1])
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else:
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# undo last mutation
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self._apply_palette_delta(self._palette_mutate_idx,
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-self._palette_mutate_delta[0],
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-self._palette_mutate_delta[1])
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# Pick a palette endpoint to move up or down
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palette_to_mutate = np.random.randint(0, 16)
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while True:
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if palette_to_mutate > 0:
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palette_lower_delta = np.random.randint(-20, 21)
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else:
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palette_lower_delta = 0
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if palette_to_mutate < 15:
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palette_upper_delta = np.random.randint(-20, 21)
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else:
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palette_upper_delta = 0
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if palette_lower_delta != 0 or palette_upper_delta != 0:
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break
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self._apply_palette_delta(palette_to_mutate, palette_lower_delta,
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palette_upper_delta)
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def _reassign_unused_palettes(self, new_line_to_palette, last_good_splits):
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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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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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max_width = 0
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split_palette_idx = -1
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idx = 0
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for lower, upper in last_good_splits:
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width = upper - lower
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if width > max_width:
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split_palette_idx = idx
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idx += 1
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lower, upper = last_good_splits[split_palette_idx]
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if upper - lower > 20:
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mid = (lower + upper) // 2
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self._palette_splits[split_palette_idx] = (
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lower, mid - 1)
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self._palette_splits[palette_idx] = (mid, upper)
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else:
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lower = np.random.randint(0, 199)
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upper = np.random.randint(lower, 200)
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self._palette_splits[palette_idx] = (lower, upper)
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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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@ -268,7 +175,6 @@ class ClusterPalette:
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self._mutate_palette_splits()
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for palette_idx in range(16):
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palette_lower, palette_upper = self._palette_splits[palette_idx]
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# TODO: dynamically tune palette cuts
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palette_pixels = self._colours_cam[
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palette_lower * 320:palette_upper * 320, :]
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@ -300,6 +206,99 @@ class ClusterPalette:
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self._palettes_accepted = False
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return new_palettes_cam, new_palettes_rgb12_iigs, new_errors
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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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# 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 _mutate_palette_splits(self):
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if self._palettes_accepted:
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# Last time was good, keep going
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self._apply_palette_delta(self._palette_mutate_idx,
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self._palette_mutate_delta[0],
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self._palette_mutate_delta[1])
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else:
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# undo last mutation
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self._apply_palette_delta(self._palette_mutate_idx,
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-self._palette_mutate_delta[0],
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-self._palette_mutate_delta[1])
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# Pick a palette endpoint to move up or down
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palette_to_mutate = np.random.randint(0, 16)
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while True:
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if palette_to_mutate > 0:
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palette_lower_delta = np.random.randint(-20, 21)
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else:
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palette_lower_delta = 0
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if palette_to_mutate < 15:
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palette_upper_delta = np.random.randint(-20, 21)
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else:
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palette_upper_delta = 0
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if palette_lower_delta != 0 or palette_upper_delta != 0:
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break
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self._apply_palette_delta(palette_to_mutate, palette_lower_delta,
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palette_upper_delta)
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def _apply_palette_delta(
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self, palette_to_mutate, palette_lower_delta, palette_upper_delta):
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old_lower, old_upper = self._palette_splits[palette_to_mutate]
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new_lower = old_lower + palette_lower_delta
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new_upper = old_upper + palette_upper_delta
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new_lower = np.clip(new_lower, 0, np.clip(new_upper, 1, 200) - 1)
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new_upper = np.clip(new_upper, new_lower + 1, 200)
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assert new_lower >= 0, new_upper - 1
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self._palette_splits[palette_to_mutate] = (new_lower, new_upper)
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self._palette_mutate_idx = palette_to_mutate
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self._palette_mutate_delta = (palette_lower_delta, palette_upper_delta)
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def _reassign_unused_palettes(self, new_line_to_palette, last_good_splits):
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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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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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max_width = 0
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split_palette_idx = -1
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idx = 0
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for lower, upper in last_good_splits:
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width = upper - lower
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if width > max_width:
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split_palette_idx = idx
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idx += 1
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lower, upper = last_good_splits[split_palette_idx]
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if upper - lower > 20:
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mid = (lower + upper) // 2
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self._palette_splits[split_palette_idx] = (
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lower, mid - 1)
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self._palette_splits[palette_idx] = (mid, upper)
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else:
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lower = np.random.randint(0, 199)
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upper = np.random.randint(lower, 200)
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self._palette_splits[palette_idx] = (lower, upper)
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def main():
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parser = argparse.ArgumentParser()
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