Apple-2-SW/ii-pix
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Fit palettes from overlapping line ranges, and map line to palette

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when dithering with two limitations:

- cannot choose the same palette as the previous line (this avoids banding)
- must be within +/- 1 of the "base" palette for the line number

This gives pretty good results!
This commit is contained in:
kris 2021-11-11 16:10:03 +00:00
parent ee2229d0ea
commit 5cab854269
2 changed files with 99 additions and 28 deletions
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75
convert.py
View File

@ -51,43 +51,68 @@ def cluster_palette(image: Image):
with colour.utilities.suppress_warnings(colour_usage_warnings=True):
colours_cam = colour.convert(colours_rgb, "RGB",
"CAM16UCS").astype(np.float32)
palettes_rgb = {}
palette_colours = collections.defaultdict(list)
for line in range(200):
palette = line_to_palette[line]
palette_colours[palette].extend(
colours_cam[line * 320:(line + 1) * 320])
palettes_cam = {}
for palette_idx in range(16):
p_lower = max(palette_idx-2, 0)
p_upper = min(palette_idx+2, 16)
palette_pixels = colours_cam[
int(p_lower * (200/16)) * 320:int(p_upper * (
200/16)) * 320, :]
# kmeans = KMeans(n_clusters=16, max_iter=10000)
# kmeans.fit_predict(palette_pixels)
# palettes_cam[palette_idx] = kmeans.cluster_centers_
fixed_centroids = None
# print(np.array(line_colours), fixed_centroids)
palettes_cam[palette_idx] = dither_pyx.k_means_with_fixed_centroids(
16, palette_pixels, fixed_centroids=fixed_centroids, tolerance=1e-6)
# palette_colours = collections.defaultdict(list)
# for line in range(200):
# palette = line_to_palette[line]
# palette_colours[palette].extend(
# colours_cam[line * 320:(line + 1) * 320])
# For each line grouping, find big palette entries with minimal total
# distance
palette_cam = None
for palette_idx in range(16):
line_colours = palette_colours[palette_idx]
#if palette_idx < 15:
# line_colours += palette_colours[palette_idx + 1]
# if palette_idx < 14:
# line_colours += palette_colours[palette_idx + 2]
# if palette_idx > 0:
# fixed_centroids = palette_cam[:8, :]
# else:
fixed_centroids = None
# print(np.array(line_colours), fixed_centroids)
palette_cam = dither_pyx.k_means_with_fixed_centroids(16, np.array(
line_colours), fixed_centroids=fixed_centroids, tolerance=1e-6)
# palette_cam = None
# for palette_idx in range(16):
# line_colours = palette_colours[palette_idx]
# #if palette_idx < 15:
# # line_colours += palette_colours[palette_idx + 1]
# # if palette_idx < 14:
# # line_colours += palette_colours[palette_idx + 2]
# # if palette_idx > 0:
# # fixed_centroids = palette_cam[:8, :]
# # else:
# fixed_centroids = None
# # print(np.array(line_colours), fixed_centroids)
# palette_cam = dither_pyx.k_means_with_fixed_centroids(16, np.array(
# line_colours), fixed_centroids=fixed_centroids, tolerance=1e-6)
#kmeans = KMeans(n_clusters=16, max_iter=10000)
#kmeans.fit_predict(line_colours)
#palette_cam = kmeans.cluster_centers_
with colour.utilities.suppress_warnings(colour_usage_warnings=True):
palette_rgb = colour.convert(palette_cam, "CAM16UCS", "RGB")
palette_rgb = colour.convert(palettes_cam[palette_idx], "CAM16UCS", "RGB")
# SHR colour palette only uses 4-bit values
palette_rgb = np.round(palette_rgb * 15) / 15
palettes_rgb[palette_idx] = palette_rgb.astype(np.float32)
# print(palettes_rgb)
return palettes_rgb, line_to_palette
# For each line, pick the palette with lowest total distance
# best_palette = 15
# for line in range(200):
# line_pixels = colours_cam[line*320:(line+1)*320]
# best_palette = dither_pyx.best_palette_for_line(
# line_pixels, palettes_cam, best_palette)
# line_to_palette[line] = best_palette
# print(line, line_to_palette[line])
return palettes_cam, palettes_rgb, line_to_palette
def main():
@ -150,15 +175,15 @@ def main():
gamma=args.gamma_correct, srgb_output=True)).astype(
np.float32) / 255
palettes_rgb, line_to_palette = cluster_palette(rgb)
palettes_cam, palettes_rgb, line_to_palette = cluster_palette(rgb)
# print(palette_rgb)
# screen.set_palette(0, (image_py.linear_to_srgb_array(palette_rgb) *
# 15).astype(np.uint8))
for i, p in palettes_rgb.items():
screen.set_palette(i, (np.round(p * 15)).astype(np.uint8))
output_4bit = dither_pyx.dither_shr(rgb, palettes_rgb, rgb_to_cam16,
line_to_palette)
output_4bit, line_to_palette = dither_pyx.dither_shr(
rgb, palettes_cam, palettes_rgb, rgb_to_cam16)
screen.set_pixels(output_4bit)
output_rgb = np.zeros((200, 320, 3), dtype=np.uint8)
for i in range(200):

52
dither.pyx
View File

@ -339,7 +339,7 @@ import colour
@cython.boundscheck(True)
@cython.wraparound(False)
def dither_shr(float[:, :, ::1] working_image, object palettes_rgb, float[:,::1] rgb_to_cam16ucs, object line_to_palette):
def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palettes_rgb, float[:,::1] rgb_to_cam16ucs):
cdef int y, x, idx, best_colour_idx
cdef float best_distance, distance
cdef float[::1] best_colour_rgb, pixel_cam, colour_rgb, colour_cam
@ -349,9 +349,24 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_rgb, float[:,::1]
cdef (unsigned char)[:, ::1] output_4bit = np.zeros((200, 320), dtype=np.uint8)
# cdef (unsigned char)[:, :, ::1] output_rgb = np.zeros((200, 320, 3), dtype=np.uint8)
cdef float[:, ::1] line_cam = np.zeros((320, 3), dtype=np.float32)
line_to_palette = {}
best_palette = 15
for y in range(200):
print(y)
palette_rgb = palettes_rgb[line_to_palette[y]]
# palette_rgb = palettes_rgb[line_to_palette[y]]
for x in range(320):
colour_cam = convert_rgb_to_cam16ucs(
rgb_to_cam16ucs, working_image[y,x,0], working_image[y,x,1], working_image[y,x,2])
line_cam[x, :] = colour_cam
best_palette = best_palette_for_line(line_cam, palettes_cam, y * 16 / 200, best_palette)
print("-->", best_palette)
palette_rgb = palettes_rgb[best_palette]
line_to_palette[y] = best_palette
for x in range(320):
pixel_cam = convert_rgb_to_cam16ucs(
rgb_to_cam16ucs, working_image[y, x, 0], working_image[y, x, 1], working_image[y, x, 2])
@ -436,11 +451,13 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_rgb, float[:,::1]
# working_image[y + 2, x + 2, i] + quant_error * (1 / 48),
# 0, 1)
return np.array(output_4bit, dtype=np.uint8) #, np.array(output_rgb, dtype=np.uint8)
return np.array(output_4bit, dtype=np.uint8), line_to_palette #, np.array(output_rgb, dtype=np.uint8)
import collections
import random
@cython.boundscheck(True)
@cython.wraparound(False)
def k_means_with_fixed_centroids(
int n_clusters, float[:, ::1] data, float[:, ::1] fixed_centroids = None,
int iterations = 10000, float tolerance = 1e-3):
@ -492,3 +509,32 @@ def k_means_with_fixed_centroids(
print(weighted_centroids)
return np.array([c for w, c in sorted(weighted_centroids, reverse=True)], dtype=np.float32)
@cython.boundscheck(True)
@cython.wraparound(False)
def best_palette_for_line(float [:, ::1] line_cam, object palettes_cam, int base_palette_idx, int last_palette_idx):
cdef int palette_idx, best_palette_idx
cdef float best_total_dist, total_dist, best_pixel_dist, pixel_dist
cdef float[:, ::1] palette_cam
cdef float[::1] pixel_cam, palette_entry
best_total_dist = 1e9
best_palette_idx = -1
for palette_idx, palette_cam in palettes_cam.items():
if palette_idx < (base_palette_idx - 1) or palette_idx > (base_palette_idx + 1):
continue
if palette_idx == last_palette_idx:
continue
total_dist = 0
best_pixel_dist = 1e9
for pixel_cam in line_cam:
for palette_entry in palette_cam:
pixel_dist = colour_distance_squared(pixel_cam, palette_entry)
if pixel_dist < best_pixel_dist:
best_pixel_dist = pixel_dist
total_dist += best_pixel_dist
# print(total_dist)
if total_dist < best_total_dist:
best_total_dist = total_dist
best_palette_idx = palette_idx
return best_palette_idx