Checkpoint
- switch to pyclustering for kmedians - allow choosing the same palette as previous line, with a multiplicative penalty to distance in case it's much better - iterate kmedians multiple times and choose the best, since it's only a local optimum
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kris
parent
643e50349e
commit
b363d60754
99
convert.py
99
convert.py
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@ -11,6 +11,8 @@ import colour
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from PIL import Image
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import numpy as np
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from pyclustering.cluster.kmedians import kmedians
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from pyclustering.cluster.kmeans import kmeans
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from pyclustering.utils.metric import distance_metric, type_metric
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from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
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import dither as dither_pyx
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@ -25,7 +27,7 @@ import screen as screen_py
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# - support HGR
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def cluster_palette(image: Image):
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line_to_palette = {}
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# line_to_palette = {}
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# shuffle_lines = liprint(st(range(200))
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# random.shuffle(shuffle_lines)
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@ -40,13 +42,13 @@ def cluster_palette(image: Image):
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# else:
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# line_to_palette[line] = np.clip(int(line / (200 / 16)) + 2, 0, 15)
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for line in range(200):
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if line % 3 == 0:
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line_to_palette[line] = int(line / (200 / 16))
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elif line % 3 == 1:
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line_to_palette[line] = np.clip(int(line / (200 / 16)) + 1, 0, 15)
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else:
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line_to_palette[line] = np.clip(int(line / (200 / 16)) + 2, 0, 15)
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# for line in range(200):
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# if line % 3 == 0:
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# line_to_palette[line] = int(line / (200 / 16))
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# elif line % 3 == 1:
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# line_to_palette[line] = np.clip(int(line / (200 / 16)) + 1, 0, 15)
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# else:
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# line_to_palette[line] = np.clip(int(line / (200 / 16)) + 2, 0, 15)
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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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@ -55,6 +57,7 @@ def cluster_palette(image: Image):
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palettes_rgb = np.empty((16, 16, 3), dtype=np.float32)
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palettes_cam = np.empty((16, 16, 3), dtype=np.float32)
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for palette_idx in range(16):
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print("Fitting palette %d" % palette_idx)
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p_lower = max(palette_idx - 2, 0)
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p_upper = min(palette_idx + 2, 16)
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palette_pixels = colours_cam[
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@ -71,12 +74,23 @@ def cluster_palette(image: Image):
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# 16, palette_pixels, fixed_centroids=fixed_centroids,
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# tolerance=1e-6)
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initial_centers = kmeans_plusplus_initializer(
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palette_pixels, 16).initialize()
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kmedians_instance = kmedians(palette_pixels, initial_centers)
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kmedians_instance.process()
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best_wce = 1e9
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best_medians = None
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for i in range(500):
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# print(i)
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initial_centers = kmeans_plusplus_initializer(
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palette_pixels, 16).initialize()
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kmedians_instance = kmedians(
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palette_pixels, initial_centers, tolerance=0.1, itermax=100,
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metric=distance_metric(type_metric.MANHATTAN))
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kmedians_instance.process()
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if kmedians_instance.get_total_wce() < best_wce:
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best_wce = kmedians_instance.get_total_wce()
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print(i, best_wce)
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best_medians = kmedians_instance
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print("Best %f" % best_wce)
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palettes_cam[palette_idx, :, :] = np.array(
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kmedians_instance.get_medians()).astype(np.float32)
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best_medians.get_medians()).astype(np.float32)
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# palette_colours = collections.defaultdict(list)
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# for line in range(200):
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@ -122,7 +136,7 @@ def cluster_palette(image: Image):
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# line_pixels, palettes_cam, best_palette)
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# line_to_palette[line] = best_palette
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# print(line, line_to_palette[line])
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return palettes_cam, palettes_rgb, line_to_palette
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return palettes_cam, palettes_rgb
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def main():
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@ -185,7 +199,7 @@ def main():
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gamma=args.gamma_correct, srgb_output=True)).astype(
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np.float32) / 255
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palettes_cam, palettes_rgb, line_to_palette = cluster_palette(rgb)
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palettes_cam, palettes_rgb = cluster_palette(rgb)
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# print(palette_rgb)
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# screen.set_palette(0, (image_py.linear_to_srgb_array(palette_rgb) *
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# 15).astype(np.uint8))
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@ -193,36 +207,37 @@ def main():
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screen.set_palette(i, (np.round(palettes_rgb[i, :, :] * 15)).astype(
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np.uint8))
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output_4bit, line_to_palette = dither_pyx.dither_shr(
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rgb, palettes_cam, palettes_rgb, rgb_to_cam16)
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screen.set_pixels(output_4bit)
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output_rgb = np.zeros((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]][
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output_4bit[i, :]] * 255).astype(np.uint8)
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output_srgb = image_py.linear_to_srgb(output_rgb).astype(np.uint8)
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for penalty in [1,2,3,4,5,6,7,8,9,10,1e9]:
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output_4bit, line_to_palette = dither_pyx.dither_shr(
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rgb, palettes_cam, palettes_rgb, rgb_to_cam16, float(penalty))
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screen.set_pixels(output_4bit)
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output_rgb = np.zeros((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]][
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output_4bit[i, :]] * 255).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, rgb_to_cam16)
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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, screen.Y_RES,
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srgb_output=False) # XXX true
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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, screen.Y_RES,
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srgb_output=False) # XXX true
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if args.show_output:
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out_image.show()
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if args.show_output:
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out_image.show()
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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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15
dither.pyx
15
dither.pyx
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@ -339,7 +339,7 @@ import colour
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def dither_shr(float[:, :, ::1] working_image, float[:, :, ::1] palettes_cam, float[:, :, ::1] palettes_rgb, float[:,::1] rgb_to_cam16ucs):
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def dither_shr(float[:, :, ::1] working_image, float[:, :, ::1] palettes_cam, float[:, :, ::1] palettes_rgb, float[:,::1] rgb_to_cam16ucs, float penalty):
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cdef int y, x, idx, best_colour_idx, best_palette
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cdef float best_distance, distance
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cdef float[::1] best_colour_rgb, pixel_cam, colour_rgb, colour_cam
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@ -354,13 +354,13 @@ def dither_shr(float[:, :, ::1] working_image, float[:, :, ::1] palettes_cam, fl
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cdef int[::1] line_to_palette = np.zeros(200, dtype=np.int32)
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best_palette = 15
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for y in range(200):
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print(y)
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# print(y)
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for x in range(320):
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colour_cam = convert_rgb_to_cam16ucs(
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rgb_to_cam16ucs, working_image[y,x,0], working_image[y,x,1], working_image[y,x,2])
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line_cam[x, :] = colour_cam
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best_palette = best_palette_for_line(line_cam, palettes_cam, <int>(y * 16 / 200), best_palette)
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best_palette = best_palette_for_line(line_cam, palettes_cam, <int>(y * 16 / 200), best_palette, penalty)
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# print("-->", best_palette)
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palette_rgb = palettes_rgb[best_palette, :, :]
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line_to_palette[y] = best_palette
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@ -509,7 +509,7 @@ def k_means_with_fixed_centroids(
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef int best_palette_for_line(float [:, ::1] line_cam, float[:, :, ::1] palettes_cam, int base_palette_idx, int last_palette_idx) nogil:
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cdef int best_palette_for_line(float [:, ::1] line_cam, float[:, :, ::1] palettes_cam, int base_palette_idx, int last_palette_idx, float last_penalty) nogil:
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cdef int palette_idx, best_palette_idx, palette_entry_idx, pixel_idx
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cdef float best_total_dist, total_dist, best_pixel_dist, pixel_dist
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cdef float[:, ::1] palette_cam
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@ -517,20 +517,23 @@ cdef int best_palette_for_line(float [:, ::1] line_cam, float[:, :, ::1] palette
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best_total_dist = 1e9
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best_palette_idx = -1
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cdef float penalty
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cdef int line_size = line_cam.shape[0]
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for palette_idx in range(16):
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palette_cam = palettes_cam[palette_idx, :, :]
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if palette_idx < (base_palette_idx - 1) or palette_idx > (base_palette_idx + 1):
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continue
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if palette_idx == last_palette_idx:
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continue
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penalty = last_penalty
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else:
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penalty = 1.0
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total_dist = 0
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best_pixel_dist = 1e9
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for pixel_idx in range(line_size):
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pixel_cam = line_cam[pixel_idx]
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for palette_entry_idx in range(16):
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palette_entry = palette_cam[palette_entry_idx, :]
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pixel_dist = colour_distance_squared(pixel_cam, palette_entry)
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pixel_dist = colour_distance_squared(pixel_cam, palette_entry) * penalty
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if pixel_dist < best_pixel_dist:
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best_pixel_dist = pixel_dist
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total_dist += best_pixel_dist
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