Use pyclustering for kmedians instead of hand-rolled
Optimize cython code
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kris
parent
52af982159
commit
0596aefe0b
80
convert.py
80
convert.py
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@ -10,7 +10,8 @@ import random
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import colour
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from PIL import Image
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import numpy as np
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from sklearn.cluster import KMeans
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from pyclustering.cluster.kmedians import kmedians
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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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import dither_pattern
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@ -26,9 +27,9 @@ import screen as screen_py
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def cluster_palette(image: Image):
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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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#for idx, line in enumerate(shuffle_lines):
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# shuffle_lines = liprint(st(range(200))
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# random.shuffle(shuffle_lines)
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# for idx, line in enumerate(shuffle_lines):
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# line_to_palette[line] = idx % 16
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# for line in range(200):
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@ -54,51 +55,60 @@ def cluster_palette(image: Image):
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palettes_rgb = {}
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palettes_cam = {}
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for palette_idx in range(16):
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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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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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int(p_lower * (200/16)) * 320:int(p_upper * (
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200/16)) * 320, :]
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int(p_lower * (200 / 16)) * 320:int(p_upper * (
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200 / 16)) * 320, :]
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# kmeans = KMeans(n_clusters=16, max_iter=10000)
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# kmeans.fit_predict(palette_pixels)
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# palettes_cam[palette_idx] = kmeans.cluster_centers_
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fixed_centroids = None
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# fixed_centroids = None
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# print(np.array(line_colours), fixed_centroids)
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palettes_cam[palette_idx] = dither_pyx.k_means_with_fixed_centroids(
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16, palette_pixels, fixed_centroids=fixed_centroids, tolerance=1e-6)
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# palettes_cam[palette_idx] = dither_pyx.k_means_with_fixed_centroids(
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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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palettes_cam[palette_idx] = np.array(
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kmedians_instance.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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# palette = line_to_palette[line]
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# palette_colours[palette].extend(
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# colours_cam[line * 320:(line + 1) * 320])
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# for line in range(200):
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# palette = line_to_palette[line]
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# palette_colours[palette].extend(
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# colours_cam[line * 320:(line + 1) * 320])
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# For each line grouping, find big palette entries with minimal total
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# distance
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# For each line grouping, find big palette entries with minimal total
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# distance
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# palette_cam = None
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# for palette_idx in range(16):
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# line_colours = palette_colours[palette_idx]
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# #if palette_idx < 15:
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# # line_colours += palette_colours[palette_idx + 1]
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# # if palette_idx < 14:
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# # line_colours += palette_colours[palette_idx + 2]
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# # if palette_idx > 0:
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# # fixed_centroids = palette_cam[:8, :]
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# # else:
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# fixed_centroids = None
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# # print(np.array(line_colours), fixed_centroids)
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# palette_cam = dither_pyx.k_means_with_fixed_centroids(16, np.array(
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# line_colours), fixed_centroids=fixed_centroids, tolerance=1e-6)
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# palette_cam = None
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# for palette_idx in range(16):
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# line_colours = palette_colours[palette_idx]
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# #if palette_idx < 15:
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# # line_colours += palette_colours[palette_idx + 1]
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# # if palette_idx < 14:
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# # line_colours += palette_colours[palette_idx + 2]
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# # if palette_idx > 0:
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# # fixed_centroids = palette_cam[:8, :]
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# # else:
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# fixed_centroids = None
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# # print(np.array(line_colours), fixed_centroids)
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# palette_cam = dither_pyx.k_means_with_fixed_centroids(16, np.array(
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# line_colours), fixed_centroids=fixed_centroids, tolerance=1e-6)
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#kmeans = KMeans(n_clusters=16, max_iter=10000)
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#kmeans.fit_predict(line_colours)
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#palette_cam = kmeans.cluster_centers_
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# kmeans = KMeans(n_clusters=16, max_iter=10000)
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# kmeans.fit_predict(line_colours)
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# palette_cam = kmeans.cluster_centers_
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with colour.utilities.suppress_warnings(colour_usage_warnings=True):
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palette_rgb = colour.convert(palettes_cam[palette_idx], "CAM16UCS", "RGB")
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palette_rgb = colour.convert(palettes_cam[palette_idx], "CAM16UCS",
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"RGB")
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# SHR colour palette only uses 4-bit values
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palette_rgb = np.round(palette_rgb * 15) / 15
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palettes_rgb[palette_idx] = palette_rgb.astype(np.float32)
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20
dither.pyx
20
dither.pyx
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@ -337,10 +337,10 @@ def dither_image(
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import colour
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@cython.boundscheck(True)
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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, object palettes_cam, object palettes_rgb, float[:,::1] rgb_to_cam16ucs):
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cdef int y, x, idx, best_colour_idx
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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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cdef float quant_error
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@ -362,8 +362,8 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palet
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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, y * 16 / 200, best_palette)
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print("-->", best_palette)
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best_palette = best_palette_for_line(line_cam, palettes_cam, <int>(y * 16 / 200), best_palette)
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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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@ -373,7 +373,8 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palet
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best_distance = 1e9
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best_colour_idx = -1
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for idx, colour_rgb in enumerate(palette_rgb):
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for idx in range(16):
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colour_rgb = palette_rgb[idx, :]
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colour_cam = convert_rgb_to_cam16ucs(rgb_to_cam16ucs, colour_rgb[0], colour_rgb[1], colour_rgb[2])
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distance = colour_distance_squared(pixel_cam, colour_cam)
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if distance < best_distance:
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@ -383,7 +384,6 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palet
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output_4bit[y, x] = best_colour_idx
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for i in range(3):
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# output_rgb[y,x,i] = <int>(best_colour_rgb[i] * 255)
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quant_error = working_image[y, x, i] - best_colour_rgb[i]
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# Floyd-Steinberg dither
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@ -451,12 +451,12 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palet
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# working_image[y + 2, x + 2, i] + quant_error * (1 / 48),
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# 0, 1)
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return np.array(output_4bit, dtype=np.uint8), line_to_palette #, np.array(output_rgb, dtype=np.uint8)
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return np.array(output_4bit, dtype=np.uint8), line_to_palette
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import collections
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import random
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@cython.boundscheck(True)
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def k_means_with_fixed_centroids(
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int n_clusters, float[:, ::1] data, float[:, ::1] fixed_centroids = None,
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@ -509,9 +509,9 @@ def k_means_with_fixed_centroids(
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print(weighted_centroids)
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return np.array([c for w, c in sorted(weighted_centroids, reverse=True)], dtype=np.float32)
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@cython.boundscheck(True)
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def best_palette_for_line(float [:, ::1] line_cam, object palettes_cam, int base_palette_idx, int last_palette_idx):
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cdef int best_palette_for_line(float [:, ::1] line_cam, object palettes_cam, int base_palette_idx, int last_palette_idx):
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cdef int palette_idx, best_palette_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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