diff --git a/convert.py b/convert.py index 53f8daa..3520459 100644 --- a/convert.py +++ b/convert.py @@ -10,7 +10,8 @@ import random import colour from PIL import Image import numpy as np -from sklearn.cluster import KMeans +from pyclustering.cluster.kmedians import kmedians +from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer import dither as dither_pyx import dither_pattern @@ -26,9 +27,9 @@ import screen as screen_py def cluster_palette(image: Image): line_to_palette = {} - #shuffle_lines = liprint(st(range(200)) - #random.shuffle(shuffle_lines) - #for idx, line in enumerate(shuffle_lines): + # shuffle_lines = liprint(st(range(200)) + # random.shuffle(shuffle_lines) + # for idx, line in enumerate(shuffle_lines): # line_to_palette[line] = idx % 16 # for line in range(200): @@ -54,51 +55,60 @@ def cluster_palette(image: Image): palettes_rgb = {} palettes_cam = {} for palette_idx in range(16): - p_lower = max(palette_idx-2, 0) - p_upper = min(palette_idx+2, 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, :] + 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 + # 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) + # palettes_cam[palette_idx] = dither_pyx.k_means_with_fixed_centroids( + # 16, palette_pixels, fixed_centroids=fixed_centroids, + # tolerance=1e-6) + + initial_centers = kmeans_plusplus_initializer( + palette_pixels, 16).initialize() + kmedians_instance = kmedians(palette_pixels, initial_centers) + kmedians_instance.process() + palettes_cam[palette_idx] = np.array( + kmedians_instance.get_medians()).astype(np.float32) # 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 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 + # 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_ + # 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(palettes_cam[palette_idx], "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) diff --git a/dither.pyx b/dither.pyx index 109611e..3ebca48 100644 --- a/dither.pyx +++ b/dither.pyx @@ -337,10 +337,10 @@ def dither_image( import colour -@cython.boundscheck(True) +@cython.boundscheck(False) @cython.wraparound(False) 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 int y, x, idx, best_colour_idx, best_palette cdef float best_distance, distance cdef float[::1] best_colour_rgb, pixel_cam, colour_rgb, colour_cam cdef float quant_error @@ -362,8 +362,8 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palet 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) + 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 @@ -373,7 +373,8 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palet best_distance = 1e9 best_colour_idx = -1 - for idx, colour_rgb in enumerate(palette_rgb): + for idx in range(16): + colour_rgb = palette_rgb[idx, :] colour_cam = convert_rgb_to_cam16ucs(rgb_to_cam16ucs, colour_rgb[0], colour_rgb[1], colour_rgb[2]) distance = colour_distance_squared(pixel_cam, colour_cam) if distance < best_distance: @@ -383,7 +384,6 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palet output_4bit[y, x] = best_colour_idx for i in range(3): - # output_rgb[y,x,i] = (best_colour_rgb[i] * 255) quant_error = working_image[y, x, i] - best_colour_rgb[i] # Floyd-Steinberg dither @@ -451,12 +451,12 @@ def dither_shr(float[:, :, ::1] working_image, object palettes_cam, object palet # working_image[y + 2, x + 2, i] + quant_error * (1 / 48), # 0, 1) - return np.array(output_4bit, dtype=np.uint8), line_to_palette #, np.array(output_rgb, dtype=np.uint8) + return np.array(output_4bit, dtype=np.uint8), line_to_palette import collections import random -@cython.boundscheck(True) +@cython.boundscheck(False) @cython.wraparound(False) def k_means_with_fixed_centroids( int n_clusters, float[:, ::1] data, float[:, ::1] fixed_centroids = None, @@ -509,9 +509,9 @@ 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.boundscheck(False) @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 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