gif.py 文件源码

def palettise(data, n_entries=256):
    height = len(data)
    width = len(data[0])
    all_colours = sum(data, [])
    print("Calculating pallete...")
    kmeans = MiniBatchKMeans(n_clusters=n_entries, random_state=0).fit(all_colours)
    pallete = [list(map(int, rgb)) for rgb in kmeans.cluster_centers_]

    print("Dithering...") # Floyd–Steinberg dithering
    for y in range(height):
        print("\r{:.1f}%".format((y/height)*100), end="")
        for x in range(width):
            bucket = kmeans.predict([data[y][x]])[0]
            error = [a-b for a, b in zip(data[y][x], pallete[bucket])]
            data[y][x] = bucket
            for dx, dy, coef in [(1, 0, 7/16), (-1, 1, 3/16), (0, 1, 5/16), (1, 1, 1/16)]:
                xn = x + dx
                yn = y + dy
                if ( 0 <= xn < width and 0 <= yn < height ):
                    data[yn][xn] = [a+b*coef for a, b in zip(data[yn][xn], error)]

    print("\r100%     ")
    return data, pallete