from PIL import Image DITHER_METHODS = { "None": Image.Dither.NONE, "Floyd-Steinberg": Image.Dither.FLOYDSTEINBERG } QUANTIZATION_METHODS = { "Median cut": Image.Quantize.MEDIANCUT, "Maximum coverage": Image.Quantize.MAXCOVERAGE, "Fast octree": Image.Quantize.FASTOCTREE, "libimagequant": Image.Quantize.LIBIMAGEQUANT } def downscale_image(image: Image, scale: int) -> Image: width, height = image.size downscaled_image = image.resize((int(width / scale), int(height / scale)), Image.NEAREST) return downscaled_image def limit_colors( image, limit: int=16, palette=None, palette_colors: int=256, quantize: Image.Quantize=Image.Quantize.MEDIANCUT, dither: Image.Dither=Image.Dither.NONE, use_k_means: bool=False ): if use_k_means: k_means_value = limit else: k_means_value = 0 if palette: palette_image = palette ppalette = palette.getcolors() if ppalette: color_palette = palette.quantize(colors=len(list(set(ppalette)))) else: colors = len(palette_image.getcolors()) if palette_image.getcolors() else palette_colors color_palette = palette_image.quantize(colors, kmeans=colors) else: # we need to get palette from image, because # dither in quantize doesn't work without it # https://pillow.readthedocs.io/en/stable/_modules/PIL/Image.html#Image.quantize color_palette = image.quantize(colors=limit, kmeans=k_means_value, method=quantize, dither=Image.Dither.NONE) new_image = image.quantize(palette=color_palette, dither=dither) return new_image def convert_to_grayscale(image): new_image = image.convert("L") return new_image.convert("RGB") def convert_to_black_and_white(image: Image, threshold: int=128, is_inversed: bool=False): if is_inversed: apply_threshold = lambda x : 255 if x < threshold else 0 else: apply_threshold = lambda x : 255 if x > threshold else 0 black_and_white_image = image.convert('L', dither=Image.Dither.NONE).point(apply_threshold, mode='1') return black_and_white_image.convert("RGB") def resize_image(image: Image, size) -> Image: width, height = size resized_image = image.resize((width, height), Image.NEAREST) return resized_image