from typing import List, Tuple from PIL import Image import math def generate_grid_configurations(size: int) -> List[Tuple[int, int]]: grid_configs = [ (2 * size, 2 * size), (1 * size, 2 * size), (1 * size, 3 * size), (1 * size, 4 * size), (4 * size, 1 * size), (3 * size, 1 * size), (2 * size, 1 * size), ] return grid_configs def select_best_resolution(original_size, possible_resolutions): """ Selects the best resolution from a list of possible resolutions based on the original size. Args: original_size (tuple): The original size of the image in the format (width, height). possible_resolutions (list): A list of possible resolutions in the format [(width1, height1), (width2, height2), ...]. Returns: tuple: The best fit resolution in the format (width, height). """ original_width, original_height = original_size best_fit = None max_effective_resolution = 0 min_wasted_resolution = float("inf") for width, height in possible_resolutions: scale = min(width / original_width, height / original_height) downscaled_width, downscaled_height = ( int(original_width * scale), int(original_height * scale), ) effective_resolution = min( downscaled_width * downscaled_height, original_width * original_height ) wasted_resolution = (width * height) - effective_resolution if effective_resolution > max_effective_resolution or ( effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution ): max_effective_resolution = effective_resolution min_wasted_resolution = wasted_resolution best_fit = (width, height) return best_fit def resize_and_pad_image(image, target_resolution): """ Resize and pad an image to a target resolution while maintaining aspect ratio. Args: image (PIL.Image.Image): The input image. target_resolution (tuple): The target resolution (width, height) of the image. Returns: PIL.Image.Image: The resized and padded image. """ original_width, original_height = image.size target_width, target_height = target_resolution scale_w = target_width / original_width scale_h = target_height / original_height if scale_w < scale_h: new_width = target_width new_height = min(math.ceil(original_height * scale_w), target_height) else: new_height = target_height new_width = min(math.ceil(original_width * scale_h), target_width) # Resize the image resized_image = image.resize((new_width, new_height)) new_image = Image.new("RGB", (target_width, target_height), (0, 0, 0)) paste_x = (target_width - new_width) // 2 paste_y = (target_height - new_height) // 2 new_image.paste(resized_image, (paste_x, paste_y)) return new_image def divide_to_patches(image, patch_size): """ Divides an image into patches of a specified size. Args: image (PIL.Image.Image): The input image. patch_size (int): The size of each patch. Returns: list: A list of PIL.Image.Image objects representing the patches. """ patches = [] width, height = image.size for i in range(0, height, patch_size): for j in range(0, width, patch_size): box = (j, i, j + patch_size, i + patch_size) patch = image.crop(box) patches.append(patch) return patches def slice_anyres_image(image, patch_size=378): grid_pinpoints = generate_grid_configurations(patch_size) best_resolution = select_best_resolution(image.size, grid_pinpoints) image_padded = resize_and_pad_image(image, best_resolution) patches = divide_to_patches(image_padded, patch_size) size = {"shortest_edge": patch_size} image_original_resize = image.resize((size["shortest_edge"], size["shortest_edge"])) image_patches = [image_original_resize] + patches return image_patches