Image-Text-to-Text
Transformers
PyTorch
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doubutsu_next
conversational
custom_code
Inference Endpoints
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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