dataset_lib/dataset_eval_MoMA.py

from PIL import Image
import numpy as np
import torch
from torchvision import transforms
from rembg import remove
import ast
import math

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 process_anyres_image(image, processor, grid_pinpoints):
    """
    Process an image with variable resolutions.

    Args:
        image (PIL.Image.Image): The input image to be processed.
        processor: The image processor object.
        grid_pinpoints (str): A string representation of a list of possible resolutions.

    Returns:
        torch.Tensor: A tensor containing the processed image patches.
    """
    if type(grid_pinpoints) is list:
        possible_resolutions = grid_pinpoints
    else:
        possible_resolutions = ast.literal_eval(grid_pinpoints)
    best_resolution = select_best_resolution(image.size, possible_resolutions)
    image_padded = resize_and_pad_image(image, best_resolution)

    patches = divide_to_patches(image_padded, processor.crop_size['height'])

    image_original_resize = image.resize((processor.size['shortest_edge'], processor.size['shortest_edge']))

    image_patches = [image_original_resize] + patches
    image_patches = [processor.preprocess(image_patch, return_tensors='pt')['pixel_values'][0]
                     for image_patch in image_patches]
    return torch.stack(image_patches, dim=0)



def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result


def process_images(images, image_processor, model_cfg):
    image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
    new_images = []
    if image_aspect_ratio == 'pad':
        for image in images:
            image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
            image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
            new_images.append(image)
    elif image_aspect_ratio == "anyres":
        for image in images:
            image = process_anyres_image(image, image_processor, model_cfg.image_grid_pinpoints)
            new_images.append(image)
    else:
        return image_processor(images, return_tensors='pt')['pixel_values']
    if all(x.shape == new_images[0].shape for x in new_images):
        new_images = torch.stack(new_images, dim=0)
    return new_images


def create_binary_mask(image):
    grayscale = image.convert("L")
    mask = grayscale.point(lambda x: 255 if x > 1 else 0, '1')
    return mask

def Dataset_evaluate_MoMA(image_pil, prompt,subject, moMA_main_modal):

    LLaVa_processor = moMA_main_modal.image_processor_llava
    llava_config = moMA_main_modal.model_llava.config
    
    transform = transforms.Compose([
        transforms.Resize((512, 512)),
    ])

    mask_pil = create_binary_mask(remove(image_pil)) # Image.open(mask_path) 
    blip2_opt = prompt
    
    if transform is not None:
        image_pil = transform(image_pil)
        mask_pil = transform(mask_pil)
    
    mask_pil = np.array(mask_pil)
    mask_pil = mask_pil[:,:,0] if len(mask_pil.shape)==3 else mask_pil
    image = torch.from_numpy(np.array(image_pil)).permute(2,0,1)
    mask = (torch.clamp((torch.from_numpy(mask_pil).unsqueeze(0)).float(),min=0.0,max=1.0)>0).float()

    res = {'image':  (image/127.5-1).unsqueeze(0),\
        'mask': mask.unsqueeze(0), \
        'text': [blip2_opt]}
    
    image_wb = image * mask + torch.ones_like(image)* (1-mask)*255
    image_pil = Image.fromarray(image_wb.permute(1,2,0).numpy().astype(np.uint8))

    res['llava_processed'] = process_images([image_pil], LLaVa_processor, llava_config)
    res['label'] = [subject]
    return res