import random from typing import Any, Dict, List, Optional, Sequence, Tuple, Union import torchvision.transforms.functional as F from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ CenterCrop, ColorJitter, Grayscale import numbers import torch import ast import math from PIL import Image from transformers.image_processing_utils import BaseImageProcessor, BatchFeature from transformers.image_utils import ImageInput from transformers.utils import TensorType class Blip3ImageProcessor(BaseImageProcessor): def __init__( self, do_resize: bool = True, resize_mode: str = "squash", interpolation_mode: str = "bicubic", size: Union[Tuple[int, int], List[int]] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.do_resize = do_resize self.resize_mode = resize_mode self.interpolation_mode = interpolation_mode self.size = size if size is not None else (378, 378) self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073] self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711] @classmethod def resize(cls, image_size, resize_mode, interpolation='bicubic', fill_color=0): interpolation_mode = InterpolationMode.BILINEAR if interpolation == 'bilinear' else InterpolationMode.BICUBIC if resize_mode == 'longest': transforms = [ ResizeKeepRatio(image_size, interpolation=interpolation_mode, longest=1), CenterCropOrPad(image_size, fill=fill_color) ] elif resize_mode == 'squash': if isinstance(image_size, int): image_size = (image_size, image_size) transforms = [ Resize(image_size, interpolation=interpolation_mode), ] else: assert resize_mode == 'shortest' if not isinstance(image_size, (tuple, list)): image_size = (image_size, image_size) if image_size[0] == image_size[1]: # simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg) transforms = [ Resize(image_size[0], interpolation=interpolation_mode) ] else: # resize shortest edge to matching target dim for non-square target transforms = [ResizeKeepRatio(image_size)] transforms += [CenterCrop(image_size)] return transforms @classmethod def convert_rgb(cls, image): return image.convert("RGB") def _preprocess(self, images: ImageInput ) -> torch.Tensor: transforms = self.resize(self.size, self.resize_mode, self.interpolation_mode) transforms.extend([ self.convert_rgb, ToTensor(), Normalize(mean=self.image_mean, std=self.image_std) ]) composed_transforms = Compose(transforms) images_tensor = composed_transforms(images) return images_tensor def preprocess(self, images: ImageInput, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs) -> BatchFeature: if 'image_aspect_ratio' in kwargs: image_aspect_ratio = kwargs['image_aspect_ratio'] else: image_aspect_ratio = 'pad' new_images = [] if image_aspect_ratio == 'pad': for image in images: image = self._preprocess(image) new_images.append(image) else: if isinstance(self.size, (tuple, list)): base_img_size = self.size[0] else: raise ValueError("size should be list or tuple") for image in images: image = process_anyres_image(image, self._preprocess, self.size, [ [base_img_size,base_img_size*2], [base_img_size*2,base_img_size], [base_img_size*2,base_img_size*2], [base_img_size*3,base_img_size], [base_img_size,base_img_size*3] ]) new_images.append(image) if all(x.shape == new_images[0].shape for x in new_images): new_images = torch.stack(new_images, dim=0) if image_aspect_ratio == 'pad': new_images = BatchFeature(data={"pixel_values": new_images.unsqueeze(0).unsqueeze(0)}, tensor_type=return_tensors) else: new_images = BatchFeature(data={"pixel_values": new_images.unsqueeze(0)}, tensor_type=return_tensors) return new_images # def preprocess(self, # images: ImageInput, # return_tensors: Optional[Union[str, TensorType]] = None, # **kwargs) -> BatchFeature: # transforms = self.resize(self.size, self.resize_mode, self.interpolation_mode) # transforms.extend([ # self.convert_rgb, # ToTensor(), # Normalize(mean=self.image_mean, std=self.image_std) # ]) # composed_transforms = Compose(transforms) # images_tensor = composed_transforms(images).unsqueeze(0).unsqueeze(1).unsqueeze(0) # encoded_outputs = BatchFeature(data={"pixel_values": images_tensor}, tensor_type=return_tensors) # return encoded_outputs class ResizeKeepRatio: """ Resize and Keep Ratio Copy & paste from `timm` """ def __init__( self, size, longest=0., interpolation=InterpolationMode.BICUBIC, random_scale_prob=0., random_scale_range=(0.85, 1.05), random_aspect_prob=0., random_aspect_range=(0.9, 1.11) ): if isinstance(size, (list, tuple)): self.size = tuple(size) else: self.size = (size, size) self.interpolation = interpolation self.longest = float(longest) # [0, 1] where 0 == shortest edge, 1 == longest self.random_scale_prob = random_scale_prob self.random_scale_range = random_scale_range self.random_aspect_prob = random_aspect_prob self.random_aspect_range = random_aspect_range @staticmethod def get_params( img, target_size, longest, random_scale_prob=0., random_scale_range=(0.85, 1.05), random_aspect_prob=0., random_aspect_range=(0.9, 1.11) ): """Get parameters """ source_size = img.size[::-1] # h, w h, w = source_size target_h, target_w = target_size ratio_h = h / target_h ratio_w = w / target_w ratio = max(ratio_h, ratio_w) * longest + min(ratio_h, ratio_w) * (1. - longest) if random_scale_prob > 0 and random.random() < random_scale_prob: ratio_factor = random.uniform(random_scale_range[0], random_scale_range[1]) ratio_factor = (ratio_factor, ratio_factor) else: ratio_factor = (1., 1.) if random_aspect_prob > 0 and random.random() < random_aspect_prob: aspect_factor = random.uniform(random_aspect_range[0], random_aspect_range[1]) ratio_factor = (ratio_factor[0] / aspect_factor, ratio_factor[1] * aspect_factor) size = [round(x * f / ratio) for x, f in zip(source_size, ratio_factor)] return size def __call__(self, img): """ Args: img (PIL Image): Image to be cropped and resized. Returns: PIL Image: Resized, padded to at least target size, possibly cropped to exactly target size """ size = self.get_params( img, self.size, self.longest, self.random_scale_prob, self.random_scale_range, self.random_aspect_prob, self.random_aspect_range ) img = F.resize(img, size, self.interpolation) return img def __repr__(self): format_string = self.__class__.__name__ + '(size={0}'.format(self.size) format_string += f', interpolation={self.interpolation})' format_string += f', longest={self.longest:.3f})' return format_string def _setup_size(size, error_msg): if isinstance(size, numbers.Number): return int(size), int(size) if isinstance(size, Sequence) and len(size) == 1: return size[0], size[0] if len(size) != 2: raise ValueError(error_msg) return size def center_crop_or_pad(img: torch.Tensor, output_size: List[int], fill=0) -> torch.Tensor: """Center crops and/or pads the given image. If the image is torch Tensor, it is expected to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. Args: img (PIL Image or Tensor): Image to be cropped. output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, it is used for both directions. fill (int, Tuple[int]): Padding color Returns: PIL Image or Tensor: Cropped image. """ if isinstance(output_size, numbers.Number): output_size = (int(output_size), int(output_size)) elif isinstance(output_size, (tuple, list)) and len(output_size) == 1: output_size = (output_size[0], output_size[0]) _, image_height, image_width = F.get_dimensions(img) crop_height, crop_width = output_size if crop_width > image_width or crop_height > image_height: padding_ltrb = [ (crop_width - image_width) // 2 if crop_width > image_width else 0, (crop_height - image_height) // 2 if crop_height > image_height else 0, (crop_width - image_width + 1) // 2 if crop_width > image_width else 0, (crop_height - image_height + 1) // 2 if crop_height > image_height else 0, ] img = F.pad(img, padding_ltrb, fill=fill) _, image_height, image_width = F.get_dimensions(img) if crop_width == image_width and crop_height == image_height: return img crop_top = int(round((image_height - crop_height) / 2.0)) crop_left = int(round((image_width - crop_width) / 2.0)) return F.crop(img, crop_top, crop_left, crop_height, crop_width) class CenterCropOrPad(torch.nn.Module): """Crops the given image at the center. If the image is torch Tensor, it is expected to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. Args: size (sequence or int): Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). """ def __init__(self, size, fill=0): super().__init__() self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") self.fill = fill def forward(self, img): """ Args: img (PIL Image or Tensor): Image to be cropped. Returns: PIL Image or Tensor: Cropped image. """ return center_crop_or_pad(img, self.size, fill=self.fill) def __repr__(self) -> str: return f"{self.__class__.__name__}(size={self.size})" def process_anyres_image(image, processor, processor_size, grid_pinpoints): """ Process an image with variable resolutions. Args: image (PIL.Image.Image): The input image to be processed. processor: The image processor object. processor_size (tuple, list): The size of the image processor. grid_pinpoints (str): A string representation of a list of possible resolutions. Returns: torch.Tensor: A tensor containing the processed image patches. """ # FIXME: determine grid_pinpoints from image sizes. 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) # processor_size = processor.transforms[0].size patches = divide_to_patches(image_padded, processor_size[0]) image_original_resize = image.resize((processor_size[0], processor_size[0])) image_patches = [image_original_resize] + patches image_patches = [processor(image_patch) for image_patch in image_patches] return torch.stack(image_patches, dim=0) 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