mirror of
https://github.com/jiaxiaojunQAQ/OmniSafeBench-MM.git
synced 2026-07-10 15:08:44 +02:00
leo0481
94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
from typing import List, Optional
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import torch
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import torchvision.transforms as T
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from torchvision.transforms.functional import InterpolationMode
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from PIL import Image
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# ImageNet statistics
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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def build_transform(input_size: int) -> T.Compose:
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return T.Compose([
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T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
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])
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def _find_closest_aspect_ratio(aspect_ratio: float, target_ratios, width: int, height: int, image_size: int):
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best_ratio_diff = float("inf")
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def _dynamic_preprocess(image: Image.Image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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target_ratios = set(
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(i, j)
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for n in range(min_num, max_num + 1)
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for i in range(1, n + 1)
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for j in range(1, n + 1)
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if (i * j) <= max_num and (i * j) >= min_num
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)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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tgt = _find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size)
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target_width = image_size * tgt[0]
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target_height = image_size * tgt[1]
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blocks = tgt[0] * tgt[1]
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resized = image.resize((target_width, target_height))
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tiles = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size,
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)
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tiles.append(resized.crop(box))
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if use_thumbnail and len(tiles) != 1:
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tiles.append(image.resize((image_size, image_size)))
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return tiles
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def load_image_to_pixel_values(image_file: str, input_size: int = 448, max_num: int = 12) -> torch.Tensor:
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image = Image.open(image_file).convert("RGB")
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transform = build_transform(input_size)
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tiles = _dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
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pixel_values = torch.stack([transform(tile) for tile in tiles])
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return pixel_values
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def load_images_batch(
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image_files: Optional[List[str]],
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input_size: int = 448,
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max_num: int = 12,
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) -> Optional[torch.Tensor]:
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if not image_files:
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return None
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batches = []
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for p in image_files:
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try:
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pv = load_image_to_pixel_values(p, input_size=input_size, max_num=max_num)
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if pv is not None and pv.numel() > 0:
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batches.append(pv)
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except Exception:
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continue
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if not batches:
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return None
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return torch.cat(batches, dim=0)
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