import math import os from typing import Optional, Tuple, Union import torch from torchvision import transforms from PIL import Image from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT from timm.data.auto_augment import rand_augment_transform, augment_and_mix_transform, auto_augment_transform from timm.data.transforms import str_to_interp_mode, str_to_pil_interp, RandomResizedCropAndInterpolation, \ ResizeKeepRatio, CenterCropOrPad, RandomCropOrPad, TrimBorder, ToNumpy, MaybeToTensor, MaybePILToTensor from timm.data.random_erasing import RandomErasing def augment_meme(img_size: Union[int, Tuple[int, int]] = 224, scale: Optional[Tuple[float, float]] = None, ratio: Optional[Tuple[float, float]] = None, train_crop_mode: Optional[str] = None, color_jitter: Union[float, Tuple[float, ...]] = 0.4, color_jitter_prob: Optional[float] = None, force_color_jitter: bool = False, grayscale_prob: float = 0., gaussian_blur_prob: float = 0., auto_augment: Optional[str] = None, interpolation: str = 'random', mean: Tuple[float, ...] = IMAGENET_DEFAULT_MEAN, std: Tuple[float, ...] = IMAGENET_DEFAULT_STD, re_prob: float = 0., re_mode: str = 'const', re_count: int = 1, re_num_splits: int = 0, use_prefetcher: bool = False, normalize: bool = True, separate: bool = False, hflip = 0, vflip = 0 ): train_crop_mode = train_crop_mode or 'rrc' assert train_crop_mode in {'rrc', 'rkrc', 'rkrr'} if train_crop_mode in ('rkrc', 'rkrr'): # FIXME integration of RKR is a WIP scale = tuple(scale or (0.8, 1.00)) ratio = tuple(ratio or (0.9, 1/.9)) primary_tfl = [ ResizeKeepRatio( img_size, interpolation=interpolation, random_scale_prob=0.5, random_scale_range=scale, random_scale_area=True, # scale compatible with RRC random_aspect_prob=0.5, random_aspect_range=ratio, ), CenterCropOrPad(img_size, padding_mode='reflect') if train_crop_mode == 'rkrc' else RandomCropOrPad(img_size, padding_mode='reflect') ] else: scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range ratio = tuple(ratio or (3. / 4., 4. / 3.)) # default imagenet ratio range primary_tfl = [] if hflip > 0.: primary_tfl += [transforms.RandomHorizontalFlip(p=hflip)] if vflip > 0.: primary_tfl += [transforms.RandomVerticalFlip(p=vflip)] secondary_tfl = [] disable_color_jitter = False if auto_augment: assert isinstance(auto_augment, str) # color jitter is typically disabled if AA/RA on, # this allows override without breaking old hparm cfgs disable_color_jitter = not (force_color_jitter or '3a' in auto_augment) if isinstance(img_size, (tuple, list)): img_size_min = min(img_size) else: img_size_min = img_size aa_params = dict( translate_const=int(img_size_min * 0.45), img_mean=tuple([min(255, round(255 * x)) for x in mean]), ) if interpolation and interpolation != 'random': aa_params['interpolation'] = str_to_pil_interp(interpolation) if auto_augment.startswith('rand'): secondary_tfl += [rand_augment_transform(auto_augment, aa_params)] elif auto_augment.startswith('augmix'): aa_params['translate_pct'] = 0.3 secondary_tfl += [augment_and_mix_transform(auto_augment, aa_params)] else: secondary_tfl += [auto_augment_transform(auto_augment, aa_params)] if color_jitter is not None and not disable_color_jitter: # color jitter is enabled when not using AA or when forced if isinstance(color_jitter, (list, tuple)): # color jitter should be a 3-tuple/list if spec brightness/contrast/saturation # or 4 if also augmenting hue assert len(color_jitter) in (3, 4) else: # if it's a scalar, duplicate for brightness, contrast, and saturation, no hue color_jitter = (float(color_jitter),) * 3 if color_jitter_prob is not None: secondary_tfl += [ transforms.RandomApply([ transforms.ColorJitter(*color_jitter), ], p=color_jitter_prob ) ] else: secondary_tfl += [transforms.ColorJitter(*color_jitter)] if grayscale_prob: secondary_tfl += [transforms.RandomGrayscale(p=grayscale_prob)] if gaussian_blur_prob: secondary_tfl += [ transforms.RandomApply([ transforms.GaussianBlur(kernel_size=23), # hardcoded for now ], p=gaussian_blur_prob, ) ] final_tfl = [] if use_prefetcher: # prefetcher and collate will handle tensor conversion and norm final_tfl += [ToNumpy()] elif not normalize: # when normalize disable, converted to tensor without scaling, keeps original dtype final_tfl += [MaybePILToTensor()] else: final_tfl += [ MaybeToTensor(), transforms.Normalize( mean=torch.tensor(mean), std=torch.tensor(std), ), ] if re_prob > 0.: final_tfl += [ RandomErasing( re_prob, mode=re_mode, max_count=re_count, num_splits=re_num_splits, device='cpu', ) ] if separate: return transforms.Compose(secondary_tfl), transforms.Compose(final_tfl) else: return transforms.Compose(primary_tfl + secondary_tfl) transform_meme = augment_meme( img_size=224, color_jitter=0.4, grayscale_prob=0.2, gaussian_blur_prob=0.5, auto_augment='rand-m9-mstd0.5', normalize=True ) # 遍历输入目录下的所有文件 input_dir = '/mnt/afs/niuyazhe/data/meme/data/Cimages/Cimages/Cimages/' N = 3 for filename in os.listdir(input_dir): # 检查文件是否为图片文件 if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')) and filename.lower()[-5]==')': # 构建输入文件的完整路径 input_path = os.path.join(input_dir, filename) # 打开图片 try: image = Image.open(input_path).convert('RGB') except Exception as e: print(e) continue # 对图片进行 N 次增强 for i in range(N): # 应用变换 try: transformed_image = transform_meme(image) # 将张量转换回 PIL 图像 # transformed_image = transforms.ToPILImage()(transformed_image) # 构建输出文件的完整路径 base_name, ext = os.path.splitext(filename) output_filename = f"{base_name}_{i}{ext}" output_path = os.path.join(input_dir, output_filename) # 保存变换后的图片 transformed_image.save(output_path) print(f"Saved {output_path}") except Exception as e: print(e) continue