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import random
import io, os
import math
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
def resize_with_random_scale(image: Image.Image, p: float = 0.5, scale_range=(0.25, 2.0)) -> Image.Image:
if random.random() > p:
return image
w, h = image.size
scale = random.uniform(*scale_range)
new_w = max(1, int(w * scale))
new_h = max(1, int(h * scale))
return image.resize((new_w, new_h), resample=Image.BICUBIC)
def pad_crop_image(image, target_width, target_height):
src_width, src_height = image.size
# 0
pad_left = pad_right = pad_top = pad_bottom = 0
crop_left = crop_top = 0
if src_width < target_width:
pad_left = random.randint(0, target_width - src_width)
pad_right = target_width - src_width - pad_left
else:
crop_left = random.randint(0, src_width - target_width)
if src_height < target_height:
pad_top = random.randint(0, target_height - src_height)
pad_bottom = target_height - src_height - pad_top
else:
crop_top = random.randint(0, src_height - target_height)
if src_width > target_width or src_height > target_height:
left = crop_left if src_width > target_width else 0
top = crop_top if src_height > target_height else 0
right = left + target_width
bottom = top + target_height
image = image.crop((left, top, right, bottom))
# Padding
if src_width < target_width or src_height < target_height:
image = ImageOps.expand(
image,
border=(pad_left, pad_top, pad_right, pad_bottom),
fill=(0, 0, 0)
)
return image
def get_start_positions(image_length, block_size, bias=0):
"""Non-overlapping full blocks, plus one boundary block when needed."""
if image_length <= block_size:
return [bias]
positions = list(range(0, image_length - block_size + 1, block_size))
if positions[-1] != image_length - block_size:
positions.append(image_length - block_size)
return [position + bias for position in positions]
def crop_image_to_tiles(image, block_size):
"""
crop to block_size x block_size
"""
width, height = image.size
x_positions = get_start_positions(width, block_size)
y_positions = get_start_positions(height, block_size)
tiles = []
for y in y_positions:
for x in x_positions:
tiles.append(image.crop((x, y, x + block_size, y + block_size)))
return tiles
def crop_image_to_tiles_new(image, block_size):
width, height = image.size
x_positions = get_start_positions(width, block_size)
y_positions = get_start_positions(height, block_size)
tiles = []
for y in y_positions:
for x in x_positions:
tiles.append(image.crop((x, y, x + block_size, y + block_size)))
if width > block_size and height > block_size:
leave_space = min(block_size, width-block_size)
x_positions = get_start_positions(width-leave_space, block_size, leave_space//2)
leave_space = min(block_size, height-block_size)
y_positions = get_start_positions(height-leave_space, block_size, leave_space//2)
for y in y_positions:
for x in x_positions:
tiles.append(image.crop((x, y, x + block_size, y + block_size)))
return tiles
def random_crop(image: Image.Image, size: int = 224, is_pad_black: bool = False) -> Image.Image:
w, h = image.size
crop_size = min(w, h, size)
if w == crop_size:
left = 0
else:
left = random.randint(0, w - crop_size)
if h == crop_size:
top = 0
else:
top = random.randint(0, h - crop_size)
cropped = image.crop((left, top, left + crop_size, top + crop_size))
if is_pad_black and crop_size < size:
padded_img = Image.new("RGB", (size, size), (0, 0, 0))
offset = ((size - crop_size) // 2, (size - crop_size) // 2)
padded_img.paste(cropped, offset)
return padded_img
else:
return cropped
def _convert_image_to_rgb(image):
return image.convert("RGB")
def clip_transform_allresize(n_px, blur_radius=None, blur_prob=0.5):
if blur_radius is None:
return transforms.Compose([
transforms.Resize((n_px, n_px), interpolation=BICUBIC),
_convert_image_to_rgb,
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
])
else:
return transforms.Compose([
transforms.Resize((n_px, n_px), interpolation=BICUBIC),
transforms.RandomApply(
[transforms.GaussianBlur(kernel_size=5, sigma=blur_radius)], p=blur_prob # (0.1, 3)
),
_convert_image_to_rgb,
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
])
def clip_transform(n_px, blur_radius=None, blur_prob=0.5):
if blur_radius is None:
return transforms.Compose([
transforms.Resize(n_px, interpolation=BICUBIC),
transforms.CenterCrop(n_px),
_convert_image_to_rgb,
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
])
else:
return transforms.Compose([
transforms.Resize(n_px, interpolation=BICUBIC),
transforms.CenterCrop(n_px),
transforms.RandomApply(
[transforms.GaussianBlur(kernel_size=5, sigma=blur_radius)], p=blur_prob # (0.1, 3)
),
_convert_image_to_rgb,
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
])
class JpegAugment():
def __init__(self, quality=None, p=1, seed=42):
self.local_random = random.Random(seed)
self.quality = quality
self.p = p
def __call__(self, img):
if self.quality is None:
return img
if self.local_random.random() < self.p:
buffer = io.BytesIO()
if self.quality == -1:
img.save(buffer, format='JPEG', quality=self.local_random.randint(30, 100))
else:
if isinstance(self.quality, tuple):
img.save(buffer, format='JPEG', quality=self.local_random.randint(*self.quality))
else:
img.save(buffer, format='JPEG', quality=self.quality)
buffer.seek(0)
return Image.open(buffer).convert('RGB')
return img
def webp_compress(img, quality=80, method=4):
buffer = io.BytesIO()
img.save(buffer, format='WEBP', quality=quality, method=method)
buffer.seek(0)
return Image.open(buffer).convert('RGB')
# Image extensions supported when scanning real/fake dirs (case-insensitive).
# Missing .JPG / .webp etc. can cause real folder to show 0 images if only those exist.
IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.webp', '.bmp', '.tif', '.tiff', '.ppm')
def find_files_with_extensions(directory, extensions=None, max_num=None):
"""Find image paths under directory. If extensions is None, use IMG_EXTENSIONS (case-insensitive)."""
if extensions is None:
extensions = IMG_EXTENSIONS
ext_lower = tuple(e.lower() for e in extensions)
matching_files = []
check_num = 0
for root, dirs, files in os.walk(directory):
for file in files:
if file.lower().endswith(ext_lower):
matching_files.append(os.path.join(root, file))
check_num += 1
if max_num is not None and check_num >= max_num:
return matching_files
return matching_files
class TiledRFDataset(Dataset):
""" Dataset -> (img, label)"""
def __init__(self, real_dirs:list, fake_dirs:list, tile_size=224, set_size=None, shuffle_seed=42, balance=True,
jpeg_augment=None, jpeg_p=0.1,
webp_augment=None, webp_p=0.1,
resize_augment=None, resize_p=0.5,
blur_augment=None, blur_p=0.5,
default_train_tiles=4, is_train=False):
self.set_size = set_size
self.shuffle_seed = shuffle_seed
self.samples = []
self.real_samples = []
self.fake_samples = []
self.tile_size=tile_size
self.transform = clip_transform(tile_size, blur_augment, blur_p)
self.default_train_tiles = default_train_tiles
self.is_train = is_train
self.jpeg_augment=jpeg_augment
self.jpeg_p = jpeg_p
self.JpegEnhance = JpegAugment(quality=jpeg_augment, p=jpeg_p, seed=shuffle_seed)
self.webp_augment = webp_augment
self.webp_p = webp_p
self.resize_augment = resize_augment
self.resize_p = resize_p
for p in real_dirs:
find_paths_real = find_files_with_extensions(p)
self.real_samples += [(f, 0) for f in find_paths_real]
for p in fake_dirs:
find_paths_fake = find_files_with_extensions(p)
self.fake_samples += [(f, 1) for f in find_paths_fake]
if balance:
target_len = min(len(self.real_samples), len(self.fake_samples))
self.real_samples = self.real_samples[:target_len]
self.fake_samples = self.fake_samples[:target_len]
self.samples = self.real_samples + self.fake_samples
# if self.set_size is not None and len(self.samples) < self.set_size:
# n_repeats = math.ceil(self.set_size / len(self.samples))
# self.samples = (self.samples * n_repeats)[:self.set_size]
if self.set_size is not None and len(self.samples) > self.set_size:
local_random = random.Random(self.shuffle_seed)
self.samples = local_random.sample(self.samples, self.set_size)
def __len__(self):
return len(self.samples)
def _get_train_item(self, idx):
path, label = self.samples[idx]
try:
img = Image.open(path).convert('RGB')
except Exception as e:
print(f"Skipping corrupted image: {path}")
return self.__getitem__((idx + 1) % len(self.samples))
if self.resize_augment is not None:
if self.resize_augment == -1:
img = resize_with_random_scale(img, p=self.resize_p)
else:
if isinstance(self.resize_augment, tuple):
img = resize_with_random_scale(img, p=self.resize_p, scale_range=self.resize_augment)
else:
img = resize_with_random_scale(img, p=self.resize_p, scale_range=(self.resize_augment, self.resize_augment))
if self.jpeg_augment is not None:
img = self.JpegEnhance(img)
# crops_one_side = 2
# img = pad_crop_image(img, crops_one_side*self.tile_size, crops_one_side*self.tile_size)
# tiles = crop_image_to_tiles(img, self.tile_size)[:crops_one_side**2]
tiles = []
for i in range(self.default_train_tiles): # 10
tiles.append(random_crop(img, self.tile_size, is_pad_black=False))
img_resize = self.transform(img)
img_list = [img_resize]
for tile in tiles:
img_list.append(self.transform(tile))
return img_list, img, label
def _get_test_item(self, idx):
path, label = self.samples[idx]
try:
img = Image.open(path).convert('RGB')
except Exception as e:
print(f"Skipping corrupted image: {path}")
return self.__getitem__((idx + 1) % len(self.samples))
if self.resize_augment is not None:
if self.resize_augment == -1:
img = resize_with_random_scale(img, p=self.resize_p)
else:
if isinstance(self.resize_augment, tuple):
img = resize_with_random_scale(img, p=self.resize_p, scale_range=self.resize_augment)
else:
img = resize_with_random_scale(img, p=self.resize_p, scale_range=(self.resize_augment, self.resize_augment))
if self.jpeg_augment is not None:
img = self.JpegEnhance(img)
if self.webp_augment is not None:
if random.random() < self.webp_p:
if isinstance(self.webp_augment, tuple):
img = webp_compress(img, quality=random.randint(*self.webp_augment))
else:
img = webp_compress(img, quality=self.webp_augment)
# testtime batchsize=1
tiles = crop_image_to_tiles(img, self.tile_size)
img_resize = self.transform(img)
img_list = [img_resize]
for tile in tiles:
img_list.append(self.transform(tile))
return img_list, img, label, path
def __getitem__(self, idx):
if self.is_train:
return self._get_train_item(idx)
else:
return self._get_test_item(idx)
def collate_img_list_to_tensor_train(batch):
"""
batch = list of (img_list, label)
- img_list: List of N Tensor[B, 3, H, W]
return：
- images: Tensor[B, N, 3, H, W]
- labels: Tensor[B]
"""
img_lists, raw_imgs, labels = zip(*batch) # len = B
# img_lists: list of length B, each is List[N x (3,H,W)]
stacked = torch.stack([torch.stack(imgs) for imgs in img_lists]) # (B, N, 3, H, W)
raw_imgs = list(raw_imgs)
labels = torch.tensor(labels)
return stacked, raw_imgs, labels
def collate_img_list_to_tensor_test(batch):
"""
batch = list of (img_list, label)
- img_list: List of N Tensor[B, 3, H, W]
return：
- images: Tensor[B, N, 3, H, W]
- labels: Tensor[B]
"""
img_lists, raw_imgs, labels, paths = zip(*batch) # len = B
# img_lists: list of length B, each is List[N x (3,H,W)]
stacked = torch.stack([torch.stack(imgs) for imgs in img_lists]) # (B, N, 3, H, W)
raw_imgs = list(raw_imgs)
labels = torch.tensor(labels)
return stacked, raw_imgs, labels, paths
if __name__ == '__main__':
block_size = 224
width = 512
leave_space = min(block_size, width-block_size)
x_positions = get_start_positions(width-leave_space, block_size, leave_space//2)
print(leave_space, x_positions)
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