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01fdb75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 | # REFUGE2 Dataset for PixelGen Medical Image Generation
# Optic disc/cup segmentation: 1200 RGB fundus images with 3-class masks
# Mask values: 0=background, 128=optic cup, 255=optic disc
import os
import torch
import random
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from torchvision.transforms import Normalize
class REFUGE2Dataset(Dataset):
"""
REFUGE2 dataset for mask-conditional image generation.
Data format:
- Images: RGB fundus photographs (varying sizes ~1600-2100px)
- Masks: 3-class segmentation (0=background, 128=optic cup, 255=optic disc)
- 400 images per split (train/val/test)
- Mask files: train/test=.bmp, val=.png
Returns format compatible with PixelGen:
- normalized_image: [3, H, W] in range [-1, 1]
- label: class label (0 for all)
- metadata: dict with 'raw_image', 'mask', 'class'
"""
def __init__(self, data_root, resolution=256, splits=('train', 'val'),
augment=True, seed=42, max_samples=None, random_flip=True,
val_ratio=0.0):
super().__init__()
self.data_root = data_root
self.resolution = resolution
self.augment = augment
self.random_flip = random_flip
# Collect files from specified splits
all_pairs = []
for split in splits:
img_dir = os.path.join(data_root, split, 'images')
mask_dir = os.path.join(data_root, split, 'mask')
img_files = sorted([f for f in os.listdir(img_dir)
if f.endswith(('.jpg', '.png', '.jpeg'))])
for img_f in img_files:
img_path = os.path.join(img_dir, img_f)
# Mask may have different extension (.bmp or .png)
base_name = os.path.splitext(img_f)[0]
mask_path = None
for ext in ['.bmp', '.png', '.jpg']:
candidate = os.path.join(mask_dir, base_name + ext)
if os.path.exists(candidate):
mask_path = candidate
break
if mask_path is not None:
all_pairs.append((img_path, mask_path))
# Optional: hold out a portion for validation
if val_ratio > 0:
random.seed(seed)
random.shuffle(all_pairs)
split_idx = int(len(all_pairs) * (1 - val_ratio))
self.pairs = all_pairs[:split_idx]
else:
self.pairs = all_pairs
# Limit samples if specified
if max_samples is not None and max_samples < len(self.pairs):
random.seed(seed)
self.pairs = random.sample(self.pairs, max_samples)
# Normalization for images ([-1, 1] range)
self.normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
print(f"[REFUGE2Dataset] splits={splits}: {len(self.pairs)} image pairs")
def __len__(self):
return len(self.pairs)
def _load_and_process(self, idx):
"""Load and process a single sample."""
img_path, mask_path = self.pairs[idx]
image = Image.open(img_path).convert('RGB')
mask = Image.open(mask_path).convert('L')
# Resize to target size (square)
image = TF.resize(image, (self.resolution, self.resolution),
interpolation=transforms.InterpolationMode.BILINEAR)
mask = TF.resize(mask, (self.resolution, self.resolution),
interpolation=transforms.InterpolationMode.NEAREST)
# Data augmentation
if self.augment:
if self.random_flip and random.random() > 0.5:
image = TF.hflip(image)
mask = TF.hflip(mask)
if self.random_flip and random.random() > 0.5:
image = TF.vflip(image)
mask = TF.vflip(mask)
# Random color jitter for image only
if random.random() > 0.5:
brightness_factor = random.uniform(0.85, 1.15)
image = TF.adjust_brightness(image, brightness_factor)
contrast_factor = random.uniform(0.85, 1.15)
image = TF.adjust_contrast(image, contrast_factor)
saturation_factor = random.uniform(0.85, 1.15)
image = TF.adjust_saturation(image, saturation_factor)
return image, mask
def __getitem__(self, idx):
max_retries = 10
for retry in range(max_retries):
try:
actual_idx = (idx + retry) % len(self.pairs)
image, mask = self._load_and_process(actual_idx)
break
except Exception as e:
if retry == max_retries - 1:
raise RuntimeError(f"Failed to load image after {max_retries} retries: {e}")
continue
raw_image = TF.to_tensor(image) # [3, H, W], range [0, 1]
normalized_image = self.normalize(raw_image)
# Normalize mask: 0->0.0, 128->0.5, 255->1.0
mask_tensor = TF.to_tensor(mask) # [1, H, W], range [0, 1]
label = 0
metadata = {
"raw_image": raw_image,
"mask": mask_tensor,
"class": label,
}
return normalized_image, label, metadata
class REFUGE2ValDataset(Dataset):
"""Validation subset from REFUGE2 (held-out from training splits)."""
def __init__(self, data_root, resolution=256, splits=('train', 'val'),
val_ratio=0.1, seed=42):
super().__init__()
self.resolution = resolution
self.normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
all_pairs = []
for split in splits:
img_dir = os.path.join(data_root, split, 'images')
mask_dir = os.path.join(data_root, split, 'mask')
img_files = sorted([f for f in os.listdir(img_dir)
if f.endswith(('.jpg', '.png', '.jpeg'))])
for img_f in img_files:
base_name = os.path.splitext(img_f)[0]
img_path = os.path.join(img_dir, img_f)
mask_path = None
for ext in ['.bmp', '.png', '.jpg']:
candidate = os.path.join(mask_dir, base_name + ext)
if os.path.exists(candidate):
mask_path = candidate
break
if mask_path is not None:
all_pairs.append((img_path, mask_path))
random.seed(seed)
random.shuffle(all_pairs)
split_idx = int(len(all_pairs) * (1 - val_ratio))
self.pairs = all_pairs[split_idx:]
print(f"[REFUGE2ValDataset] {len(self.pairs)} val samples")
def __len__(self):
return len(self.pairs)
def __getitem__(self, idx):
img_path, mask_path = self.pairs[idx]
image = Image.open(img_path).convert('RGB')
mask = Image.open(mask_path).convert('L')
image = TF.resize(image, (self.resolution, self.resolution),
interpolation=transforms.InterpolationMode.BILINEAR)
mask = TF.resize(mask, (self.resolution, self.resolution),
interpolation=transforms.InterpolationMode.NEAREST)
raw_image = TF.to_tensor(image)
normalized_image = self.normalize(raw_image)
mask_tensor = TF.to_tensor(mask)
label = 0
metadata = {
"raw_image": raw_image,
"mask": mask_tensor,
"class": label,
}
return normalized_image, label, metadata
class REFUGE2RandnDataset(Dataset):
"""Random noise dataset for evaluation/prediction with REFUGE2 masks."""
def __init__(self, data_root, resolution=256, max_num_instances=1000,
noise_scale=1.0, seed=42, splits=('train', 'val', 'test')):
super().__init__()
self.resolution = resolution
self.noise_scale = noise_scale
# Collect all mask paths
all_masks = []
for split in splits:
mask_dir = os.path.join(data_root, split, 'mask')
if not os.path.exists(mask_dir):
continue
mask_files = sorted([f for f in os.listdir(mask_dir)
if f.endswith(('.bmp', '.png', '.jpg'))])
for mf in mask_files:
all_masks.append(os.path.join(mask_dir, mf))
random.seed(seed)
if max_num_instances <= len(all_masks):
self.mask_paths = random.sample(all_masks, max_num_instances)
else:
self.mask_paths = all_masks * (max_num_instances // len(all_masks) + 1)
self.mask_paths = self.mask_paths[:max_num_instances]
print(f"[REFUGE2RandnDataset] {len(self.mask_paths)} samples for generation")
def __len__(self):
return len(self.mask_paths)
def __getitem__(self, idx):
xT = self.noise_scale * torch.randn(3, self.resolution, self.resolution)
mask = Image.open(self.mask_paths[idx]).convert('L')
mask = TF.resize(mask, (self.resolution, self.resolution),
interpolation=transforms.InterpolationMode.NEAREST)
mask_tensor = TF.to_tensor(mask)
label = 0
metadata = {
"mask": mask_tensor,
"class": label,
}
return xT, label, metadata
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