File size: 9,539 Bytes
d2ce7dd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 | import os
import cv2
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
from torchvision import transforms
from typing import List, Tuple, Dict
import random
class FundusQualityDataset(Dataset):
"""
Dataset for fundus quality classification
Combines:
- Labeled invalid images
- Labeled valid images
- Unlabeled raw images (optional)
"""
def __init__(
self,
invalid_dir: str,
valid_dir: str,
raw_dir: str = None,
img_size: int = 512,
transform=None,
use_raw: bool = False,
raw_pseudo_label_threshold: float = 0.5
):
self.img_size = img_size
self.transform = transform
self.use_raw = use_raw
self.raw_pseudo_label_threshold = raw_pseudo_label_threshold
# Load labeled data
self.invalid_paths = self._load_image_paths(invalid_dir)
self.valid_paths = self._load_image_paths(valid_dir)
# Load unlabeled raw data
self.raw_paths = []
if use_raw and raw_dir is not None:
self.raw_paths = self._load_image_paths(raw_dir)
# Combine all paths and create labels
self.image_paths = []
self.labels = []
self.is_labeled = []
# Invalid images (label=0)
for path in self.invalid_paths:
self.image_paths.append(path)
self.labels.append(0)
self.is_labeled.append(True)
# Valid images (label=1)
for path in self.valid_paths:
self.image_paths.append(path)
self.labels.append(1)
self.is_labeled.append(True)
# Raw images (unlabeled, label=-1 initially)
if use_raw:
for path in self.raw_paths:
self.image_paths.append(path)
self.labels.append(-1) # Pseudo label placeholder
self.is_labeled.append(False)
# Pseudo labels dict (for raw images)
self.pseudo_labels = {}
print(f"Dataset loaded: {len(self.invalid_paths)} invalid, "
f"{len(self.valid_paths)} valid, "
f"{len(self.raw_paths)} raw")
# f:\oinn\image_filter\FQ-ManifoldNet\model_ASRMNet\asrm_preprocess.py
# 修改第30行附近的 _load_image_paths 函数
def _load_image_paths(self, directory: str) -> List[str]:
"""Load all image paths from directory"""
if not os.path.exists(directory):
print(f"⚠️ 目录不存在: {directory}")
return []
image_paths = []
# 支持的扩展名（包含DICOM格式）
valid_extensions = ('.png', '.jpg', '.jpeg', '.tif', '.tiff', '.bmp', '.dcm')
# 列出所有文件进行调试
all_files = os.listdir(directory)
print(f"📂 {os.path.basename(directory)}: 找到 {len(all_files)} 个文件")
# 尝试多种方式扫描
for filename in all_files:
# 方法1: 检查小写扩展名
if filename.lower().endswith(valid_extensions):
image_paths.append(os.path.join(directory, filename))
print(f"✅ {os.path.basename(directory)}: 成功加载 {len(image_paths)} 张图像")
# 如果还是0，尝试直接使用所有文件（跳过目录）
if len(image_paths) == 0:
print(f"🔍 {os.path.basename(directory)}: 尝试直接使用所有文件...")
for filename in all_files:
filepath = os.path.join(directory, filename)
if os.path.isfile(filepath):
image_paths.append(filepath)
print(f" 第二次尝试: 加载了 {len(image_paths)} 张")
return image_paths
def set_pseudo_labels(self, pseudo_label_dict: Dict[str, float]):
"""
Set pseudo labels for raw images
Args:
pseudo_label_dict: {image_path: quality_score} (0-1)
"""
self.pseudo_labels = pseudo_label_dict
def __len__(self) -> int:
return len(self.image_paths)
def __getitem__(self, idx: int) -> Dict:
img_path = self.image_paths[idx]
# Load image
img = cv2.imread(img_path)
if img is None:
# Return empty image if loading fails
img = np.zeros((self.img_size, self.img_size, 3), dtype=np.uint8)
# Convert to RGB
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Resize
img = cv2.resize(img, (self.img_size, self.img_size))
# Apply transforms
if self.transform is not None:
img = self.transform(img)
# Get label
if self.is_labeled[idx]:
label = self.labels[idx]
else:
# Use pseudo label if available, else return -1
if img_path in self.pseudo_labels:
score = self.pseudo_labels[img_path]
label = 1 if score > self.raw_pseudo_label_threshold else 0
else:
label = -1
return {
'image': img,
'label': label,
'is_labeled': self.is_labeled[idx],
'path': img_path
}
def get_train_transforms(img_size: int = 512):
"""Training data transforms with augmentation"""
return transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomRotation(degrees=15),
transforms.ColorJitter(
brightness=0.2,
contrast=0.2,
saturation=0.2,
hue=0.1
),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
def get_val_transforms(img_size: int = 512):
"""Validation/test data transforms (no augmentation)"""
return transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
def create_data_loaders(
invalid_dir: str,
valid_dir: str,
raw_dir: str = None,
img_size: int = 512,
batch_size: int = 32,
num_workers: int = 8,
use_raw: bool = False,
use_weighted_sampler: bool = True
) -> Tuple[DataLoader, DataLoader]:
"""
Create train and validation data loaders
Args:
invalid_dir: Directory with invalid fundus images
valid_dir: Directory with valid fundus images
raw_dir: Directory with raw unlabeled images (optional)
img_size: Target image size
batch_size: Batch size
num_workers: Number of data loading workers
use_raw: Whether to include raw unlabeled images
use_weighted_sampler: Whether to use weighted sampler for class imbalance
Returns:
(train_loader, val_loader)
"""
# Create train dataset
train_dataset = FundusQualityDataset(
invalid_dir=invalid_dir,
valid_dir=valid_dir,
raw_dir=raw_dir,
img_size=img_size,
transform=get_train_transforms(img_size),
use_raw=use_raw
)
# Split into train/val
dataset_size = len(train_dataset)
val_size = int(0.2 * dataset_size)
train_size = dataset_size - val_size
from torch.utils.data import random_split
train_subset, val_subset = random_split(
train_dataset,
[train_size, val_size],
generator=torch.Generator().manual_seed(42)
)
# Create samplers
if use_weighted_sampler:
# Weighted sampler to handle class imbalance
labeled_indices = [i for i in range(len(train_dataset))
if train_dataset.is_labeled[i]]
if len(labeled_indices) > 0:
labeled_labels = [train_dataset.labels[i] for i in labeled_indices]
class_counts = np.bincount(labeled_labels, minlength=2)
class_weights = 1.0 / (class_counts + 1e-8)
# Create weights for all samples (unlabeled get weight 1)
weights = torch.ones(len(train_subset))
for idx_in_subset, idx_in_dataset in enumerate(train_subset.indices):
if train_dataset.is_labeled[idx_in_dataset]:
label = train_dataset.labels[idx_in_dataset]
weights[idx_in_subset] = class_weights[label]
sampler = WeightedRandomSampler(weights, len(weights))
else:
sampler = None
else:
sampler = None
# Create data loaders
train_loader = DataLoader(
train_subset,
batch_size=batch_size,
sampler=sampler,
shuffle=(sampler is None),
num_workers=num_workers,
pin_memory=True,
drop_last=True
)
val_loader = DataLoader(
val_subset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True
)
return train_loader, val_loader
|