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import os
from PIL import Image
import numpy as np
import cv2
import torch
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
from torchvision import transforms
from sklearn.model_selection import train_test_split
IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]
class CLAHETransform:
"""Apply CLAHE to the L channel of LAB color space.
Operates on PIL Images; returns a PIL Image.
"""
def __init__(self, clip_limit: float = 2.0, tile_grid_size: tuple = (8, 8)):
self.clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)
def __call__(self, img: Image.Image) -> Image.Image:
img_np = np.array(img) # RGB uint8
lab = cv2.cvtColor(img_np, cv2.COLOR_RGB2LAB)
lab[:, :, 0] = self.clahe.apply(lab[:, :, 0])
result = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
return Image.fromarray(result)
class DRDataset(Dataset):
"""Diabetic Retinopathy dataset from flat class-folder layout."""
def __init__(self, image_paths: list, labels: list, transform=None):
self.image_paths = image_paths
self.labels = labels
self.transform = transform
def __len__(self) -> int:
return len(self.image_paths)
def __getitem__(self, idx: int):
img = Image.open(self.image_paths[idx]).convert('RGB')
label = self.labels[idx]
if self.transform is not None:
img = self.transform(img)
return img, label
def split_dataset(data_root: str, seed: int = 42):
"""Scan class folders 0–4 and return stratified 70/15/15 splits.
Returns:
(train_paths, train_labels, val_paths, val_labels, test_paths, test_labels)
"""
all_paths, all_labels = [], []
for class_idx in range(5):
class_dir = os.path.join(data_root, str(class_idx))
for fname in sorted(os.listdir(class_dir)):
if fname.lower().endswith(('.jpg', '.jpeg', '.png')):
all_paths.append(os.path.join(class_dir, fname))
all_labels.append(class_idx)
# 70% train, 30% temp
train_paths, temp_paths, train_labels, temp_labels = train_test_split(
all_paths, all_labels, test_size=0.30, stratify=all_labels, random_state=seed
)
# 50/50 of temp -> 15% val, 15% test
val_paths, test_paths, val_labels, test_labels = train_test_split(
temp_paths, temp_labels, test_size=0.50, stratify=temp_labels, random_state=seed
)
print(f"Dataset split — train: {len(train_paths)}, val: {len(val_paths)}, test: {len(test_paths)}")
return train_paths, train_labels, val_paths, val_labels, test_paths, test_labels
def build_transforms(use_clahe: bool, training: bool, img_size: int = 224):
"""Build a transform pipeline for training or eval.
Training path uses RandomResizedCrop for scale/position augmentation.
Eval path uses a deterministic Resize to preserve comparability.
CLAHE is applied before spatial transforms at a slightly larger size
so the crop has room to operate.
"""
ops = []
if training:
if use_clahe:
# Resize larger so RandomResizedCrop still sees enough context after CLAHE
ops.append(transforms.Resize((img_size + 32, img_size + 32)))
ops.append(CLAHETransform(clip_limit=2.0, tile_grid_size=(8, 8)))
ops += [
transforms.RandomResizedCrop(img_size, scale=(0.8, 1.0), ratio=(0.9, 1.1)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomVerticalFlip(p=0.5),
transforms.RandomRotation(degrees=30),
transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.2, hue=0.02),
transforms.GaussianBlur(kernel_size=3, sigma=(0.1, 1.0)),
]
else:
ops.append(transforms.Resize((img_size, img_size)))
if use_clahe:
ops.append(CLAHETransform(clip_limit=2.0, tile_grid_size=(8, 8)))
ops += [
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
]
return transforms.Compose(ops)
def build_dataloaders(
train_paths, train_labels,
val_paths, val_labels,
test_paths, test_labels,
use_clahe: bool,
batch_size: int = 32,
num_workers: int = 4,
img_size: int = 224,
):
"""Create DataLoaders for all three splits.
The training DataLoader uses WeightedRandomSampler so that each class
is sampled at approximately equal frequency, counteracting class imbalance.
"""
train_ds = DRDataset(train_paths, train_labels, transform=build_transforms(use_clahe, training=True, img_size=img_size))
val_ds = DRDataset(val_paths, val_labels, transform=build_transforms(use_clahe, training=False, img_size=img_size))
test_ds = DRDataset(test_paths, test_labels, transform=build_transforms(use_clahe, training=False, img_size=img_size))
# Per-sample weights: sqrt-inverse-frequency for moderate class balancing.
# Full inverse (1/count) causes over-prediction of minorities; sqrt gives a
# softer boost that retains enough majority-class signal.
labels_arr = np.array(train_labels)
class_counts = np.bincount(labels_arr, minlength=5)
class_sample_weights = 1.0 / np.sqrt(class_counts.astype(float))
sample_weights = class_sample_weights[labels_arr]
sampler = WeightedRandomSampler(
weights=torch.from_numpy(sample_weights).float(),
num_samples=len(train_labels),
replacement=True,
)
train_loader = DataLoader(
train_ds, batch_size=batch_size, sampler=sampler,
num_workers=num_workers, pin_memory=True,
persistent_workers=True, prefetch_factor=4,
)
val_loader = DataLoader(
val_ds, batch_size=batch_size * 2, shuffle=False,
num_workers=num_workers, pin_memory=True,
persistent_workers=True, prefetch_factor=4,
)
test_loader = DataLoader(
test_ds, batch_size=batch_size * 2, shuffle=False,
num_workers=num_workers, pin_memory=True,
persistent_workers=True, prefetch_factor=4,
)
return train_loader, val_loader, test_loader
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