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Dataset Loader for BraTS 2021 PNG Dataset
Handles loading of FLAIR, RGB images and segmentation masks
"""
import os
import numpy as np
import pandas as pd
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
from typing import Tuple, Dict, List, Optional
import albumentations as A
from albumentations.pytorch import ToTensorV2
class BraTSDataset(Dataset):
"""BraTS 2021 Dataset Loader for PNG images"""
def __init__(
self,
data_path: str,
manifest_csv: Optional[str] = None,
image_size: int = 256,
mode: str = 'train',
transform: Optional[A.Compose] = None,
use_cache: bool = False
):
"""
Args:
data_path: Path to brats_png directory
manifest_csv: Path to CSV manifest file
image_size: Target image size
mode: 'train', 'val', or 'test'
transform: Albumentations transform
use_cache: Cache images in memory
"""
self.data_path = data_path
self.image_size = image_size
self.mode = mode
self.transform = transform
self.use_cache = use_cache
self.cache = {}
# Load data paths
self.data_list = self._load_data_list(manifest_csv)
def _load_data_list(self, manifest_csv: Optional[str]) -> List[Dict]:
"""Load list of image paths"""
data_list = []
if manifest_csv and os.path.exists(manifest_csv):
# Load from CSV manifest
df = pd.read_csv(manifest_csv)
data_list = df.to_dict('records')
else:
# Scan directory structure
for patient_folder in sorted(os.listdir(self.data_path)):
patient_path = os.path.join(self.data_path, patient_folder)
if not os.path.isdir(patient_path):
continue
# Get all slices for this patient
files = os.listdir(patient_path)
# Group by slice number
slices = {}
for f in files:
if '_FLAIR.png' in f:
slice_num = f.split('_slice_')[1].split('_')[0]
slices[slice_num] = {
'patient_id': patient_folder,
'slice_num': slice_num
}
# Add full paths
for slice_num, info in slices.items():
base_name = f"{patient_folder}_slice_{slice_num}"
data_list.append({
'patient_id': patient_folder,
'slice_num': slice_num,
'flair_path': os.path.join(patient_path, f"{base_name}_FLAIR.png"),
'rgb_path': os.path.join(patient_path, f"{base_name}_RGB.png"),
'et_mask_path': os.path.join(patient_path, f"{base_name}_ET.png"),
'tc_mask_path': os.path.join(patient_path, f"{base_name}_TC.png"),
'wt_mask_path': os.path.join(patient_path, f"{base_name}_WT.png"),
})
return data_list
def __len__(self) -> int:
return len(self.data_list)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
"""Get a single sample"""
# Check cache
if self.use_cache and idx in self.cache:
return self.cache[idx]
item = self.data_list[idx]
# Load images
flair = np.array(Image.open(item['flair_path']).convert('L'))
rgb = np.array(Image.open(item['rgb_path']).convert('RGB'))
# Load masks
et_mask = np.array(Image.open(item['et_mask_path']).convert('L'))
tc_mask = np.array(Image.open(item['tc_mask_path']).convert('L'))
wt_mask = np.array(Image.open(item['wt_mask_path']).convert('L'))
# Combine masks into multi-channel
mask = np.stack([et_mask, tc_mask, wt_mask], axis=-1)
# Normalize masks to 0-1
mask = (mask > 0).astype(np.float32)
# Apply transforms
if self.transform:
transformed = self.transform(image=rgb, mask=mask)
rgb = transformed['image']
mask = transformed['mask']
else:
# Default: resize and convert to tensor
rgb = torch.from_numpy(rgb).permute(2, 0, 1).float() / 255.0
mask = torch.from_numpy(mask).permute(2, 0, 1).float()
sample = {
'image': rgb,
'mask': mask,
'patient_id': item['patient_id'],
'slice_num': item['slice_num'],
'flair': torch.from_numpy(flair).unsqueeze(0).float() / 255.0
}
# Cache if enabled
if self.use_cache:
self.cache[idx] = sample
return sample
def get_transforms(image_size: int = 256, mode: str = 'train') -> A.Compose:
"""Get augmentation transforms"""
if mode == 'train':
return A.Compose([
A.Resize(image_size, image_size),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.RandomRotate90(p=0.5),
A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=15, p=0.5),
A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),
A.GaussNoise(var_limit=(10.0, 50.0), p=0.3),
A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensorV2()
])
else:
return A.Compose([
A.Resize(image_size, image_size),
A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensorV2()
])
def create_dataloaders(
data_path: str,
batch_size: int = 8,
image_size: int = 256,
num_workers: int = 4,
train_split: float = 0.7,
val_split: float = 0.15,
test_split: float = 0.15
) -> Tuple[DataLoader, DataLoader, DataLoader]:
"""Create train, validation, and test dataloaders"""
# Create full dataset
full_dataset = BraTSDataset(
data_path=data_path,
image_size=image_size,
mode='train'
)
# Split dataset
total_size = len(full_dataset)
train_size = int(total_size * train_split)
val_size = int(total_size * val_split)
test_size = total_size - train_size - val_size
train_dataset, val_dataset, test_dataset = torch.utils.data.random_split(
full_dataset,
[train_size, val_size, test_size],
generator=torch.Generator().manual_seed(42)
)
# Apply transforms
train_dataset.dataset.transform = get_transforms(image_size, 'train')
val_dataset.dataset.transform = get_transforms(image_size, 'val')
test_dataset.dataset.transform = get_transforms(image_size, 'test')
# Create dataloaders
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True
)
val_loader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True
)
test_loader = DataLoader(
test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True
)
return train_loader, val_loader, test_loader
if __name__ == "__main__":
# Test dataset loader
data_path = "D:/major projrct PNG folder/brats_png"
dataset = BraTSDataset(data_path=data_path, image_size=256)
print(f"Dataset size: {len(dataset)}")
sample = dataset[0]
print(f"Image shape: {sample['image'].shape}")
print(f"Mask shape: {sample['mask'].shape}")
print(f"Patient ID: {sample['patient_id']}")
print(f"Slice number: {sample['slice_num']}")
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