o M4h@sddlZddlZddlZddlmZmZddlmZddlZ ddl Z ddl m Z ddl ZddlmZdddZdd d ZGd d d eZGddde jZdS)N)Dataset DataLoader) transforms)train_test_split)get_smaller_structures_mask#~/DP/data/syn_3d.py/extracted_data/cCszg}g}tj|d}tj|d}ttj|dD]}tj|}|tj|||tj||q||fS)z Load raw image and corresponding segmentation mask paths from the dataset directory. Args: root (str): Root directory containing 'raw' and 'seg' subdirectories. Returns: tuple: Two lists of paths - images and masks. rawsegz*.nii.gz)ospathjoinglobbasenameappend)rootimagesmasksZ images_pathZ masks_pathfile image_namer-/home/xmoravc/AI_project/dataloader/syn_3d.py load_datasets s  r*c CsJt|t||d|d\}}}}t||d|d\}}}}||||||fS)a0 Split the dataset into train, validation, and test sets. Args: images (list): List of image file paths. masks (list): List of mask file paths. random_seed (int): Random seed for reproducibility. Returns: tuple: Lists of train/val/test images and masks. g?)Z test_size random_state)randomseedr) rr random_seed train_images test_images train_masks test_masks val_images val_masksrrr make_splits%s r#c@sFeZdZdZ    dddZddZdd d Zdd d ZddZdS)Syn3Da PyTorch Dataset for loading 3D synthetic medical image volumes with optional augmentation and noise. Args: root_dir (str): Path to dataset root. images (list): List of image file paths (.nii.gz). masks (list): List of corresponding mask file paths (.nii.gz). split (str): One of ['train', 'val', 'test'] to determine augmentation behavior. add_noise (float): Std of Gaussian noise to add. If < 0, a random std [0, 0.45] will be sampled each time. NtrainrcCs,||_||_||_||_t|_||_dSN)root_dirrrsplitrToTensor transform add_noise)selfr'rrr(r+rrr__init__Es   zSyn3D.__init__cCs t|jSr&)lenrr,rrr__len__Rs z Syn3D.__len__rrrc Cs|j\}}}td||d}td||d}td||d} ||||d|||d| | |df}||||d|||d| | |df}||fS)aA Perform random cropping of 3D image and mask volumes. Args: image (ndarray): 3D image volume. mask (ndarray): 3D segmentation mask. crop_factor (tuple): Desired output dimensions (z, y, x). Returns: tuple: Cropped image and mask volumes. r)shaperrandint) r,imagemask crop_factorwhdzyxrrr random_cropUs 22zSyn3D.random_cropcCs6|dur|j}t||}||}t|dd}|S)a Add Gaussian noise to the 3D image tensor. Args: image (Tensor): Input image tensor of shape (1, D, H, W). std (float, optional): Standard deviation of the noise. Returns: Tensor: Noisy image tensor. Nrr2)r+torch randn_likeclamp)r,r6stdnoise noisy_imagerrradd_gausian_noisejs  zSyn3D.add_gausian_noisecCst|j|}t|j|}|j||dd\}}t|dd}tj|dtj d}tj|tj d}| d}| d}tj|tj d d}|j }|j dkrYt dd}|j||d }|||fS) z Load and process a sample from the dataset. Returns: tuple: (noisy_image, ground_truth_mask, postprocessed_mask) )rGrG)r8)Zker_size)dtyperg?)rC)nibloadrZ get_fdatarr?rr@tensorfloat32 unsqueezer+runiformrF)r,idxr6r7Z opened_maskrCrErrr __getitem__|s      zSyn3D.__getitem__)NNr%r)r1r&) __name__ __module__ __qualname____doc__r-r0r?rFrRrrrrr$:s     r$csDeZdZdZdfdd Zddd Zd d Zd d ZddZZ S)Syn3DLightningaN PyTorch Lightning DataModule for handling the Syn3D dataset. Args: root_dir (str): Root path to the dataset. batch_size (int): Number of samples per batch. num_workers (int): Number of subprocesses used for data loading. add_noise (float): Standard deviation for Gaussian noise to apply. r2rcs<tt|||_||_||_||_d|_d|_d|_ dSr&) superrWr-r' batch_size num_workersr+ train_dataset val_dataset test_dataset)r,r'rZr[r+ __class__rrr-s zSyn3DLightning.__init__Nc Cst|jd\}}t||\}}}}}} |dks|dur3t|j||d|jd|_t|j||d|jd|_|dks;|durIt|j|| d|jd|_dSdS)z Setup datasets for different stages: 'fit' (train/val) and 'test'. Args: stage (str, optional): Either 'fit', 'test', or None for both. )rfitNr%)r'rrr(r+valtest)rr'r#r$r+r\r]r^) r,stagerrrrr!r"rr rrrsetups zSyn3DLightning.setupcCt|j|jd|jdS)NTrZshuffler[)rr\rZr[r/rrrtrain_dataloader zSyn3DLightning.train_dataloadercCrfNFrg)rr]rZr[r/rrrval_dataloaderrjzSyn3DLightning.val_dataloadercCrfrk)rr^rZr[r/rrrtest_dataloaderrjzSyn3DLightning.test_dataloader)r2rXrr&) rSrTrUrVr-rerirlrm __classcell__rrr_rrWs rW)r)r)r r r@torch.utils.datarr torchvisionrpytorch_lightningplrZsklearn.model_selectionrZnibabelrKZutils.image_processingrrr#r$LightningDataModulerWrrrrs     a