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import SimpleITK as sitk
import os
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import pandas as pd
from tqdm import tqdm
from typing import (
TYPE_CHECKING,
Any,
BinaryIO,
Callable,
Dict,
Iterable,
Iterator,
List,
Optional,
Tuple,
Union,
overload,
)
import h5py
######################
# base dataset for ict-UNET
def Load_from_HDF5(file_path=None, file_format= 'hdf5'):
if file_format == 'hdf5':
# read hdf5
# Replace 'your_file.h5' with the path to your HDF5 file
# file_path = r'E:\LoDoPaB\ground_truth_train\ground_truth_train_000.hdf5'
if file_path is None:
raise ValueError("Please provide a file path to the HDF5 file.")
# Open the HDF5 file and load the dataset
with h5py.File(file_path, 'r') as f:
dataset = f['data'][:]
elif file_format == 'dicom':
# read dicom
if file_path is None:
raise ValueError("Please provide a file path to the DICOM file.")
else:
patient_folder = file_path
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(patient_folder)
reader.SetFileNames(dicom_names)
image = reader.Execute()
# Added a call to PermuteAxes to change the axes of the data
#image = sitk.PermuteAxes(image, [2, 1, 0])
dataset = sitk.GetArrayFromImage(image)
return dataset
import json
VERBOSE = False
class basejsonDataset(Dataset):
def __init__(self, json_path, mode='train', transform_list=None, do_normalize=False, slice_axis=2, use_saved_slice_info=False, slice_info_path="./data_table./slice_info.json"):
"""
Args:
file_ids (list): List of file ids to load data from.
mode (str): 'train' or 'test'. Determines if augmentation is applied.
transform_list (list of callable, optional): List of transforms to be applied on a sample.
slice_axis (int): The axis along which to slice the 3D volumes (0, 1, or 2).
"""
self.mode = mode
self.transform_list = transform_list
self.do_normalize = do_normalize
self.json_path = json_path
self.slice_axis = slice_axis
self.data_info = self._load_json()
self.slice_info_file=slice_info_path
if use_saved_slice_info and os.path.exists(self.slice_info_file):
self.slice_info = self._load_slice_info()
else:
self.slice_info = self._calculate_slice_info()
def __len__(self):
return len(self.slice_info)
def _load_json(self):
with open(self.json_path, 'r') as file:
data_info = json.load(file)
return data_info
def _load_slice_info(self):
with open(self.slice_info_file, 'r') as f:
slice_info = json.load(f)
return slice_info
def _calculate_slice_info(self):
slice_info = []
for entry in tqdm(self.data_info, desc="Calculating slice info"):
data_img = self._load_file(entry['ground_truth'])
num_slices = data_img.shape[self.slice_axis]
for i in range(num_slices):
slice_info.append((entry, i))
with open(self.slice_info_file, 'w') as f:
json.dump(slice_info, f, indent=4)
return slice_info
def __getitem__(self, idx: Union[int, List[int]]) -> Union[Dict, List[Dict]]:
'''
for huggingface dataset, batch should be a dictionary:
batch = {
"original_image": [img1, img2, img3],
"ground_truth_image": [edited_img1, edited_img2, edited_img3],
"edit_prompt": ["prompt1", "prompt2", "prompt3"]
}
'''
if isinstance(idx, int):
return self.__get_single_item__(idx)
elif isinstance(idx, list):
return self.__get_batch_items__(idx)
else:
raise TypeError(f"Invalid index type: {type(idx)}. Expected int or list of int.")
def __get_single_item__(self, idx: int) -> Dict:
entry, slice_idx = self.slice_info[idx]
# original = ground_truth = label = img
# edited = oberservation = data = sino
input_image = self._load_file(entry['observation'])
input_slice = self._slice_volume(input_image, slice_idx)
ground_truth_image = self._load_file(entry['ground_truth'])
ground_truth_slice = self._slice_volume(ground_truth_image, slice_idx)
# Normalize
if self.do_normalize:
scale_factor=3000
ground_truth_slice = (ground_truth_slice - np.min(ground_truth_slice)) / scale_factor
# Expand dimensions to include channel dimension
input_slice = np.expand_dims(input_slice, axis=0)
ground_truth_slice = np.expand_dims(ground_truth_slice, axis=0)
# Convert to torch tensors
input_slice = torch.from_numpy(input_slice).float()
ground_truth_slice = torch.from_numpy(ground_truth_slice).float()
# Resize
#resize = ResizeWithPadOrCrop(spatial_size=(512, 512), mode="minimum")
#input_slice = resize(input_slice)
#ground_truth_slice = resize(ground_truth_slice)
single_item = {"data": input_slice,
"label": ground_truth_slice}
return single_item
def __get_batch_items__(self, indices: List[int]) -> Dict[str, List]:
batch = {"input_image": [], "ground_truth_image": []}
for idx in indices:
item = self.__get_single_item__(idx)
for key in batch.keys():
batch[key].append(item[key])
return batch
def _load_file(self, file_id):
if file_id.endswith('.nrrd') or file_id.endswith('.nii.gz'):
data_img = sitk.ReadImage(file_id)
data_img = sitk.GetArrayFromImage(data_img)
elif file_id.endswith('.hdf5'):
data_img = Load_from_HDF5(file_path=file_id, file_format= 'hdf5')
data_img = np.moveaxis(data_img, 0, -1)
if VERBOSE:
self._check_images(data_img)
return data_img
def _slice_volume(self, data_img, slice_idx):
if self.slice_axis == 0:
data_slice = data_img[slice_idx, :, :]
elif self.slice_axis == 1:
data_slice = data_img[:, slice_idx, :]
elif self.slice_axis == 2:
data_slice = data_img[:, :, slice_idx]
else:
raise ValueError(f"Invalid axis: {self.slice_axis}. Axis must be 0, 1, or 2.")
return data_slice
def _preprocess(self, data):
if self.mode == 'train':
for sample in range(data.shape[0]):
interval = 10
variation = np.random.randint(-interval, interval)
data[sample, :, :, 0] = data[sample, :, :, 0] + variation
interval = 2
variation = np.random.randint(-interval, interval)
data[sample, :, :, 1] = data[sample, :, :, 1] + variation
data = self.normalize(data)
if data.ndim < 4:
data = np.expand_dims(data, axis=-1)
return data
@staticmethod
def adapt_to_task(data_img, label_img):
return data_img, label_img
def _check_images(self, data, lbl):
print(' Data : ', data.shape, np.max(data), np.min(data))
print(' Label: ', lbl.shape, np.max(lbl), np.min(lbl))
print('-------------------------------------------')
pass
def example_json_dataset():
dataset_name = 'xcat'
json_path = f"./data_table/{dataset_name}_dataset.json"
slice_info_path = f"./data_table/{dataset_name}_slice_info.json"
dataset = basejsonDataset(json_path=json_path,
mode='train',
transform_list=None,
slice_axis=2,
use_saved_slice_info=True,
slice_info_path=slice_info_path)
dataloader=DataLoader(dataset, batch_size=4, shuffle=True)
print("Length of dataset:", len(dataset))
for batch in dataloader:
data = batch["data"]
label = batch["label"]
print(data.shape)
print(label.shape)
break
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