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# ------------------------------------------------------------ #
#
# file : utils/io/read.py
# author : CM
# Function to read dataset
#
# ------------------------------------------------------------ #
import os
import sys
import nibabel as nib
import numpy as np
# read nii file and load it into a numpy 3d array
def niiToNp(filename):
data = nib.load(filename).get_data().astype('float16')
return data/data.max()
# read a dataset and load it into a numpy 4d array
def readDataset(folder, size, size_x, size_y, size_z):
dataset = np.empty((size, size_x, size_y, size_z), dtype='float16')
i = 0
files = os.listdir(folder)
files.sort()
for filename in files:
if(i>=size):
break
print(filename)
dataset[i, :, :, :] = niiToNp(os.path.join(folder, filename))
i = i+1
return dataset
# return dataset affine
def getAffine_subdir(folder):
subdir = os.listdir(folder)
subdir.sort()
files = os.listdir(folder+subdir[0])
path = folder + subdir[0]
image = nib.load(os.path.join(path, files[0]))
return image.affine
def getAffine(folder):
files = os.listdir(folder)
files.sort()
image = nib.load(os.path.join(folder, files[0]))
return image.affine
# reshape the dataset to match keras input shape (add channel dimension)
def reshapeDataset(d):
return d.reshape(d.shape[0], d.shape[1], d.shape[2], d.shape[3], 1)
# read a dataset and load it into a numpy 3d array as raw data (no normalisation)
def readRawDataset(folder, size, size_x, size_y, size_z, dtype):
files = os.listdir(folder)
files.sort()
if(len(files) < size):
sys.exit(2)
count = 0
# astype depend on your dataset type.
dataset = np.empty((size, size_x, size_y, size_z)).astype(dtype)
for filename in files:
if(count>=size):
break
dataset[count, :, :, :] = nib.load(os.path.join(folder, filename)).get_data()
count += 1
print(count, '/', size, os.path.join(folder, filename))
return dataset
def readTrainValid(config):
print("Loading training dataset")
train_gd_dataset = readRawDataset(config["dataset_train_gd_path"],
config["dataset_train_size"],
config["image_size_x"],
config["image_size_y"],
config["image_size_z"],
'uint16')
print("Training ground truth dataset shape", train_gd_dataset.shape)
print("Training ground truth dataset dtype", train_gd_dataset.dtype)
train_in_dataset = readRawDataset(config["dataset_train_mra_path"],
config["dataset_train_size"],
config["image_size_x"],
config["image_size_y"],
config["image_size_z"],
'uint16')
print("Training input image dataset shape", train_in_dataset.shape)
print("Training input image dataset dtype", train_in_dataset.dtype)
print("Loading validation dataset")
valid_gd_dataset = readRawDataset(config["dataset_valid_gd_path"],
config["dataset_valid_size"],
config["image_size_x"],
config["image_size_y"],
config["image_size_z"],
'uint16')
print("Validation ground truth dataset shape", valid_gd_dataset.shape)
print("Validation ground truth dataset dtype", valid_gd_dataset.dtype)
valid_in_dataset = readRawDataset(config["dataset_valid_mra_path"],
config["dataset_valid_size"],
config["image_size_x"],
config["image_size_y"],
config["image_size_z"],
'uint16')
print("Validation input image dataset shape", valid_in_dataset.shape)
print("Validation input image dataset dtype", valid_in_dataset.dtype)
return train_gd_dataset, train_in_dataset, valid_gd_dataset, valid_in_dataset
# read a dataset and load it into a numpy 3d without any preprocessing
def getDataset(folder, size, type=None):
files = os.listdir(folder)
files.sort()
if(len(files) < size):
sys.exit(0x2001)
image = nib.load(os.path.join(folder, files[0]))
if type==None:
dtype = image.get_data_dtype()
else:
dtype = type
dataset = np.empty((size, image.shape[0], image.shape[1], image.shape[2])).astype(dtype)
del image
count = 0
for filename in files:
dataset[count, :, :, :] = nib.load(os.path.join(folder, filename)).get_data()
count += 1
if(count>=size):
break
return dataset
# read a dataset and load it into a numpy 3d without any preprocessing with "start" index and number of files
def readDatasetPart(folder, start, size, type=None):
files = os.listdir(folder)
files.sort()
if(len(files) < start + size):
sys.exit("readDatasetPart : len(files) < start + size")
image = nib.load(os.path.join(folder, files[0]))
if type==None:
dtype = image.get_data_dtype()
else:
dtype = type
dataset = np.empty(((size), image.shape[0], image.shape[1], image.shape[2])).astype(dtype)
del image
count = 0
for i in range(start, start + size):
dataset[count, :, :, :] = nib.load(os.path.join(folder, files[i])).get_data()
count += 1
return dataset
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