utils/io/read.py

# ------------------------------------------------------------ #
#
# 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