AD_Standard_2DRandomSlicesData.py

import nibabel as nib
import os
from torch.utils.data import Dataset
import numpy as np
import matplotlib.pyplot as plt
from skimage.transform import resize 
from PIL import Image
import random


AX_INDEX = 78
COR_INDEX = 79
SAG_INDEX = 57
AX_SCETION = "[:, :, slice_i]"
COR_SCETION = "[:, slice_i, :]"
SAG_SCETION = "[slice_i, :, :]"


class AD_Standard_2DRandomSlicesData(Dataset):
    """labeled Faces in the Wild dataset."""
    
    def __init__(self, root_dir, data_file, transform=None, slice = slice):
        """
        Args:
            root_dir (string): Directory of all the images.
            data_file (string): File name of the train/test split file.
            transform (callable, optional): Optional transform to be applied on a sample.
            data_augmentation (boolean): Optional data augmentation.
        """
        self.root_dir = root_dir
        self.data_file = data_file
        self.transform = transform
    
    def __len__(self):
        return sum(1 for line in open(self.data_file))
    
    def __getitem__(self, idx):
        df = open(self.data_file)
        lines = df.readlines()
        lst = lines[idx].split()
        img_name = lst[0]
        img_label = lst[1]
        image_path = os.path.join(self.root_dir, img_name)
        image = nib.load(image_path)
        samples = []
        if img_label == 'Normal':
            label = 0
        elif img_label == 'AD':
            label = 1
        elif img_label == 'MCI':
            label = 2

        AXimageList = axRandomSlice(image)
        CORimageList = corRandomSlice(image)
        SAGimageList = sagRandomSlice(image)

        for img2DList in (AXimageList, CORimageList, SAGimageList):
            for image2D in img2DList:
                if self.transform:
                    image2D = self.transform(image2D)
                sample = {'image': image2D, 'label': label}
                samples.append(sample)
        random.shuffle(samples)
        return samples


def getRandomSlice(image_array, keyIndex, section, step = 1):
    slice_p = keyIndex
    slice_2Dimgs = []
    slice_select_0 = None
    slice_select_1 = None
    slice_select_2 = None

    randomShift = random.randint(-9, 9)
    slice_p = slice_p + randomShift
    i = 0
    for slice_i in range(slice_p-step, slice_p+step+1, step):
        slice_select = eval("image_array"+section)
        exec("slice_select_"+str(i)+"=slice_select")
        i += 1
    slice_2Dimg = np.stack((slice_select_0, slice_select_1, slice_select_2), axis = 2)
    slice_2Dimgs.append(slice_2Dimg)
    return slice_2Dimgs

def axRandomSlice(image):
    image_array = np.array(image.get_data())
    return getRandomSlice(image_array, AX_INDEX, AX_SCETION)


def corRandomSlice(image):
    image_array = np.array(image.get_data())
    return getRandomSlice(image_array, COR_INDEX, COR_SCETION)


def sagRandomSlice(image):
    image_array = np.array(image.get_data())
    return getRandomSlice(image_array, SAG_INDEX, SAG_SCETION)