| | 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 |
| | import torch |
| |
|
| | NON_AX = (0, 1) |
| | NON_COR = (0, 2) |
| | NON_SAG = (1, 2) |
| |
|
| |
|
| | class AD_Standard_3DRandomPatch(Dataset): |
| | """labeled Faces in the Wild dataset.""" |
| | |
| | def __init__(self, root_dir, data_file): |
| | """ |
| | Args: |
| | root_dir (string): Directory of all the images. |
| | data_file (string): File name of the train/test split file. |
| | """ |
| | self.root_dir = root_dir |
| | self.data_file = data_file |
| | |
| | def __len__(self): |
| | with open(self.data_file) as df: |
| | summation = sum(1 for line in df) |
| | return summation |
| | |
| | def __getitem__(self, idx): |
| | with open(self.data_file) as df: |
| | lines = df.readlines() |
| | lst = lines[idx].split() |
| | img_name = lst[0] |
| | image_path = os.path.join(self.root_dir, img_name) |
| | image = nib.load(image_path) |
| |
|
| | image_array = np.array(image.get_data()) |
| | patch_samples = getRandomPatches(image_array) |
| | patch_dict = {"patch": patch_samples} |
| | return patch_dict |
| |
|
| |
|
| | def customToTensor(pic): |
| | if isinstance(pic, np.ndarray): |
| | img = torch.from_numpy(pic) |
| | img = torch.unsqueeze(img,0) |
| | |
| | return img.float() |
| |
|
| | def getRandomPatches(image_array): |
| | patches = [] |
| | mean_ax = np.ndarray.mean(image_array, axis = NON_AX) |
| | mean_cor = np.ndarray.mean(image_array, axis = NON_COR) |
| | mean_sag = np.ndarray.mean(image_array, axis = NON_SAG) |
| |
|
| | first_ax = int(round(list(mean_ax).index(filter(lambda x: x>0, mean_ax)[0]))) |
| | last_ax = int(round(list(mean_ax).index(filter(lambda x: x>0, mean_ax)[-1]))) |
| | first_cor = int(round(list(mean_cor).index(filter(lambda x: x>0, mean_cor)[0]))) |
| | last_cor = int(round(list(mean_cor).index(filter(lambda x: x>0, mean_cor)[-1]))) |
| | first_sag = int(round(list(mean_sag).index(filter(lambda x: x>0, mean_sag)[0]))) |
| | last_sag = int(round(list(mean_sag).index(filter(lambda x: x>0, mean_sag)[-1]))) |
| |
|
| | first_ax = first_ax + 20 |
| | last_ax = last_ax - 5 |
| |
|
| | ax_samples = [random.randint(first_ax - 3, last_ax - 3) for r in xrange(10000)] |
| | cor_samples = [random.randint(first_cor - 3, last_cor - 3) for r in xrange(10000)] |
| | sag_samples = [random.randint(first_sag - 3, last_sag - 3) for r in xrange(10000)] |
| |
|
| | for i in range(1000): |
| | ax_i = ax_samples[i] |
| | cor_i = cor_samples[i] |
| | sag_i = sag_samples[i] |
| | patch = image_array[ax_i-3:ax_i+4, cor_i-3:cor_i+4, sag_i-3:sag_i+4] |
| | while (np.ndarray.sum(patch) == 0): |
| | ax_ni = random.randint(first_ax - 3, last_ax - 4) |
| | cor_ni = random.randint(first_cor - 3, last_cor - 4) |
| | sag_ni = random.randint(first_sag - 3, last_sag - 4) |
| | patch = image_array[ax_ni-3:ax_ni+4, cor_ni-3:cor_ni+4, sag_ni-3:sag_ni+4] |
| | patch = customToTensor(patch) |
| | patches.append(patch) |
| | return patches |
| |
|
| |
|
| | |
| | |
| |
|
| |
|
| |
|
| |
|
| |
|
