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data3D.py
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data3D.py
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# import package
# linear algebra
import numpy as np
# data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import pydicom
import os
import scipy.ndimage
import sys
import matplotlib.pyplot as plt
from skimage import measure, morphology, io
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
# Some constants
INPUT_FOLDER = '../input/CEFIESTA>/CEFIESTA'
INPUT_FOLDER_GT = '../input/CEFIESTA>/ground_truth'
image_rows = int(512)
image_cols = int(512)
image_depth = 16
# Flag of data argument
argument = 0
patients = os.listdir(INPUT_FOLDER)
patients_gt = os.listdir(INPUT_FOLDER_GT)
patients.sort()
patients_gt.sort()
# Load the scans in given folder path
def load_scan(path):
slices = [pydicom.read_file(path + '/' + s) for s in os.listdir(path)]
slices.sort(key=lambda x: float(x.ImagePositionPatient[2]))
try:
slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])
except:
slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)
for s in slices:
s.SliceThickness = slice_thickness
return slices
def get_pixels_hu(slices):
image = np.stack([s.pixel_array for s in slices])
# Convert to int16 (from sometimes int16),
# should be possible as values should always be low enough (<32k)
image = image.astype(np.int16)
# Set outside-of-scan pixels to 0
# The intercept is usually -1024, so air is approximately 0
image[image == -2000] = 0
# Convert to Hounsfield units (HU)
for slice_number in range(len(slices)):
intercept = 0
slope = 1
if hasattr(slices[slice_number], 'RescaleIntercept'):
intercept = slices[slice_number].RescaleIntercept
if hasattr(slices[slice_number], 'RescaleSlope'):
slope = slices[slice_number].RescaleSlope
if slope != 1:
image[slice_number] = slope * image[slice_number].astype(np.float64)
image[slice_number] = image[slice_number].astype(np.int16)
image[slice_number] += np.int16(intercept)
return np.array(image, dtype=np.int16)
def create_train_data():
# TRAIN_NUM = int((len(patients) - 1) * 0.7)
TRAIN_NUM = int((len(patients) - 1) * 0.1)
if argument == 1:
# MAX of slices per patient: 216
total = 0
for k in range(0, TRAIN_NUM):
images = os.listdir(INPUT_FOLDER + '/' + patients[k + 1])
total += int(np.floor(len(images) / 8))
total += 1
# total = int(TRAIN_NUM * 27)
imgs = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.int16)
imgs_mask = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.int16)
imgs_temp = np.ndarray((total, image_depth//2, image_rows, image_cols), dtype=np.int16)
imgs_mask_temp = np.ndarray((total, image_depth//2, image_rows, image_cols), dtype=np.int16)
num = 0
print('-' * 30)
print('Creating training images...')
print('-' * 30)
for i in range(0, TRAIN_NUM):
j = 0
patient = load_scan(INPUT_FOLDER + '/' + patients[i + 1])
patient_pixels = get_pixels_hu(patient)
count = total
for k in range(0, patient_pixels.shape[0]):
imgs_temp[num, j] = patient_pixels[k]
j += 1
if j % (image_depth/2) == 0:
j = 0
num += 1
if (num % 100) == 0:
print('Done: {0}/{1} 3d images'.format(num, count))
for x in range(0, imgs_temp.shape[0] - 1):
imgs[x] = np.append(imgs_temp[x], imgs_temp[x+1], axis=0)
print('Loading of train data done.')
print('-' * 30)
print('Creating training masks...')
print('-' * 30)
num = 0
for i in range(0, TRAIN_NUM):
j = 0
patient_gt = load_scan(INPUT_FOLDER_GT + '/' + patients_gt[i + 1])
patient_pixels_gt = get_pixels_hu(patient_gt)
count = total
for k in range(0, patient_pixels_gt.shape[0]):
imgs_mask_temp[num, j] = patient_pixels_gt[k]
j += 1
if j % (image_depth / 2) == 0:
j = 0
num += 1
if (num % 100) == 0:
print('Done: {0}/{1} 3d images'.format(num, count))
for x in range(0, imgs_mask_temp.shape[0] - 1):
imgs_mask[x] = np.append(imgs_mask_temp[x], imgs_mask_temp[x + 1], axis=0)
print('Loading of masks done.')
imgs_mask = preprocess(imgs_mask)
imgs = preprocess(imgs)
print('Preprocessing of masks done.')
np.save('imgs_train_3D.npy', imgs)
np.save('imgs_mask_train_3D.npy', imgs_mask)
imgs = preprocess_squeeze(imgs)
imgs_mask = preprocess_squeeze(imgs_mask)
print('Saving to .npy files done.')
elif argument == 0:
# MAX of slices per patient: 216
total = 0
for k in range(0, TRAIN_NUM):
images = os.listdir(INPUT_FOLDER + '/' + patients[k + 1])
total += len(images)
total = int(np.ceil(total / image_depth))
# total = int(TRAIN_NUM * 27)
imgs = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.int16)
imgs_mask = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.int16)
print('-' * 30)
print('Creating training images...')
print('-' * 30)
num = 0
j = 0
for i in range(0, TRAIN_NUM):
patient = load_scan(INPUT_FOLDER + '/' + patients[i + 1])
patient_pixels = get_pixels_hu(patient)
count = total
for k in range(0, patient_pixels.shape[0]):
imgs[num, j] = patient_pixels[k]
j += 1
if j % image_depth == 0:
j = 0
num += 1
if (num % 100) == 0:
print('Done: {0}/{1} 3d images'.format(num, count))
print('Loading of train data done.')
print('-' * 30)
print('Creating training masks...')
print('-' * 30)
num = 0
j = 0
for i in range(0, TRAIN_NUM):
patient_gt = load_scan(INPUT_FOLDER_GT + '/' + patients_gt[i + 1])
patient_pixels_gt = get_pixels_hu(patient_gt)
count = total
for k in range(0, patient_pixels_gt.shape[0]):
imgs_mask[num, j] = patient_pixels_gt[k]
j += 1
if j % image_depth == 0:
j = 0
num += 1
if (num % 100) == 0:
print('Done: {0}/{1} 3d images'.format(num, count))
print('Loading of masks done.')
imgs_mask = preprocess(imgs_mask)
imgs = preprocess(imgs)
print('Preprocessing of masks done.')
np.save('imgs_train_3D.npy', imgs)
np.save('imgs_mask_train_3D.npy', imgs_mask)
imgs = preprocess_squeeze(imgs)
imgs_mask = preprocess_squeeze(imgs_mask)
print('Saving to .npy files done.')
def load_train_data():
imgs_train = np.load('imgs_train_3D.npy')
imgs_mask_train = np.load('imgs_mask_train_3D.npy')
return imgs_train, imgs_mask_train
def create_test_data():
# TRAIN_NUM = int((len(patients) - 1) * 0.7)
TRAIN_NUM = int((len(patients) - 1) * 0.1)
# TEST_NUM = 91
TEST_NUM = 15
if argument == 1:
total = 0
for k in range(TRAIN_NUM, TEST_NUM):
images = os.listdir(INPUT_FOLDER + '/' + patients[k + 1])
total += len(images)
# total += int(np.floor((len(images) - 2) / (image_depth - 2)))
total = int(np.ceil((total - 2) / (image_depth - 2)))
imgs = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.int16)
imgs_mask = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.int16)
print('-' * 30)
print('Creating test images...')
print('-' * 30)
num = 0
j = 0
for i in range(TRAIN_NUM, TEST_NUM):
patient = load_scan(INPUT_FOLDER + '/' + patients[i + 1])
patient_pixels = get_pixels_hu(patient)
count = total
for k in range(0, patient_pixels.shape[0]):
imgs[num, j] = patient_pixels[k]
j += 1
if j % (image_depth - 1) == 0:
imgs[num + 1, 0] = patient_pixels[k]
if j % image_depth == 0:
imgs[num + 1, 1] = patient_pixels[k]
j = 2
num += 1
print('Done: {0}/{1} test 3d images'.format(num, count))
print('Loading of test data done.')
print('-' * 30)
print('Creating test masks...')
print('-' * 30)
num = 0
j = 0
for i in range(TRAIN_NUM, TEST_NUM):
patient_gt = load_scan(INPUT_FOLDER_GT + '/' + patients_gt[i + 1])
patient_pixels_gt = get_pixels_hu(patient_gt)
count = total
for k in range(0, patient_pixels_gt.shape[0]):
imgs_mask[num, j] = patient_pixels_gt[k]
j += 1
if j % (image_depth - 1) == 0:
imgs_mask[num + 1, 0] = patient_pixels_gt[k]
if j % image_depth == 0:
imgs_mask[num + 1, 1] = patient_pixels_gt[k]
j = 2
num += 1
print('Done: {0}/{1} test mask 3d images'.format(num, count))
print('Loading of test masks done.')
imgs = preprocess(imgs)
imgs_mask = preprocess(imgs_mask)
np.save('imgs_test_3D.npy', imgs)
np.save('imgs_mask_test_3D.npy', imgs_mask)
imgs = preprocess_squeeze(imgs)
imgs_mask = preprocess_squeeze(imgs_mask)
count_processed = 0
pred_dir = 'test_preprocessed_3D'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs.shape[0]):
for y in range(0, imgs.shape[1]):
io.imsave(os.path.join(pred_dir, 'pre_processed_' + str(count_processed) + '.png'), imgs[x][y])
count_processed += 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(count_processed, imgs.shape[0] * imgs.shape[1]))
count_processed = 0
pred_dir = 'test_mask_preprocessed_3D'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs_mask.shape[0]):
for y in range(0, imgs_mask.shape[1]):
io.imsave(os.path.join(pred_dir, 'pre_processed_mask_' + str(count_processed) + '.png'), imgs_mask[x][y])
count_processed += 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(count_processed, imgs_mask.shape[0] * imgs_mask.shape[1]))
print('Saving to .npy files done.')
elif argument == 0:
total = 0
for k in range(TRAIN_NUM, TEST_NUM):
images = os.listdir(INPUT_FOLDER + '/' + patients[k + 1])
total += len(images)
total = int(np.ceil(total / image_depth))
imgs = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.int16)
imgs_mask = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.int16)
print('-' * 30)
print('Creating test images...')
print('-' * 30)
num = 0
j = 0
for i in range(TRAIN_NUM, TEST_NUM):
patient = load_scan(INPUT_FOLDER + '/' + patients[i + 1])
patient_pixels = get_pixels_hu(patient)
count = total
for k in range(0, patient_pixels.shape[0]):
imgs[num, j] = patient_pixels[k]
j += 1
if j % image_depth == 0:
j = 0
num += 1
print('Done: {0}/{1} test 3d images'.format(num, count))
print('Loading of test data done.')
print('-' * 30)
print('Creating test masks...')
print('-' * 30)
num = 0
j = 0
for i in range(TRAIN_NUM, TEST_NUM):
patient_gt = load_scan(INPUT_FOLDER_GT + '/' + patients_gt[i + 1])
patient_pixels_gt = get_pixels_hu(patient_gt)
count = total
for k in range(0, patient_pixels_gt.shape[0]):
imgs_mask[num, j] = patient_pixels_gt[k]
j += 1
if j % image_depth == 0:
j = 0
num += 1
print('Done: {0}/{1} test mask 3d images'.format(num, count))
print('Loading of test masks done.')
imgs = preprocess(imgs)
imgs_mask = preprocess(imgs_mask)
np.save('imgs_test_3D.npy', imgs)
np.save('imgs_mask_test_3D.npy', imgs_mask)
imgs = preprocess_squeeze(imgs)
imgs_mask = preprocess_squeeze(imgs_mask)
count_processed = 0
pred_dir = 'test_preprocessed_3D'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs.shape[0]):
for y in range(0, imgs.shape[1]):
io.imsave(os.path.join(pred_dir, 'pre_processed_' + str(count_processed) + '.png'), imgs[x][y])
count_processed += 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(count_processed, imgs.shape[0] * imgs.shape[1]))
count_processed = 0
pred_dir = 'test_mask_preprocessed_3D'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs_mask.shape[0]):
for y in range(0, imgs_mask.shape[1]):
io.imsave(os.path.join(pred_dir, 'pre_processed_mask_' + str(count_processed) + '.png'),
imgs_mask[x][y])
count_processed += 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(count_processed, imgs_mask.shape[0] * imgs_mask.shape[1]))
print('Saving to .npy files done.')
def load_test_data():
imgs_test = np.load('imgs_test_3D.npy')
return imgs_test
def preprocess(imgs):
imgs = np.expand_dims(imgs, axis=4)
print(' ---------------- preprocessed -----------------')
return imgs
def preprocess_squeeze(imgs):
imgs = np.squeeze(imgs, axis=4)
print(' ---------------- preprocessed squeezed -----------------')
return imgs
if __name__ == '__main__':
create_train_data()
create_test_data()