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data2D_reg.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
from distance import *
# Some constants
INPUT_FOLDER = '../input/CEFIESTA>/CEFIESTA'
INPUT_FOLDER_GT = '../input/CEFIESTA>/ground_truth'
image_rows = int(512)
image_cols = int(512)
image_depth = 1
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)
imgs_temp = np.ndarray((0, image_rows, image_cols), dtype=np.int16)
imgs_mask_temp = np.ndarray((0, image_rows, image_cols), dtype=np.int16)
print('-' * 30)
print('Creating training images...')
print('-' * 30)
for i in range(1, TRAIN_NUM + 1):
patient = load_scan(INPUT_FOLDER + '/' + patients[i])
patient_pixels = get_pixels_hu(patient)
imgs_temp = np.concatenate((imgs_temp, patient_pixels), axis=0)
print('Done: {0}/{1} 2d images'.format(i, TRAIN_NUM))
imgs = imgs_temp
print('Loading of train data done.')
print('-' * 30)
print('Creating training masks...')
print('-' * 30)
for i in range(1, TRAIN_NUM + 1):
patient_gt = load_scan(INPUT_FOLDER_GT + '/' + patients_gt[i])
patient_pixels_gt = get_pixels_hu(patient_gt)
for k in range(0, patient_pixels_gt.shape[0]):
mask_df = patient_pixels_gt[k][:, :, np.newaxis]
sdf = generate_sdf(mask_df != 0)
sdf = sdf[:, :, 0]
sdf = sdf[np.newaxis, :, :]
imgs_mask_temp = np.concatenate((imgs_mask_temp, sdf), axis=0)
print('Done: {0}/{1} mask 2d images'.format(i, TRAIN_NUM))
imgs_mask = imgs_mask_temp
print('Loading of train masks done.')
imgs = preprocess(imgs)
imgs_mask = preprocess(imgs_mask)
print('Preprocessing of masks done.')
np.save('imgs_train_reg.npy', imgs)
np.save('imgs_mask_train_reg.npy', imgs_mask)
imgs = preprocess_squeeze(imgs)
imgs_mask = preprocess_squeeze(imgs_mask)
# count_processed = 0
# pred_dir = 'train_preprocessed'
# if not os.path.exists(pred_dir):
# os.mkdir(pred_dir)
# for x in range(0, imgs.shape[0]):
# io.imsave(os.path.join(pred_dir, 'pre_processed_' + str(count_processed) + '.png'), imgs[x])
# count_processed += 1
# if (count_processed % 100) == 0:
# print('Done: {0}/{1} train images'.format(count_processed, imgs.shape[0]))
#
# count_processed = 0
# pred_dir = 'mask_preprocessed'
# if not os.path.exists(pred_dir):
# os.mkdir(pred_dir)
# for x in range(0, imgs.shape[0]):
# io.imsave(os.path.join(pred_dir, 'pre_processed_' + str(count_processed) + '.png'), imgs_mask[x])
# count_processed += 1
# if (count_processed % 100) == 0:
# print('Done: {0}/{1} train images'.format(count_processed, imgs.shape[0]))
print('Saving to .npy files done.')
def load_train_data():
imgs_train = np.load('imgs_train_reg.npy')
imgs_mask_train = np.load('imgs_mask_train_reg.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
imgs_temp = np.ndarray((0, image_rows, image_cols), dtype=np.int16)
imgs_mask_temp = np.ndarray((0, image_rows, image_cols), dtype=np.int16)
print('-' * 30)
print('Creating test images...')
print('-' * 30)
for i in range(TRAIN_NUM + 1, TEST_NUM + 1):
patient = load_scan(INPUT_FOLDER + '/' + patients[i])
patient_pixels = get_pixels_hu(patient)
imgs_temp = np.concatenate((imgs_temp, patient_pixels), axis=0)
print('Done: {0}/{1} 2d images'.format(i - TRAIN_NUM - 1, TEST_NUM - TRAIN_NUM))
imgs = imgs_temp
print('Loading of test data done.')
print('-' * 30)
print('Creating test masks...')
print('-' * 30)
for i in range(TRAIN_NUM + 1, TEST_NUM + 1):
patient_gt = load_scan(INPUT_FOLDER_GT + '/' + patients_gt[i])
patient_pixels_gt = get_pixels_hu(patient_gt)
for k in range(0, patient_pixels_gt.shape[0]):
mask_df = patient_pixels_gt[k][:, :, np.newaxis]
sdf = generate_sdf(mask_df != 0)
sdf = sdf[:, :, 0]
sdf = sdf[np.newaxis, :, :]
imgs_mask_temp = np.concatenate((imgs_mask_temp, sdf), axis=0)
print('Done: {0}/{1} 2d images'.format(i - TRAIN_NUM - 1, TEST_NUM - TRAIN_NUM))
imgs_mask = imgs_mask_temp
print('Loading of test masks done.')
imgs = preprocess(imgs)
imgs_mask = preprocess(imgs_mask)
np.save('imgs_test_reg.npy', imgs)
np.save('imgs_mask_test_reg.npy', imgs_mask)
imgs = preprocess_squeeze(imgs)
imgs_mask = preprocess_squeeze(imgs_mask)
count_processed = 0
pred_dir = 'test_preprocessed_reg'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs.shape[0]):
io.imsave(os.path.join(pred_dir, 'pre_processed_' + str(count_processed) + '.png'), imgs[x])
count_processed += 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(count_processed, imgs.shape[0]))
count_processed = 0
pred_dir = 'test_mask_preprocessed_reg'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs.shape[0]):
io.imsave(os.path.join(pred_dir, 'pre_processed_mask_' + str(count_processed) + '.png'), imgs_mask[x])
count_processed += 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(count_processed, imgs.shape[0]))
print('Saving to .npy files done.')
def load_test_data():
imgs_test = np.load('imgs_test_reg.npy')
return imgs_test
def preprocess(imgs):
imgs = np.expand_dims(imgs, axis=3)
print(' ---------------- preprocessed -----------------')
return imgs
def preprocess_squeeze(imgs):
imgs = np.squeeze(imgs, axis=3)
print(' ---------------- preprocessed squeezed -----------------')
return imgs
if __name__ == '__main__':
create_train_data()
create_test_data()
# example = snowy.load('test_mask_preprocessed/pre_processed_mask_38.png')
# example = example[:, :, 1]
# example = example[:, :, np.newaxis]
# sdf = snowy.unitize(snowy.generate_sdf(example != 0))
# snowy.show(snowy.hstack([example, sdf]))
# imgs, imgs_mask = create_train_data()
# print(np.amax(imgs))
# print(np.amax(imgs_mask))
#
# first_patient = load_scan(INPUT_FOLDER + '/' + patients[1])
# second_patient = load_scan(INPUT_FOLDER + '/' + patients[2])
# first_patient_pixels = get_pixels_hu(first_patient)
# second_patient_pixels = get_pixels_hu(second_patient)
# pixels = np.concatenate((first_patient_pixels, second_patient_pixels), axis=0)
#
# first_patient_gt = load_scan(INPUT_FOLDER + '/' + patients_gt[1])