from __future__ import print_function
# import packages
from functools import partial
import os
import time
import numpy as np
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import Adam
from keras import callbacks
from keras import backend as K
from keras.utils import plot_model
import nibabel as nib
from PIL import Image
from sklearn.feature_extraction.image import extract_patches
# import load data
from data_handling_2d_patch import load_train_data, load_validatation_data
from train_main_2d_patch import get_unet_default, get_unet_reduced, get_unet_extended
# import configurations
import configs
K.set_image_data_format('channels_last') # TF dimension ordering in this code
image_type = configs.IMAGE_TYPE
# init configs
image_rows = configs.VOLUME_ROWS
image_cols = configs.VOLUME_COLS
image_depth = configs.VOLUME_DEPS
num_classes = configs.NUM_CLASSES
# patch extraction parameters
patch_size = configs.PATCH_SIZE
BASE = configs.BASE
smooth = configs.SMOOTH
nb_epochs = configs.NUM_EPOCHS
batch_size = configs.BATCH_SIZE
unet_model_type = configs.MODEL
extraction_step = 1
extraction_reconstruct_step = configs.extraction_reconstruct_step
# init
train_imgs_path = '../data_new/Test_Set'
print('path: ', train_imgs_path)
checkpoint_filename = 'best_4classes_32_default_tuned_8925.h5'
print('weight file: ', checkpoint_filename)
write_path = 'predict2D'
# for each slice estract patches and stack
def create_slice_testing(slice_number, img_dir_name):
# empty matrix to hold patches
patches_training_imgs_2d = np.empty(shape=[0, patch_size, patch_size], dtype='int16')
patches_training_gtruth_2d = np.empty(shape=[0, patch_size, patch_size, num_classes], dtype='int16')
images_train_dir = os.listdir(train_imgs_path)
j = 0
# volume
img_name = img_dir_name + '_hist.nii.gz'
print('Image: ', img_name)
img_name = os.path.join(train_imgs_path, img_dir_name, img_name)
# mask
img_mask_name = img_dir_name + '_mask.nii.gz'
img_mask_name = os.path.join(train_imgs_path, img_dir_name, img_mask_name)
# load volume and mask
img = nib.load(img_name)
img_data = img.get_data()
img_data = np.squeeze(img_data)
img_mask = nib.load(img_mask_name)
img_mask_data = img_mask.get_data()
img_mask_data = np.squeeze(img_mask_data)
patches_training_imgs_2d_temp = np.empty(shape=[0, patch_size, patch_size], dtype='int16')
patches_training_gtruth_2d_temp = np.empty(shape=[0, patch_size, patch_size, num_classes], dtype='int16')
rows = []; cols = []
if np.count_nonzero(img_mask_data[:, :, slice_number]) and np.count_nonzero(img_data[:, :, slice_number]):
# extract patches of the jth volume image
imgs_patches, rows, cols = extract_2d_patches_one_slice(img_data[:, :, slice_number],
img_mask_data[:, :, slice_number])
# update database
patches_training_imgs_2d_temp = np.append(patches_training_imgs_2d_temp, imgs_patches, axis=0)
patches_training_imgs_2d = np.append(patches_training_imgs_2d, patches_training_imgs_2d_temp, axis=0)
j += 1
X = patches_training_imgs_2d.shape
Y = patches_training_gtruth_2d.shape
# convert to single precision
patches_training_imgs_2d = patches_training_imgs_2d.astype('float32')
patches_training_imgs_2d = np.expand_dims(patches_training_imgs_2d, axis=3)
S = patches_training_imgs_2d.shape
label_predicted = np.zeros((img_data.shape[0], img_data.shape[1]), dtype=np.uint8)
return label_predicted, patches_training_imgs_2d, rows, cols
# extract patches in one slice
def extract_2d_patches_one_slice(img_data, mask_data):
patch_shape = (patch_size, patch_size)
# empty matrix to hold patches
imgs_patches_per_slice = np.empty(shape=[0, patch_size, patch_size], dtype='int16')
img_patches = extract_patches(img_data, patch_shape, extraction_reconstruct_step)
mask_patches = extract_patches(mask_data, patch_shape, extraction_reconstruct_step)
Sum = np.sum(mask_patches, axis=(2, 3))
rows, cols = np.nonzero(Sum)
N = len(rows)
# select non-zero patches index
selected_img_patches = img_patches[rows, cols, :, :]
# update database
imgs_patches_per_slice = np.append(imgs_patches_per_slice, selected_img_patches, axis=0)
return imgs_patches_per_slice, rows, cols
# write predicted label to the final result
def write_slice_predict(imgs_valid_predict, rows, cols):
label_predicted_filled = np.zeros((image_rows, image_cols, num_classes))
label_final = np.zeros((image_rows, image_cols))
Count = len(rows)
count_write = len(rows)
for index in range(0, len(rows)):
row = rows[index]; col = cols[index]
start_row = row * extraction_reconstruct_step
start_col = col * extraction_reconstruct_step
patch_volume = imgs_valid_predict[index, :, :, :]
for i in range(0, patch_size):
for j in range(0, patch_size):
prob_class0_new = patch_volume[i][j][0]
prob_class1_new = patch_volume[i][j][1]
prob_class2_new = patch_volume[i][j][2]
prob_class3_new = patch_volume[i][j][3]
label_predicted_filled[start_row + i][start_col + j][0] = prob_class0_new
label_predicted_filled[start_row + i][start_col + j][1] = prob_class1_new
label_predicted_filled[start_row + i][start_col + j][2] = prob_class2_new
label_predicted_filled[start_row + i][start_col + j][3] = prob_class3_new
for i in range(0, 256):
for j in range(0, 128):
prob_class0 = label_predicted_filled[i][j][0]
prob_class1 = label_predicted_filled[i][j][1]
prob_class2 = label_predicted_filled[i][j][2]
prob_class3 = label_predicted_filled[i][j][3]
prob_max = max(prob_class0, prob_class1, prob_class2, prob_class3)
if prob_class0 == prob_max:
label_final[i][j] = 0
elif prob_class1 == prob_max:
label_final[i][j] = 1
elif prob_class2 == prob_max:
label_final[i][j] = 2
else:
label_final[i][j] = 3
print('Number of processed patches: ', count_write)
print('Number of extracted patches: ', Count)
return label_final
# predict function
def predict(img_dir_name):
if unet_model_type == 'default':
model = get_unet_default()
elif unet_model_type == 'reduced':
model = get_unet_reduced()
elif unet_model_type == 'extended':
model = get_unet_extended()
checkpoint_filepath = 'outputs/' + checkpoint_filename
model.load_weights(checkpoint_filepath)
model.summary()
SegmentedVolume = np.zeros((image_rows,image_cols,image_depth))
img_mask_name = img_dir_name + '_mask.nii.gz'
img_mask_name = os.path.join(train_imgs_path, img_dir_name, img_mask_name)
img_mask = nib.load(img_mask_name)
img_mask_data = img_mask.get_data()
# for each slice, extract patches and predict
for iSlice in range(0,256):
mask = img_mask_data[2:254, 2:127, iSlice]
if np.sum(mask, axis=(0,1))>0:
print('-' * 30)
print('Slice number: ', iSlice)
label_predicted, patches_training_imgs_2d, rows, cols = create_slice_testing(iSlice, img_dir_name)
imgs_valid_predict = model.predict(patches_training_imgs_2d)
label_predicted_filled = write_slice_predict(imgs_valid_predict, rows, cols)
for i in range(0, SegmentedVolume.shape[0]):
for j in range(0, SegmentedVolume.shape[1]):
if img_mask_data.item((i, j, iSlice)) == 1:
SegmentedVolume.itemset((i,j,iSlice), label_predicted_filled.item((i, j)))
else:
label_predicted_filled.itemset((i, j), 0)
print ('done')
# utilize mask to write output
data = SegmentedVolume
img = nib.Nifti1Image(data, np.eye(4))
if num_classes == 3:
img_name = img_dir_name + '_predicted_3class_' + str(patch_size) + '_' + unet_model_type + '_tuned_8925.nii.gz'
else:
img_name = img_dir_name + '_predicted_4class_' + str(patch_size) + '_' + unet_model_type + '_tuned_8925.nii.gz'
nib.save(img, os.path.join('../data_new', write_path, img_name))
print('-' * 30)
# main
if __name__ == '__main__':
# folder to hold outputs
if 'outputs' not in os.listdir(os.curdir):
os.mkdir('outputs')
images_train_dir = os.listdir(train_imgs_path)
j=0
for img_dir_name in images_train_dir:
j=j+1
if j:
start_time = time.time()
print('*'*50)
print('Segmenting: volume {0} / {1} volume images'.format(j, len(images_train_dir)))
# print('-'*30)
if num_classes == 3:
img_name = img_dir_name + '_predicted_3class_' + str(patch_size) + '_' + unet_model_type + '_tuned_8925.nii.gz'
else:
img_name = img_dir_name + '_predicted_4class_' + str(patch_size) + '_' + unet_model_type + '_tuned_8925.nii.gz'
print ('Path: ', os.path.join('../data_new', write_path, img_name))
print('*' * 50)
predict(img_dir_name)
end_time = time.time()
elapsed_time = end_time - start_time
print ('Elapsed time is: ', elapsed_time)
