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)
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_depth,image_rows,image_cols))
img_mask_data=np.load('../npy_data/test_mask.npy')
img_mask_data=img_mask_data[0]
# for each slice, extract patches and predict
for iSlice in [28]:
mask = img_mask_data[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[1]):
for j in range(0, SegmentedVolume.shape[2]):
if img_mask_data.item((iSlice,i, j)) == 1:
SegmentedVolume.itemset((iSlice,i,j), label_predicted_filled.item((i, j)))
else:
label_predicted_filled.itemset((i, j), 0)
print ('done')
# utilize mask to write output
data = SegmentedVolume
kkk=np.array(data)
print(np.unique(kkk))
misc.imsave("28layer_test.bmp",kkk[28]*80)
test_label=np.load(img_dir_name+'test_y.npy')
test_label=test_label[0,28]
dice(kkk[28],test_label)
def resume():
print('-' * 30)
print('Loading and preprocessing train data...')
print('-' * 30)
imgs_train, imgs_gtruth_train = load_train_data()
print('-' * 30)
print('Loading and preprocessing validation data...')
print('-' * 30)
imgs_val, imgs_gtruth_val = load_validatation_data()
print('-' * 30)
print('Creating and compiling model...')
print('-' * 30)
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_best = 'outputs/' + 'best_4classes_32_reduced_tuned_8915.h5'
print(checkpoint_filepath_best)
model.load_weights(checkpoint_filepath_best)
model.summary()
print('-' * 30)
print('Fitting model...')
print('-' * 30)
#============================================================================
print('training starting..')
log_filename = 'outputs/' + image_type + '_model_train.csv'
#Callback that streams epoch results to a csv file.
csv_log = callbacks.CSVLogger(log_filename, separator=',', append=True)
early_stopping = callbacks.EarlyStopping(monitor='val_loss',
min_delta=0,
patience=10,
verbose=0,
mode='min')
#checkpoint_filepath = 'outputs/' + image_type +"_best_weight_model_{epoch:03d}_{val_loss:.4f}.hdf5"
checkpoint_filepath = 'outputs/' + 'weights.h5'
checkpoint = callbacks.ModelCheckpoint(checkpoint_filepath,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
#callbacks_list = [csv_log, checkpoint]
callbacks_list = [csv_log, early_stopping, checkpoint]
#============================================================================
hist = model.fit(
imgs_train,
imgs_gtruth_train,
batch_size=batch_size,
nb_epoch=nb_epochs,
verbose=1,
validation_data=(imgs_val, imgs_gtruth_val),
shuffle=True,
callbacks=callbacks_list) # validation_split=0.2,
model_name = 'outputs/' + image_type + '_model_last'
model.save(model_name) # creates a HDF5 file 'my_model.h5'