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test.py
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test.py
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'''
Capsules for Object Segmentation (SegCaps)
Original Paper by Rodney LaLonde and Ulas Bagci (https://arxiv.org/abs/1804.04241)
Code written by: Rodney LaLonde
If you use significant portions of this code or the ideas from our paper, please cite it :)
If you have any questions, please email me at lalonde@knights.ucf.edu.
This file is used for testing models. Please see the README for details about testing.
'''
from __future__ import print_function
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
from os.path import join
from os import makedirs
import csv
import SimpleITK as sitk
from tqdm import tqdm
import numpy as np
import scipy.ndimage.morphology
from skimage import measure, filters
from metrics import dc, jc, assd
from keras import backend as K
K.set_image_data_format('channels_last')
from keras.utils import print_summary
from load_3D_data import generate_test_batches
def threshold_mask(raw_output, threshold):
if threshold == 0:
try:
threshold = filters.threshold_otsu(raw_output)
except:
threshold = 0.5
print('\tThreshold: {}'.format(threshold))
raw_output[raw_output > threshold] = 1
raw_output[raw_output < 1] = 0
all_labels = measure.label(raw_output)
props = measure.regionprops(all_labels)
props.sort(key=lambda x: x.area, reverse=True)
thresholded_mask = np.zeros(raw_output.shape)
if len(props) >= 2:
if props[0].area / props[1].area > 5: # if the largest is way larger than the second largest
thresholded_mask[all_labels == props[0].label] = 1 # only turn on the largest component
else:
thresholded_mask[all_labels == props[0].label] = 1 # turn on two largest components
thresholded_mask[all_labels == props[1].label] = 1
elif len(props):
thresholded_mask[all_labels == props[0].label] = 1
thresholded_mask = scipy.ndimage.morphology.binary_fill_holes(thresholded_mask).astype(np.uint8)
return thresholded_mask
def test(args, test_list, model_list, net_input_shape):
if args.weights_path == '':
weights_path = join(args.check_dir, args.output_name + '_model_' + args.time + '.hdf5')
else:
weights_path = join(args.data_root_dir, args.weights_path)
output_dir = join(args.data_root_dir, 'results', args.net, 'split_' + str(args.split_num))
raw_out_dir = join(output_dir, 'raw_output')
fin_out_dir = join(output_dir, 'final_output')
fig_out_dir = join(output_dir, 'qual_figs')
try:
makedirs(raw_out_dir)
except:
pass
try:
makedirs(fin_out_dir)
except:
pass
try:
makedirs(fig_out_dir)
except:
pass
if len(model_list) > 1:
eval_model = model_list[1]
else:
eval_model = model_list[0]
try:
eval_model.load_weights(weights_path)
except:
print('Unable to find weights path. Testing with random weights.')
print_summary(model=eval_model, positions=[.38, .65, .75, 1.])
# Set up placeholders
outfile = ''
if args.compute_dice:
dice_arr = np.zeros((len(test_list)))
outfile += 'dice_'
if args.compute_jaccard:
jacc_arr = np.zeros((len(test_list)))
outfile += 'jacc_'
if args.compute_assd:
assd_arr = np.zeros((len(test_list)))
outfile += 'assd_'
# Testing the network
print('Testing... This will take some time...')
with open(join(output_dir, args.save_prefix + outfile + 'scores.csv'), 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
row = ['Scan Name']
if args.compute_dice:
row.append('Dice Coefficient')
if args.compute_jaccard:
row.append('Jaccard Index')
if args.compute_assd:
row.append('Average Symmetric Surface Distance')
writer.writerow(row)
for i, img in enumerate(tqdm(test_list)):
sitk_img = sitk.ReadImage(join(args.data_root_dir, 'imgs', img[0]))
img_data = sitk.GetArrayFromImage(sitk_img)
num_slices = img_data.shape[0]
output_array = eval_model.predict_generator(generate_test_batches(args.data_root_dir, [img],
net_input_shape,
batchSize=args.batch_size,
numSlices=args.slices,
subSampAmt=0,
stride=1),
steps=num_slices, max_queue_size=1, workers=1,
use_multiprocessing=False, verbose=1)
if args.net.find('caps') != -1:
output = output_array[0][:,:,:,0]
#recon = output_array[1][:,:,:,0]
else:
output = output_array[:,:,:,0]
output_img = sitk.GetImageFromArray(output)
print('Segmenting Output')
output_bin = threshold_mask(output, args.thresh_level)
output_mask = sitk.GetImageFromArray(output_bin)
output_img.CopyInformation(sitk_img)
output_mask.CopyInformation(sitk_img)
print('Saving Output')
sitk.WriteImage(output_img, join(raw_out_dir, img[0][:-4] + '_raw_output' + img[0][-4:]))
sitk.WriteImage(output_mask, join(fin_out_dir, img[0][:-4] + '_final_output' + img[0][-4:]))
# Load gt mask
sitk_mask = sitk.ReadImage(join(args.data_root_dir, 'masks', img[0]))
gt_data = sitk.GetArrayFromImage(sitk_mask)
# Plot Qual Figure
print('Creating Qualitative Figure for Quick Reference')
f, ax = plt.subplots(1, 3, figsize=(15, 5))
ax[0].imshow(img_data[img_data.shape[0] // 3, :, :], alpha=1, cmap='gray')
ax[0].imshow(output_bin[img_data.shape[0] // 3, :, :], alpha=0.5, cmap='Blues')
ax[0].imshow(gt_data[img_data.shape[0] // 3, :, :], alpha=0.2, cmap='Reds')
ax[0].set_title('Slice {}/{}'.format(img_data.shape[0] // 3, img_data.shape[0]))
ax[0].axis('off')
ax[1].imshow(img_data[img_data.shape[0] // 2, :, :], alpha=1, cmap='gray')
ax[1].imshow(output_bin[img_data.shape[0] // 2, :, :], alpha=0.5, cmap='Blues')
ax[1].imshow(gt_data[img_data.shape[0] // 2, :, :], alpha=0.2, cmap='Reds')
ax[1].set_title('Slice {}/{}'.format(img_data.shape[0] // 2, img_data.shape[0]))
ax[1].axis('off')
ax[2].imshow(img_data[img_data.shape[0] // 2 + img_data.shape[0] // 4, :, :], alpha=1, cmap='gray')
ax[2].imshow(output_bin[img_data.shape[0] // 2 + img_data.shape[0] // 4, :, :], alpha=0.5,
cmap='Blues')
ax[2].imshow(gt_data[img_data.shape[0] // 2 + img_data.shape[0] // 4, :, :], alpha=0.2,
cmap='Reds')
ax[2].set_title(
'Slice {}/{}'.format(img_data.shape[0] // 2 + img_data.shape[0] // 4, img_data.shape[0]))
ax[2].axis('off')
fig = plt.gcf()
fig.suptitle(img[0][:-4])
plt.savefig(join(fig_out_dir, img[0][:-4] + '_qual_fig' + '.png'),
format='png', bbox_inches='tight')
plt.close('all')
row = [img[0][:-4]]
if args.compute_dice:
print('Computing Dice')
dice_arr[i] = dc(output_bin, gt_data)
print('\tDice: {}'.format(dice_arr[i]))
row.append(dice_arr[i])
if args.compute_jaccard:
print('Computing Jaccard')
jacc_arr[i] = jc(output_bin, gt_data)
print('\tJaccard: {}'.format(jacc_arr[i]))
row.append(jacc_arr[i])
if args.compute_assd:
print('Computing ASSD')
assd_arr[i] = assd(output_bin, gt_data, voxelspacing=sitk_img.GetSpacing(), connectivity=1)
print('\tASSD: {}'.format(assd_arr[i]))
row.append(assd_arr[i])
writer.writerow(row)
row = ['Average Scores']
if args.compute_dice:
row.append(np.mean(dice_arr))
if args.compute_jaccard:
row.append(np.mean(jacc_arr))
if args.compute_assd:
row.append(np.mean(assd_arr))
writer.writerow(row)
print('Done.')