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.')
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 FileExistsError:
pass
try:
makedirs(fin_out_dir)
except FileExistsError:
pass
try:
makedirs(fig_out_dir)
except FileExistsError:
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 FileNotFoundError:
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'),
'w') 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)):
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=1,
max_queue_size=0,
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]
print('output')
print(output.shape)
# output_img = sitk.GetImageFromArray(output)
print('Segmenting Output')
output_bin = threshold_mask(output, args.thresh_level)
output_bin = output_bin[0, :, :]
# (raw_output, threshold)
# output_mask = sitk.GetImageFromArray(output_bin)
path_to_np = join(args.data_root_dir, 'np_files',
img[0][:-3] + 'npz')
sitk_mask = np.load(path_to_np)
print('mask')
gt_data = sitk_mask['mask']
gt_data = gt_data[:, :, 0]
intn_data = sitk_mask['img']
intn_data = intn_data[:, :, 0]
print(gt_data.shape)
print('Saving Output')
indiv_fig_dir = join(fig_out_dir, args.save_prefix)
try:
makedirs(indiv_fig_dir)
except FileExistsError:
pass
# Generarte image
f, ax = plt.subplots(1, 3, figsize=(15, 5))
ax[0].imshow(intn_data, alpha=1, cmap='gray')
ax[0].imshow(output_bin, alpha=0.2, cmap='Reds')
ax[0].set_title('Predict Mask')
ax[1].imshow(intn_data, alpha=1, cmap='gray')
ax[1].imshow(gt_data, alpha=0.2, cmap='Blues')
ax[1].set_title('True Mask')
ax[2].imshow(output_bin, alpha=0.3, cmap='Reds')
ax[2].imshow(gt_data, alpha=0.3, cmap='Blues')
ax[2].set_title('Comparison')
fig = plt.gcf()
fig.suptitle(img[0][:-4])
plt.savefig(join(indiv_fig_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])
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.')