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:
logging.info('\nWeights_path=%s' % (weights_path))
eval_model.load_weights(weights_path)
except:
logging.warning(
'\nUnable 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
logging.info('\nTesting... 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((test_list)):
sitk_img = sitk.ReadImage(join(args.data_root_dir, 'imgs', img[0]))
img_data = sitk.GetArrayFromImage(
sitk_img) # 3d:(slices, 512, 512), 2d:(512, 512, channels=4)
# Change RGB to single slice of grayscale image for MS COCO 17 dataset.
if args.dataset == 'mscoco17':
img_data = convert_img_data(img_data, 3)
num_slices = 1
logging.info('\ntest.test: eval_model.predict_generator')
_, _, generate_test_batches = get_generator(args.dataset)
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=4,
use_multiprocessing=args.use_multiprocessing,
verbose=1)
logging.info('\ntest.test: output_array=%s' % (output_array))
if args.net.find('caps') != -1:
# A list with two images [mask, recon], get mask image.#3d:
# output_array=[mask(Slices, x=512, y=512, 1), recon(slices, x=512, y=512, 1)]
output = output_array[0][:, :, :,
0] # output = (slices, 512, 512)
#recon = output_array[1][:,:,:,0]
else:
output = output_array[:, :, :, 0]
#output_image = RTTI size:[512, 512, 119]
output_img = sitk.GetImageFromArray(output)
print('Segmenting Output')
# output_bin (119, 512, 512)
output_bin = threshold_mask(output, args.thresh_level)
# output_mask = RIIT (512, 512, 119)
output_mask = sitk.GetImageFromArray(output_bin)
if args.dataset == 'luna16':
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:]))
else: # MS COCO 17
plt.imshow(output[0, :, :], cmap='gray')
plt.imsave(
join(raw_out_dir,
img[0][:-4] + '_raw_output' + img[0][-4:]),
output[0, :, :])
plt.imshow(output_bin[0, :, :], cmap='gray')
plt.imsave(
join(fin_out_dir,
img[0][:-4] + '_final_output' + img[0][-4:]),
output_bin[0, :, :])
# Load gt mask
# sitk_mask: 3d RTTI(512, 512, slices)
sitk_mask = sitk.ReadImage(
join(args.data_root_dir, 'masks', img[0]))
# gt_data: 3d=(slices, 512, 512), Ground Truth data
gt_data = sitk.GetArrayFromImage(sitk_mask)
# Change RGB to single slice of grayscale image for MS COCO 17 dataset.
if args.dataset == 'mscoco17':
gt_data = convert_mask_data(gt_data)
# Reshape numpy from 2 to 3 dimensions (slices, heigh, width)
gt_data = gt_data.reshape(
[1, gt_data.shape[0], gt_data.shape[1]])
# Plot Qual Figure
print('Creating Qualitative Figure for Quick Reference')
f, ax = plt.subplots(1, 3, figsize=(15, 5))
if args.dataset == 'mscoco17':
pass
else: # 3D data
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')
# Compute metrics
row = [img[0][:-4]]
if args.compute_dice:
logging.info('\nComputing Dice')
dice_arr[i] = dc(output_bin, gt_data)
logging.info('\tDice: {}'.format(dice_arr[i]))
row.append(dice_arr[i])
if args.compute_jaccard:
logging.info('\nComputing Jaccard')
jacc_arr[i] = jc(output_bin, gt_data)
logging.info('\tJaccard: {}'.format(jacc_arr[i]))
row.append(jacc_arr[i])
if args.compute_assd:
logging.info('\nComputing ASSD')
assd_arr[i] = assd(output_bin,
gt_data,
voxelspacing=sitk_img.GetSpacing(),
connectivity=1)
logging.info('\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.')
prediction, dim=1) #返回每一行中最大值的那个元素，且返回其索引（返回最大元素在这一行的列索引）
# print(prediction.shape) #torch.Size([1, 256, 256])
# print(prediction)
prediction = prediction.squeeze(0).cpu().detach().numpy().astype(
np.uint8)
# print(prediction.shape) (256, 256)
predictions_in_order.append(prediction)
# overlay_in_orders = draw_many_slices(slices_in_order, predictions_in_order, labels_in_order)
# imwrite(predictions_in_order, overlay_in_orders, prediction_path+str(i) + '/')
# metrics
v_prediction = np.stack(predictions_in_order).astype(np.uint)
v_label = np.stack(labels_in_order).astype(np.uint)
dice = m.dc(v_prediction, v_label)
dice_list.append(dice)
print('DICE:', dice)
iou = m.jc(v_prediction, v_label)
iou_list.append(iou)
print('IOU:', iou)
voe = 1 - iou
voe_list.append(voe)
print('VOE:', voe)
rvd = m.ravd(v_prediction, v_label)
rvd_list.append(rvd)
print('RVD:', rvd)