def measure(self, generated, vessels, masks, num_data, iter_time, phase,
total_time):
# masking
vessels_in_mask, generated_in_mask = utils.pixel_values_in_mask(
vessels, generated, masks)
# averaging processing time
avg_pt = (total_time / num_data) * 1000 # average processing tiem
# evaluate Area Under the Curve of ROC and Precision-Recall
auc_roc = utils.AUC_ROC(vessels_in_mask, generated_in_mask)
auc_pr = utils.AUC_PR(vessels_in_mask, generated_in_mask)
# binarize to calculate Dice Coeffient
binarys_in_mask = utils.threshold_by_otsu(generated, masks)
dice_coeff = utils.dice_coefficient_in_train(vessels_in_mask,
binarys_in_mask)
acc, sensitivity, specificity = utils.misc_measures(
vessels_in_mask, binarys_in_mask)
score = auc_pr + auc_roc + dice_coeff + acc + sensitivity + specificity
# # auc_sum for saving best model in training
# auc_sum = auc_roc + auc_pr
# if self.flags.stage == 2:
# #auc_sum = auc_roc + auc_pr
# auc_sum = auc_roc + auc_pr
# else:
# auc_sum = auc_roc + auc_pr
auc_sum = dice_coeff + acc + auc_pr
# print information
ord_output = collections.OrderedDict([('auc_pr', auc_pr),
('auc_roc', auc_roc),
('dice_coeff', dice_coeff),
('acc', acc),
('sensitivity', sensitivity),
('specificity', specificity),
('score', score),
('auc_sum', auc_sum),
('best_auc_sum',
self.best_auc_sum),
('avg_pt', avg_pt)])
utils.print_metrics(iter_time, ord_output)
# write in tensorboard when in train mode only
if phase == 'train':
self.model.measure_assign(auc_pr, auc_roc, dice_coeff, acc,
sensitivity, specificity, score,
iter_time)
elif phase == 'test':
# write in npy format for evaluation
utils.save_obj(vessels_in_mask, generated_in_mask,
os.path.join(self.auc_out_dir, "auc_roc.npy"),
os.path.join(self.auc_out_dir, "auc_pr.npy"))
return auc_sum
def measure(self, generated, vessels, masks, num_data, iter_time, phase,
total_time):
vessels_in_mask, generated_in_mask = utils.pixel_values_in_mask(
vessels, generated, masks)
avg_pt = (total_time / num_data) * 1000 # average processing tiem
# evaluation
auc_roc = utils.AUC_ROC(vessels_in_mask, generated_in_mask)
auc_pr = utils.AUC_PR(vessels_in_mask, generated_in_mask)
binarys_in_mask = utils.threshold_by_otsu(generated, masks)
dice_coeff = utils.dice_coefficient_in_train(vessels_in_mask,
binarys_in_mask)
acc, sensitivity, specificity = utils.misc_measures(
vessels_in_mask, binarys_in_mask)
score = auc_pr + auc_roc + dice_coeff + acc + sensitivity + specificity
# print information
ord_output = collections.OrderedDict([('auc_pr', auc_pr),
('auc_roc', auc_roc),
('dice_coeff', dice_coeff),
('acc', acc),
('sensitivity', sensitivity),
('specificity', specificity),
('score', score),
('best_dice_coeff',
self.best_dice_coeff),
('avg_pt', avg_pt)])
utils.print_metrics(iter_time, ord_output)
# write in tensorboard
if phase == 'train':
self.model.measure_assign(auc_pr, auc_roc, dice_coeff, acc,
sensitivity, specificity, score,
iter_time)
if phase == 'test':
# write in npy format for evaluation
utils.save_obj(vessels_in_mask, generated_in_mask,
os.path.join(self.auc_out_dir, "auc_roc.npy"),
os.path.join(self.auc_out_dir, "auc_pr.npy"))
return dice_coeff
# visualize results
if "V-GAN" in result or "DRIU" in result or "1st_manual" in result:
test_dir = testdata.format(os.path.basename(dataset))
ori_imgs = utils.load_images_under_dir(test_dir)
vessels_dir = vessels_out.format(os.path.basename(dataset),
os.path.basename(result))
filenames = utils.all_files_under(result)
if not os.path.isdir(vessels_dir):
os.makedirs(vessels_dir)
for index in range(gt_vessels.shape[0]):
# thresholded_vessel=utils.threshold_by_f1(np.expand_dims(gt_vessels[index,...], axis=0),
# np.expand_dims(pred_vessels[index,...], axis=0),
# np.expand_dims(masks[index,...], axis=0),
# flatten=False)*255
thresholded_vessel = utils.threshold_by_otsu(
np.expand_dims(pred_vessels[index, ...], axis=0),
np.expand_dims(masks[index, ...], axis=0),
flatten=False) * 255
ori_imgs[index, ...][np.squeeze(thresholded_vessel, axis=0)
== 0] = (0, 0, 0)
# ori_imgs[index,...]*=np.tile(np.expand_dims(pred_vessels[index,...], axis=3), (1,1,3))
Image.fromarray(ori_imgs[index, ...].astype(
np.uint8)).save(
os.path.join(vessels_dir,
os.path.basename(filenames[index])))
# compare with the ground truth
comp_dir = comparison_out.format(os.path.basename(dataset),
os.path.basename(result))
if not os.path.isdir(comp_dir):
os.makedirs(comp_dir)
dice_list = []
# G
gan_x_test, gan_y_test=utils.input2gan(val_imgs, val_vessels, d_out_shape)
loss,acc=gan.evaluate(gan_x_test,gan_y_test, batch_size=batch_size, verbose=0)
utils.print_metrics(n_round+1, acc=acc, loss=loss, type='GAN')
# save the weights
g.save_weights(os.path.join(model_out_dir,"g_{}_{}_{}.h5".format(n_round,FLAGS.discriminator,FLAGS.ratio_gan2seg)))
# update step sizes, learning rates
scheduler.update_steps(n_round)
K.set_value(d.optimizer.lr, scheduler.get_lr())
K.set_value(gan.optimizer.lr, scheduler.get_lr())
# evaluate on test images
if n_round in rounds_for_evaluation:
generated=g.predict(test_imgs,batch_size=batch_size)
generated=np.squeeze(generated, axis=3)
vessels_in_mask, generated_in_mask = utils.pixel_values_in_mask(test_vessels, generated , test_masks)
auc_roc=utils.AUC_ROC(vessels_in_mask,generated_in_mask,os.path.join(auc_out_dir,"auc_roc_{}.npy".format(n_round)))
auc_pr=utils.AUC_PR(vessels_in_mask, generated_in_mask,os.path.join(auc_out_dir,"auc_pr_{}.npy".format(n_round)))
binarys_in_mask=utils.threshold_by_otsu(generated,test_masks)
dice_coeff=utils.dice_coefficient_in_train(vessels_in_mask, binarys_in_mask)
acc, sensitivity, specificity=utils.misc_measures(vessels_in_mask, binarys_in_mask)
utils.print_metrics(n_round+1, auc_pr=auc_pr, auc_roc=auc_roc, dice_coeff=dice_coeff,
acc=acc, senstivity=sensitivity, specificity=specificity, type='TESTING')
# print test images
segmented_vessel=utils.remain_in_mask(generated, test_masks)
for index in range(segmented_vessel.shape[0]):
Image.fromarray((segmented_vessel[index,:,:]*255).astype(np.uint8)).save(os.path.join(img_out_dir,str(n_round)+"_{:02}_segmented.png".format(index+1)))