print("Calculate metrics (Ensemble + Z-filtering per crop) . . .")
ens_zfil_iou_per_image = jaccard_index_numpy(
Y_test, (ens_zfil_preds_test > 0.5).astype(np.uint8))
ens_zfil_ov_iou_per_image = voc_calculation(
Y_test, (ens_zfil_preds_test > 0.5).astype(np.uint8),
ens_zfil_iou_per_image)
del Y_test_ensemble, ens_zfil_preds_test
print("############################################################\n"
"# Metrics (per image, merging crops with 50% of overlap) #\n"
"############################################################\n")
print("Merge X_test to crop it again with 50% overlap and make the prediction")
X_test = merge_3D_data_with_overlap(X_test, orig_test_shape, overlap=overlap)
X_test = crop_3D_data_with_overlap(X_test,
test_3d_desired_shape,
overlap=(0.5, 0.5, 0.5))
Y_test_50ov = model.predict(X_test, batch_size=batch_size_value, verbose=1)
if n_classes > 1:
Y_test_50ov = np.expand_dims(np.argmax(Y_test_50ov, -1), -1)
Y_test_50ov = merge_3D_data_with_overlap(Y_test_50ov,
orig_test_shape,
overlap=(0.5, 0.5, 0.5))
print("Saving 50% overlap predicted images . . .")
save_img(Y=(Y_test_50ov > 0.5).astype(np.float32),
mask_dir=result_bin_dir_50ov,
prefix="test_out_bin_50ov")
ov_iou = 0
index = 0
for i in tqdm(range(len(orig_test_shape))):
original_3d_shape = orig_test_shape[i]
crop_3d_shape = crop_test_shapes[i]
f_name = filenames[1][i]
orig_X_test, orig_Y_test = merge_3D_data_with_overlap(
X_test[index:index + crop_3d_shape[0]],
original_3d_shape,
data_mask=Y_test[index:index + crop_3d_shape[0]],
overlap=overlap,
verbose=False)
orig_X_test = crop_3D_data_with_overlap(orig_X_test,
test_3d_desired_shape,
overlap=(0.5, 0.5, 0.5),
verbose=False)
Y_test_50ov = model.predict(orig_X_test,
batch_size=batch_size_value,
verbose=1)
if n_classes > 1:
Y_test_50ov = np.expand_dims(np.argmax(Y_test_50ov, -1), -1)
Y_test_50ov = merge_3D_data_with_overlap(Y_test_50ov,
original_3d_shape,
overlap=(0.5, 0.5, 0.5),
verbose=False)
Y_test_50ov = Y_test_50ov.astype(np.float32)
