print("Making the predictions on test data . . .")
preds_test = model.predict(X_test, batch_size=batch_size_value, verbose=1)
if n_classes > 1:
preds_test = np.expand_dims(np.argmax(preds_test, -1), -1)
Y_test = np.expand_dims(np.argmax(Y_test, -1), -1)
print("########################################\n"
"# Metrics (per image, merging crops) #\n"
"########################################\n")
# Merge crops
print("Reconstruct X_test/Y_test . . .")
X_test, Y_test = merge_data_with_overlap(X_test,
orig_test_shape,
Y_test,
overlap=overlap)
print("Reconstruct preds_test . . .")
preds_test = merge_data_with_overlap(preds_test,
orig_test_shape,
overlap=overlap)
print("Saving predicted images . . .")
save_img(Y=(preds_test > 0.5).astype(np.uint8),
mask_dir=result_bin_dir_per_image,
prefix="test_out_bin")
save_img(Y=preds_test,
mask_dir=result_no_bin_dir_per_image,
prefix="test_out_no_bin")
if np.max(X_test) > 2:
X_test = X_test.astype('float32')
X_test *= 1. / 255
if n_classes > 1:
Y_test_one_hot = np.zeros(Y_test.shape[:3] + (n_classes, ))
for i in range(Y_test.shape[0]):
Y_test_one_hot[i] = np.asarray(
img_to_onehot_encoding(Y_test[i], n_classes))
Y_test = Y_test_one_hot
del Y_test_one_hot
# Merge crops
print("Reconstruct X_test/Y_test . . .")
X_test, Y_test = merge_data_with_overlap(X_test,
orig_test_shape,
Y_test,
overlap=overlap)
print("########################\n"
"# Metrics (full image) #\n"
"########################\n")
print("Making the predictions on test data . . .")
preds_test_full = model.predict(X_test, batch_size=batch_size_value, verbose=1)
if n_classes > 1:
preds_test_full = np.expand_dims(np.argmax(preds_test_full, -1), -1)
print("Saving predicted images . . .")
save_img(Y=preds_test_full, mask_dir=result_no_bin_dir_full)
