# Merge the volumes and convert them into 2D data
preds_test, Y_test = merge_3D_data_with_overlap(preds_test,
orig_test_shape,
data_mask=Y_test,
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")
print("Calculate metrics (per image) . . .")
iou_per_image = jaccard_index_numpy(Y_test,
(preds_test > 0.5).astype(np.uint8))
ov_iou_per_image = voc_calculation(Y_test, (preds_test > 0.5).astype(np.uint8),
iou_per_image)
det_per_image = -1
print("~~~~ 16-Ensemble (per image) ~~~~")
Y_test_ensemble = np.zeros(X_test.shape, dtype=np.float32)
for i in tqdm(range(X_test.shape[0])):
predictions_ensemble = ensemble16_3d_predictions(
X_test[i],
pred_func=(lambda img_batch_subdiv: model.predict(img_batch_subdiv)),
n_classes=n_classes)
Y_test_ensemble[i] = predictions_ensemble
# Merge the volumes and convert them into 2D data
Y_test_ensemble = merge_3D_data_with_overlap(Y_test_ensemble,
Y_test = merge_data_with_overlap(Y_test, orig_test_shape, crop_shape[0],
test_ov_crops)
print("Reconstruct preds_test . . .")
preds_test = merge_data_with_overlap(preds_test, orig_test_shape,
crop_shape[0], test_ov_crops)
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")
print("Calculate metrics (per image) . . .")
iou_per_image = jaccard_index_numpy(Y_test,
(preds_test > 0.5).astype(np.uint8))
ov_iou_per_image = voc_calculation(Y_test, (preds_test > 0.5).astype(np.uint8),
iou_per_image)
det_per_image = DET_calculation(Y_test, (preds_test > 0.5).astype(np.uint8),
det_eval_ge_path, det_eval_path, det_bin,
n_dig, args.job_id)
print("~~~~ Blending (per image) ~~~~")
Y_test_blending = np.zeros(X_test.shape, dtype=np.float32)
for i in tqdm(range(X_test.shape[0])):
predictions_blending = predict_img_with_smooth_windowing(
X_test[i],
window_size=crop_shape[0],
subdivisions=2,
nb_classes=1,
pred_func=(lambda img_batch_subdiv: model.predict(img_batch_subdiv)),
preds_test_full = np.zeros([165, 768, 1024, 2], dtype=np.float32)
for i in range(0, len(preds)):
preds_test_full[i] = transform.resize(preds[i], [768, 1024, 2], order=3)
print("Saving predicted images . . .")
save_img(Y=(np.expand_dims(preds_test_full[..., 1], -1) > 0.5).astype(
np.uint8),
mask_dir=result_bin_dir_full,
prefix="test_out_bin_full")
save_img(Y=np.expand_dims(preds_test_full[..., 1], -1),
mask_dir=result_no_bin_dir_full,
prefix="test_out_no_bin_full")
print("Calculate metrics (full image) . . .")
iou_full = jaccard_index_numpy(
Y_test,
(np.expand_dims(preds_test_full[..., 1], -1) > 0.5).astype(np.uint8))
ov_iou_full = voc_calculation(
Y_test,
(np.expand_dims(preds_test_full[..., 1], -1) > 0.5).astype(np.uint8),
iou_full)
print("~~~~ 8-Ensemble (full image) ~~~~")
Y_test_ensemble = np.zeros(X_test.shape, dtype=(np.float32))
for i in tqdm(range(X_test.shape[0])):
pred_ensembled = ensemble8_2d_predictions(
X_test[i],
pred_func=(lambda img_batch_subdiv: model.predict(img_batch_subdiv)),
n_classes=n_classes)
Y_test_ensemble[i] = pred_ensembled
verbose=False)
orig_preds_test = orig_preds_test.astype(np.float32)
orig_Y_test = orig_Y_test.astype(np.float32)
print("Saving predicted images . . .")
os.makedirs(result_bin_dir_per_image, exist_ok=True)
aux = np.expand_dims((orig_preds_test > 0.5).astype(np.uint8), 1)
f = os.path.join(result_bin_dir_per_image, f_name)
imsave(f, aux, imagej=True, metadata={'axes': 'ZCYXS'})
os.makedirs(result_no_bin_dir_per_image, exist_ok=True)
aux = np.expand_dims(orig_preds_test.astype(np.uint8), 1)
f = os.path.join(result_no_bin_dir_per_image, f_name)
imsave(f, aux, imagej=True, metadata={'axes': 'ZCYXS'})
print("Calculate the Jaccard of the image 3D")
j = jaccard_index_numpy(orig_Y_test,
(orig_preds_test > 0.5).astype(np.uint8))
v = voc_calculation(orig_Y_test, (orig_preds_test > 0.5).astype(np.uint8),
j)
print("Image {} ; Foreground IoU: {} ; Overall IoU: {}".format(i, j, v))
iou += j
ov_iou += v
index += crop_3d_shape[0]
del orig_preds_test
print("Calculate metrics (per image) . . .")
iou_per_image = iou / len(orig_test_shape)
ov_iou_per_image = ov_iou / len(orig_test_shape)
print("~~~~ 16-Ensemble (per image) ~~~~")
Y_test_ensemble = np.zeros(Y_test.shape, dtype=np.float32)
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)
print("Calculate metrics (full image) . . .")
iou_full = jaccard_index_numpy(Y_test,
(preds_test_full > 0.5).astype(np.uint8))
overall_IoU_full = voc_calculation(Y_test,
(preds_test_full > 0.5).astype(np.uint8),
iou_full)
print("~~~~ 8-Ensemble (full image) ~~~~")
Y_test_ensemble = np.zeros(X_test.shape, dtype=(np.float32))
for i in tqdm(range(X_test.shape[0])):
pred_ensembled = ensemble8_2d_predictions(
X_test[i],
pred_func=(lambda img_batch_subdiv: model.predict(img_batch_subdiv)),
n_classes=n_classes)
Y_test_ensemble[i] = pred_ensembled
print("Saving smooth predicted images . . .")
