def test_get_ncr_net(volume):
just_ncr_net = brats_labels.get_ncr_net(volume.copy())
plot_3_view("ncr_net", just_ncr_net[:, :, :], 100, save=True)
plot_3_view("whole", volume[:, :, :], 100, save=True)
assert [0, 1] == list(np.unique(just_ncr_net))
def tta_uncertainty_loop(model,
images,
device,
brain_mask,
iterations=2,
monte_carlo=False):
prediction_labels_maps, prediction_score_vectors = [], []
data_transformations = _get_transforms()
range_transforms = range(0, len(data_transformations))
for i in tqdm(range(iterations), desc="Predicting.."):
random_transform_idx = np.random.choice(range_transforms)
transform = data_transformations[random_transform_idx]
subject, _, _ = transform((images, None, brain_mask))
prediction_four_channels, vector_prediction_scores = predict.predict(
model, subject.astype(float), device, monte_carlo=monte_carlo)
pred_map = predict.get_prediction_map(prediction_four_channels)
plot_3_view(f"pred_map_{i}_{random_transform_idx}",
pred_map[:, :, :],
40,
save=True)
plot_3_view(f"subject_{i}_{random_transform_idx}",
subject[0, :, :, :],
40,
save=True)
prediction_labels_maps.append(pred_map)
prediction_score_vectors.append(vector_prediction_scores)
return prediction_labels_maps, prediction_score_vectors
def test_visual_test():
volume, volume_patch, seg_patch = patching_strategy(patching, (64, 64, 64))
# plot_3_view("random_tumor_flair", volume[0, :, :, :], 100, save=save)
plot_3_view("random_tumor_patch_flair",
volume_patch[0, :, :, :],
32,
save=save)
plot_3_view("random_tumor_path_seg", seg_patch[:, :, :], 32, save=save)
def test_random_rotation_90_real_patient(patient):
volume, seg, brain_mask = patient
rot = RandomRotation90(p=1)
rot_volume, rot_seg, _ = rot.__call__(img_and_mask=(volume, seg,
brain_mask))
plot_3_view("rotated_volume", rot_volume[0, :, :, :], 100, save=True)
plot_3_view("rotated_seg", rot_seg[:, :, :], 100, save=True)
plot_3_view("volume", volume[0, :, :, :], 100, save=True)
plot_3_view("seg", seg[:, :, :], 100, save=True)
def plot(volume: np.ndarray, patch: np.ndarray, volume_slice: int = 100):
plot_3_view("flair", volume[0, :, :, :], volume_slice, save=True)
plot_3_view("patch_flair", patch[0, :, :, :], volume_slice, save=True)
def test_visual_test():
volume, volume_patch, seg_patch = patching_strategy(patching, (80, 80, 80))
plot_3_view("equal_flair", volume[0, :, :, :], 100, save=save)
plot_3_view("equal_patch_flair", volume_patch[0, :, :, :], 40, save=save)
plot_3_view("equal_path_seg", seg_patch[:, :, :], 40, save=save)
def test_visual_test():
volume, volume_patch, seg_patch = patching_strategy(
patching, (160, 160, 128))
plot_3_view("center_flair", volume[0, :, :, :], 100, save=save)
plot_3_view("center_patch_flair", volume_patch[0, :, :, :], 100, save=save)
plot_3_view("center_path_seg", seg_patch[:, :, :], 100, save=save)