def preproc_and_norm(data, preprocess_method, norm_params):
if preprocess_method is not None:
print('Applying preprocess by {}...'.format(preprocess_method))
if preprocess_method == 'window_1_99':
data = window_intensities_data(data)
else:
raise Exception('Unknown preprocess: {}'.format(preprocess_method))
if norm_params is not None and any(norm_params.values()):
data = normalize_data(data,
mean=norm_params['mean'],
std=norm_params['std'])
return data
def preproc_and_norm(data, preprocess_method=None, norm_params=None, scale=None, preproc=None):
if preprocess_method is not None:
print('Applying preprocess by {}...'.format(preprocess_method))
if preprocess_method == 'window_1_99':
data = window_intensities_data(data)
else:
raise Exception('Unknown preprocess: {}'.format(preprocess_method))
if scale is not None:
data = ndimage.zoom(data, scale)
if preproc is not None:
preproc_func = getattr(fetal_net.preprocess, preproc)
data = preproc_func(data)
# data = normalize_data(data, mean=data.mean(), std=data.std())
if norm_params is not None and any(norm_params.values()):
data = normalize_data(data, mean=norm_params['mean'], std=norm_params['std'])
return data
def main(input_mat_path, output_mat_path, config, overlap_factor, model_path, preprocess_method=None, norm_params=None):
print(model_path)
model = load_old_model(get_last_model_path(model_path))
print('Loading mat from {}...'.format(input_mat_path))
mat = loadmat(input_mat_path)
print('Predicting mask...')
data = mat['volume'].astype(np.float)
if preprocess_method is not None:
print('Applying preprocess by {}...'.format(preprocess_method))
if preprocess_method == 'window_1_99':
data = window_intensities_data(data)
else:
raise Exception('Unknown preprocess: {}'.format(preprocess_method))
if norm_params is not None and any(norm_params.values()):
data = normalize_data(data, mean=norm_params['mean'], std=norm_params['std'])
prediction = \
patch_wise_prediction(model=model,
data=np.expand_dims(data, 0),
overlap_factor=overlap_factor,
patch_shape=config["patch_shape"] + [config["patch_depth"]])
print('Post-processing mask...')
if prediction.shape[-1] > 1:
prediction = prediction[..., 1]
prediction = prediction.squeeze()
mat['masks'][0, 9] = \
process_pred(prediction, gaussian_std=0, threshold=0.2) # .astype(np.uint8)
mat['masks'][0, 8] = \
process_pred(prediction, gaussian_std=1, threshold=0.5) # .astype(np.uint8)
mat['masks'][0, 7] = \
process_pred(prediction, gaussian_std=0.5, threshold=0.5) # .astype(np.uint8)
print('Saving mat to {}'.format(output_mat_path))
savemat(output_mat_path, mat)
print('Finished.')
def main(pred_dir,
config,
split='test',
overlap_factor=1,
preprocess_method=None):
padding = [16, 16, 8]
prediction2_dir = os.path.abspath(
os.path.join(config['base_dir'], 'predictions2', split))
model = load_old_model(get_last_model_path(config["model_file"]))
with open(os.path.join(opts.config_dir, 'norm_params.json'), 'r') as f:
norm_params = json.load(f)
for sample_folder in glob(os.path.join(pred_dir, split, '*')):
mask_path = os.path.join(sample_folder, 'prediction.nii.gz')
truth_path = os.path.join(sample_folder, 'truth.nii.gz')
subject_id = Path(sample_folder).name
dest_folder = os.path.join(prediction2_dir, subject_id)
Path(dest_folder).mkdir(parents=True, exist_ok=True)
truth = nib.load(truth_path)
nib.save(truth, os.path.join(dest_folder, Path(truth_path).name))
mask = nib.load(mask_path)
mask = process_pred(mask.get_data(), gaussian_std=0.5, threshold=0.5)
bbox_start, bbox_end = find_bounding_box(mask)
check_bounding_box(mask, bbox_start, bbox_end)
if padding is not None:
bbox_start = np.maximum(bbox_start - padding, 0)
bbox_end = np.minimum(bbox_end + padding, mask.shape)
print("BBox: {}-{}".format(bbox_start, bbox_end))
volume = nib.load(
os.path.join(original_data_folder, subject_id, 'volume.nii'))
orig_volume_shape = np.array(volume.get_data().shape)
volume = cut_bounding_box(volume, bbox_start,
bbox_end).get_data().astype(np.float)
if preprocess_method is not None:
print('Applying preprocess by {}...'.format(preprocess_method))
if preprocess_method == 'window_1_99':
volume = window_intensities_data(volume)
else:
raise Exception(
'Unknown preprocess: {}'.format(preprocess_method))
if norm_params is not None and any(norm_params.values()):
volume = normalize_data(volume,
mean=norm_params['mean'],
std=norm_params['std'])
prediction = patch_wise_prediction(model=model,
data=np.expand_dims(volume, 0),
patch_shape=config["patch_shape"] +
[config["patch_depth"]],
overlap_factor=overlap_factor)
prediction = prediction.squeeze()
padding2 = list(zip(bbox_start, orig_volume_shape - bbox_end))
print(padding2)
prediction = np.pad(prediction,
padding2,
mode='constant',
constant_values=0)
assert all(
[s1 == s2 for s1, s2 in zip(prediction.shape, orig_volume_shape)])
prediction = get_image(prediction)
nib.save(prediction, os.path.join(dest_folder, Path(mask_path).name))