import argparse
import json
import os
from pathlib import Path
import nibabel as nib
import fetal_net.preprocess
from fetal.preprocess import window_intensities_data
from fetal.utils import get_last_model_path
from fetal_net.normalize import normalize_data
from fetal_net.postprocess import postprocess_prediction as process_pred
from fetal_net.prediction import patch_wise_prediction, predict_augment, predict_flips
from fetal_net.preprocess import *
from fetal_net.training import load_old_model
from fetal_net.utils.cut_relevant_areas import find_bounding_box, check_bounding_box
from fetal_net.utils.utils import read_img, get_image
def save_nifti(data, path):
nifti = get_image(data)
nib.save(nifti, path)
def secondary_prediction(mask, vol, config2, model2_path=None,
preprocess_method2=None, norm_params2=None,
overlap_factor=0.9, augment2=None, num_augment=32, return_all_preds=False):
model2 = load_old_model(get_last_model_path(model2_path), config=config2)
pred = mask
bbox_start, bbox_end = find_bounding_box(pred)
check_bounding_box(pred, bbox_start, bbox_end)
padding = [16, 16, 8]
if padding is not None:
bbox_start = np.maximum(bbox_start - padding, 0)
bbox_end = np.minimum(bbox_end + padding, mask.shape)
data = vol.astype(np.float)[
bbox_start[0]:bbox_end[0],
bbox_start[1]:bbox_end[1],
bbox_start[2]:bbox_end[2]
]
data = preproc_and_norm(data, preprocess_method2, norm_params2)
prediction = get_prediction(data, model2, augment=augment2, num_augments=num_augment, return_all_preds=return_all_preds,
overlap_factor=overlap_factor, config=config2)
padding2 = list(zip(bbox_start, np.array(vol.shape) - bbox_end))
if return_all_preds:
padding2 = [(0, 0)] + padding2
print(padding2)
print(prediction.shape)
prediction = np.pad(prediction, padding2, mode='constant', constant_values=0)
return prediction
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 get_prediction(data, model, augment, num_augments, return_all_preds, overlap_factor, config):
if augment is not None:
patch_shape = config["patch_shape"] + [config["patch_depth"]]
if augment == 'all':
prediction = predict_augment(data, model=model, overlap_factor=overlap_factor, num_augments=num_augments, patch_shape=patch_shape)
elif augment == 'flip':
prediction = predict_flips(data, model=model, overlap_factor=overlap_factor, patch_shape=patch_shape, config=config)
else:
raise ("Unknown augmentation {}".format(augment))
if not return_all_preds:
prediction = np.median(prediction, axis=0)
else:
prediction = \
patch_wise_prediction(model=model,
data=np.expand_dims(data, 0),
overlap_factor=overlap_factor,
patch_shape=config["patch_shape"] + [config["patch_depth"]])
prediction = prediction.squeeze()
return prediction
def main(input_path, output_path, overlap_factor,
config, model_path, preprocess_method=None, norm_params=None, augment=None, num_augment=0,
config2=None, model2_path=None, preprocess_method2=None, norm_params2=None, augment2=None, num_augment2=0,
z_scale=None, xy_scale=None, return_all_preds=False):
print(model_path)
model = load_old_model(get_last_model_path(model_path), config=config)
print('Loading nifti from {}...'.format(input_path))
nifti = read_img(input_path)
print('Predicting mask...')
data = nifti.get_fdata().astype(np.float).squeeze()
print('original_shape: ' + str(data.shape))
scan_name = Path(input_path).name.split('.')[0]
if (z_scale is None):
z_scale = 1.0
if (xy_scale is None):
xy_scale = 1.0
if z_scale != 1.0 or xy_scale != 1.0:
data = ndimage.zoom(data, [xy_scale, xy_scale, z_scale])
data = preproc_and_norm(data, preprocess_method, norm_params,
scale=config.get('scale_data', None),
preproc=config.get('preproc', None))
save_nifti(data, os.path.join(output_path, scan_name + '_data.nii.gz'))
data = np.pad(data, 3, 'constant', constant_values=data.min())
print('Shape: ' + str(data.shape))
prediction = get_prediction(data=data, model=model, augment=augment,
num_augments=num_augment, return_all_preds=return_all_preds,
overlap_factor=overlap_factor, config=config)
# unpad
prediction = prediction[3:-3, 3:-3, 3:-3]
# revert to original size
if config.get('scale_data', None) is not None:
prediction = ndimage.zoom(prediction.squeeze(), np.divide([1, 1, 1], config.get('scale_data', None)), order=0)[..., np.newaxis]
save_nifti(prediction, os.path.join(output_path, scan_name + '_pred.nii.gz'))
if z_scale != 1.0 or xy_scale != 1.0:
prediction = ndimage.zoom(prediction.squeeze(), [1.0 / xy_scale, 1.0 / xy_scale, 1.0 / z_scale], order=1)[..., np.newaxis]
# if prediction.shape[-1] > 1:
# prediction = prediction[..., 1]
if config2 is not None:
prediction = prediction.squeeze()
mask = process_pred(prediction, gaussian_std=0.5, threshold=0.5) # .astype(np.uint8)
nifti = read_img(input_path)
prediction = secondary_prediction(mask, vol=nifti.get_fdata().astype(np.float),
config2=config2, model2_path=model2_path,
preprocess_method2=preprocess_method2, norm_params2=norm_params2,
overlap_factor=overlap_factor, augment2=augment2, num_augment=num_augment2,
return_all_preds=return_all_preds)
save_nifti(prediction, os.path.join(output_path, scan_name + 'pred_roi.nii.gz'))
print('Saving to {}'.format(output_path))
print('Finished.')
def get_params(config_dir):
with open(os.path.join(config_dir, 'config.json'), 'r') as f:
__config = json.load(f)
with open(os.path.join(config_dir, 'norm_params.json'), 'r') as f:
__norm_params = json.load(f)
__model_path = os.path.join(config_dir, os.path.basename(__config['model_file']))
return __config, __norm_params, __model_path
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--input_nii", help="specifies mat file dir path",
type=str, required=True)
parser.add_argument("--output_folder", help="specifies mat file dir path",
type=str, required=True)
parser.add_argument("--overlap_factor", help="specifies overlap between prediction patches",
type=float, default=0.9)
parser.add_argument("--z_scale", help="specifies overlap between prediction patches",
type=float, default=1)
parser.add_argument("--xy_scale", help="specifies overlap between prediction patches",
type=float, default=1)
parser.add_argument("--return_all_preds", help="output std for prediction",
type=int, default=0)
# Params for primary prediction
parser.add_argument("--config_dir", help="specifies config dir path",
type=str, required=True)
parser.add_argument("--preprocess", help="what preprocess to do",
type=str, required=False, default=None)
parser.add_argument("--augment", help="what augment to do",
type=str, required=False, default=None) # one of 'flip, all'
parser.add_argument("--num_augment", help="what augment to do",
type=int, required=False, default=0) # one of 'flip, all'
# Params for secondary prediction
parser.add_argument("--config2_dir", help="specifies config dir path",
type=str, required=False, default=None)
parser.add_argument("--preprocess2", help="what preprocess to do",
type=str, required=False, default=None)
parser.add_argument("--augment2", help="what augment to do",
type=str, required=False, default=None) # one of 'flip, all'
parser.add_argument("--num_augment2", help="what augment to do",
type=int, required=False, default=0) # one of 'flip, all'
opts = parser.parse_args()
Path(opts.output_folder).mkdir(exist_ok=True)
# 1
_config, _norm_params, _model_path = get_params(opts.config_dir)
# 2
if opts.config2_dir is not None:
_config2, _norm_params2, _model2_path = get_params(opts.config2_dir)
else:
_config2, _norm_params2, _model2_path = None, None, None
main(opts.input_nii, opts.output_folder, overlap_factor=opts.overlap_factor,
config=_config, model_path=_model_path, preprocess_method=opts.preprocess, norm_params=_norm_params, augment=opts.augment,
num_augment=opts.num_augment,
config2=_config2, model2_path=_model2_path, preprocess_method2=opts.preprocess2, norm_params2=_norm_params2, augment2=opts.augment2,
num_augment2=opts.num_augment2,
z_scale=opts.z_scale, xy_scale=opts.xy_scale, return_all_preds=opts.return_all_preds)
