import argparse
import json
import os
import numpy as np
from brats.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
from fetal_net.training import load_old_model
from brats.preprocess import window_intensities_data
from scipy.io import loadmat, savemat
from fetal_net.utils.cut_relevant_areas import find_bounding_box, check_bounding_box
def secondary_prediction(mask, vol, config2, model2_path=None,
preprocess_method2=None, norm_params2=None,
overlap_factor=0.9):
model2 = load_old_model(get_last_model_path(model2_path))
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 = \
patch_wise_prediction(model=model2,
data=np.expand_dims(data, 0),
overlap_factor=overlap_factor,
patch_shape=config2["patch_shape"] + [config2["patch_depth"]])
prediction = prediction.squeeze()
padding2 = list(zip(bbox_start, np.array(vol.shape) - bbox_end))
print(padding2)
print(prediction.shape)
prediction = np.pad(prediction, padding2, mode='constant', constant_values=0)
return prediction
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 main(input_mat_path, output_mat_path, overlap_factor,
config, model_path, preprocess_method=None, norm_params=None,
config2=None, model2_path=None, preprocess_method2=None, norm_params2=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)
data = preproc_and_norm(data, preprocess_method, norm_params)
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()
print("Storing prediction in [7-9], 7 should be the best...")
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)
if config2 is not None:
print('Making secondary prediction... [6]')
prediction = secondary_prediction(mat['masks'][0, 7], vol=mat['volume'].astype(np.float),
config2=config2, model2_path=model2_path,
preprocess_method2=preprocess_method2, norm_params2=norm_params2,
overlap_factor=0.9)
mat['masks'][0, 6] = \
process_pred(prediction, gaussian_std=0, threshold=0.2) # .astype(np.uint8)
mat['masks'][0, 5] = \
process_pred(prediction, gaussian_std=1, threshold=0.5) # .astype(np.uint8)
mat['masks'][0, 4] = \
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 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_mat", help="specifies mat file dir path",
type=str, required=True)
parser.add_argument("--output_mat", 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)
# 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)
# 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)
opts = parser.parse_args()
# 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_mat, opts.output_mat, overlap_factor=opts.overlap_factor,
config=_config, model_path=_model_path, preprocess_method=opts.preprocess, norm_params=_norm_params,
config2=_config2, model2_path=_model2_path, preprocess_method2=opts.preprocess2, norm_params2=_norm_params2)
