--- a
+++ b/prod/predict_mat2.py
@@ -0,0 +1,155 @@
+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)