# Copyright 2020 MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import numpy as np from monai.transforms import DivisiblePad STRUCTURES = ( "BrainStem", "Chiasm", "Mandible", "OpticNerve_L", "OpticNerve_R", "Parotid_L", "Parotid_R", "Submandibular_L", "Submandibular_R", ) def get_filenames(path, maskname=STRUCTURES): """ create file names according to the predefined folder structure. Args: path: data folder name maskname: target structure names """ maskfiles = [] for seg in maskname: if os.path.exists(os.path.join(path, "./structures/" + seg + "_crp_v2.npy")): maskfiles.append(os.path.join(path, "./structures/" + seg + "_crp_v2.npy")) else: # the corresponding mask is missing seg, path.split("/")[-1] maskfiles.append(None) return os.path.join(path, "img_crp_v2.npy"), maskfiles def load_data_and_mask(data, mask_data): """ Load data filename and mask_data (list of file names) into a dictionary of {'image': array, "label": list of arrays, "name": str}. """ pad_xform = DivisiblePad(k=32) img = np.load(data) # z y x img = pad_xform(img[None])[0] item = dict(image=img, label=[]) for maskfnm in mask_data: if maskfnm is None: ms = np.zeros(img.shape, np.uint8) else: ms = np.load(maskfnm).astype(np.uint8) assert ms.min() == 0 and ms.max() == 1 mask = pad_xform(ms[None])[0] item["label"].append(mask) assert len(item["label"]) == 9 item["name"] = str(data) return item