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nnUNet_calvingfront_detection / nnunet /evaluation /add_mean_dice_to_json.py
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# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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 json
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import subfiles
from collections import OrderedDict
def foreground_mean(filename):
with open(filename, 'r') as f:
res = json.load(f)
class_ids = np.array([int(i) for i in res['results']['mean'].keys() if (i != 'mean')])
class_ids = class_ids[class_ids != 0]
class_ids = class_ids[class_ids != -1]
class_ids = class_ids[class_ids != 99]
tmp = res['results']['mean'].get('99')
if tmp is not None:
_ = res['results']['mean'].pop('99')
metrics = res['results']['mean']['1'].keys()
res['results']['mean']["mean"] = OrderedDict()
for m in metrics:
foreground_values = [res['results']['mean'][str(i)][m] for i in class_ids]
res['results']['mean']["mean"][m] = np.nanmean(foreground_values)
with open(filename, 'w') as f:
json.dump(res, f, indent=4, sort_keys=True)
def run_in_folder(folder):
json_files = subfiles(folder, True, None, ".json", True)
json_files = [i for i in json_files if not i.split("/")[-1].startswith(".") and not i.endswith("_globalMean.json")] # stupid mac
for j in json_files:
foreground_mean(j)
if __name__ == "__main__":
folder = "/media/fabian/Results/nnUNetOutput_final/summary_jsons"
run_in_folder(folder)