odir_runner.py

# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
#
# 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.
# ==============================================================================
from absl import app
import logging
import logging.config
import time
from odir_training_data_parser import DataParser


def main(argv):
    # Path to the annotation data
    file = r'dataset\ODIR-5K_Training_Annotations(Updated)_V2.xlsx'
    # Produce all the patient information and store it in an dictionary
    start = time.time()
    logger.debug('Produce all the patient information and store it in an dictionary')
    parser = DataParser(file, 'Sheet1')
    logger.debug('File ' + file + ' parsed successfully!')
    logger.debug('Excel to Patients DTO')
    patients = parser.generate_patients()
    logger.debug('Excel to Patients DTO Finished')

    # Additional quality check, can be commented out
    logger.debug('Ensuring Training Data Quality')
    parser.check_data_quality()
    logger.debug('Ensuring Training Data Quality Finished')

    # Additional CSV Generation
    logger.debug('Ensuring CSV generation')
    parser.generate_ground_truth_csv()
    logger.debug('Ensuring CSV generation Finished')

    # Additional CSV for single class labelling
    logger.debug('Ensuring CSV generation')
    parser.generate_ground_truth_class_csv()
    logger.debug('Ensuring CSV generation Finished')
    # Transform patients into TFRecord (from the treated images folder)
    images_path = r'C:\temp\ODIR-5K_Training_Dataset_treated'
    # With treated images we reduce the TFRecord size from 91,117,187,072 bytes
    # to 98,721,792 bytes, 99.89% reduction
    #generator = GenerateTFRecord(images_path)
    #generator.patients_to_tfrecord(patients, 'images.tfrecord')
    end = time.time()
    logger.debug('All Done in ' + str(end - start) + ' seconds')


if __name__ == '__main__':
    # create logger
    logging.config.fileConfig('logging.conf')
    logger = logging.getLogger('odir')
    app.run(main)