Spaces:
Running
Running
| #!/usr/bin/python | |
| # Copyright 2018 The TensorFlow Authors. 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. | |
| # ============================================================================== | |
| import tensorflow as tf | |
| import csv | |
| import os | |
| import argparse | |
| """ | |
| usage: | |
| Processes all .jpg, .png, .bmp and .gif files found in the specified directory and its subdirectories. | |
| --PATH ( Path to directory of images or path to directory with subdirectory of images). e.g Path/To/Directory/ | |
| --Model_PATH path to the tensorflow model | |
| """ | |
| parser = argparse.ArgumentParser(description='Crystal Detection Program') | |
| parser.add_argument('--PATH', type=str, help='path to image directory. Recursively finds all image files in directory and sub directories') # path to image directory or containing sub directories. | |
| parser.add_argument('--MODEL_PATH', type=str, default='./savedmodel',help='the file path to the tensorflow model ') | |
| args = vars(parser.parse_args()) | |
| PATH = args['PATH'] | |
| model_path = args['MODEL_PATH'] | |
| crystal_images = [os.path.join(dp, f) for dp, dn, filenames in os.walk(PATH) for f in filenames if os.path.splitext(f)[1] in ['.jpg','png','bmp','gif']] | |
| size = len(crystal_images) | |
| def load_images(file_list): | |
| for i in file_list: | |
| files = open(i,'rb') | |
| yield {"image_bytes":[files.read()]},i | |
| iterator = load_images(crystal_images) | |
| with open(PATH +'results.csv', 'w') as csvfile: | |
| Writer = csv.writer(csvfile, delimiter=' ',quotechar=' ', quoting=csv.QUOTE_MINIMAL) | |
| predicter= tf.contrib.predictor.from_saved_model(model_path) | |
| dic = {} | |
| k = 0 | |
| for _ in range(size): | |
| data,name = next(iterator) | |
| results = predicter(data) | |
| vals =results['scores'][0] | |
| classes = results['classes'][0] | |
| dictionary = dict(zip(classes,vals)) | |
| print('Image path: '+ name+' Crystal: '+str(dictionary[b'Crystals'])+' Other: '+ str(dictionary[b'Other'])+' Precipitate: '+ str(dictionary[b'Precipitate'])+' Clear: '+ str(dictionary[b'Clear'])) | |
| Writer.writerow(['Image path: '+ name,'Crystal: '+str(dictionary[b'Crystals']),'Other: '+ str(dictionary[b'Other']),'Precipitate: '+ str(dictionary[b'Precipitate']),'Clear: '+ str(dictionary[b'Clear'])]) | |