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processes all crystal growth images in directory and  sub directories and prints results to console, and a comma seperated file. Removes dependencies of google SDK(only dependent on tensorflow and python 2.7)

research/marco/Automated_Marco.py

+#!/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:
+        file = open(i)
+        yield {"image_bytes":[file.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]
+                vals = vals*100 
+                print('Image path: '+ name,'Crystal: '+str(vals[0]),'Other: '+ str(vals[1]),'Precipitate: '+ str(vals[2]),'Clear '+ str(vals[3]))
+                Writer.writerow(['Image path: '+ name,'Crystal: '+str(vals[0]),'Other: '+ str(vals[1]),'Precipitate: '+ str(vals[2]),'Clear: '+ str(vals[3])])
+