classify.py

import tensorflow as tf
import sys
import os

# speicherorte fuer trainierten graph und labels in train.sh festlegen ##

# Disable tensorflow compilation warnings
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

image_path = sys.argv[1]
# angabe in console als argument nach dem aufruf  


#bilddatei readen
image_data = tf.gfile.FastGFile(image_path, 'rb').read()

# holt labels aus file in array 
label_lines = [line.rstrip() for line 
                   in tf.gfile.GFile("tf_files/retrained_labels.txt")]
# !! labels befinden sich jeweils in eigenen lines -> keine aenderung in retrain.py noetig -> falsche darstellung im windows editor !!
				   
# graph einlesen, wurde in train.sh -> call retrain.py trainiert
with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
 
    graph_def = tf.GraphDef()	## The graph-graph_def is a saved copy of a TensorFlow graph; objektinitialisierung
    graph_def.ParseFromString(f.read())	#Parse serialized protocol buffer data into variable
    _ = tf.import_graph_def(graph_def, name='')	# import a serialized TensorFlow GraphDef protocol buffer, extract objects in the GraphDef as tf.Tensor
	
	#https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/inception.py ; ab zeile 276

with tf.Session() as sess:

    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
	# return: Tensor("final_result:0", shape=(?, 4), dtype=float32); stringname definiert in retrain.py, zeile 1064 

    predictions = sess.run(softmax_tensor, \
             {'DecodeJpeg/contents:0': image_data})
    # gibt prediction values in array zuerueck:
	
    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
	# sortierung; circle -> 0, plus -> 1, square -> 2, triangle -> 3; array return bsp [3 1 2 0] -> sortiert nach groesster uebereinstimmmung

	# output
    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
        print('%s (score = %.5f)' % (human_string, score))