classify.py

import tensorflow as tf
import sys
import os
import time

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

image_path = sys.argv[1]

# Read the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
warm_up_image_data = tf.gfile.FastGFile('000002.jpg', 'rb').read()
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line 
                   in tf.gfile.GFile("tf_files/retrained_labels.txt")]

# Unpersists graph from file
with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')

with tf.Session() as sess:
    # Feed the image_data as input to the graph and get first prediction
    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
    print("Warm-up start")
    for i in range(10):
        print("Warm-up for time {}".format(i))
        predictions = sess.run(softmax_tensor,
                           {'DecodeJpeg/contents:0': warm_up_image_data})
 
    print("Warm-up finished")
 
    # # record the start time of the actual prediction
    start_time = time.time()
    
    predictions = sess.run(softmax_tensor, \
             {'DecodeJpeg/contents:0': image_data})
    
    # Sort to show labels of first prediction in order of confidence
    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
    
    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
        print('%s (score = %.5f)' % (human_string, score))
    print("Prediction used time:{} S".format(time.time() - start_time))