label_image.py

"""
Applies the trained network to label an image.
Author: Landon Wiedenman
"""
import tensorflow as tf
import sys

image_path = sys.argv[1]

# Read in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()

# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line in tf.gfile.GFile("/Users/landonwiedenman/Documents/development_projects/landongw/tensorflow_work/image_classifier/retrained_labels.txt")]

# Unpersists graph from file
with tf.gfile.FastGFile("/Users/landonwiedenman/Documents/development_projects/landongw/tensorflow_work/image_classifier/retrained_graph.pb", 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')

# Feed the image_data as input to the graph and get first prediction
with tf.Session() as sess:
    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
    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))