predict.py

#!/usr/bin/env python2
# -*- coding: utf-8 -*-

import sys
import tensorflow as tf
import os, argparse
from tensorflow.python.framework import graph_util
import scipy.misc
import numpy as np
from scipy import ndimage
import cv2
import glob
import itertools
import os.path
import re
import tarfile
import Queue
import time

dir = os.path.dirname(os.path.realpath(__file__))
FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_integer('image_size', 299,
                            """Provide square images of this size.""")

class NodeLookup(object):
  """Converts integer node ID's to human readable labels."""

  def __init__(self,
               label_lookup_path=None,
               uid_lookup_path=None):
    if not label_lookup_path:
      label_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
    if not uid_lookup_path:
      uid_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)

  def load(self, label_lookup_path, uid_lookup_path):
    """Loads a human readable English name for each softmax node.
    Args:
      label_lookup_path: string UID to integer node ID.
      uid_lookup_path: string UID to human-readable string.
    Returns:
      dict from integer node ID to human-readable string.
    """
    if not tf.gfile.Exists(uid_lookup_path):
      tf.logging.fatal('File does not exist %s', uid_lookup_path)
    if not tf.gfile.Exists(label_lookup_path):
      tf.logging.fatal('File does not exist %s', label_lookup_path)

    # Loads mapping from string UID to human-readable string
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r'[n\d]*[ \S,]*')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      uid = parsed_items[0]
      human_string = parsed_items[2]
      uid_to_human[uid] = human_string

    # Loads mapping from string UID to integer node ID.
    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
      if line.startswith('  target_class:'):
        target_class = int(line.split(': ')[1])
      if line.startswith('  target_class_string:'):
        target_class_string = line.split(': ')[1]
        node_id_to_uid[target_class] = target_class_string[1:-2]

    # Loads the final mapping of integer node ID to human-readable string
    node_id_to_name = {}
    for key, val in node_id_to_uid.items():
      if val not in uid_to_human:
        tf.logging.fatal('Failed to locate: %s', val)
      name = uid_to_human[val]
      node_id_to_name[key] = name

    return node_id_to_name

  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return ''
    return self.node_lookup[node_id]

def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the 
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the 
    # current default Graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def, 
            input_map=None, 
            return_elements=None, 
            name="prefix", 
            op_dict=None, 
            producer_op_list=None
        )
    return graph

def create_image_batch(folder):
	image_list = glob.glob(folder)
	img_np_list=[]
	for img in image_list:
		img_np =cv2.imread(img,1)
		img_np_list.append(img_np)
	img_pad = np.zeros(np.shape(img_np))
	input_list = list(itertools.izip_longest(*[iter(img_np_list)]*64,fillvalue=img_pad))
	return input_list

if __name__ == '__main__':
    # Let's allow the user to pass the filename as an argument
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_dir",type=str,default='./')
    parser.add_argument("--num_top_predictions",type=int,default=1)
    parser.add_argument("--frozen_model_filename", default="frozen_model.pb", type=str, help="Frozen model file to import")
    parser.add_argument("--image_dir",type=str,help="directory of images to predict on")
    parser.add_argument("--layer",type=str,help="Output layer")

    FLAGS,unparsed = parser.parse_known_args()

    # Create input queue
    img_q = Queue.Queue()
    map(img_q.put, glob.glob(image_dir+'/*.jpg'))

    # We use our "load_graph" function
    graph = load_graph(FLAGS.frozen_model_filename)
	
    # We can verify that we can access the list of operations in the graph
    #for op in graph.get_operations():
        #print(op.name)
        # prefix/Placeholder/inputs_placeholder
        # ...
        # prefix/Accuracy/predictions
        
    # We access the input and output nodes 
    x = graph.get_tensor_by_name('prefix/DecodeJpeg/contents:0') # Input tensor
    if FLAGS.layer=='pool':
    	y = graph.get_tensor_by_name('prefix/pool_3/_reshape:0') # Output tensor
    elif FLAGS.layer=='softmax':
	y = graph.get_tensor_by_name('prefix/softmax:0')
    
    # We launch a Session
    start = time.time()
    with tf.Session(graph=graph) as sess:
       # Note: we didn't initialize/restore anything, everything is stored in the graph_def
       embedding_list = []
       prediction_list = []
       while not img_q.empty():
       	image_data = tf.gfile.FastGFile(img_q.get(),'rb').read()
       	embedding = sess.run(y, feed_dict={x:image_data})
	prediction = sess.run(graph.get_tensor_by_name('prefix/softmax:0'),feed_dict={x:image_data})
	predictions = np.squeeze(prediction)

    	# Creates node ID --> English string lookup.
    	node_lookup = NodeLookup()
    	top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
    	for node_id in top_k:
      		human_string = node_lookup.id_to_string(node_id)
      		score = predictions[node_id]

	embedding_list.append(embedding)
	prediction_list.append(human_string)
       print ('total time: %s seconds'%(time.time()-start))