image_reco_service.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os.path
import re
import sys
import tarfile

import numpy as np
from six.moves import urllib
import tensorflow as tf

#!flask/bin/python
from flask import Flask, jsonify
from flask import make_response
from flask import request, render_template
from flask_bootstrap import Bootstrap

from werkzeug import secure_filename



app = Flask(__name__)
bootstrap = Bootstrap(app)


@app.route('/')
def index():
    return render_template("upload.html")

# import default command line flags from TensorFlow
FLAGS = tf.app.flags.FLAGS

# define directory that the model is stored in (default is the current directory)
tf.app.flags.DEFINE_string(
  'model_dir', '/home/ubuntu/flaskapp',
  """Path to output_graph.pb, """
  """output_labels.txt""")

tf.app.flags.DEFINE_integer('num_top_predictions', 3,
                            """Display this many predictions.""")

# Classificaiton endpoint
@app.route("/classify", methods=["POST"])
def classify():

  create_graph()
  print("Model loaded")

  node_lookup = NodeLookup()
  print("Node lookup loaded")


  predictions = dict(run_inference_on_image(request.data))
  print(predictions)
  return jsonify(predictions=predictions)


@app.errorhandler(404)
def not_found(error):
    return make_response(jsonify({'error': 'Not found'}), 404)

# The following code performs the recognition, and is derived from the examples
# provided in the Tensorflow package
# ==============================================================================
#
# Copyright 2015 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.
# ==============================================================================

"""Simple image classification with Inception.
Run image classification with Inception trained on ImageNet 2012 Challenge data
set.
This program creates a graph from a saved GraphDef protocol buffer,
and runs inference on an input JPEG image. It outputs human readable
strings of the top 5 predictions along with their probabilities.
Change the --image_file argument to any jpg image to compute a
classification of that image.
Please see the tutorial and website for a detailed description of how
to use this script to perform image recognition.
https://tensorflow.org/tutorials/image_recognition/
"""


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

  def __init__(self, label_lookup_path=None):
    if not label_lookup_path:
      label_lookup_path = os.path.join(
          FLAGS.model_dir, 'output_labels.txt')

    self.node_lookup = self.load(label_lookup_path)

  def load(self, label_lookup_path):
    """Loads a human readable English name for each softmax node.
    Args:
      label_lookup_path: string UID to integer node ID.
    Returns:
      dict from integer node ID to human-readable string.
    """

    node_id_to_name = {}

    label_file = open(label_lookup_path)
    i = 0

    # labels are ordered from 0 to N in the lookup file

    for line in label_file:
      node_id_to_name[i] = line.strip()
      i = i + 1

    return node_id_to_name

  # return the friendly name for the given node_id
  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return ''
    return self.node_lookup[node_id]


def create_graph():
  """Creates a graph from saved GraphDef file and returns a saver."""
  # Creates graph from saved graph_def.pb.
  with tf.gfile.FastGFile(os.path.join(
      FLAGS.model_dir, 'output_graph.pb'), 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')

# sess = None
# node_lookup = None


def run_inference_on_image(image_data):
  """Runs inference on an image.
  Args:
    image_data: Image data.
  Returns:
    Nothing
  """
  sess = tf.Session()
  print("Tensorflow session ready")
  node_lookup = NodeLookup()
  print("Node lookup loaded")
  # Runs the softmax tensor by feeding the image_data as input to the graph.
  softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
  predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0': image_data})
  predictions = np.squeeze(predictions)

  # sort the predictions
  top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]

  # map to the friendly names and return the tuples
  return [(node_lookup.id_to_string(node_id), float(predictions[node_id])) for node_id in top_k]




if __name__ == '__main__':

  create_graph()
  print("Model loaded")

  node_lookup = NodeLookup()
  print("Node lookup loaded")

  sess = tf.Session()
  print("Tensorflow session ready")

  print("Launching web application...")
  app.run(debug=True)