Add label_image.py that works with retrain.py.

PiperOrigin-RevId: 156468713

4 files changed, 199 insertions, 0 deletions

diff --git a/tensorflow/examples/image_retraining/BUILD b/tensorflow/examples/image_retraining/BUILD
index 5a885e33c2..394c413b33 100644
--- a/tensorflow/examples/image_retraining/BUILD
+++ b/tensorflow/examples/image_retraining/BUILD
@@ -24,13 +24,30 @@ py_binary(
     ],
 )
 
+py_binary(
+    name = "label_image",
+    srcs = [
+        "label_image.py",
+    ],
+    srcs_version = "PY2AND3",
+    visibility = ["//tensorflow:__subpackages__"],
+    deps = [
+        "//tensorflow:tensorflow_py",
+    ],
+)
+
 py_test(
     name = "retrain_test",
     size = "small",
     srcs = [
+        "label_image.py",
         "retrain.py",
         "retrain_test.py",
     ],
+    data = [
+        ":data/labels.txt",
+        "//tensorflow/examples/label_image:data/grace_hopper.jpg",
+    ],
     srcs_version = "PY2AND3",
     deps = [
         ":retrain",
diff --git a/tensorflow/examples/image_retraining/data/labels.txt b/tensorflow/examples/image_retraining/data/labels.txt
new file mode 100644
index 0000000000..bc1131ac45
--- /dev/null
+++ b/tensorflow/examples/image_retraining/data/labels.txt
@@ -0,0 +1,3 @@
+Runner-up
+Winner
+Loser
diff --git a/tensorflow/examples/image_retraining/label_image.py b/tensorflow/examples/image_retraining/label_image.py
new file mode 100644
index 0000000000..ecfa672462
--- /dev/null
+++ b/tensorflow/examples/image_retraining/label_image.py
@@ -0,0 +1,147 @@
+# Copyright 2016 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 your model.
+
+This script is usually used with retrain.py found in this same
+directory.
+
+This program creates a graph from a saved GraphDef protocol buffer,
+and runs inference on an input JPEG image. You are required
+to pass in the graph file and the txt file.
+
+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.
+
+Example usage:
+python label_image.py --graph=retrained_graph.pb
+  --labels=retrained_labels.txt
+  --image=flower_photos/daisy/54377391_15648e8d18.jpg
+
+NOTE: To learn to use this file and retrain.py, please see:
+
+https://codelabs.developers.google.com/codelabs/tensorflow-for-poets
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+
+import tensorflow as tf
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    '--image', required=True, type=str, help='Absolute path to image file.')
+parser.add_argument(
+    '--num_top_predictions',
+    type=int,
+    default=5,
+    help='Display this many predictions.')
+parser.add_argument(
+    '--graph',
+    required=True,
+    type=str,
+    help='Absolute path to graph file (.pb)')
+parser.add_argument(
+    '--labels',
+    required=True,
+    type=str,
+    help='Absolute path to labels file (.txt)')
+parser.add_argument(
+    '--output_layer',
+    type=str,
+    default='final_result:0',
+    help='Name of the result operation')
+parser.add_argument(
+    '--input_layer',
+    type=str,
+    default='DecodeJpeg/contents:0',
+    help='Name of the input operation')
+
+
+def load_image(filename):
+  """Read in the image_data to be classified."""
+  return tf.gfile.FastGFile(filename, 'rb').read()
+
+
+def load_labels(filename):
+  """Read in labels, one label per line."""
+  return [line.rstrip() for line in tf.gfile.GFile(filename)]
+
+
+def load_graph(filename):
+  """Unpersists graph from file as default graph."""
+  with tf.gfile.FastGFile(filename, 'rb') as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+    tf.import_graph_def(graph_def, name='')
+
+
+def run_graph(image_data, labels, input_layer_name, output_layer_name,
+              num_top_predictions):
+  with tf.Session() as sess:
+    # Feed the image_data as input to the graph.
+    #   predictions  will contain a two-dimensional array, where one
+    #   dimension represents the input image count, and the other has
+    #   predictions per class
+    softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
+    predictions, = sess.run(softmax_tensor, {input_layer_name: image_data})
+
+    # Sort to show labels in order of confidence
+    top_k = predictions.argsort()[-num_top_predictions:][::-1]
+    for node_id in top_k:
+      human_string = labels[node_id]
+      score = predictions[node_id]
+      print('%s (score = %.5f)' % (human_string, score))
+
+    return 0
+
+
+def main(argv):
+  """Runs inference on an image."""
+  if argv[1:]:
+    raise ValueError('Unused Command Line Args: %s' % argv[1:])
+
+  if not tf.gfile.Exists(FLAGS.image):
+    tf.logging.fatal('image file does not exist %s', FLAGS.image)
+
+  if not tf.gfile.Exists(FLAGS.labels):
+    tf.logging.fatal('labels file does not exist %s', FLAGS.labels)
+
+  if not tf.gfile.Exists(FLAGS.graph):
+    tf.logging.fatal('graph file does not exist %s', FLAGS.graph)
+
+  # load image
+  image_data = load_image(FLAGS.image)
+
+  # load labels
+  labels = load_labels(FLAGS.labels)
+
+  # load graph, which is stored in the default session
+  load_graph(FLAGS.graph)
+
+  run_graph(image_data, labels, FLAGS.input_layer, FLAGS.output_layer,
+            FLAGS.num_top_predictions)
+
+
+if __name__ == '__main__':
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=sys.argv[:1]+unparsed)
diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py
index 00ccea174f..8af5cc7114 100644
--- a/tensorflow/examples/image_retraining/retrain_test.py
+++ b/tensorflow/examples/image_retraining/retrain_test.py
@@ -19,7 +19,9 @@ from __future__ import division
 from __future__ import print_function
 
 import tensorflow as tf
+import os
 
+from tensorflow.examples.image_retraining import label_image
 from tensorflow.examples.image_retraining import retrain
 from tensorflow.python.framework import test_util
 
@@ -81,5 +83,35 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
       gt = tf.placeholder(tf.float32, [1], name='gt')
       self.assertIsNotNone(retrain.add_evaluation_step(final, gt))
 
+  def testLabelImage(self):
+
+    image_filename = ('../label_image/data/grace_hopper.jpg')
+
+    # Load some default data
+    label_path = os.path.join(tf.resource_loader.get_data_files_path(),
+                              'data/labels.txt')
+    labels = label_image.load_labels(label_path)
+    self.assertEqual(len(labels), 3)
+
+    image_path = os.path.join(tf.resource_loader.get_data_files_path(),
+                              image_filename)
+
+    image = label_image.load_image(image_path)
+    self.assertEqual(len(image), 61306)
+
+    # Create trivial graph; note that the two nodes don't meet
+    with tf.Graph().as_default():
+      jpeg = tf.constant(image)
+      # Input node that doesn't lead anywhere.
+      tf.image.decode_jpeg(jpeg, name='DecodeJpeg')
+
+      # Output node, that always outputs a constant.
+      tf.constant([[10, 30, 5]], name='final')
+
+      # As label_image outputs via print, we assume that
+      # if it returns, everything is OK.
+      result = label_image.run_graph(image, labels, jpeg, 'final:0', 3)
+      self.assertEqual(result, 0)
+
 if __name__ == '__main__':
   tf.test.main()