[25/45] tika git commit: Added the script file for Tensorflow classify images

[ma...@apache.org]

Added the script file for Tensorflow classify images


Project: http://git-wip-us.apache.org/repos/asf/tika/repo
Commit: http://git-wip-us.apache.org/repos/asf/tika/commit/6b56ad12
Tree: http://git-wip-us.apache.org/repos/asf/tika/tree/6b56ad12
Diff: http://git-wip-us.apache.org/repos/asf/tika/diff/6b56ad12

Branch: refs/heads/master
Commit: 6b56ad12cd16e23de90205f47f5ef18ed36c333f
Parents: f3e9d82
Author: Thamme Gowda <th...@apache.org>
Authored: Sun Jun 12 19:19:07 2016 -0700
Committer: Thamme Gowda <th...@apache.org>
Committed: Sun Jun 12 19:19:07 2016 -0700

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 .../parser/recognition/tf/classify_image.py     | 212 +++++++++++++++++++
 1 file changed, 212 insertions(+)
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http://git-wip-us.apache.org/repos/asf/tika/blob/6b56ad12/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/classify_image.py
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diff --git a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/classify_image.py b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/classify_image.py
new file mode 100644
index 0000000..6192683
--- /dev/null
+++ b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/classify_image.py
@@ -0,0 +1,212 @@
+# 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/
+"""
+
+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
+
+FLAGS = tf.app.flags.FLAGS
+
+# classify_image_graph_def.pb:
+#   Binary representation of the GraphDef protocol buffer.
+# imagenet_synset_to_human_label_map.txt:
+#   Map from synset ID to a human readable string.
+# imagenet_2012_challenge_label_map_proto.pbtxt:
+#   Text representation of a protocol buffer mapping a label to synset ID.
+tf.app.flags.DEFINE_string(
+    'model_dir', '/tmp/imagenet',
+    """Path to classify_image_graph_def.pb, """
+    """imagenet_synset_to_human_label_map.txt, and """
+    """imagenet_2012_challenge_label_map_proto.pbtxt.""")
+tf.app.flags.DEFINE_string('image_file', '',
+                           """Absolute path to image file.""")
+tf.app.flags.DEFINE_integer('num_top_predictions', 5,
+                            """Display this many predictions.""")
+
+# pylint: disable=line-too-long
+DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+# pylint: enable=line-too-long
+
+
+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 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, 'classify_image_graph_def.pb'), 'rb') as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+    _ = tf.import_graph_def(graph_def, name='')
+
+
+def run_inference_on_image(image):
+  """Runs inference on an image.
+
+  Args:
+    image: Image file name.
+
+  Returns:
+    Nothing
+  """
+  if not tf.gfile.Exists(image):
+    tf.logging.fatal('File does not exist %s', image)
+  image_data = tf.gfile.FastGFile(image, 'rb').read()
+
+  # Creates graph from saved GraphDef.
+  create_graph()
+
+  with tf.Session() as sess:
+    # Some useful tensors:
+    # 'softmax:0': A tensor containing the normalized prediction across
+    #   1000 labels.
+    # 'pool_3:0': A tensor containing the next-to-last layer containing 2048
+    #   float description of the image.
+    # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
+    #   encoding of the image.
+    # Runs the softmax tensor by feeding the image_data as input to the graph.
+    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
+    predictions = sess.run(softmax_tensor,
+                           {'DecodeJpeg/contents:0': image_data})
+    predictions = np.squeeze(predictions)
+
+    # 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]
+      print('%s (score = %.5f)' % (human_string, score))
+
+
+def maybe_download_and_extract():
+  """Download and extract model tar file."""
+  dest_directory = FLAGS.model_dir
+  if not os.path.exists(dest_directory):
+    os.makedirs(dest_directory)
+  filename = DATA_URL.split('/')[-1]
+  filepath = os.path.join(dest_directory, filename)
+  if not os.path.exists(filepath):
+    def _progress(count, block_size, total_size):
+      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+          filename, float(count * block_size) / float(total_size) * 100.0))
+      sys.stdout.flush()
+    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
+    print()
+    statinfo = os.stat(filepath)
+    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
+  tarfile.open(filepath, 'r:gz').extractall(dest_directory)
+
+
+def main(_):
+  maybe_download_and_extract()
+  image = (FLAGS.image_file if FLAGS.image_file else
+           os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
+  run_inference_on_image(image)
+
+
+if __name__ == '__main__':
+  tf.app.run()