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 <[email protected]> Authored: Sun Jun 12 19:19:07 2016 -0700 Committer: Thamme Gowda <[email protected]> Committed: Sun Jun 12 19:19:07 2016 -0700 ---------------------------------------------------------------------- .../parser/recognition/tf/classify_image.py | 212 +++++++++++++++++++ 1 file changed, 212 insertions(+) ---------------------------------------------------------------------- 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 ---------------------------------------------------------------------- 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()
