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July 10, 2016 06:23
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classify_image.py
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# Copyright 2015 Google Inc. 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/ | |
""" | |
import os.path | |
import re | |
import sys | |
import tarfile | |
# pylint: disable=unused-import,g-bad-import-order | |
import tensorflow.python.platform | |
from six.moves import urllib | |
import numpy as np | |
import tensorflow as tf | |
# pylint: enable=unused-import,g-bad-import-order | |
from tensorflow.python.platform import gfile | |
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 gfile.Exists(uid_lookup_path): | |
tf.logging.fatal('File does not exist %s', uid_lookup_path) | |
if not 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 = 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 = 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 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 gfile.Exists(image): | |
tf.logging.fatal('File does not exist %s', image) | |
image_data = 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, | |
reporthook=_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() |
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