""" Simple image classification with Inception. """

# This is a modified version of the following file:

# https://github.com/tensorflow/models/blob/master/tutorials/image/imagenet/classify_image.py # noqa

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import os

import re

import numpy as np

from tensorpy import settings

import tensorflow as tf

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

FLAGS = tf.app.flags.FLAGS

tf.logging.set_verbosity(tf.logging.ERROR)

tf.app.flags.DEFINE_string(

'model_dir', settings.IMAGENET_FOLDER,

"""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', 1,

"""Display this many predictions.""")

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):

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():

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='') # noqa

def run_inference_on_image(image, external_run=False):

if not tf.gfile.Exists(image):

tf.logging.fatal('File does not exist %s', image)

image_data = tf.gfile.FastGFile(image, 'rb').read()

create_graph()

with tf.Session() as sess:

softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')

predictions = sess.run(softmax_tensor,

{'DecodeJpeg/contents:0': image_data})

predictions = np.squeeze(predictions)

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]

if external_run:

return str(human_string).split(',')[0]

else:

print('%s (score = %.5f)' % (human_string, score))

def main(_):

if FLAGS.image_file:

image = FLAGS.image_file

print(run_inference_on_image(image))

else:

print("No image file specified! (Usage: --image_file=IMAGE_FILE.jpg)")

def external_run(image):

best_guess = run_inference_on_image(image, external_run=True)

return best_guess

if __name__ == '__main__':

tf.app.run()