classify_image.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
# Modifications Copyright 2018 Team Crystal. 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 argparse
import os.path
import re
import sys
import tarfile
import cv2
import math

import numpy as np
from six.moves import urllib
import tensorflow as tf

FLAGS = None
TEST_DIR = './tests/'
PATH_TO_MODEL = './crystal-clear/object_detection/tmp/imagenet'
GUESS_COUNT = 1


# 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(
				PATH_TO_MODEL, 'imagenet_2012_challenge_label_map_proto.pbtxt')
		if not uid_lookup_path:
			uid_lookup_path = os.path.join(
				PATH_TO_MODEL, '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(
			PATH_TO_MODEL, '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()[-GUESS_COUNT:][::-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))
		return human_string, score

def maybe_download_and_extract():
	"""Download and extract model tar file."""
	dest_directory = PATH_TO_MODEL
	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('Successfully 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, 'C:/Users/TO/Documents/TensorFlow/pencil.mp4'))
	image = (FLAGS.image_file if FLAGS.image_file else
			 os.path.join(TEST_DIR, 'pencil.jpg'))
	run_inference_on_image(image)


if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	# 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.
	parser.add_argument(
		'--model_dir',
		type=str,
		default='./tmp/imagenet',
		help="""\
	  Path to classify_image_graph_def.pb,
	  imagenet_synset_to_human_label_map.txt, and
	  imagenet_2012_challenge_label_map_proto.pbtxt.\
	  """
	)
	parser.add_argument(
		'--image_file',
		type=str,
		default='',
		help='Absolute path to image file.'
	)
	parser.add_argument(
		'--num_top_predictions',
		type=int,
		default=5,
		help='Display this many predictions.'
	)
	FLAGS, unparsed = parser.parse_known_args()
	tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)