import numpy as np
import tensorflow as tf
import re 

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(
                '/tmp/imagenet/', 'imagenet_2012_challenge_label_map_proto.pbtxt')
        if not uid_lookup_path:
            uid_lookup_path = os.path.join(
                '/tmp/imagenet/', '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]

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

with tf.gfile.FastGFile('classify_image_graph_def.pb', 'rb') as f:
  graph_def = tf.GraphDef()
  graph_def.ParseFromString(f.read())
  tf.import_graph_def(graph_def, name='')
SESSION = tf.InteractiveSession()
softmax_tensor = tf.get_default_graph().get_tensor_by_name('softmax:0')

predictions = SESSION.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data})

predictions = np.squeeze(predictions)
node_lookup = NodeLookup(label_lookup_path='imagenet_2012_challenge_label_map_proto.pbtxt',
  uid_lookup_path='imagenet_synset_to_human_label_map.txt')

top_k = predictions.argsort()[-5:][::-1]
strResult = '%s (score = %.5f)' % (node_lookup.id_to_string(top_k[0]), predictions[top_k[0]])
print()
for node_id in top_k:
    human_string = node_lookup.id_to_string(node_id)
    score = predictions[node_id]
    print('%s - %s (score = %.5f)' % (node_id, human_string, score))



import numpy as np
import tensorflow as tf
import re 

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(
                '/tmp/imagenet/', 'imagenet_2012_challenge_label_map_proto.pbtxt')
        if not uid_lookup_path:
            uid_lookup_path = os.path.join(
                '/tmp/imagenet/', '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]

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

with tf.gfile.FastGFile('classify_image_graph_def.pb', 'rb') as f:
  graph_def = tf.GraphDef()
  graph_def.ParseFromString(f.read())
  tf.import_graph_def(graph_def, name='')
SESSION = tf.InteractiveSession()
softmax_tensor = tf.get_default_graph().get_tensor_by_name('softmax:0')

predictions = SESSION.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data})

predictions = np.squeeze(predictions)
node_lookup = NodeLookup(label_lookup_path='imagenet_2012_challenge_label_map_proto.pbtxt',
  uid_lookup_path='imagenet_synset_to_human_label_map.txt')

top_k = predictions.argsort()[-5:][::-1]
strResult = '%s (score = %.5f)' % (node_lookup.id_to_string(top_k[0]), predictions[top_k[0]])
print()
for node_id in top_k:
    human_string = node_lookup.id_to_string(node_id)
    score = predictions[node_id]
    print('%s - %s (score = %.5f)' % (node_id, human_string, score))



Converts integer node ID's to human readable labels. # Loads mapping from string UID to human-readable string # Loads mapping from string UID to integer node ID. # Loads the final mapping of integer node ID to human-readable string