inception.py

"""
This implementation is based on the image recognition tutorial on Tensorflow's website.
Find more on https://www.tensorflow.org/tutorials/image_recogniton
"""
import tensorflow as tf
import numpy as np
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 = 'model/imagenet_2012_challenge_label_map_proto.pbtxt'
        if not uid_lookup_path:
            uid_lookup_path = '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 label_lookup_path:
            label_lookup_path = 'model/imagenet_2012_challenge_label_map_proto.pbtxt'
        if not uid_lookup_path:
            uid_lookup_path = 'model/imagenet_synset_to_human_label_map.txt'
        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(graph):
    """Creates a graph from saved GraphDef file and returns a saver."""
    # Creates graph from saved graph_def.pb.
    with tf.gfile.FastGFile(graph, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')

def run_inference_on_image(node_lookup, image_in, count = 5):
    res = []
    
    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_in})
        predictions = np.squeeze(predictions)

        # Creates node ID --> English string lookup.
        top_k = predictions.argsort()[-count:][::-1]
        for node_id in top_k:
            human_string = node_lookup.id_to_string(node_id)
            score = predictions[node_id]
            res.append((human_string, float(score)))

    return res