model.py

import numpy as np
import tensorflow as tf
import os
import os.path
import datetime as dt

import input_data

from tensorflow.python.platform import gfile

# image_data_tensor_name = 'DecodeJpeg/contents'
image_data_tensor_name = 'input_images'
final_tensor_name = 'final_result'

def ensure_name_has_port(tensor_name):
  """Makes sure that there's a port number at the end of the tensor name.

  Args:
    tensor_name: A string representing the name of a tensor in a graph.

  Returns:
    The input string with a :0 appended if no port was specified.
  """
  if ':' not in tensor_name:
    name_with_port = tensor_name + ':0'
  else:
    name_with_port = tensor_name
  return name_with_port

def create_graph():
    """Creates a graph from saved GraphDef file and returns a saver."""
    # Creates graph from saved graph_def.pb.
    with tf.Session() as sess:
        with tf.gfile.FastGFile(os.path.join("", 'output_graph.pb'), 'rb') as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            _ = tf.import_graph_def(graph_def, name='')
    return sess.graph

class inference:

    def __init__(self):
        graph = create_graph()
        self.sess = tf.Session()

        # Set up all our weights to their initial default values.
        init = tf.initialize_all_variables()
        self.sess.run(init)

    def direction(self, image_data):
        steering = self.sess.run(ensure_name_has_port(final_tensor_name),
            { ensure_name_has_port(image_data_tensor_name): image_data })

        steering = (steering* (1979 - 980)) + 1552
        return steering, 1650
        # print(result)
        argmax = np.argmax(result)
        if argmax == 0:
            return 'f'
        elif argmax == 1:
            return 'l'
        elif argmax == 2:
            return 'r'
        else:
            return 's'