object_det_num100_serve.py

# -*- coding: utf-8 -*-
"""
Created on Wed Jan 24 14:47:35 2018

@author: Vasantha kumar
"""

import tensorflow as tf
import os
import numpy as np
import os,glob,cv2
import sys,argparse
# First, pass the path of the image
dir_path = os.path.dirname(os.path.realpath(file))
image_path=sys.argv[1]
filename = dir_path +'/' +image_path
image_size=128
num_channels=3
images = []
 
##  Reading the image using OpenCV
image = cv2.imread(filename)
 
##   Resizing the image to our desired size and preprocessing will be done exactly as done during training
image = cv2.resize(image, (image_size, image_size), cv2.INTER_LINEAR)
images.append(image)
images = np.array(images, dtype=np.uint8)
images = images.astype('float32')
images = np.multiply(images, 1.0/255.0)
##   The input to the network is of shape [None image_size image_size num_channels]. 
## Hence we reshape.
 
x_batch = images.reshape(1, image_size,image_size,num_channels)
 
frozen_graph="/numplate/numplate29517_100img/frozen_inference_graph.pb"
with tf.gfile.GFile(frozen_graph, "rb") as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
 
with tf.Graph().as_default() as graph:
      tf.import_graph_def(graph_def,
                          input_map=None,
                          return_elements=None,
                          name=""
      )
## NOW the complete graph with values has been restored
y_pred = graph.get_tensor_by_name("y_pred:0")
## Let's feed the images to the input placeholders
x= graph.get_tensor_by_name("x:0")
y_test_images = np.zeros((1, 2))
sess= tf.Session(graph=graph)
### Creating the feed_dict that is required to be fed to calculate y_pred 
feed_dict_testing = {x: x_batch}
result=sess.run(y_pred, feed_dict=feed_dict_testing)
print(result)