predict.py

import tensorflow as tf
import numpy as np
import os,glob,cv2
import sys,argparse

classes = ['Bus','car','cat','dog']
# First, pass the path of the image
dir_path = os.path.dirname(os.path.realpath(__file__))
image_path=sys.argv[1]
filename = image_path
print(filename)
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)

## Let us restore the saved model 
sess = tf.Session()
# Step-1: Recreate the network graph. At this step only graph is created.
saver = tf.train.import_meta_graph('/Users/pavanpss/Documents/model/tm.meta')
# Step-2: Now let's load the weights saved using the restore method.
saver.restore(sess, tf.train.latest_checkpoint("/Users/pavanpss/Documents/model"))

# Accessing the default graph which we have restored
graph = tf.get_default_graph()

# Now, let's get hold of the op that we can be processed to get the output.
# In the original network y_pred is the tensor that is the prediction of the network
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_true = graph.get_tensor_by_name("y_true:0")
y_test_images = np.zeros((1, 4))


### Creating the feed_dict that is required to be fed to calculate y_pred 
feed_dict_testing = {x: x_batch, y_true: y_test_images}
result=sess.run(y_pred, feed_dict=feed_dict_testing)
# result is of this format [probabiliy_of_rose probability_of_sunflower]
print(result)
r=result[0]
high_prob = max(r)
print("High Probability:", high_prob)
for i in range (0,len(r)):
    if r[i] == high_prob:
        index = i

print("The input object is ", classes[index], "with probability of ", high_prob)