predict.py

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


def predict_image(filename, sess):
	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), 0, 0, 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)

	# 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, 2))

	# 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)
	return result


def test_data(testing_path, classes, session):
	error = 0
	success = 0
	for class_idx, classe in enumerate(classes):
		path = os.path.dirname(os.path.realpath(__file__)) + "/" + testing_path + "/"+classe
		for file in glob.glob(path + "/*.png"):
			res = predict_image(file, session)[0]
			if max(res) == res[class_idx]:
				success += 1
			else:
				error += 1

	return success, error


# Get Image
dir_path = os.path.dirname(os.path.realpath(__file__))

# Restore the saved model
sess = tf.Session()
# Step-1: Recreate the network graph.
saver = tf.train.import_meta_graph(dir_path + '/model-20/.meta')
# Step-2: Load the weights saved using the restore method.
saver.restore(sess, tf.train.latest_checkpoint(dir_path + '/model-20/'))

classes = ['rl', 'uni']
success, error = test_data("testing_data", classes, sess)
print(success, error)
taux = success / (success + error)

print("Succes rate is " + str(taux))