How to classify image in real time using tensorflow?

I'm trying to use raspberry pi camera to capture image and classify the image in real time into three classes. What I did is using the code below. It can predict in the first iteration. The problem is that it shows me ran out of memory after the second iteration. Is there anyway to fix this?

import numpy as np
import tensorflow as tf
import argparse
import os
import sys

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


def run_inference(images, out_file, labels, model_file, k=5):

    # Creates graph from saved GraphDef.
    create_graph(model_file)

    if out_file:
        out_file = open(out_file, 'wb', 1)

    with tf.Session() as sess:
        softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
        for img in images:
            if not tf.gfile.Exists(img):
                tf.logging.fatal('File does not exist %s', img)
                continue
            image_data = tf.gfile.FastGFile(img, 'rb').read()


            predictions = sess.run(softmax_tensor,
                               {'DecodeJpeg/contents:0': image_data})
            predictions = np.squeeze(predictions)
            top_k = predictions.argsort()[-k:][::-1]  # Getting top k predictions

            vals = []
            for node_id in top_k:
                human_string = labels[node_id]
                score = predictions[node_id]
                vals.append('%s=%.5f' % (human_string, score))
           rec = "%s\t %s" % (img, ", ".join(vals))
            if out_file:
                out_file.write(rec)
                out_file.write("\n")
            else:
                print(rec)    
    if out_file:
        print("Output stored to a file")
        out_file.close()

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Classify Image(s)')
    parser.add_argument('-i','--in', help='Input Image file ')
    parser.add_argument('-li','--list', help='List File having input image paths')
    parser.add_argument('-o','--out', help='Output file for storing the content')
    parser.add_argument('-m','--model', help='model file path (protobuf)', required=True)
    parser.add_argument('-l','--labels', help='labels text file', required=True)
   parser.add_argument('-r','--root', help='path to root directory of input data')
    args = vars(parser.parse_args())
    # Read input
    if not args['in'] and not args['list']:
        print("Either -in or -list option is required.")
        sys.exit(1)
    if args['in']:
        images = [args['in']]
    else:  # list must be given
        with open(args['list']) as ff:
            images = filter(lambda x: x, map(lambda y: y.strip(), ff.readlines()))

    # if a separate root directory given then make a new path
    if args['root']:
        print("Input data from  : %s" % args['root'])
        images = map(lambda p: os.path.join(args['root'], p), images)

    with open(args['labels'], 'rb') as f:
        labels = [str(w).replace("\n", "") for w in f.readlines()]

    while True:
        imagename='/home/pi/Desktop/camerasnap.jpg'
        images=raspi.capture(imagename)
        run_inference(images=images, out_file=args['out'], labels=labels, model_file=args['model'])