test_queue=tf.train.string_input_producer(images)
reader=tf.WholeFileReader()
key , image_data = reader.read(test_queue)
decoded_image = tf.image.decode_jpeg(image_data, channels=MODEL_INPUT_DEPTH)
init_op=tf.initialize_all_variables()
with tf.Session() as sess:
  sess.run(init_op)
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(sess=sess,coord=coord)
  for i in range(len(images)):
    try:
      image_tensor = decoded_image.eval()
      shape=image_tensor.shape
      if shape[0]<FLAGS.min_width or shape[1]<FLAGS.min_width:
        print "bad too small ",shape[0],"x",shape[1]," ",images[i]
    except Exception as e:
      print "bad some other reason above" + images[i]
  coord.request_stop()
  coord.join(threads)