//  [python]
def getPrediction(options):
    tf.reset_default_graph()

    options.batchSize = 1
    min_after_dequeue = 1000

    reader = RecordReaderAll()
    if options.dataset == 'SUNCG':
        filename_queue = tf.train.string_input_producer(['/mnt/vision/PlaneNet/planes_SUNCG_val.tfrecords'], num_epochs=10000)
    elif options.dataset == 'NYU_RGBD':
        filename_queue = tf.train.string_input_producer(['/mnt/vision/PlaneNet/planes_nyu_rgbd_val.tfrecords'], num_epochs=1)
        options.deepSupervision = 0
        options.predictLocal = 0
    elif options.dataset == 'matterport':
        filename_queue = tf.train.string_input_producer(['/mnt/vision/PlaneNet/planes_matterport_val.tfrecords'], num_epochs=1)
    else:
        filename_queue = tf.train.string_input_producer(['/mnt/vision/PlaneNet/planes_scannet_val.tfrecords'], num_epochs=1)
        pass

    img_inp, global_gt_dict, local_gt_dict = reader.getBatch(filename_queue, numOutputPlanes=options.numOutputPlanes, batchSize=options.batchSize, min_after_dequeue=min_after_dequeue, getLocal=True, random=False)



    training_flag = tf.constant(False, tf.bool)

    options.gpu_id = 0
    global_pred_dict, local_pred_dict, deep_pred_dicts = build_graph(img_inp, img_inp, training_flag, options)

    var_to_restore = tf.all_variables()


    config=tf.ConfigProto()
    config.gpu_options.allow_growth=True
    config.allow_soft_placement=True
    init_op = tf.group(tf.global_variables_initializer(),
                       tf.local_variables_initializer())


    width_high_res = 640
    height_high_res = 480


    pred_dict = {}
    with tf.Session(config=config) as sess:
        sess.run(init_op)
        #var_to_restore = [v for v in var_to_restore if 'res4b22_relu_non_plane' not in v.name]
        loader = tf.train.Saver(var_to_restore)
        loader.restore(sess, "%s/checkpoint.ckpt"%(options.checkpoint_dir))
        #loader.restore(sess, options.fineTuningCheckpoint)

        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)


        try:
            predDepths = []
            predPlanes = []
            predSegmentations = []
            predSemantics = []
            predNonPlaneDepths = []
            predNonPlaneNormals = []
            predNonPlaneMasks = []
            predBoundaries = []
            for index in xrange(options.startIndex + options.numImages):
                if index % 10 == 0:
                    print(('image', index))
                    pass
                t0=time.time()

                img, global_gt, global_pred = sess.run([img_inp, global_gt_dict, global_pred_dict])

                if index < options.startIndex:
                    continue


                pred_p = global_pred['plane'][0]
                pred_s = global_pred['segmentation'][0]

                pred_np_m = global_pred['non_plane_mask'][0]
                pred_np_d = global_pred['non_plane_depth'][0]
                pred_np_n = global_pred['non_plane_normal'][0]

                if global_gt['info'][0][19] > 1 and global_gt['info'][0][19] < 4 and False:
                    pred_np_n = calcNormal(pred_np_d.squeeze(), global_gt['info'][0])
                    pass


                #pred_b = global_pred['boundary'][0]
                predNonPlaneMasks.append(pred_np_m)
                predNonPlaneDepths.append(pred_np_d)
                predNonPlaneNormals.append(pred_np_n)
                #predBoundaries.append(pred_b)

                all_segmentations = np.concatenate([pred_s, pred_np_m], axis=2)

                plane_depths = calcPlaneDepths(pred_p, width_high_res, height_high_res, global_gt['info'][0])

                pred_np_d = np.expand_dims(cv2.resize(pred_np_d.squeeze(), (width_high_res, height_high_res)), -1)
                all_depths = np.concatenate([plane_depths, pred_np_d], axis=2)

                all_segmentations = np.stack([cv2.resize(all_segmentations[:, :, planeIndex], (width_high_res, height_high_res)) for planeIndex in xrange(all_segmentations.shape[-1])], axis=2)

                segmentation = np.argmax(all_segmentations, 2)
                pred_d = all_depths.reshape(-1, options.numOutputPlanes + 1)[np.arange(height_high_res * width_high_res), segmentation.reshape(-1)].reshape(height_high_res, width_high_res)

                if 'semantics' in global_pred:
                    #cv2.imwrite('test/semantics.png', drawSegmentationImage(np.argmax(global_pred['semantics'][0], axis=-1)))
                    #exit(1)
                    predSemantics.append(np.argmax(global_pred['semantics'][0], axis=-1))
                else:
                    predSemantics.append(np.zeros((HEIGHT, WIDTH)))
                    pass

                predDepths.append(pred_d)
                predPlanes.append(pred_p)
                predSegmentations.append(segmentation)
                continue
            pred_dict['plane'] = np.array(predPlanes)
            pred_dict['segmentation'] = np.array(predSegmentations)
            pred_dict['depth'] = np.array(predDepths)
            #pred_dict['semantics'] = np.array(predSemantics)
            pred_dict['np_depth'] = np.array(predNonPlaneDepths)
            #pred_dict['np_normal'] = np.array(predNonPlaneNormals)
            pred_dict['np_mask'] = np.array(predNonPlaneMasks)
            #pred_dict['boundary'] = np.array(predBoundaries)
            pass
        except tf.errors.OutOfRangeError:
            print('Done training -- epoch limit reached')
        finally:
            # When done, ask the threads to stop.
            coord.request_stop()
            pass

        # Wait for threads to finish.
        coord.join(threads)
        sess.close()
        pass
    return pred_dict