from matplotlib import pyplot as plt
import set_paths
FLAGS = set_paths.FLAGS
slim = tf.contrib.slim
from tf_image_segmentation.models.fcn_8s import FCN_8s
from matplotlib import pyplot as plt
from tf_image_segmentation.utils.pascal_voc import pascal_segmentation_lut
from tf_image_segmentation.utils.tf_records import read_tfrecord_and_decode_into_image_annotation_pair_tensors
from tf_image_segmentation.utils.inference import adapt_network_for_any_size_input
from tf_image_segmentation.utils.visualization import visualize_segmentation_adaptive
pascal_voc_lut = pascal_segmentation_lut()
tfrecord_filename = 'pascal_augmented_val.tfrecords'
number_of_classes = 21
filename_queue = tf.train.string_input_producer([tfrecord_filename],
num_epochs=1)
image, annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors(
filename_queue)
# Fake batch for image and annotation by adding
# leading empty axis.
image_batch_tensor = tf.expand_dims(image, axis=0)
annotation_batch_tensor = tf.expand_dims(annotation, axis=0)
parser.add_option('--labels', dest="labels", type="int",
help="Number of labels")
parser.add_option('--iter', dest="iter", type="int",
help="iter")
(options, args) = parser.parse_args()
restoremodel = options.checkpoint
slim = tf.contrib.slim
pascal_voc_lut = pascal_segmentation_lut()
tfrecord_filename = options.tf_records
number_of_classes = options.labels
filename_queue = tf.train.string_input_producer(
[tfrecord_filename], num_epochs=1)
image, annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors(filename_queue)
# Fake batch for image and annotation by adding
# leading empty axis.
image_batch_tensor = tf.expand_dims(image, axis=0)
annotation_batch_tensor = tf.expand_dims(annotation, axis=0)
