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)