Python scale_image_with_crop_padding Exemples

Exemple #1

Fichier : test.py Projet : UW-COSMOS/deeplab_v3

    4: 'text'
}

class_labels = [v for v in range((args.number_of_classes + 1))]
class_labels[-1] = 255

LOG_FOLDER = './tboard_logs'
TEST_DATASET_DIR = "./dataset/tfrecords"
TEST_FILE = 'test.tfrecords'

test_filenames = [os.path.join(TEST_DATASET_DIR, TEST_FILE)]
test_dataset = tf.data.TFRecordDataset(test_filenames)
test_dataset = test_dataset.map(
    tf_record_parser)  # Parse the record into tensors.
test_dataset = test_dataset.map(
    lambda image, annotation, image_shape: scale_image_with_crop_padding(
        image, annotation, image_shape, args.crop_size))
test_dataset = test_dataset.shuffle(buffer_size=100)
test_dataset = test_dataset.batch(args.batch_size)

iterator = test_dataset.make_one_shot_iterator()
batch_images_tf, batch_labels_tf, batch_shapes_tf = iterator.get_next()

logits_tf = network.deeplab_v3(batch_images_tf,
                               args,
                               is_training=False,
                               reuse=False)

valid_labels_batch_tf, valid_logits_batch_tf = training.get_valid_logits_and_labels(
    annotation_batch_tensor=batch_labels_tf,
    logits_batch_tensor=logits_tf,
    class_labels=class_labels)

Exemple #2

resnet_checkpoints_path = './resnet/checkpoints/'
download_resnet_checkpoint_if_necessary(resnet_checkpoints_path, args.resnet_model)



# =============================================================================
# # defining training and validation dataset
# =============================================================================

training_filenames = [os.path.join(TRAIN_DATASET_DIR,TRAIN_FILE)]
training_dataset = tf.data.TFRecordDataset(training_filenames)
training_dataset = training_dataset.map(tf_record_parser) # Parse the record in tensors
training_dataset = training_dataset.map(rescale_image_and_annotation_by_factor)
training_dataset = training_dataset.map(distort_randomly_image_color)
training_dataset = training_dataset.map(lambda image,annotation,image_shape:scale_image_with_crop_padding(image,annotation,image_shape,crop_size))
training_dataset = training_dataset.map(random_flip_image_and_annotation) 
training_dataset = training_dataset.repeat()  # no of epochs (no values means inf time repeat)
training_dataset = training_dataset.shuffle(buffer_size=500)
training_dataset = training_dataset.batch(args.batch_size)

validation_filenames = [os.path.join(TRAIN_DATASET_DIR,VALIDATION_FILE)]
validation_dataset = tf.data.TFRecordDataset(validation_filenames)
validation_dataset = validation_dataset.map(tf_record_parser) # Parse the record in tensors
validation_dataset = validation_dataset.map(lambda image,annotation,image_shape:scale_image_with_crop_padding(image,annotation,image_shape,crop_size))
validation_dataset = validation_dataset.shuffle(buffer_size = 100)
validation_dataset = validation_dataset.batch(args.batch_size)

class_labels = [ v for v in range(args.number_of_classes+1)]
class_labels[-1] = 255