my own data set about fcis HOT 13 CLOSED

def create_pascal_voc_sbd_dataset(raw_learning_examples, out_dir, split_thresh=0.9, split_name='val.txt'):
    """
    Creates SBD dataset for training instance segmentation networks. For more info check
    http://home.bharathh.info/pubs/codes/SBD/download.html
    :param raw_learning_examples: list of learning examples
    :param out_dir: the output dataset directory
    :param split_thresh: the percentage of the training data.  Defaults to use 90% for training.
    :param split_name: the name of the non-training output file.  Defaults to val.txt file.
    """

    # create dataset dir
    layout_dir = os.path.join(out_dir, 'ImageSets', 'Main')
    images_dir = os.path.join(out_dir, 'img')
    inst_dir = os.path.join(out_dir, 'inst')
    cls_dir = os.path.join(out_dir, 'cls')

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)
    if not os.path.exists(layout_dir):
        os.makedirs(layout_dir)
    if not os.path.exists(images_dir):
        os.makedirs(images_dir)
    if not os.path.exists(inst_dir):
        os.makedirs(inst_dir)
    if not os.path.exists(cls_dir):
        os.makedirs(cls_dir)

    # shuffle the provided dataset to avoid construction bias
    shuffle(raw_learning_examples)

    # create the instance segmentation dataset
    train_cutoff = int(len(raw_learning_examples) * split_thresh)
    for learning_example_count, (img, xml_file) in enumerate(raw_learning_examples):
        print('preparing learning example {}/{}'.format(learning_example_count + 1, len(raw_learning_examples)))
        label_filename, detection_objects = parse(xml_file)
        filename_prefix = label_filename.split('.')[0]
        img_filename = filename_prefix + '.jpg'
        w, h = img.size
        cls_mask = np.zeros((h, w))
        inst_mask = np.zeros((h, w))

        for obj_id, obj in enumerate(detection_objects, 1):
            x_points = []
            y_points = []
            for x, y in obj.poly:
                x_points.append(x)
                y_points.append(y)
                # the problem in hand has one class only, hard code it as 1
                cls_mask[skipoly(np.array(y_points), np.array(x_points))] = 1
                inst_mask[skipoly(np.array(y_points),
                                  np.array(x_points))] = obj_id

        if learning_example_count < train_cutoff:
            layout_filename = 'train.txt'
        else:
            layout_filename = split_name

        # write the learning example to the proper layout file (train or validation))
        with open(os.path.join(layout_dir, layout_filename), 'a') as out_file:
            # write the learning example image
            out_file.write(filename_prefix + '\n')
        img.save(os.path.join(images_dir, img_filename))

        # write instance mask to inst folder
        np.save(os.path.join(inst_dir, filename_prefix), inst_mask)

        # write class mask to inst folder
        np.save(os.path.join(cls_dir, filename_prefix), cls_mask)