def METHOD_NAME(self):
    filenames = self._load_filenames()
    for filename in filenames:
        filename_path = self.annotations_path + filename
        tree = ElementTree.parse(filename_path)
        root = tree.getroot()
        image_name = root.find('filename').text
        box_data = []
        difficulties = []
        size_tree = root.find('size')
        width = float(size_tree.find('width').text)
        height = float(size_tree.find('height').text)
        # check evaluate flag
        if self.evaluate:
            width = 1
            height = 1
        for object_tree in root.findall('object'):
            difficulty = int(object_tree.find('difficult').text)
            if difficulty == 1 and not (self.with_difficult_samples):
                continue
            class_name = object_tree.find('name').text
            if class_name in self.class_names:
                class_arg = self.class_to_arg[class_name]
                bounding_box = object_tree.find('bndbox')
                # VOC dataset format follows Matlab,
                # in which indexes start from 0
                xmin = (float(bounding_box.find('xmin').text) - 1.0) / width
                ymin = (float(bounding_box.find('ymin').text) - 1.0) / height
                xmax = (float(bounding_box.find('xmax').text) - 1.0) / width
                ymax = (float(bounding_box.find('ymax').text) - 1.0) / height
                box_data.append([xmin, ymin, xmax, ymax, class_arg])
                difficulties.append(difficulty)
        if len(box_data) == 0:
            continue
        # self.data[self.images_path + image_name] = label_data
        image_path = self.images_path + image_name
        box_data = np.asarray(box_data)
        difficulties = np.asarray(difficulties, dtype=bool)
        if self.evaluate:
            self.data.append({'image': image_path,
                              'boxes': box_data,
                              'difficulties': difficulties})
        else:
            self.data.append({'image': image_path, 'boxes': box_data})