def __init__()

in siammot/data/image_dataset.py [0:0]

• 27 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self,
                 dataset: COCO,
                 image_dir,
                 transforms=None,
                 frames_per_image=1,
                 amodal=False,
                 skip_empty=True,
                 min_object_area=0,
                 use_crowd=False,
                 include_bg=False,
                 ):
        """
        :param dataset: the ingested dataset with COCO-format
        :param transforms: image transformation
        :param frames_per_image: how many image copies are generated from a single image
        :param amodal: whether to use amodal ground truth (no image boundary clipping)
        :param include_bg: whether to include the full background images during training
        """

        self.dataset = dataset
        self.image_dir = image_dir
        self.transforms = transforms
        self.frames_per_image = frames_per_image

        self._skip_empty = skip_empty
        self._min_object_area = min_object_area
        self._use_crowd = use_crowd
        self._amodal = amodal
        self._include_bg = include_bg
        self._det_classes = [c['name'] for c in self.dataset.loadCats(self.dataset.getCatIds())]

        # These are tha mapping table of COCO labels
        self.json_category_id_to_contiguous_id = {
            v: i+1 for i, v in enumerate(self.dataset.getCatIds())
        }

        self._labels, self._im_aspect_ratios, self._items, self._ids \
            = self._dataset_preprocess()

        self.id_to_img_map = {k: v for k, v in enumerate(self._ids)}