def entity2target()

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

• 11 lines of code
• 7 McCabe index (conditional complexity)

    def entity2target(self, im: Image, entities: [AnnoEntity]):
        """
        Wrap up the entity to maskrcnn-benchmark compatible format - BoxList
        """
        boxes = [entity.bbox for entity in entities]
        ids = [int(entity.id) for entity in entities]
        # we only consider person tracking for now,
        # thus all the labels are 1,
        # reserve category 0 for background during training
        int_labels = [1 for _ in entities]

        boxes = torch.as_tensor(boxes).reshape(-1, 4)
        boxes = BoxList(boxes, im.size, mode='xywh').convert('xyxy')
        if not self.amodal:
            boxes = boxes.clip_to_image(remove_empty=False)
        boxes.add_field('labels', torch.as_tensor(int_labels, dtype=torch.int64))
        boxes.add_field('ids', torch.as_tensor(ids, dtype=torch.int64))

        return boxes