def __getitem__()

in datasets/coco_panoptic.py [0:0]

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

    def __getitem__(self, idx):
        ann_info = self.coco['annotations'][idx] if "annotations" in self.coco else self.coco['images'][idx]
        img_path = Path(self.img_folder) / ann_info['file_name'].replace('.png', '.jpg')
        ann_path = Path(self.ann_folder) / ann_info['file_name']

        img = Image.open(img_path).convert('RGB')
        w, h = img.size
        if "segments_info" in ann_info:
            masks = np.asarray(Image.open(ann_path), dtype=np.uint32)
            masks = rgb2id(masks)

            ids = np.array([ann['id'] for ann in ann_info['segments_info']])
            masks = masks == ids[:, None, None]

            masks = torch.as_tensor(masks, dtype=torch.uint8)
            labels = torch.tensor([ann['category_id'] for ann in ann_info['segments_info']], dtype=torch.int64)

        target = {}
        target['image_id'] = torch.tensor([ann_info['image_id'] if "image_id" in ann_info else ann_info["id"]])
        if self.return_masks:
            target['masks'] = masks
        target['labels'] = labels

        target["boxes"] = masks_to_boxes(masks)

        target['size'] = torch.as_tensor([int(h), int(w)])
        target['orig_size'] = torch.as_tensor([int(h), int(w)])
        if "segments_info" in ann_info:
            for name in ['iscrowd', 'area']:
                target[name] = torch.tensor([ann[name] for ann in ann_info['segments_info']])

        if self.transforms is not None:
            img, target = self.transforms(img, target)

        return img, target