def __call__()

in seamseg/data/transform.py [0:0]

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

    def __call__(self, img, msk, cat, iscrowd):
        # Random flip
        if self.random_flip:
            img, msk = self._random_flip(img, msk)

        # Adjust scale, possibly at random
        if self.random_scale is not None:
            target_size = self._random_target_size()
        else:
            target_size = self.shortest_size
        scale = self._adjusted_scale(img.size[0], img.size[1], target_size)

        out_size = tuple(int(dim * scale) for dim in img.size)
        img = img.resize(out_size, resample=Image.BILINEAR)
        msk = [m.resize(out_size, resample=Image.NEAREST) for m in msk]

        # Wrap in np.array
        cat = np.array(cat, dtype=np.int32)
        iscrowd = np.array(iscrowd, dtype=np.uint8)

        # Image transformations
        img = tfn.to_tensor(img)
        img = self._normalize_image(img)

        # Label transformations
        msk = np.stack([np.array(m, dtype=np.int32, copy=False) for m in msk], axis=0)
        msk, cat, iscrowd = self._compact_labels(msk, cat, iscrowd)

        # Convert labels to torch and extract bounding boxes
        msk = torch.from_numpy(msk.astype(np.long))
        cat = torch.from_numpy(cat.astype(np.long))
        iscrowd = torch.from_numpy(iscrowd)
        bbx = extract_boxes(msk, cat.numel())

        return dict(img=img, msk=msk, cat=cat, iscrowd=iscrowd, bbx=bbx)