Source code for torchaug.transforms._meta

# ==================================
# Copyright: CEA-LIST/DIASI/SIALV/
# Author : Torchaug Developers
# License: CECILL-C
# ==================================

# Code partially based on Torchvision (BSD 3-Clause License), available at:
#   https://github.com/pytorch/vision

from __future__ import annotations

from typing import Any, Dict, Union

from torchaug import ta_tensors

from . import functional as F
from ._transform import Transform


[docs] class ConvertBoundingBoxFormat(Transform): """Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY". Args: format: output bounding box format. Possible values are defined by :class:`~torchaug.ta_tensors.BoundingBoxFormat` and string values match the enums, e.g. "XYXY" or "XYWH" etc. """ _transformed_types = (ta_tensors.BoundingBoxes, ta_tensors.BatchBoundingBoxes) def __init__(self, format: Union[str, ta_tensors.BoundingBoxFormat]) -> None: super().__init__() self.format = format def _transform( self, inpt: Union[ta_tensors.BoundingBoxes, ta_tensors.BatchBoundingBoxes], params: Dict[str, Any], ) -> Union[ta_tensors.BoundingBoxes, ta_tensors.BatchBoundingBoxes]: return F.convert_bounding_box_format(inpt, new_format=self.format) # type: ignore[return-value, arg-type]
[docs] class ClampBoundingBoxes(Transform): """Clamp bounding boxes to their corresponding image dimensions. The clamping is done according to the bounding boxes' ``canvas_size`` meta-data. """ _transformed_types = (ta_tensors.BoundingBoxes, ta_tensors.BatchBoundingBoxes) def __init__(self) -> None: super().__init__() def _transform( self, inpt: Union[ta_tensors.BoundingBoxes, ta_tensors.BatchBoundingBoxes], params: Dict[str, Any], ) -> Union[ta_tensors.BoundingBoxes, ta_tensors.BatchBoundingBoxes]: return F.clamp_bounding_boxes(inpt) # type: ignore[return-value]