dev(narugo): fulfill docstrings (4f475a6d) · Commits · git-mirror / Imgutils

imgutils/preprocess/pillow.py

+51 −0

Original line number	Diff line number	Diff line
		@@ -801,6 +801,18 @@ def _parse_rescale(obj):


		class PillowPadToSize:
		"""
		A class for padding images to a specified size.

		This class provides functionality to pad images to a target size while maintaining
		the original image content. It supports various padding colors and interpolation methods.

		:param size: Target size as (width, height) tuple or single integer for square
		:param background_color: Color for padding area (RGB, RGBA, string color name, or integer)
		:param interpolation: PIL interpolation method for resizing
		:type interpolation: int
		"""

		def __init__(self, size: Union[Tuple[int, int], int],
		background_color: Union[str, int, Tuple[int, int, int], Tuple[int, int, int, int]] = 'white',
		interpolation: int = Image.BILINEAR):
		@@ -812,6 +824,16 @@ class PillowPadToSize:
		_parse_color_to_rgba(self.background_color)

		def __call__(self, pic):
		"""
		Apply padding transformation to the input image.

		:param pic: Input PIL Image
		:type pic: PIL.Image.Image

		:return: Padded image
		:rtype: PIL.Image.Image
		:raises TypeError: If input is not a PIL Image
		"""
		if not isinstance(pic, Image.Image):
		raise TypeError('pic should be PIL Image. Got {}'.format(type(pic)))

		@@ -823,6 +845,12 @@ class PillowPadToSize:
		)

		def __repr__(self) -> str:
		"""
		Return string representation of the class.

		:return: String representation
		:rtype: str
		"""
		interpolate_str = _PILLOW_TO_STR[self.interpolation]
		detail = f"(size={self.size}, interpolation={interpolate_str}, background_color={self.background_color})"
		return f"{self.__class__.__name__}{detail}"
		@@ -832,6 +860,19 @@ class PillowPadToSize:
		def _create_pad_to_size(size: Union[Tuple[int, int], int],
		background_color: Union[str, int, Tuple[int, int, int], Tuple[int, int, int, int]] = 'white',
		interpolation: str = 'bilinear'):
		"""
		Factory function to create PillowPadToSize instance.

		:param size: Target size for padding
		:type size: Union[Tuple[int, int], int]
		:param background_color: Color for padding area
		:type background_color: Union[str, int, Tuple[int, int, int], Tuple[int, int, int, int]]
		:param interpolation: Interpolation method name
		:type interpolation: str

		:return: PillowPadToSize instance
		:rtype: PillowPadToSize
		"""
		return PillowPadToSize(
		size=size,
		background_color=background_color,
		@@ -841,6 +882,16 @@ def _create_pad_to_size(size: Union[Tuple[int, int], int],

		@register_pillow_parse('pad_to_size')
		def _parse_pad_to_size(obj):
		"""
		Parse PillowPadToSize object to dictionary configuration.

		:param obj: Object to parse
		:type obj: Any

		:return: Configuration dictionary
		:rtype: dict
		:raises NotParseTarget: If object is not PillowPadToSize instance
		"""
		if not isinstance(obj, PillowPadToSize):
		raise NotParseTarget

imgutils/preprocess/torchvision.py

+61 −0

Original line number	Diff line number	Diff line
		@@ -78,6 +78,24 @@ def _get_interpolation_mode(value):


		def _get_int_from_interpolation_mode(value):
		"""
		Convert a torchvision.transforms.InterpolationMode enum value to its corresponding integer representation.

		This function performs the reverse operation of _get_interpolation_mode, converting
		InterpolationMode enum values back to their integer representations.

		:param value: The InterpolationMode enum value to convert
		:type value: InterpolationMode

		:return: The integer representation of the interpolation mode
		:rtype: int

		:raises TypeError: If the input value is not an InterpolationMode enum value

		:examples:
		>>> mode = InterpolationMode.BILINEAR
		>>> _get_int_from_interpolation_mode(mode) # Returns 2
		"""
		from torchvision.transforms import InterpolationMode
		if not isinstance(value, InterpolationMode):
		raise TypeError(
		@@ -358,6 +376,14 @@ if _HAS_TORCHVISION:
		"""
		Resize and center-pad PIL image to target size with background color.
		TorchVision-compatible transform that can be composed.

		:param size: Target size as (height, width) tuple or single int for square output
		:type size: Union[Tuple[int, int], int]
		:param background_color: Color to use for padding. Can be string name, RGB/RGBA tuple, or single int
		:param interpolation: Interpolation mode for resizing, defaults to BILINEAR
		:type interpolation: InterpolationMode

		:raises ValueError: If size or background_color format is invalid
		"""

		def __init__(self, size: Union[Tuple[int, int], int],
		@@ -371,6 +397,16 @@ if _HAS_TORCHVISION:
		_parse_color_to_rgba(self.background_color)

		def forward(self, pic):
		"""
		Apply padding transform to input image.

		:param pic: Input PIL Image
		:type pic: PIL.Image.Image

		:return: Padded image with target size
		:rtype: PIL.Image.Image
		:raises TypeError: If input is not a PIL Image
		"""
		if not isinstance(pic, Image.Image):
		raise TypeError('pic should be PIL Image. Got {}'.format(type(pic)))

		@@ -393,6 +429,20 @@ else:
		def _create_pad_to_size(size: Union[Tuple[int, int], int],
		background_color: Union[str, int, Tuple[int, int, int], Tuple[int, int, int, int]] = 'white',
		interpolation='bilinear'):
		"""
		Factory function to create PadToSize transform instance.

		:param size: Target size as (height, width) tuple or single int
		:type size: Union[Tuple[int, int], int]
		:param background_color: Color for padding
		:type background_color: Union[str, int, Tuple[int, int, int], Tuple[int, int, int, int]]
		:param interpolation: Interpolation mode name
		:type interpolation: str

		:return: PadToSize transform instance
		:rtype: PadToSize
		:raises AssertionError: If torchvision is not available
		"""
		assert PadToSize is not None
		return PadToSize(
		size=size,
		@@ -403,6 +453,17 @@ def _create_pad_to_size(size: Union[Tuple[int, int], int],

		@_register_parse('pad_to_size', safe=False)
		def _parse_pad_to_size(obj):
		"""
		Parse PadToSize transform object for serialization.

		:param obj: Transform object to parse
		:type obj: Any

		:return: Dictionary containing transform parameters
		:rtype: dict
		:raises NotParseTarget: If object is not a PadToSize instance
		:raises AssertionError: If torchvision is not available
		"""
		assert PadToSize is not None
		if not isinstance(obj, PadToSize):
		raise NotParseTarget