def motion_blur_param_generator(
batch_size: int,
kernel_size: Union[int, Tuple[int, int]],
angle: UnionFloat,
direction: UnionFloat,
border_type: Union[int, str, BorderType] = BorderType.CONSTANT.name,
same_on_batch: bool = True) -> Dict[str, torch.Tensor]:
angle_bound: torch.Tensor = _check_and_bound(angle, 'angle', center=0.)
direction_bound: torch.Tensor = _check_and_bound(direction,
'direction',
center=0.,
bounds=(-1, 1))
if isinstance(kernel_size, int):
ksize_factor = torch.tensor([kernel_size] * batch_size)
elif isinstance(kernel_size, tuple):
ksize_x, ksize_y = kernel_size
ksize_factor = _adapted_uniform(
(batch_size, ), ksize_x // 2, ksize_y // 2,
same_on_batch).int() * 2 + 1
else:
raise TypeError(f"Unsupported type: {type(kernel_size)}")
angle_factor = _adapted_uniform((batch_size, ), angle_bound[0],
angle_bound[1], same_on_batch)
direction_factor = _adapted_uniform((batch_size, ), direction_bound[0],
direction_bound[1], same_on_batch)
return dict(ksize_factor=ksize_factor,
angle_factor=angle_factor,
direction_factor=direction_factor,
border_type=torch.tensor(BorderType.get(border_type).value))
def random_color_jitter_generator(
batch_size: int,
brightness: FloatUnionType = 0.,
contrast: FloatUnionType = 0.,
saturation: FloatUnionType = 0.,
hue: FloatUnionType = 0.,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random color jiter parameters for a batch of images.
Args:
batch_size (int): the number of images.
brightness (float or tuple): Default value is 0
contrast (float or tuple): Default value is 0
saturation (float or tuple): Default value is 0
hue (float or tuple): Default value is 0
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
brightness_bound: torch.Tensor = _check_and_bound(brightness,
'brightness',
center=1.,
bounds=(0, float('inf')))
contrast_bound: torch.Tensor = _check_and_bound(contrast,
'contrast',
center=1.,
bounds=(0, float('inf')))
saturation_bound: torch.Tensor = _check_and_bound(saturation,
'saturation',
center=1.,
bounds=(0, float('inf')))
hue_bound: torch.Tensor = _check_and_bound(hue, 'hue', bounds=(-0.5, 0.5))
brightness_factor = _adapted_uniform((batch_size, ), brightness_bound[0],
brightness_bound[1], same_on_batch)
contrast_factor = _adapted_uniform((batch_size, ), contrast_bound[0],
contrast_bound[1], same_on_batch)
hue_factor = _adapted_uniform((batch_size, ), hue_bound[0], hue_bound[1],
same_on_batch)
saturation_factor = _adapted_uniform((batch_size, ), saturation_bound[0],
saturation_bound[1], same_on_batch)
return dict(brightness_factor=brightness_factor,
contrast_factor=contrast_factor,
hue_factor=hue_factor,
saturation_factor=saturation_factor,
order=torch.randperm(4))
def random_color_jitter_generator(
batch_size: int,
brightness: FloatUnionType = 0.,
contrast: FloatUnionType = 0.,
saturation: FloatUnionType = 0.,
hue: FloatUnionType = 0.,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random color jiter parameters for a batch of images.
Args:
batch_size (int): the number of images.
brightness (float or tuple): Default value is 0
contrast (float or tuple): Default value is 0
saturation (float or tuple): Default value is 0
hue (float or tuple): Default value is 0
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
dict: generated parameter dictionary.
See :class:`~kornia.augmentation.ColorJitter` for details.
"""
brightness_bound: torch.Tensor = _check_and_bound(brightness,
'brightness',
center=1.,
bounds=(0, 2))
contrast_bound: torch.Tensor = _check_and_bound(contrast,
'contrast',
center=1.)
saturation_bound: torch.Tensor = _check_and_bound(saturation,
'saturation',
center=1.)
hue_bound: torch.Tensor = _check_and_bound(hue, 'hue', bounds=(-0.5, 0.5))
brightness_factor = _adapted_uniform((batch_size, ), brightness_bound[0],
brightness_bound[1], same_on_batch)
contrast_factor = _adapted_uniform((batch_size, ), contrast_bound[0],
contrast_bound[1], same_on_batch)
hue_factor = _adapted_uniform((batch_size, ), hue_bound[0], hue_bound[1],
same_on_batch)
saturation_factor = _adapted_uniform((batch_size, ), saturation_bound[0],
saturation_bound[1], same_on_batch)
return {
"brightness_factor": brightness_factor,
"contrast_factor": contrast_factor,
"hue_factor": hue_factor,
"saturation_factor": saturation_factor,
"order": torch.randperm(4)
}
def random_solarize_generator(
batch_size: int,
thresholds: FloatUnionType = 0.1,
additions: FloatUnionType = 0.1,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random solarize parameters for a batch of images. For each pixel in the image less than threshold,
we add 'addition' amount to it and then clip the pixel value to be between 0 and 1.0
Args:
batch_size (int): the number of images.
thresholds (float or tuple): Pixels less than threshold will selected. Otherwise, subtract 1.0 from the pixel.
Default value is 0.1
additions (float or tuple): The value is between -0.5 and 0.5. Default value is 0.1
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
thresholds_bound: torch.Tensor = _check_and_bound(thresholds,
'thresholds',
center=0.5,
bounds=(0., 1.))
additions_bound: torch.Tensor = _check_and_bound(additions,
'additions',
bounds=(-0.5, 0.5))
thresholds_factor = _adapted_uniform(
(batch_size, ), thresholds_bound[0].float(),
thresholds_bound[1].float(), same_on_batch)
additions_factor = _adapted_uniform(
(batch_size, ), additions_bound[0].float(), additions_bound[1].float(),
same_on_batch)
return dict(thresholds_factor=thresholds_factor,
additions_factor=additions_factor)