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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.image.d_lambda import spectral_distortion_index
from torchmetrics.functional.image.d_s import spatial_distortion_index
from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE
if not _TORCHVISION_AVAILABLE:
__doctest_skip__ = ["quality_with_no_reference"]
def quality_with_no_reference(
preds: Tensor,
ms: Tensor,
pan: Tensor,
pan_lr: Optional[Tensor] = None,
alpha: float = 1,
beta: float = 1,
norm_order: int = 1,
window_size: int = 7,
reduction: Literal["elementwise_mean", "sum", "none"] = "elementwise_mean",
) -> Tensor:
"""Calculate `Quality with No Reference`_ (QualityWithNoReference_) also known as QNR.
Metric is used to compare the joint spectral and spatial distortion between two images.
Args:
preds: High resolution multispectral image.
ms: Low resolution multispectral image.
pan: High resolution panchromatic image.
pan_lr: Low resolution panchromatic image.
alpha: Relevance of spectral distortion.
beta: Relevance of spatial distortion.
norm_order: Order of the norm applied on the difference.
window_size: Window size of the filter applied to degrade the high resolution panchromatic image.
reduction: A method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean (default)
- ``'sum'``: takes the sum
- ``'none'``: no reduction will be applied
Return:
Tensor with QualityWithNoReference score
Raises:
ValueError:
If ``alpha`` or ``beta`` is not a non-negative real number.
Example:
>>> from torch import rand
>>> from torchmetrics.functional.image import quality_with_no_reference
>>> preds = rand([16, 3, 32, 32])
>>> ms = rand([16, 3, 16, 16])
>>> pan = rand([16, 3, 32, 32])
>>> quality_with_no_reference(preds, ms, pan)
tensor(0.9694)
"""
if not isinstance(alpha, (int, float)) or alpha < 0:
raise ValueError(f"Expected `alpha` to be a non-negative real number. Got alpha: {alpha}.")
if not isinstance(beta, (int, float)) or beta < 0:
raise ValueError(f"Expected `beta` to be a non-negative real number. Got beta: {beta}.")
d_lambda = spectral_distortion_index(preds, ms, norm_order, reduction)
d_s = spatial_distortion_index(preds, ms, pan, pan_lr, norm_order, window_size, reduction)
return (1 - d_lambda) ** alpha * (1 - d_s) ** beta
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