o .wi @sddlmZddlmZddlmZddlmZddlm Z ddl m Z e s)dgZ  dd ed ededeede de dedededdefddZdS))Optional)Tensor)Literal)spectral_distortion_index)spatial_distortion_index)_TORCHVISION_AVAILABLEquality_with_no_referenceNelementwise_meanpredsmspanpan_lralphabeta norm_order window_size reduction)r sumnonereturnc Cst|ttfr |dkrtd|dt|ttfr|dkr&td|dt||||} t|||||||} d| |d| |S)a`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) rz>Expected `alpha` to be a non-negative real number. Got alpha: .zsF