o .wiW+@sddlmZddlZddlmZddlmZddlmZddlm Z ddl m Z e s.dd gZ dd ed ed ed eede eeeeeff ddZ    dd ed ed ed eededededdefddZ    dd ed ed ed eededededdefdd ZdS))OptionalN)Tensor)Literal)universal_image_quality_index)reduce)_TORCHVISION_AVAILABLE!_spatial_distortion_index_computespatial_distortion_indexpredsmspanpan_lrreturnc Cst|jdkrtd|jd|j|jkr#td|jd|jd|j|jkr6td|jd|jd|durM|j|jkrMtd |jd |jdt|jdkr]td |jdt|jdkrmtd |jd|durt|jdkrtd |jd|jdd|jddkrtd|jd|jd|jdd|jddkrtd|jd|jd|dur|jdd|jddkrtd|jd |jd|jdd\}}|jdd\}}|jdd\}} ||krtd|d||| krtd|d| ||dkr td|d|d||dkr2td|d|d||dkrDtd|d|d| |dkrVtd| d|d|dur|jdd\} } | |krttd|d | d| |krtd|d | d||||fS)aUpdate and returns variables required to compute Spatial Distortion Index. Args: preds: High resolution multispectral image. ms: Low resolution multispectral image. pan: High resolution panchromatic image. pan_lr: Low resolution panchromatic image. Return: A tuple of Tensors containing ``preds``, ``ms``, ``pan`` and ``pan_lr``. Raises: TypeError: If ``preds``, ``ms``, ``pan`` and ``pan_lr`` don't have the same data type. ValueError: If ``preds``, ``ms``, ``pan`` and ``pan_lr`` don't have ``BxCxHxW shape``. ValueError: If ``preds``, ``ms``, ``pan`` and ``pan_lr`` don't have the same batch and channel sizes. ValueError: If ``preds`` and ``pan`` don't have the same dimension. ValueError: If ``ms`` and ``pan_lr`` don't have the same dimension. ValueError: If ``preds`` and ``pan`` don't have dimension which is multiple of that of ``ms``. z3Expected `preds` to have BxCxHxW shape. Got preds: .zAExpected `preds` and `ms` to have the same data type. Got preds: z and ms: zBExpected `preds` and `pan` to have the same data type. Got preds: z and pan: NzEExpected `preds` and `pan_lr` to have the same data type. Got preds: z and pan_lr: z-Expected `ms` to have BxCxHxW shape. Got ms: z/Expected `pan` to have BxCxHxW shape. Got pan: z5Expected `pan_lr` to have BxCxHxW shape. Got pan_lr: zOExpected `preds` and `ms` to have the same batch and channel sizes. Got preds: zPExpected `preds` and `pan` to have the same batch and channel sizes. Got preds: zSExpected `preds` and `pan_lr` to have the same batch and channel sizes. Got preds: z?Expected `preds` and `pan` to have the same height. Got preds: z>Expected `preds` and `pan` to have the same width. Got preds: rzHExpected height of `preds` to be multiple of height of `ms`. Got preds: zFExpected width of `preds` to be multiple of width of `ms`. Got preds: zFExpected height of `pan` to be multiple of height of `ms`. Got preds: zDExpected width of `pan` to be multiple of width of `ms`. Got preds: z>> from torch import rand >>> preds = rand([16, 3, 32, 32]) >>> ms = rand([16, 3, 16, 16]) >>> pan = rand([16, 3, 32, 32]) >>> preds, ms, pan, pan_lr = _spatial_distortion_index_update(preds, ms, pan) >>> _spatial_distortion_index_compute(preds, ms, pan, pan_lr) tensor(0.0090) r#rNzNExpected `window_size` to be smaller than dimension of `ms`. Got window_size: rzWhen `pan_lr` is not provided as input to metric Spatial distortion index, torchvision should be installed. Please install with `pip install torchvision` or `pip install torchmetrics[image]`.r)resize)_uniform_filter)r'F)size antialias)device)rrr!torchvision.transforms.functionalr+#torchmetrics.functional.image.utilsr,torchzerosr/rangerabsr)r r r r r&r'r(lengthrrr+r, pan_degradedm1m2idiffr r r!rs. )     68cCsnt|tr |dkrtd|dt|tr|dkr"td|dt||||\}}}}t|||||||S)aCalculate `Spatial Distortion Index`_ (SpatialDistortionIndex_) also known as D_s. Metric is used to compare the 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. 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 SpatialDistortionIndex score Raises: TypeError: If ``preds``, ``ms``, ``pan`` and ``pan_lr`` don't have the same data type. ValueError: If ``preds``, ``ms``, ``pan`` and ``pan_lr`` don't have ``BxCxHxW shape``. ValueError: If ``preds``, ``ms``, ``pan`` and ``pan_lr`` don't have the same batch and channel sizes. ValueError: If ``preds`` and ``pan`` don't have the same dimension. ValueError: If ``ms`` and ``pan_lr`` don't have the same dimension. ValueError: If ``preds`` and ``pan`` don't have dimension which is multiple of that of ``ms``. ValueError: If ``norm_order`` is not a positive integer. ValueError: If ``window_size`` is not a positive integer. Example: >>> from torch import rand >>> from torchmetrics.functional.image import spatial_distortion_index >>> preds = rand([16, 3, 32, 32]) >>> ms = rand([16, 3, 16, 16]) >>> pan = rand([16, 3, 32, 32]) >>> spatial_distortion_index(preds, ms, pan) tensor(0.0090) rz@Expected `norm_order` to be a positive integer. Got norm_order: rzBExpected `window_size` to be a positive integer. Got window_size: ) isinstanceintrr"r)r r r r r&r'r(r r r!r s 9)N)Nr#r$r%)typingrr2rtyping_extensionsr!torchmetrics.functional.image.uqir"torchmetrics.utilities.distributedrtorchmetrics.utilities.importsr__doctest_skip__tupler"r=rr r r r r!s~       j N