o / iR@sddlmZddlZddlZddlmZmZddlmZm Z m Z ddl m Z m Z mZddlmZddlZddlmZddlmZmZdd lmZdd lmZmZmZgd ZGd d d eZGdddeZGdddeZ dS)) annotationsN)ABCabstractmethod)CallableIterableSequence)_emptyisclass signature)Any)iter_patch_position) BaseWSIReader WSIReader)convert_to_tensor)PathLike ensure_tupleensure_tuple_rep)SplitterSlidingWindowSplitterWSISlidingWindowSplitterc@sFeZdZdZddd d ZedddZedddZedddZdS)ra9 A base class for splitting the inputs into iterable tuple of patches and locations Extend this class to support operations for `PatchInference`, e.g. SlidingPatchSplitter. Args: patch_size: the size of patches to be generated. device: the device where the patches are generated. N patch_sizeSequence[int] | intdevicetorch.device | str | NonereturnNonecCs||_||_dSNrr)selfrrrY/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/inferers/splitter.py__init__)s zSplitter.__init__inputsr tuplecCtd|jjd)aD Return the input spatial shape. Args: inputs: either a tensor of shape BCHW[D], representing a batch of images, or a filename (str) or list of filenames to the image(s). Raises: NotImplementedError: When the subclass does not override this method. Subclass  must implement this method.NotImplementedError __class____name__rr"rrr get_input_shape-s zSplitter.get_input_shapecCr$)a Return the actual spatial shape covered by the output split patches. For instance, if the input image is padded, the actual spatial shape will be enlarged and not the same as input spatial shape. Args: inputs: either a tensor of shape BCHW[D], representing a batch of images, or a filename (str) or list of filenames to the image(s). Raises: NotImplementedError: When the subclass does not override this method. r%r&r'r+rrr get_padded_shape<szSplitter.get_padded_shape,Iterable[tuple[torch.Tensor, Sequence[int]]]cCr$)a Split the input image (or batch of images) into patches and return pairs of (patch, location). Where location is the coordinate of top left [front] corner of a patch. Args: inputs: either a tensor of shape BCHW[D], representing a batch of images, or a filename (str) or list of filenames to the image(s). Raises: NotImplementedError: When the subclass does not override this method. r%r&r'r+rrr __call__MszSplitter.__call__r)rrrrrrr"r rr#r"r rr.) r* __module__ __qualname____doc__r!rr,r-r/rrrr rs   rcsveZdZdZ      d.d/fdd ZeddZddZd0ddZd1d$d%Z d2d'd(Z d2d)d*Z d3d,d-Z Z S)4ra Splits the input into patches with sliding window strategy and a possible overlap. It also allows offsetting the starting position and filtering the patches. Args: patch_size : the size of the patches to be generated. offset: the amount of offset for the patches with respect to the original input. Defaults to 0. overlap: the amount of overlap between patches in each dimension. It can be either a float in the range of [0.0, 1.0) that defines relative overlap to the patch size, or it can be a non-negative int that defines number of pixels for overlap. Defaults to 0.0. filter_fn: a callable to filter patches. It should accepts exactly two parameters (patch, location), and return True for a patch to keep. Defaults to no filtering. pad_mode: string define the mode for `torch.nn.functional.pad`. The acceptable values are `"constant"`, `"reflect"`, `"replicate"`, `"circular"` or `None`. Default to `"constant"`. If None, no padding will be applied, so it will drop the patches crossing the border of the image (either when the offset is negative or the image is non-divisible by the patch_size). pad_value: the value for `"constant"` padding. Defaults to 0. device: the device where the patches are generated. Defaults to the device of inputs. Note: When a scaler value is provided for `patch_size`, `offset`, or `overlap`, it is broadcasted to all the spatial dimensions. rNconstantrroverlap-Sequence[float] | float | Sequence[int] | intoffset filter_fnCallable | Nonepad_mode str | None pad_value float | intrrrrcstj||d||_tt|dtr'tddt|Dr'td|dtddt|Dr:td|d ||_| ||_ ||_ ||_ |j s`td dt|Drbtd |j d dSdS) Nrrcss |] }|dkp |dkVqdS)r5g?Nr.0ovrrr sz1SlidingWindowSplitter.__init__..z1Relative overlap must be between 0.0 and 1.0 but zZ is given. If you wish to use number of pixels as overlap, please provide integer numbers.cs|]}|dkVqdSrNrr@rrr rCz1Number of pixels for overlap cannot be negative. z is given. csrDrErrAoffrrr rCrFzDNegative `offset`requires a valid padding mode but `mode` is set to .) superr!r9 isinstancerfloatany ValueErrorr7_validate_filter_fnr:r<r>)rrr7r9r:r<r>rr)rr r!ws (  zSlidingWindowSplitter.__init__cCst|r9t|}t|j}tdd|jD}|dkr(td|d|d|dkr7td|d|d|S|durGtd t|d |S) NcSsg|] }|jtur|qSr)defaultr)rAvrrr sz=SlidingWindowSplitter._validate_filter_fn..z``filter_fn` requires to accept at least two parameters (patch, location).The provided callable (z) has z parameters.z``filter_fn` can have at most two positional parameters (patch, location).The provided callable (z positional parameters.zN`filter_fn` should be a callable with two input parameters (patch, location). is given.)callabler len parametersvaluesrNtype)r:sign_paramsZnum_pos_paramsrrr rOs8  z)SlidingWindowSplitter._validate_filter_fnc Csdgd|}|js|dfSdd|D|ddd<g}t||||D]2\}} } } | dkr0d} n t| trD| || t| | | } n | || t| | } || q#||ddd<|t|dddfS)NrrTFcss|] }t|d VqdSrE)minrGrrr rCz.)r<ziprKrLroundappendrM) r spatial_shape spatial_ndimrr9r7pad_sizeZ end_paddingshrHpsrB pad_amountrrr _calculate_pad_sizes  z)SlidingWindowSplitter._calculate_pad_sizerc Sequence[int]Ltuple[tuple[int, ...], tuple[float, ...] | tuple[int, ...], tuple[int, ...]]cst|}t|j|}t|j|tfddt|Dtddt|Dr5tdd|dt|j|}t|||D]$\}}}|| krVtd|d|d ||kretd |d |dqA||fS) Nc3s,|]\}}|r |ntddVqdSrE)rZ)rAopr7rr rCs*zDSlidingWindowSplitter._get_valid_shape_parameters..css|] \}}||kVqdSrr)rArBrgrrr rCr^z `overlap` (z$) cannot be larger than patch size (z).zNegative `offset` (z&) cannot be larger than `patch_size` (z) in magnitude.z `offset` (z%) cannot be larger than inputs size () rWrrr7r#r`rMrNr9)rrcrdrr9rHrgrfrrnr _get_valid_shape_parameterss     z1SlidingWindowSplitter._get_valid_shape_parametersr"r locationtuple[int, ...]cCs.tdfdtddt||D}||S)NrTcss"|] \}}t|||VqdSr)slice)rAlocrgrrr rC z3SlidingWindowSplitter._get_patch..)rrr#r`)rr"rprslicesrrr _get_patchs&z SlidingWindowSplitter._get_patchr#cCst|jddS)a  Return the input spatial shape. Args: inputs: either a tensor of shape BCHW[D], representing a batch of images, or a filename (str) or list of filenames to the image(s). Returns: spatial_shape rTN)r#shaper+rrr r,s z%SlidingWindowSplitter.get_input_shapec Cst||}|js |St|}||\}}}||||||\}}tddt||ddd|dddD} | S)a Return the actual spatial shape covered by the output split patches. For instance, if the input image is padded, the actual spatial shape will be enlarged and not the same as input spatial shape. Args: inputs: either a tensor of shape BCHW[D], representing a batch of images, or a filename (str) or list of filenames to the image(s). Returns: padded_spatial_shape cs"|] \}}}|||VqdSrrrAssrgperrr rCrtz9SlidingWindowSplitter.get_padded_shape..r_NrT)r,r<rWrorir#r`) rr"rcrdrr7r9re_padded_spatial_shaperrr r-s .z&SlidingWindowSplitter.get_padded_shaper.c cs@t|tjstdt|d|jdd}t|}||\}}}||||||\}}|j rbt |rbtj j j ||ddd|j |jd}|jdd}|rbtddt||d ddD}t||||d D]3} ||| |} t| |jd } |rtd dt| |d ddD} |jdus|| | r| | fVqjdS) a/Split the input tensor into patches and return patches and locations. Args: inputs: either a torch.Tensor with BCHW[D] dimensions, representing an image or a batch of images Yields: tuple[torch.Tensor, Sequence[int]]: yields tuple of patch and location zThe input should be a tensor. rUrTN)modevaluecs|] \}}||VqdSrrrArHrmrrr rCr^z1SlidingWindowSplitter.__call__..r_Frcs|] \}}||VqdSrrrArsrmrrr rC!r^)rKtorchTensorrNrZrwrWrorir<rMnn functionalpadr>r#r`r rvrrr:) rr"rcrdrr7r9reis_start_paddedrppatchrrr r/s* $"" zSlidingWindowSplitter.__call__)r5rNr6rN)rrr7r8r9rr:r;r<r=r>r?rrrr)rcrjrrkr"r rprqrrqrr r0r1)r*r2r3r4r! staticmethodrOrirorvr,r-r/ __classcell__rrrPr r^s"     rcsXeZdZdZ      d)d*fdd Zd+ddZd,d d!Zd-d#d$Zd.d'd(ZZ S)/ra$ Splits the whole slide image input into patches with sliding window strategy and a possible overlap. This extracts patches from file without loading the entire slide into memory. It also allows offsetting the starting position and filtering the patches. Args: patch_size : the size of the patches to be generated. offset: the amount of offset for the patches with respect to the original input. Defaults to 0. overlap: the amount of overlap between patches in each dimension. It can be either a float in the range of [0.0, 1.0) that defines relative overlap to the patch size, or it can be a non-negative int that defines number of pixels for overlap. Defaults to 0.0. filter_fn: a callable to filter patches. It should accepts exactly two parameters (patch, location), and return True for a patch to keep. Defaults to no filtering. pad_mode: define the mode for padding. Either "constant" or None. Default to "constant". Padding is only supported with "OpenSlide" or "cuCIM" backend, and the filling value is 256. device: the device where the patches are generated. Defaults to the device of inputs. reader: the module to be used for loading whole slide imaging. If `reader` is - a string, it defines the backend of `monai.data.WSIReader`. Defaults to "OpenSlide". - a class (inherited from `BaseWSIReader`), it is initialized and set as wsi_reader. - an instance of a class inherited from `BaseWSIReader`, it is set as the wsi_reader. To obtain an optimized performance please use either "cuCIM" or "OpenSlide" backend. reader_kwargs: the arguments to pass to `WSIReader` or the provided whole slide reader class. For instance, level=2, dtype=torch.float32, etc. Note that if `level` is not provided, `level=0` is assumed. Note: When a scaler value is provided for `patch_size`, `offset`, or `overlap`, it is broadcasted to all the spatial dimensions. r5rNr6 OpenSliderrr7r8r9r:r;r<r=rrreader0str | BaseWSIReader | type[BaseWSIReader] | None reader_kwargsdictrrc  sr|r|dkrtd|dtj||||||d||||jjdvr7td|jjddSdS)Nr6zFThe underlying wsi readers only support for constant padding. pad_mod=rU)rr7r9r:rr<) openslidecucimzWSIReader with z backend is not supported for efficiently loading patches. This may cause an significant slow down and a large memory foot print. Please use other backends such as 'OpenSlide' or 'cuCIM' instead.) rNrJr! _set_readerrbackendlowerwarningswarn) rrr7r9r:r<rrrrPrr r!Hs    z!WSISlidingWindowSplitter.__init__cCsz|||_t|trtdd|i|j|_dSt|r+t|tr+|di|j|_dSt|tr5||_dStd|d)a Set the WSI reader object based on the input reader Args: reader: the module to be used for loading whole slide imaging. If `reader` is - a string, it defines the backend of `monai.data.WSIReader`. Defaults to cuCIM. - a class (inherited from `BaseWSIReader`), it is initialized and set as wsi_reader. - an instance of a class inherited from `BaseWSIReader`, it is set as the wsi_reader. rzUnsupported reader type: rINr) rrKstrrrr issubclassr rN)rrrrrr rds    z$WSISlidingWindowSplitter._set_readerr"r rprqcCs|jj|||d\}}|dS)N)wsirpsize)rget_data)rr"rprrr|rrr rvzsz#WSISlidingWindowSplitter._get_patchr#cCs(|j|}|jdd}|j||S)a  Return the input spatial shape. Args: inputs: either a tensor of shape BCHW[D], representing a batch of images, or a filename (str) or list of filenames to the image(s). Returns: spatial_shape levelr)rreadrgetget_size)rr"rrrrr r,~s z(WSISlidingWindowSplitter.get_input_shapePathLike | Sequence[PathLike]r.c #st|tst|trt|dkrtd|d}t|ttjfs+tdt|d|j |}|j dd}|j |||j ||}t|}|dkrVtd|d ||\}}}||||||\} } t| rtd d t|| dd d| d d dD}| rtd d t|| dd dD}t||||dD]>} tfdd | D} ||| |} t| |jd} | rtdd t| | dd dD} |jd us|| | r| | fVqd S)zSplit the input tensor into patches and return patches and locations. Args: inputs: the file path to a whole slide image. Yields: tuple[torch.Tensor, Sequence[int]]: yields tuple of patch and location r_zSOnly batch size of one would work for wsi image. Please provide one path at a time.rz7The input should be the path to the whole slide image. rUrrTz"WSIReader only support 2D images. z spatial dimension is provided.csrxrrryrrr rCrtz4WSISlidingWindowSplitter.__call__..Ncsrrrrrrr rCr^Fc3s|] }t|VqdSr)ra)rArsdownsample_ratiorr rCr^rcsrrrrrrr rCr^)rKrrrWrNosrrZrrrrget_downsample_ratiorrorirMr#r`r rvrrr:)rr"rrrcrdrr7r9rerrp location_rrrr r/s<  ."" z!WSISlidingWindowSplitter.__call__)r5rNr6Nr)rrr7r8r9rr:r;r<r=rrrrrrrr)rrrrrrrr0)r"rrr.) r*r2r3r4r!rrvr,r/rrrrPr r's#   r)! __future__rrrabcrrcollections.abcrrrinspectrr r typingr rmonai.data.utilsr monai.data.wsi_readerr rmonai.transforms.utility.arrayrmonai.utils.miscrrr__all__rrrrrrr s"    ?J