o oi[8@sddlmZddlZddlZddlZddlmZddlmZm Z m Z m Z ddl Z ddl mZmZmZmZddlmZddlmZdd lmZGd d d ZGd d d eeeZGdddeeeZdS)) annotationsNwraps)AnyCallableOptionalUnion)Module SequentialTensor from_numpy)PILImage)numpy)ONNXExportMixinc@steZdZUdZded<  d.d/d d Zd0ddZd1ddZd2ddZd3ddZ d4ddZ d5d6d'd(Z d7d8d,d-Z dS)9ImageModuleMixInzA MixIn that handles image-based operations. This modules accepts multiple input and output data types, provides end-to-end visualization, file saving features. Note that this MixIn fits the classes that return one image tensor only. r _output_imageNtensorinput_names_to_handleOptional[list[Any]] output_typestrreturnCallable[[Any], Any]csdfdd }|S)aTConvert input and output types for a function. Args: input_names_to_handle: List of input names to convert, if None, handle all inputs. output_type: Desired output type ('tensor', 'numpy', or 'pil'). Returns: Callable: Decorated function with converted input and output types. funcrrcs tdfdd }|S) NargsrkwargsrUnion[Any, list[Any]]c s0durtfdd|D}fdd|D}n3t|}tt|jjD]\}\}}|vr9|||<q(|D]\}}|vrM|||<q>|i|}t|tfs^|f}g}|D])}dkrn| |qbdkr{|  |qbdkr|  |qbt dt |d kr|S|d S) Nc3s(|]}|r|n|VqdSN _is_valid_arg to_tensor).0argselfF/home/ubuntu/.local/lib/python3.10/site-packages/kornia/core/module.py Bs&z\ImageModuleMixIn.convert_input_output..decorator..wrapper..cs*i|]\}}||r|n|qSr&r)r"kvr$r&r' Ds*z]ImageModuleMixIn.convert_input_output..decorator..wrapper..rrpilzCOutput type not supported. Choose from 'tensor', 'numpy', or 'pil'.r r)tupleitemslist enumeratezip__code__ co_varnamesr! isinstanceappendto_numpyto_pil ValueErrorlen) rrir#namevaluetensor_outputsoutputsoutput)rrrr%r&r'wrapper=s2  zIImageModuleMixIn.convert_input_output..decorator..wrapper)rrrrrrr)rr@rrr%)rr' decorator<s&z8ImageModuleMixIn.convert_input_output..decoratorN)rrrrr&)r%rrrBr&rAr'convert_input_output,s*z%ImageModuleMixIn.convert_input_outputr#boolcCsRt|tfrtj|rdSt|tfrdSt|tjfrdSt|tjr'dSdS)zCheck if the argument is a valid type for conversion. Args: arg: The argument to check. Returns: bool: True if valid, False otherwise. TF) r4rospathexistsr npndarrayImage)r%r#r&r&r'r hs   zImageModuleMixIn._is_valid_argxr cCst|tfrtj|tjjjdSt|tfr|St|tj fr*tj j |dSt|t j frAtt|ddddStd)zConvert input to tensor. Supports image path, numpy array, PIL image, and raw tensor. Args: x: The input to convert. Returns: Tensor: The converted tensor. rr Input type not supported)r4rkorniaio load_image ImageLoadType UNCHANGEDr rHrIutilsimageimage_to_tensorrJr arraypermutefloat TypeErrorr%rKr&r&r'r!}s   zImageModuleMixIn.to_tensornp.arraycCsNt|tfr|St|tjfr|St|tjfr#t|St d)zConvert input to numpy array. Args: x: The input to convert. Returns: np.array: The converted numpy array. rN) r4r cpudetachrrHrIrJrWrZr[r&r&r'r6s  zImageModuleMixIn.to_numpy Image.ImagecCst|tfr]|}|jdkr:|d|ddd }|j ddkr5|dddddf}t |S|jdkr[|d|dddd}| }dd |DSt t|tjfrft t|t j fro|Std ) zConvert input to PIL image. Args: x: The input to convert. Returns: Image.Image: The converted PIL image. rLr rMrNcSsg|]}t|qSr&)rJ fromarray)r"imgr&r&r' z+ImageModuleMixIn.to_pil..rN)r4r r^r]ndimmul_rX contiguousbytershaperJrbNotImplementedErrorrHrIrZ)r%rKrarr tensor_chw numpy_batchr&r&r'r7s&       zImageModuleMixIn.to_pil output_image*Union[Tensor, list[Tensor], tuple[Tensor]]csZt|tfr |St|ttfr t|fdd|DStd|dt|d)awDetach the input tensor (or list/tuple of tensors) from the GPU and move it to the CPU. Args: output_image (Union[Tensor, list[Tensor], tuple[Tensor]]): The input tensor(s) to be moved. Returns: Union[Tensor, list[Tensor], tuple[Tensor]]: The tensor(s) moved to the CPU and detached from the computational graph. csg|]}|qSr&)_detach_tensor_to_cpu)r"outr$r&r'rdrez:ImageModuleMixIn._detach_tensor_to_cpu..zUnexpected object z with a type of ``)r4r r^r]r/r-type RuntimeError)r%ror&r$r'rqs z&ImageModuleMixIn._detach_tensor_to_cpur,Tn_row Optional[int]backenddisplay Optional[Any]cCs|jdur tdt|jjdkr|j}n%t|jjdkr8|dur,t|jjdd}tjjj |j|dd}nt|d krX|rXt | d dd d tjdS|d krpt | d dd d tjStd |d )zReturn PIL images. Args: n_row: Number of images displayed in each row of the grid. backend: visualization backend. Only PIL is supported now. display: whether to display the image. Nz5No pre-computed images found. Needs to execute first.r`rar?rMpaddingr,r rLzUnsupported backend `z`.)rr8r9rjmathceilrOrTrU make_gridrJrbrXsqueezerastyperHuint8show)r%rvrxry out_imager&r&r'rs  ,(zImageModuleMixIn.showr; Optional[str]NonecCs|durdtjjtjjddd}t|jjdkr|j}t|jjdkrA|dur6t |jjdd }t j j j |j|d d }t j||d dS) zSave the output image(s) to a directory. Args: name: Directory to save the images. n_row: Number of images displayed in each row of the grid. NzKornia-)tzz %Y%m%d%H%M%Sz.jpgr`rarr{rMr|go@)datetimenowtimezoneutcstrftimer9rrjr~rrOrTrUrrP write_imagemulri)r%r;rvrr&r&r'saves zImageModuleMixIn.save)Nr)rrrrrr)r#rrrD)rKrrr )rKrrr\)rKrrr_)rorprrp)Nr,T)rvrwrxrryrDrrz)NN)r;rrvrwrr) __name__ __module__ __qualname____doc____annotations__rCr r!r6r7rqrrr&r&r&r'r#s  <     rcXeZdZdZdfdd Zedd d Zejdddd ZddddfddZZ S) ImageModuleaHandles image-based operations. This modules accepts multiple input and output data types, provides end-to-end visualization, file saving features. Note that this module fits the classes that return one image tensor only. Note: The additional add-on features increase the use of memories. To restore the original behaviour, you may set `disable_features = True`. rrrrrctj|i|d|_dSNFsuper__init___disable_featuresr%rr __class__r&r'r zImageModule.__init__rDcC|jSrrr$r&r&r'disable_featureszImageModule.disable_featuresTr<cC ||_dSrrr%r<r&r&r'r  Nrrrinputsrrrrcb|js&|j||dtj}||i|}|dkr!|||_|S||_|Stj|i|}|SaOverwrite the __call__ function to handle various inputs. Args: inputs: Inputs to operate on. input_names_to_handle: List of input names to convert, if None, handle all inputs. output_type: Desired output type ('tensor', 'numpy', or 'pil'). kwargs: Additional arguments. Returns: Callable: Decorated function with converted input and output types. rrrrCr__call__rqrr%rrrrdecorated_forwardrrr&r'r$ zImageModule.__call__rrrrrrrrDTr<rDrr rrrrrrrrrr rrrrrpropertyrsetterr __classcell__r&r&rr'r   rcr)ImageSequentialaHandles image-based operations as a sequential module. This modules accepts multiple input and output data types, provides end-to-end visualization, file saving features. Note that this module fits the classes that return one image tensor only. Note: The additional add-on features increase the use of memories. To restore the original behaviour, you may set `disable_features = True`. rrrrrcrrrrrr&r'rSrzImageSequential.__init__rDcCrrrr$r&r&r'rWrz ImageSequential.disable_featuresTr<cCrrrrr&r&r'r[rNrrrrrrrcrrrrrr&r'r_rzImageSequential.__call__rrrrrrr&r&rr'rFrr) __future__rrr~rE functoolsrtypingrrrrrO_backendr r r r externalrrJrrH mixin.onnxrrrrr&r&r&r's     i;