hxSrSSKJr SSKrSSKJr SSKJrJr SSK r SSK J r SSKJr SSKJr S /r"S S \5rg) a Lambda transform module for creating custom user-defined transformations. This module provides a flexible transform class that allows users to define their own custom transformation functions for different targets (image, mask, keypoints, bboxes). It's particularly useful for implementing custom logic that isn't available in the standard transforms. The Lambda transform accepts different callable functions for each target type and applies them when the transform is executed. This allows for maximum flexibility while maintaining compatibility with the Albumentations pipeline structure. Key features: - Apply different custom functions to different target types - Compatible with all Albumentations pipeline features - Support for all image types and formats - Ability to handle any number of channels - Warning system for lambda expressions and multiprocessing compatibility Note that using actual lambda expressions (rather than named functions) can cause issues with multiprocessing, as lambdas cannot be properly pickled. ) annotationsN) LambdaType)AnyCallable) functional)NoOp) format_argsLambdac^\rSrSrSrS S U4SjjjrSSjrSSjrSSjrSSjr \ SSj5r SS jr SS jr S rU=r$)r &aA flexible transformation class for using user-defined transformation functions per targets. Function signature must include **kwargs to accept optional arguments like interpolation method, image size, etc: Args: image (Callable[..., Any] | None): Image transformation function. mask (Callable[..., Any] | None): Mask transformation function. keypoints (Callable[..., Any] | None): Keypoints transformation function. bboxes (Callable[..., Any] | None): BBoxes transformation function. p (float): probability of applying the transform. Default: 1.0. Targets: image, mask, bboxes, keypoints, volume, mask3d Image types: uint8, float32 Number of channels: Any cT>[T U]US9 XPl[R S[ R 5UlUUUUS.R5HRupxUcM [U[5(a%URS:Xa[R"SSS9 XRU'MT g)N)p)imagemask keypointsbboxeszzaUsing lambda is incompatible with multiprocessing. Consider using regular functions or partial().) stacklevel)super__init__namedictfromkeysfpixelnoopcustom_apply_fnsitems isinstancer__name__warningswarn) selfrrrrrr target_namecustom_apply_fn __class__s m/var/www/fran/franai/venv/lib/python3.13/site-packages/albumentations/augmentations/other/lambda_transform.pyrLambda.__init__<s 1 $ .VX^XcXc d" - %' - (K *oz::?W?W[e?eMMI#$ 6E%%k2-c 2URSnU"U40UD6$)aApply the Lambda transform to the input image. Args: img (np.ndarray): The input image to apply the Lambda transform to. **params (Any): Additional parameters (not used in this transform). Returns: np.ndarray: The image with the applied Lambda transform. rr)r"imgparamsfns r&apply Lambda.applyYs$ " "7 +#   r(c 2URSnU"U40UD6$)a Apply the Lambda transform to the input mask. Args: mask (np.ndarray): The input mask to apply the Lambda transform to. **params (Any): Additional parameters (not used in this transform). Returns: np.ndarray: The mask with the applied Lambda transform. rr*)r"rr,r-s r& apply_to_maskLambda.apply_to_maskgs$ " "6 *$!&!!r(c 2URSnU"U40UD6$)a,Apply the Lambda transform to the input bounding boxes. Args: bboxes (np.ndarray): The input bounding boxes to apply the Lambda transform to. **params (Any): Additional parameters (not used in this transform). Returns: np.ndarray: The bounding boxes with the applied Lambda transform. rr*)r"rr,r-s r&apply_to_bboxesLambda.apply_to_bboxesus$ " "8 ,&#F##r(c 2URSnU"U40UD6$)a Apply the Lambda transform to the input keypoints. Args: keypoints (np.ndarray): The input keypoints to apply the Lambda transform to. **params (Any): Additional parameters (not used in this transform). Returns: np.ndarray: The keypoints with the applied Lambda transform. rr*)r"rr,r-s r&apply_to_keypointsLambda.apply_to_keypointss$ " "; /)&v&&r(cg)z|Check if the Lambda transform is serializable. Returns: bool: True if the transform is serializable, False otherwise. F)clss r&is_serializableLambda.is_serializablesr(cpURc Sn[U5eUR5URS.$)z}Convert the Lambda transform to a dictionary. Returns: dict[str, Any]: The dictionary representation of the transform. zTo make a Lambda transform serializable you should provide the `name` argument, e.g. `Lambda(name='my_transform', image=, ...)`.)__class_fullname__r)r ValueErrorget_class_fullname)r"msgs r&to_dict_privateLambda.to_dict_privates= 99 N S/ !&*&=&=&?TYYWWr(cSUR0nURURR55 URUR 55 UR R S[U5S3$)zReturn the string representation of the Lambda transform. Returns: str: The string representation of the Lambda transform. r())rupdaterrget_base_init_argsr%rr )r"states r&__repr__Lambda.__repr__sf# T**0023 T,,./..))*!K,>+?qAAr()rr)NNNNNg?) rCallable[..., Any] | NonerrMrrMrrMrz str | Nonerfloat)r+ np.ndarrayr,rreturnrO)rrOr,rrPrO)rrOr,rrPrO)rrOr,rrPrO)rPbool)rPzdict[str, Any])rPstr)r __module__ __qualname____firstlineno____doc__rr.r1r4r7 classmethodr<rCrK__static_attributes__ __classcell__)r%s@r&r r &s.,0*./3,0E(E(E- E * E  E EE: ! " $ ' X B Br()rV __future__rr typesrtypingrrnumpynp"albumentations.augmentations.pixelrr(albumentations.core.transforms_interfaceralbumentations.core.utilsr __all__r r:r(r&rcs;,# C91 *NBTNBr(