o  iF'@sddlmZddlZddlZddlmZddlZddlm Z ddl m Z m Z m Z mZmZmZmZddlmZ    d$d%ddZddZd d!Zd"d#ZdS)&) annotationsN)Sequence)PathLike)ComposeEnsureChannelFirstd LoadImaged OrientationdSpacingd SqueezeDimd Transform)GridSampleModeimagelabelFdatalist list[dict] output_dirstr dimensionintpixdimSequence[float] | float image_key label_keybase_dirPathLike | Nonelimit relative_pathbool transformsTransform | Nonereturnc Cs|dvrtdt|std| durt|||n| } g} t|D]\} } |r/| |kr/| S| |} | |d}|rMtj|| } |rKtj||nd}tj| } |r[tj|nd}t d| d|rg|nd| || ||i}||}||}t d|j d|durdn|j t |dd }|durt |dd }t d |j d|durdn|j |d krt| ||||d }n t| ||||d }| |q"| S) a8 Utility to pre-process and create dataset list for Deepgrow training over on existing one. The input data list is normally a list of images and labels (3D volume) that needs pre-processing for Deepgrow training pipeline. Args: datalist: A list of data dictionary. Each entry should at least contain 'image_key': . For example, typical input data can be a list of dictionaries:: [{'image': , 'label':