U PhF' @sddlmZddlZddlZddlmZddlZddlm Z ddl m Z m Z m Z mZmZmZmZddlmZdd d d d d d dd ddd d ddZddZddZddZdS)) annotationsN)Sequence)PathLike)ComposeEnsureChannelFirstd LoadImaged OrientationdSpacingd SqueezeDimd Transform)GridSampleModeimagelabelFz list[dict]strintzSequence[float] | floatzPathLike | NoneboolzTransform | None) datalist output_dir dimensionpixdim image_key label_keybase_dirlimit relative_path transformsreturnc Cs|dkrtdt|s td| dkr4t|||n| } g} t|D]f\} } |r`| |kr`q| |} | |d}|rtj|| } |rtj||nd}tj| } |rtj|nd}t d| d|r|nd| || ||i}||}||}t d|j d|dkrdn|j t |dd }|dk rJt |dd }t d |j d|dkrfdn|j |d krt| ||||d }nt| ||||d }| |qD| 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':