remove nifti script to avoid sklearn dependency

Author: hotzenklotz

docs/src/cli/index.md

@@ -9,7 +9,6 @@ The WEBKNOSSOS CLI offers many useful commands to work with WEBKNOSSOS datasets:
 - `webknossos compress`: Compress a WEBKNOSSOS dataset
 - `webknossos convert`: Convert an image stack (e.g., `tiff`, `jpg`, `png`, `bmp`, `dm3`, `dm4`) to a WEBKNOSSOS dataset
 - `webknossos convert-knossos`: Converts a KNOSSOS dataset to a WEBKNOSSOS dataset
-- `webknossos convert-nifti`: Converts a NIFTI image or a directory with NIFTI files into a WEBKNOSSOS dataset
 - `webknossos convert-raw`: Converts a RAW image file to a WEBKNOSSOS dataset
 - `webknossos convert-zarr`: Converts a Zarr dataset to a WEBKNOSSOS dataset 
 - `webknossos download`: Download a dataset from a WEBKNOSSOS server as WKW format
@@ -57,12 +56,6 @@ webknossos compress data/target
 # Convert Knossos cubes to wkw cubes
 webknossos convert-knossos --layer-name color --voxel-size 11.24,11.24,25 data/source/mag1 data/target
 
-# Convert NIFTI file to wkw file
-webknossos convert-nifti --layer-name color --voxel-size 10,10,30 data/source/nifti_file data/target
-
-# Convert folder with NIFTI files to wkw files
-webknossos convert-nifti --color-file one_nifti_file --segmentation-file another_nifti --voxel-size 10,10,30 data/source/ data/target
-
 # Convert RAW file to wkw file
 webknossos convert-raw --layer-name color --voxel-size 10,10,30 --dtype uint8 --shape 2048,2048,1024 data/source/raw_file.raw data/target
 

webknossos/Changelog.md

@@ -226,7 +226,6 @@ For upgrade instructions, please check the respective _Breaking Changes_ section
     - `python -m wkcuber`, `python -m wkcuber.convert_image_stack_to_wkw` -> `webknossos convert`
     - `python -m wkcuber.export_wkw_as_tiff` -> `webknossos export-wkw-as-tiff`
     - `python -m wkcuber.convert_knossos` -> `webknossos convert-knossos`
-    - `python -m wkcuber.convert_nifti` -> `webknossos convert-nifti`
     - `python -m wkcuber.convert_raw` -> `webknossos convert-raw`
     - `python -m wkcuber.downsampling` -> `webknossos downsample`
     - `python -m wkcuber.compress` -> `webknossos compress`