Users should be able to add their own annotations, in the same format as the dataset, by using the Create annotation frontend and a folder. Basic support should include pngseq. The required code changes are:
[ ] backend/app/app/api/api_v1/endpoints/utils.py:train_unet2d pass annotation filetype to celery task with new param annotation_filetype
frontend
[ ] regenerate the code from the backend API as shown in the documentation. This should add the correct file types in the annotation form.
gpu_worker/app/worker.py:train_unet2d
[ ] add new param annotation_filetype
[ ] app/worker.py:train_unet2d Skip step Convert JSON annotations to .pngs if filetype is not json.
dash_frontend
[ ] disable the edit annotation button if the type is not json.
Possible issues:
The string representing location of the annotation will change from only a file annotations.json to either a file or a folder. This can break unknown stuff that depended on this assumption e.g. the annotation tools from the dash_frontend viewer or the folder creation in the gpu_worker.
Users should be able to add their own annotations, in the same format as the dataset, by using the
Create annotationfrontend and a folder. Basic support should includepngseq. The required code changes are:backend
schemas/annotation.py:AnnoationFileType: addpngseq = "pngseq"backend/app/app/api/api_v1/endpoints/utils.py:train_unet2dpass annotation filetype to celery task with new paramannotation_filetypefrontend
gpu_worker/app/worker.py:train_unet2dannotation_filetypeapp/worker.py:train_unet2dSkip stepConvert JSON annotations to .pngsif filetype is not json.dash_frontend
Possible issues:
annotations.jsonto either a file or a folder. This can break unknown stuff that depended on this assumption e.g. the annotation tools from the dash_frontend viewer or the folder creation in the gpu_worker.