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We currently have annotations_to_coco, but we also need to convert prediction files to the COCO .json format, to facilitate the extraction of cliplabels.json (see #10).
For SLEAP .slp file, it could be that annotation_to_coco can already do this? Though we'd need to double-check how labeled vs predicted instances are handled by sleap-io here.
For DLC (and LP) files, I suspect we'll for sure need a separate predictions_to_coco function. We'd discussed maybe doing it via movement (e.g. DLC predictions -> movement dataset -> SLEAP file -> sleap-io -> COCO), but:
Not sure that would work
Probably it's more efficient to not go via movement in this case.
Note
Because we are talking about predictions inputs here, we can probably use movement sample data for testing.
We currently have
annotations_to_coco, but we also need to convert prediction files to the COCO .json format, to facilitate the extraction ofcliplabels.json(see #10).For SLEAP .slp file, it could be that
annotation_to_cococan already do this? Though we'd need to double-check how labeled vs predicted instances are handled bysleap-iohere.For DLC (and LP) files, I suspect we'll for sure need a separate
predictions_to_cocofunction. We'd discussed maybe doing it viamovement(e.g. DLC predictions ->movementdataset -> SLEAP file ->sleap-io-> COCO), but:movementin this case.Note
Because we are talking about predictions inputs here, we can probably use
movementsample data for testing.