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Here I added Cellpose method into the cellpose component.
Cellpose is imported from txsim.
as an input for the method itself I give
image = sdata['morphology_mip']['scale0'].image.compute().to_numpy()
transformation = sdata['morphology_mip']['scale0'].image.transform.copy()
img_arr = tx.preprocessing.segment_cellpose(image[0], hyperparameters)
as far as you mentioned that we will switch to another input format: it would not be zarr file with iteration across all scales and images, but the cropped image itself. As far as in the pipeline it's still missing, I added it's like that
All test successfully passed
![image](https://private-user-images.githubusercontent.com/92544773/389018613-5c5638c2-70af-4618-9df1-7955cfd38eab.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.x4Mlr3Ktnn_EMsfWzSOMvwHlz65VyAiRWZAItHMlkco)