Comments (4)
Additionally, I am looking at the logs and I'm having a bit of a trouble trying to get an understanding for progress based on on the values at each section. The inequality for the optimization problem makes sense, but is there any built in indication of progress?
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For anybody who is having slow performance issues, I realized that my issue was due to incorrect array formatting:
I was passing in data in the form (x, y, z) (ex. 512,512,50) rather than (z, x, y) (50,512,512).
This ended up being up to how I went from a list of images as 2D numpy arrays to a 3D numpy array:
np.dstack(masks)
creates an array of the shape (512,512,50)np.array(masks)
creates an array of the shape (50, 512, 512)
Small mistake, but stumped me for a little bit and created excessively long run times. Still curious about the CUDA implementation though!
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Hi @Prehani - sorry it took a while to get back to you, and it seems like you solved the problem! Hope the results are good.
Re: the CUDA implementation, I made a start on this but never got round to finishing it. Parallelizing the bayesian update code is quite tricky.
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No worries--preliminary testing appears to be very promising! We sent over some UI suggestions to the Napari team for tracks and a few other things, but so far BTrack has been working flawlessly after that small mix up on my end.
Out of curiosity, do you have any recommended segmentation packages for timeseries data? We've had decent luck with Cellpose, but it seems that there could be potential for a temporally aware algorithm using a similar tracking mechanism to generate an updated mask on a frame by frame basis rather than segmenting each image individually.
Re Cuda: totally understandable, I know it can be an absolute nightmare to work with. It seems for now our timeseries data is fine running solely on the CPU end of thing.
Thanks again for all the work you've done on this!
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Related Issues (20)
- [FEATURE REQUEST] Update napari hub preview page with new plugin details
- [FEATURE REQUEST] Should example configs be taken from `lowe-lab-ucl/btrack-examples`? HOT 2
- [FEATURE REQUEST] Remove default values for `napari` widgets
- `generation` missing from list of `Tracklet` attributes
- [BUG] Values in `Optimiser` tab aren't being updated from config HOT 3
- [FEATURE REQUEST] Add a warning if `enable_optimisation == False` but optimising HOT 1
- [FEATURE REQUEST] In tests change `prob_not_assign` to `0.001` HOT 1
- [FEATURE REQUEST] If `enable_optimisation` is False then disable tab/nested widgets HOT 2
- [FEATURE REQUEST] Rename `Method` tab to `Basic` HOT 1
- [BUG] `volume` isn't set as expected, in the `test_tracker_graph` test HOT 4
- [FEATURE REQUEST] Write a test for the new `enable_optimisation` flag
- [BUG] Default config name is used when saving config from napari plugin HOT 8
- [BUG] configuration volume is None HOT 2
- [FEATURE REQUEST] 2D tracking with no branching and short links HOT 2
- [FEATURE REQUEST] Vector field to aid tracking HOT 5
- [BUG] not all objects passed to btrack are tracked - some seem to get lost HOT 1
- Filter tracks by value HOT 2
- [FEATURE REQUEST] Add constraints to tracking and possibly variable search radius.
- [FEATURE REQUEST] Better documentation on the config file
- combine cellpose segmentation with tracking result into h5 file[FEATURE REQUEST] HOT 2
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