Hi @Cryaaa, I need your help with one thing, maybe you can pinpoint the issue faster. The issue might have something to do with the code for the visualization of the cluster image, but it also might be somewhere deeper.
After seeing that video yesterday I was playing around with timelapses on this branch, and so far what works:
- Getting measurements for a timelapse. It loops through the whole timelapse and gets reg props data for each time point and in the end concatenates all (works for now just for area & intensity measurements, not with neighborhood data). There is also a "timepoint" column, which we might rename to "frame" to be consistent with Trackmate results.
- Then you can plot timepoint against the label, and nicely see if visualization works correctly (6th cell divides at 6th timepoint, so we get one more label, and it's good for looking for bugs):

It works well for 2-6 labels, but not 1st and 7th. This is what it displays if the 1st label is selected with a picker tool:

I was hoping we could discuss this next week in one of the meetings when we are both there, so I am just putting this here so if you wanted you could already take a look. Tell me if you want to send me this segmented dataset, it's 4D but I am using just 16 timepoints, so it's great to try out stuff because it's not computationally demanding.
Hi @Cryaaa, I need your help with one thing, maybe you can pinpoint the issue faster. The issue might have something to do with the code for the visualization of the cluster image, but it also might be somewhere deeper.
After seeing that video yesterday I was playing around with timelapses on this branch, and so far what works:
It works well for 2-6 labels, but not 1st and 7th. This is what it displays if the 1st label is selected with a picker tool:

I was hoping we could discuss this next week in one of the meetings when we are both there, so I am just putting this here so if you wanted you could already take a look. Tell me if you want to send me this segmented dataset, it's 4D but I am using just 16 timepoints, so it's great to try out stuff because it's not computationally demanding.