- User Since
- Aug 1 2016, 12:10 PM (286 w, 1 d)
Dec 3 2021
Dec 2 2021
Nov 2 2021
Crazy stuff and good find 👍 Thanks Stefan!
Oct 26 2021
Yes it looks like I am running OOM. Strange stuff. Loading each image individually MITK barely uses any RAM. As soon as I load both RAM usage jumps up to 20GB (out of my 32GB ) right before it crashes. Also CPU util is high just before that.
Oct 25 2021
(I was painting in axial slice which was 2048x2048)
Oct 20 2021
strange... the color in here is displayed correctly. It's then my png viewer that must be messing things up...
Oct 14 2021
The labels in my example are consecutive integers. And even if they weren't the color mapping should skip unused integer values. Use equivalent of np.unique(segmentation) to determine used labels and set the map accordingly.
see T28736, does not really work for me
Unfortunately still slow. Just checked with 2021-10-01 shapshot on Ubuntu 18.04 (same system as tests above).
13.6s for intersection
14s for difference
didn't bother clocking union
Oct 4 2021
Edit: After a long time the 3d Model appeared but no Data Manager object was created for it:
Strange stuff (I deleted the segmentation tht's why the manager is empty)
Aug 24 2021
This is still not fixed, can we please re-open it?
Jun 4 2021
As it is an inexpensive operation my first guess would be an issue related to the amount of used memory. For example, the image above eats 3,5 GB, Activating the interpolation already triples it. Confirming adds at least another 3,5 GB. That could also explain why it is so slow (swapping). But at this point it is just a first assumption. On a 16 GB Windows machine I was able to complete the interpolation without crash but it took a while.
Jun 2 2021
@kislinsk I know you have plenty of things to do, but this bug (along with T28516) is a blocker at the moment. I would like to recommend MITK as an annotation tool for our collaborators but cannot do that as along as the slice interpolation does not work (fast enough, T28516) for large images :-)
Jun 1 2021
@kislinsk sorry for the late reply. I am not using the convert to segmentation functionality because it draws contours by default and that is annoying because it obstructs too much from the image. Multilabel color map does exactly the right thing, is easy to use and does not require and additional mouse clicks. It is just more convenient :-)
May 25 2021
May 20 2021
import numpy as np import colorsys from skimage import io