[scikit-image] Memory consumption of measure.label (compared to matlab)

[Martin Fleck]

Hello skimage users and devs,

in an image analysis of mine, I use skimage.measure.label and
skimage.morphology of  skimage 0.13.0 .

My problem my analysis uses much more memory than I expect.
I attached output from the memory_profiler package, with which I tried
to keep track of the memory consumption of my analysis.
You can see that for an ~8MiB file that I used for testing,
skimage.measure.label needs to use 56MiB of memory, which surprised me.
My first question is: Am I doing something wrong here?
The memory consumption is skimage.morphology.remove_small_objects is
surprisingly high, too.

I want to be able to process images of several GiB on my machine.
I hope I can excuse the following comparison with matlab by pointing to
the similarity between skimage.measure.regionprops and matlabs regionprops:
With Matlab I don't run into any memory problems on the very same machine.

I would be very glad if I could continue using python for this analysis
and not have to rely on matlab.
Is there a way for me to reduce the memory consumption if I want to use
skimage.morphology.remove_small_objects() and
skimage.measure.regionprops() for which I need the label()?

Best Regards,
Martin
-------------- next part --------------
Line #    Mem usage    Increment   Line Contents
================================================
   154  102.523 MiB    0.000 MiB   @profile
   155                             def run():
   156  110.500 MiB    7.977 MiB       image = data.imread(image_filename)
   157  110.500 MiB    0.000 MiB       image = color.rgb2gray(image)
   158                                 
   159                                 # make binary image with threshold using Li's method
   160  117.328 MiB    6.828 MiB       bwSource = image<filters.threshold_minimum(image)
   161                                 
   162                                 # Filter bwSource image -> remove small specks
   163  131.715 MiB   14.387 MiB       bwFiltered = morphology.remove_small_objects(bwSource, min_size=minSingleDislocationArea, connectivity=2)
   164                                 
   165                                 # analyze regions:
   166  187.551 MiB   55.836 MiB       label_img = label(bwFiltered)
   167  187.809 MiB    0.258 MiB       regions = regionprops(label_img)