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
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)
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