Hi Pawel,
I would recommend scikit-image for these types of analysis. Here's a start:
---
from skimage import io, measure
import numpy as np
image = io.imread('disc.png')
thresholded = image > 10
labels = measure.label(image)
regions = measure.regionprops(labels)
regions_small = [r for r in regions if r.area < 100000]
mu = np.mean([r.area for r in regions_small])
M = np.max([r.area for r in regions_small])
print(f"Mean area: {mu}")
print(f"Max area: {M}")
---
That's a pretty crude way of rejecting the background areas, and can be
improved in various ways. Feel free to also post to the scikit-image user
forum at https://forum.image.sc/tag/scikit-image
Best regards,
Stéfan
On Sat, Jan 22, 2022, at 10:45, [email protected] wrote:
> Hello,
>
> I am not sure that this is correct group for my problem but I hope
> someone can help me :)
>
> I try to analyze picture with porous material
> (https://python.neocast.eu/disc.png). I calculate a total quantity of
> each pores but I would like also calculate a porosity. To make I need a
> size of sample or total pixels which are in the sample. How to do it ?
>
> thanks in advance
> Pavel
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