regionprops Very slow on centroid identification

[jeff witz]

Hello,

With the process I've explained before it works at least at 70 FPS, we 
still have room for a lot of improvement !

For example we will compute each mark on a specific process.

Finally we don't use regionprops for the real-time purposes because 
regionprops can find several zones in a ZOI and we want to considere the 
choosen zone to be unique. We use numpy.where to find where white pixels 
are and numpy.min() and numpy.max() to find the bounding box instead of 
bbox from regionprops. bbox is a little faster than our numpy version but 
can find several zones.
We notice that the a median filter on each ZOI increase stability. Once we 
get something clean I will send an example. 

We already use cv2 as we have implemented the camera grabber class in 
OpenCV  (if someone need a complete ximea opencv class mail me), I could 
test and compare the speed.

Regards 



Le mardi 18 novembre 2014 00:38:35 UTC+1, Stefan van der Walt a écrit :
>
> Hi Jeff 
>
> On 2014-11-14 17:27:42, jeff witz <witz... at gmail.com <javascript:>> 
> wrote: 
> > In order to deal with data in real time I have to be fast (over 100 
> fps). 
> > So I first identify the Zones Of Interests using this example : 
> > http://scikit-image.org/docs/dev/auto_examples/plot_label.html 
>
> I'm afraid that for a 100 fps applications, you'll currently have to 
> look at OpenCV.  We'd love to get those kinds of execution times, but 
> it's not easily achievable with our current stack.  That said, we are 
> working on improving regionprops calculations. 
>
> Regards 
> Stéfan 
>
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