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.


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...@gmail.com> 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.