what is the fastest way to do patch-based image analysis

edit

why would the patch-based version be faster ?

What do you mean Berak? its way faster using c++ opencv, ok I know c++ is faster than python but the difference is more than i expected. and also I am doing very simple calculation on every patch why should it take that long?

How fast is the C++ version?

ah I am looking for high luminance occurrence in every patch:

img= cv2.imread("3.png")
window= 5
overlap= 4
h, w= img.shape

    gridx = xrange(0, w - window, window - overlap)
    gridx = np.hstack((gridx, w - window))

    gridy = xrange(0, h - window, window - overlap)
    gridy = np.hstack((gridy, h - window))

    for i in xrange(len(gridx)):
        for j in xrange(len(gridy)):
            xx = gridx[i]
            yy = gridy[j]
            patch = img[yy: yy + window, xx: xx + window]
            mean = np.mean(patch)
            if (central_v-mean) >(np.std(patch)/2):
                        print "HL"

about 20 sec FooBar

ah, ok. iterating with ranges over pixels is sloooow.

basically, you're looking for local maxima ?

yep, at this moment

start at least with parallel for loops for each patch since the operation is independent for each patch :)

StevenPuttemans: I would need later to go for neighborhood, so don't think parallel can help me :(

It depends, if you need a 4*4 neighborhood, then simply go for block processing in the parallel loop instead of single regions. Will still increase performance since you can do multiple blocks at a time.

Try using integral for fast mean, stddev calculation.