How to optimize this image iteration in numpy? [duplicate]

Question

I'm using this code to detect green color in the image.

The problem is this iteration is really slow.

How to make it faster? If it is using numpy, How to do it in numpy way?

def convertGreen(rawimg):
width, height, channels = rawimg.shape
size = (w, h, channels) = (width, height, 1)
processedimg = np.zeros(size, np.uint8)
for wimg in range(0,width):
    for himg in range(0,height):
        blue = rawimg.item(wimg,himg,0)
        green = rawimg.item(wimg,himg,1)
        red = rawimg.item(wimg,himg,2)
        exg = 2*green-red-blue
        if(exg > 50):
            processedimg.itemset((wimg,himg,0),exg)

return processedimg

Marcus Müller · Accepted Answer · 2017-03-30 08:56:14Z

2

I would go for something like this (untested):

def convertGreen(rawimg):
    red, green, blue = rawimg[:,:,0], rawimg[:,:,1], rawimg[:,:,2]
    exg = 2*green - red - blue
    processedimg = exg.copy();
    processedimg[processedimg < 50] = 0

    return processedimg

The copy operation can actually be omitted, but I kept it to stay a bit more in line with the original code.

Note that in general programming questions are actually offtopic here, and more suitable for StackOverflow.

edited Mar 30, 2017 at 8:56

Marcus Müller

36.9k4 gold badges59 silver badges105 bronze badges

answered Mar 24, 2017 at 8:25

Maximilian Matthé

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