如何在numpy中优化这个图像迭代？ [英] How to optimize this image iteration in numpy?

问题描述

我正在使用此代码检测图像中的绿色。

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

问题是此迭代非常慢。

The problem is this iteration is really slow.

如何让它更快？如果它正在使用numpy，如何以numpy方式进行？

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

推荐答案

试试这个：

blue = rawimg[:,:,0]
green = rawimg[:,:,1]
red = rawimg[:,:,2]
exg = 2*green-red-blue
processedimg = np.where(exg > 50, exg, 0)

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