如何在 numpy 中优化这个图像迭代？

Question

我正在使用此代码检测图像中的绿色。

问题是这次迭代真的很慢。

如何让它更快？如果是用numpy，怎么用numpy的方式做？

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

Answer 1

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)

Answer 2

作为业余爱好者，我只接触过 numpy，但我相信您可以利用 fromfunction 创建一个新的来自现有数组的 np 数组 https://docs.scipy.org/doc/numpy/reference/generated/numpy.fromfunction.html

以下是我认为在这种情况下可能起作用的方法——这将利用 numpy 的速度：

def handle_colors(img, x, y):
    blue = img.item(x,y,0)
    green = img.item(x,y,1)
    red = img.item(x,y,2)
    exg = 2*green-red-blue
    if exg > 50:
        return (exg, green, red)
    return blue, green, red

def convertGreen(rawimg):
    processedimg = np.fromfunction(lambda i, j: handle_colors(rawimg, i, j), rawimg.shape)
    return processedimg

如何在 numpy 中优化这个图像迭代？

How to optimize this image iteration in numpy?

python

opencv

numpy

image-processing

opencv3.0