快速查找大图像中连接组件的最小和最大坐标 [英] Quickly find the min and max coordinates of connected component in a large image

问题描述

我的图片大小 50000x50000 。它有大约 25000 连接不同的连接组件。我正在使用 ndimage.label 来标记每个，然后我找到非零点，最后得到min x，max x，min y和max y值。但是，我必须找到每个 25000 连接组件的坐标。这是昂贵的，因为我必须在 50000x50000 图像 25000 25000 25000 c> c000 code>次。这是我刚才提到的代码片段。

I have an image that is of size 50000x50000. It has around 25000 connected different connected components. I'm using ndimage.label to label each of them and then I find the non zero points and finally get the min x, max x, min y and max y values. However, I have to find these coordinates is for each of the 25000 connected components. This is expensive as I have to run np.nonzero on the 50000x50000 image 25000 times. Here is a snippet of the code doing what I just mentioned.

im, _ = ndimage.label(im)
num_instances = np.max(np.max(im))
for instance_id in range(1,num_instances+1):
    im_inst = im == instance_id 
    points = np.nonzero(im_inst) # running this is expensive as im is 50000x50000

    cropped_min_x_1 = np.min(points[0])
    cropped_min_y_1 = np.min(points[1]) 
    cropped_max_x_1 = np.max(points[0])+1 
    cropped_max_y_1 = np.max(points[1])+1

有谁知道我能做些什么来大大加快这个过程？

Does anyone know what I can do to significantly speed up this process?

推荐答案

如果标记像素的分数不是太大：

If the fraction of labelled pixels is not too large:

nz = np.flatnonzero(im)
order = np.argsort(im.ravel()[nz])
nz = nz[order]
blocks = np.searchsorted(im.ravel()[nz], np.arange(2, num_instances+1))
# or (which is faster will depend on numbers)
blocks = 1 + np.where(np.diff(im.ravel()[nz]))[0]
coords = np.array(np.unravel_index(nz, (50000, 50000)))
groups = np.split(coords, blocks, axis=-1)

groups将是2xn_i坐标的列表其中n_i是组件i的大小。

groups will be a list of 2xn_i coordinates where n_i is the size of component i.

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