最快的3D到2D投影 [英] Numpy fastest 3D to 2D projection

问题描述

我有一个二进制数据的3D数组.我想将其投影到3个2D图像-侧面，头部，鸟瞰图.

I have a 3D array of binary data. I want to project this to 3 2D images - side on, head on, birds eye.

我已经编写了代码:

for x in range(data.shape[2]):
    for y in range(data.shape[0]):
        val = 0
        for z in range(data.shape[1]):
            if data[y][z][x] > 0:
                val = 255
                break
        side[y][x] = val

但是对于约700x300x300的矩阵，这太慢了(75秒！).

But this is horrifically slow (75s!) for a ~700x300x300 matrix.

完成这项任务的最快方法是什么?

What is the fastest way of achieving this task?

为了保存图像，我使用了:

To save the image, I have used:

sideImage = Image.fromarray(side)
sideImage.convert('RGB').save("sideImage.png")

推荐答案

您可以按以下方式进行计算:

You can compute it as follows:

>>> data = np.random.random_sample((200, 300, 100)) > 0.5
>>> data.any(axis=-1).shape # show the result has the shape we want
(200, 300)
>>> data.any(axis=-1)
array([[ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       ...,
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True],
       [ True,  True,  True, ...,  True,  True,  True]], dtype=bool)
>>>

如果需要，您可以缩放值

You can scale values if you need

>>> data.any(axis=-1) * 255
array([[255, 255, 255, ..., 255, 255, 255],
       [255, 255, 255, ..., 255, 255, 255],
       [255, 255, 255, ..., 255, 255, 255],
       ...,
       [255, 255, 255, ..., 255, 255, 255],
       [255, 255, 255, ..., 255, 255, 255],
       [255, 255, 255, ..., 255, 255, 255]])
>>>

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