user2290362 user2290362 - 9 days ago 6
Python Question

Numpy fastest 3D to 2D projection

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

I have written the code:

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


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

What is the fastest way of achieving this task?

EDIT:

To save the image, I have used:

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

YXD YXD
Answer

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)
>>>

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]])
>>>