CODEWITHSUNDEEP
python
Toykan Ozdeger
Toykan Ozdeger

Reputation: 169

How to rotate a 3D array without rounding, by using Python?

I have a 3D numpy array that I want to rotate with an angle that I want. I have tried using scipy.ndimage.rotate function and it does the job. However, it does a lot of rounding when rotating. This causes me a problem because my 3D array is representation of an object and numbers in each pixel represent the material that pixel is filled with (which I store in a different file). Therefore, I need a way to rotate the array without doing approximation or rounding and making the object blurry is not a problem

Here is what I got with the function I used:

Before

After

Upvotes: 1

Views: 2056

Answers (3)

Bartian
Bartian

Reputation: 33

For anyone with this problem stumbling upon this thread: brechmos' comment under the OP put me in the right direction for an actual solution. rotate() by default uses a third-order spline interpolation, which gives nice smooth edges. We want sharp edges though, without numbers in between. Setting order = 0 does exactly this. No need for extra functions or implementing anything yourself, just change a single argument.

Upvotes: 1

user11563547
user11563547

Reputation:

The problem you are dealing with is essentially a sampling issue. Your resolution is too low for the data you are dealing with. One possibility to solve this is to increase the resolution of the image you are working with, enforce the color values as you rotate (ie no blending colors at the edges), and create a size/shape template that must be met after the rotation.

Edit: For clarity, it isn't the data that is at too low of a resolution, it's the image in which the data is stored that should be at a high enough resolution. The wikipedia page on multidimensional sampling is good for this topic: https://en.wikipedia.org/wiki/Multidimensional_sampling

Upvotes: 2

brechmos
brechmos

Reputation: 1316

I think the way I would approach it, outside of someone knowing an actual package to do this, is start with the indices and rotate them, then, given they may be floating point, round them. This may not be the best, but I think it should work.

Most of this example is loading a 3D dataset I found to use as an example.

import matplotlib.pyplot as plt
import os
import numpy as np
from scipy.ndimage import rotate

def load_example_data():
    # Found data as an example
    from urllib.request import urlopen
    import tarfile
    opener = urlopen( 'http://graphics.stanford.edu/data/voldata/MRbrain.tar.gz')
    tar_file = tarfile.open('MRbrain.tar.gz')
    try:
        os.mkdir('mri_data')
    except:
        pass
    tar_file.extractall('mri_data')
    tar_file.close()

    import numpy as np
    data = np.array([np.fromfile(os.path.join('mri_data', 'MRbrain.%i' % i),
                                            dtype='>u2') for i in range(1, 110)])
    data.shape = (109, 256, 256)

    return data

def rotate_nn(data, angle, axes):
    """
    Rotate a `data` based on rotating coordinates.
    """

    # Create grid of indices
    shape = data.shape
    d1, d2, d3 = np.mgrid[0:shape[0], 0:shape[1], 0:shape[2]]

    # Rotate the indices
    d1r = rotate(d1, angle=angle, axes=axes)
    d2r = rotate(d2, angle=angle, axes=axes)
    d3r = rotate(d3, angle=angle, axes=axes)

    # Round to integer indices
    d1r = np.round(d1r)
    d2r = np.round(d2r)
    d3r = np.round(d3r)

    d1r = np.clip(d1r, 0, shape[0])
    d2r = np.clip(d2r, 0, shape[1])
    d3r = np.clip(d3r, 0, shape[2])

    return data[d1r, d2r, d3r]


data = load_example_data()

# Rotate the coordinates indices
angle = 5
axes = (0, 1)

data_r = rotate_nn(data, angle, axes)

I think the general idea will work. You will have to consider what the axis is to rotate around.

Upvotes: 1

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