Unification ct scan voxel size by using interpolation in Python

Question

I have used interp2 in Matlab, such as the following code, that is part of @rayryeng's answer in: Three dimensional (3D) matrix interpolation in Matlab:

d = size(volume_image)
[X,Y] = meshgrid(1:1/scaleCoeff(2):d(2), 1:1/scaleCoeff(1):d(1));
for ind = z
    %Interpolate each slice via interp2   
    M2D(:,:,ind) = interp2(volume_image(:,:,ind), X, Y);   
end

Example of Dimensions:

The image size is 512x512 and the number of slices is 133. So:
volume_image(rows, columns, slices in 3D dimenson) : 512x512x133 in 3D dimenson
X: 288x288
Y: 288x288
scaleCoeff(2): 0.5625
scaleCoeff(1): 0.5625
z = 1 up to 133 ,hence z: 1x133
ind: 1 up to 133
M2D(:,:,ind) finally is 288x288x133 in 3D dimenson

Aslo, Matlabs syntax for size: (rows, columns, slices in 3rd dimenson) and Python syntax for size: (slices in 3rd dim, rows, columns). However, after convert the Matlab code to Python code occurred an error, ValueError: Invalid length for input z for non rectangular grid:

for ind in range(0, len(z)+1):
    M2D[ind, :, :] = interpolate.interp2d(X, Y, volume_image[ind, :, :]) # ValueError: Invalid length for input z for non rectangular grid

What is wrong? Thank you so much.

Answer 1

In MATLAB, interp2 has as arguments:

result = interp2(input_x, input_y, input_z, output_x, output_y)

You are using only the latter 3 arguments, the first two are assumed to be input_x = 1:size(input_z,2) and input_y = 1:size(input_z,1).

In Python, scipy.interpolate.interp2 is quite different: it takes the first 3 input arguments of the MATLAB function, and returns an object that you can call to get interpolated values:

f = scipy.interpolate.interp2(input_x, input_y, input_z)
result = f(output_x, output_y)

Following the example from the documentation, I get to something like this:

from scipy import interpolate
x = np.arange(0, volume_image.shape[2])
y = np.arange(0, volume_image.shape[1])
f = interpolate.interp2d(x, y, volume_image[ind, :, :])
xnew = np.arange(0, volume_image.shape[2], 1/scaleCoeff[0])
ynew = np.arange(0, volume_image.shape[1], 1/scaleCoeff[1])
M2D[ind, :, :] = f(xnew, ynew)

[Code not tested, please let me know if there are errors.]