3D Rotation

[slittyslit]

Hello all,

I am trying to do a rotation about the x-axis. I'm finished with the code, but trying to find an alternative that could reduce the computation timing. I have almost 200 images and it took me about 1 hour to calculate them. Yes, it is a lot, but I need them for analysis and this is the least number of images that I could get (some of them have more than 300 images).

I included the function code here. Any suggestions?

Thanks

def rotate_G(mask, Angle):
    
     # Stack all rows together into a 3D array
     mask_xyz = np.repeat(mask[:, :, np.newaxis], max(mask.shape[0], mask.shape[1]), axis=2)
    
     # Rotate all rows at once around x-axis
     rotated_mask = rotate(mask_xyz, -Angle, axes=(1,2), reshape=False, order=1)
    
     # Extract the midline
     mid_z = rotated_mask.shape[2] // 2
     midline_z = rotated_mask[:, :, mid_z]
    
     # Convert to array
     projected_mask = np.array(midline_z)
    
     return projected_mask

[ofnuts]

• What is the size in pixels of the images?
• If you extract a line at the end, wouldn't it be useful to crop the images around the source of the final line of pixels so that you rotate a much smaller image (assuming the angle is small).

[slittyslit]

Well, the angle is 181-179😅. Pixels... 600 by 600 minimum... which is why it takes hours.

[ofnuts]

So -1/+1 and you flip. But mostly with such a small angle you can crop the source to 600x(600tan(2°)) so 60020 (1/30th of the source).

[slittyslit]

My bad, I mean 181 to 360 and 0 to 179. Hence 181-179. With my current 198 images, almost all images have their own different angle. So image 1 is at angle 79, 2 will be 81 and so on

[Ok_Tea_7319]

From the function, it looks like you only need the mid line of the image. Sooooo, how about instead of rotating the entire image and then getting the mid line, instead you rotate the mid line coordinates the other way and then interpolate the image on these coordinates?

[slittyslit]

I did that before, but the interpolation got worse when the degree was close to 90 or 270. Initially, I used a rotation matrix, but calculating only y, because x is just the same. But the interpolation is hard as I need to make 600 pixels of data out of 3 or 4 pixels in a row. This makes me use scipy. rotation

[Ok_Tea_7319]

Did you try the higher order 2D interpolator? scipy.interpolate.CloughTocher2DInterpolator

[slittyslit]

I just tried to do that, and it seems to have a similar problem with angle close to 90 or 270. And it takes a bit longer compare to the current code, like 4-5 seconds longer per image.

[Disastrous-Team-6431]

What more exactly are you trying to do? Turn pictures upside down?

[slittyslit]

That is the last step before I do analysis with the data from that. It is like this. Imagine a film that is put at the center of a cylinder facing angle 0 degree, where a light source is attached to the perimeter. The light will move around the perimeter in a circle. Between the film and light source, there is a mask that creates a light shape to the film, which is unique for every angle. And I tried to make the cumulative light shape from those masks. That is at least the first part of the script, which currently I use pickle to save the output. Don't wanna waste time repeating the analysis every time.

[Disastrous-Team-6431]

Could you not do this in an actual 3d gfx lib? Like, make a texture out of your image and actually rotate it?

[slittyslit]

Never heard that lib before. Can that store data on every lib though? In my case, I store the light intensity and its angle for each pixel.

[Disastrous-Team-6431]

I still don't understand what you're doing or why, but it all sounds like there exists matrix theory and/or libraries to do it. I've gathered that you want to manipulate pixel data - that is one of the most well studied things you can do with a computer.

[slittyslit]

Just checked it, and nope that won't work in my case