Write a function that takes in two arbitrary sized matrices, multiplies them together, and returns the resulting matrix.

Assemble a rotation matrix based on one particular angle. You'll use this same matrix for your entire code.

If you are rotating by $45$ degrees, on the first iteration you'll rotate from $0$ to $45,$ on the second from $45$ to $90,$ etc.

You will not be rotating the original image by increasingly large angles.

Load in your image and copy into a larger, buffered image $($PA $0)$

Loop through the number of rotations you need to do to go from $0$ to $360$

If you're rotating by $180$ degrees, this will be $2$ times

If you're rotating by $45$ degrees, this will be $8$ times

For each rotation loop:

Iterate over all pixels in image

Translate each pixel by $(-$cx$,-$cy$)$

Rotate using matrix multiplication function

Translate back by $($cx$,$ cy$)$

If your final pixel is within your image boundaries $($positive value that's less than the width and height of the image$)$

Copy color between corresponding pixel in original image and pixel in rotated image. Absolute color error calculation: Code to compute the absolute error between the colors

of the original image$$s pixels and the colors of the image after it$$s been rotated $360$

$$ Pixel rounding error calculation: Code to compute the distance between a rotated pixel$$s

actual location $($float value$)$ and the location where it is displayed $($rounded integer value$).$

This total value should be divided by:

$($numPixelsInImage $*$ numTimesImageWasRotated$)$

You can use the OpenCV and Numpy libraries for opening, displaying, and saving images and to

create empty arrays$/$images$.$ You may also use the math library built into Python for sine$,$ cosine,

and PI calculations. You may not use any other built$-$in functions or libraries.