As with the article on linear interpolation, this article is in preparation for the upcoming discussion on image rotation. I just realised that if I don’t write this, I’m going to take a long time explaining the image rotation code.

We’ve already covered Cartesian coordinates. So what are raster and polar coordinates?

### Raster coordinates

I did some research, and to my surprise, there’s no such thing as a raster coordinate system! So where did this term enter my memory? Hmm…

That’s fine, we’ll define it here then. Raster coordinates are actually very simple.

The horizontal axis is similar to the usual x-axis, just only with non-negative values. The vertical axis is an inverted y-axis (so values increase *downwards* instead of upwards), and also has only non-negative values. I’ve only used raster coordinates with images, so negative indices don’t make sense.

For illustration, suppose w is the width of the image and h is the height of the image (in pixels). Then (0,0) is the top left corner of the image. (w,0) is the top right corner of the image. (0,h) is the bottom left corner of the image. And (w,h) is the bottom right corner of the image.

You’ll encounter raster coordinates when you deal with texture mapping. But that’s another story for another day…

### Polar coordinates

Polar coordinates have two components, a length and an angle. 2D Cartesian coordinates also have two components, an x and a y. So what’s the difference?

Note that angles are measured from the positive x-axis and goes anti-clockwise. Remember the quadrants of the 2D Cartesian plane? I’ve included an example in the illustration with the line in the 3rd quadrant.

So how are polar coordinates related to Cartesian coordinates?

You’ll have to revise your trigonometry lessons. I’ll leave it to you to find out the derivation.

### Converting from raster to Cartesian to polar coordinates. And back.

Why would anyone convert from raster to Cartesian to polar, only to convert back from polar to Cartesian to raster? Ahh… let’s look at a diagram.

We can only do proper rotation at the polar coordinate stage. But we start with an image, with raster coordinates. So we convert from (image) raster coordinates to Cartesian, then to polar, do the rotation part, convert back to Cartesian, and then back to raster coordinates.

To determine the formula for raster-Cartesian conversion, let’s look at the four corners of the image. We want

raster (0,0) -> Cartesian (-w/2,h/2)

raster (w,0) -> Cartesian (w/2,h/2)

raster (0,h) -> Cartesian (-w/2,-h/2)

raster (w,h) -> Cartesian (w/2,-h/2)

Based on that, the formula is

x = rasterX – w/2

y = h/2 – rasterY

For Cartesian-polar conversion, we have

r = sqrt(x*x + y*y)

theta = PI/2 if x=0 and y>0

theta = 3*PI/2 if x=0 and y<0
theta = arctan(y/x) otherwise
I don't need to restrict theta to be within [0,2*PI) interval, even though it's mentioned here.

[short digression]

The square bracket [ means 0 is included in the interval. The round bracket ) means 2*PI is *not* included. The sine and cosine functions take in any real values. 2*PI + 1 radians automatically wraps to 1 radian by sine and cosine.

[end digression]

I don’t really need to explain why there are 3 conditional assignments for theta, right?

Alright, fine. x is a denominator as a parameter in the arctan function. That means you need to check for x equal to zero. Next, if x=0, the angle can either be 90 degrees (positive y-axis) or 270 degrees (negative y-axis). Hence spawning the other 2 conditions.

For polar-Cartesian conversion, we have

x = r * cos(theta)

y = r * sin(theta)

For Cartesian-raster conversion, we have

rasterX = x – w/2

rasterY = h/2 – x

As for some of the edge cases, we’ll look at them when we get to the code. Oh yes, there’s code. Lot’s of it. Stay tuned.

**UPDATE**: “I’m dying to look at the code for rotating images with bilinear interpolation! Bring me there!”