In this case, I was looking for ways of analysing an image and looking for specific objects. I'm doing this based on colour, as it seems easiest. Python has a function called Pixelarray which will take an image and convert it into a 2D array of individual pixel colours. For example:
Pixel = (255,0,0) would mean that the pixel on row 0 and column 0 (top left of the image) is coloured red (the three numbers in the brackets represent the amount of red, green and blue in the colour)
This is fine when you are wanting to change the colour of an individual pixel, but it doesn't work quite as well if you are wanting to READ the colours. For that you need some extra tweaking.
My program here has a small calibration sequence which waits for you to press space and then grabs and remembers whatever colour appears under the blue cross at the top of the screen.
Then it switches to a black-and-white display which discards all colours except the target colour.
Then, it starts tracking the object by searching for solid blocks of the target colour. Due to the grainy nature of cheap video cameras (especially under poor light conditions), lots of speckling occurs which may include colours close to the target colour. My program tries to discard those speckles by focusing on blocks where at least 90% of the pixels are the target colour. It essentially scans the whole image quickly, then when it finds a potential block it starts counting how many neighbouring pixels match.
There are software libraries that do this sort of thing much more cleverly, but I don't understand them. This seems to work for now.