Ok, I'll do. I will assume that we already have the image in an 1-dimensional array, each element containing an array of the RGB values. Then we can convert it to grayscale with one line of python.
gray = map(lambda p:sum(p)/3,IMG)
If you want to test it, you can use this 3x3 sample image (or just load one):
IMG = [[5, 5, 5], [6, 8, 9], [94, 123, 4], [54, 5, 32], [44, 3, 3], [34, 234, 33], [5, 5, 5], [6, 8, 9], [94, 123, 4]]
The result will look like this:
[5, 7, 73, 30, 16, 100, 5, 7, 73]
If your image happens to be loaded into a 2-dimensional array, use this:
gray = map(lambda row:map(lambda p:sum(p)/3,row), IMG)
Sobel is slightly more complicated, but can be written like this (you'll need a larger sample image though):
width, height #width and height of our image
IMG #2D array of our RGB values
sobel #result image
# The actual filter starts here:
for x in range(1,width-1):
for y in range(1,height-1):
sx = IMG[x-1][y-1]+IMG[x][y-1]*2+IMG[x+1][y-1]-IMG[x-1][y+1]-IMG[x][y+1]*2-IMG[x+1][y+1]
sy = IMG[x+1][y-1]+IMG[x+1][y]*2+IMG[x+1][y+1]-IMG[x-1][y-1]-IMG[x-1][y]*2-IMG[x-1][y+1]
sobel[x][y] = Math.sqrt(sx*sx+sy*sy)
A total of 5 lines.
And I have to thank you, because I finally just learned what convolutions are while doing this.