Hi everyone
I have a quick problem. I'm trying to write a routine to "demosaic" video data
extremely quickly. I thought my algorithm would make good use of my
processor's vector math units, but it isn't fast enough (it currently takes
about 300ms for each 8 megapixel frame) Maybe you can help me speed it up?
Lets just look at one piece of the algorithm - interpolating the green pixels:
My camera hands me a frame as an int* c array of 8 bit integers. Forget the
problem of turning that into a numpy array for now and assume I have the image
as a numpy uint8 array "ain" . Also assume that Greenmask is a precomputed
array of 1s and 0s of the same size as ain. Now, I do the following:
#interpolate the green pixels from the bayer filter image ain
g = greenMask * ain
gi = g[:-2, 1:-1].astype('uint16')
gi += g[2:, 1:-1]
gi += g[1:-1, :-2]
gi += g[1:-1, 2:]
gi /= 4
gi += g[1:-1, 1:-1]
return gi
I do something similar for red and blue, then stack the interpolated red, green
and blue integers into an array of 24 bit integers and blit to the screen.
I was hoping that none of the lines would have to iterate over pixels, and
would instead do the adds and multiplies as single operations. Perhaps numpy
has to iterate when copying a subset of an array? Is there a faster array
"crop" ? Any hints as to how I might code this part up using ctypes?
Thanks in advance,
Brendan
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