How to speed up Python code using numpy?

Question

Orginal and Mutated are images. I need to get the difference of each r,g,b separately. I got this code to work, but it is to slow. Any help on making this fast would be nice! :)

Orginal = np.asarray(Orginal).copy()
Mutated = np.asarray(Mutated).copy()    

Fittnes = 0

for x in range(0, 299):

    for y in range(0, 299):

        DeltaRed   = (Orginal[x][y][0] - Mutated[x][y][0])
        DeltaGreen = (Orginal[x][y][1] - Mutated[x][y][1])
        DeltaBlue  = (Orginal[x][y][2] - Mutated[x][y][2])

        Fittnes += (DeltaRed * DeltaRed + DeltaGreen * DeltaGreen + DeltaBlue * DeltaBlue)

return Fittnes

You want to redo python part? Because there is a way to speed-up numpy itself, using intel MKL software.intel.com/en-us/articles/numpyscipy-with-intel-mkl — Severin Pappadeux
– Severin Pappadeux, Commented Nov 15, 2015 at 2:09
@SeverinPappadeux MKL would only be relevant for linear algebra ops, e.g. matrix products. Simple vectorized subtraction and summation are handled by numpy's internals rather than external libraries. — ali_m
– ali_m, Commented Nov 15, 2015 at 11:59

Carsten · Accepted Answer · 2015-11-15 02:20:57Z

3

It should be a whole lot faster if you didn't go the extra mile of zipping and then summing up each dimension instead of using numpy's sum function:

DeltaRed   = np.sum(OR) - np.sum(MR)
DeltaGreen = np.sum(OG) - np.sum(MG)
DeltaBlue = np.sum(OB) - np.sum(MB)

answered Nov 15, 2015 at 2:20

Carsten

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2 Comments

Funktiona Over a year ago

Any way to improve it more?

Carsten Over a year ago

On my computer, this gives you at least a 100x speed improvement. For anything beyond I'd have to know abot your particular use case. If you're summing up the whole image, all additional improvements will be small.

Divakar · Accepted Answer · 2015-11-15 18:03:32Z

1

Here's one approach to do all those in one summation with ndarray.sum -

DeltaRed, DeltaGreen, DeltaBlue = Orginal.sum((0,1)) - Mutated.sum((0,1))

Here's another with np.einsum and hopefully faster one, when working with uint8 images -

org_diff = np.einsum('ijk->k',Orginal.astype('uint64'))
mut_diff = np.einsum('ijk->k',Mutated.astype('uint64'))
DeltaRed, DeltaGreen, DeltaBlue = org_diff - mut_diff

edited Nov 15, 2015 at 18:03

answered Nov 15, 2015 at 8:43

Divakar

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17 Comments

Funktiona Over a year ago

This gets me the total RGB changes, I need them to calculate the RGB difference for each pixel, and get the total RGB changes for all pixels combined.

Divakar Over a year ago

@Funktiona ` RGB difference for each pixel` would be Orginal - Mutated, right? And total RGB changes for all pixels combined would be as listed in the solution?

Funktiona Over a year ago

Look my code bellow. They does not do the same as your solutions

Divakar Over a year ago

@Funktiona What's the datatype of Orginal and Mutated?

Funktiona Over a year ago

they are numpy.asarray

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Funktiona · Accepted Answer · 2015-11-15 17:40:45Z

0

This was the code from beginning that worked.

    Fittnes = 0

    for x in range(0, 299):

        for y in range(0, 299):

            DeltaRed   = (Orginal[x][y][0] - Mutated[x][y][0])
            DeltaGreen = (Orginal[x][y][1] - Mutated[x][y][1])
            DeltaBlue  = (Orginal[x][y][2] - Mutated[x][y][2])

            Fittnes += (DeltaRed * DeltaRed + DeltaGreen * DeltaGreen + DeltaBlue * DeltaBlue)

   return Fittnes

answered Nov 15, 2015 at 17:40

Funktiona

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