CODEWITHSUNDEEP
pythonperformancenumpy
Funktiona
Funktiona

Reputation: 113

How to speed up Python code using numpy?

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

Upvotes: 1

Views: 144

Answers (3)

Divakar
Divakar

Reputation: 221714

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

Upvotes: 1

Funktiona
Funktiona

Reputation: 113

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

Upvotes: 0

Carsten
Carsten

Reputation: 18446

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)

Upvotes: 3

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