如何使用numpy加速Python代码？

Question

Orginal和Mutated是图像。 我需要分别得到r，g，b的差值。我有这个代码工作，但它是慢。任何帮助都会很好！：）

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

Answer 1

如果你不进行额外的压缩，然后对每个维度求和，而不是使用numpy的求和函数，速度应该会快很多：

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

Answer 2

有一种方法可以用^{}一次求和

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

这是另一个使用^{}的，希望更快，当使用uint8图像时-

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

Answer 3

这是从一开始就有效的代码。你知道吗

    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