我有两个数组(G和G_)。它们的形状和大小相同,我想要对它们进行卷积。我找到了numpy.convolve和fftconvolve函数。
我的代码如下:
foldedX = getFoldGradientsFFT(G, G_)
foldedY = getFoldGradientsNumpy(G, G_)
def getFoldGradientsFFT(G, G_):
# convolve via scipy fast fourier transform
X =signal.fftconvolve(G,G_, "same)
X*=255.0/numpy.max(X);
return X
def getFoldGradientsNumpy(G, G_):
# convolve via numpy.convolve
Y = ndimage.convolve(G, G_)
Y*=255.0/numpy.max(Y);
return Y
但结果并不相同。 结果类似于: Numpy.concolve()
[ 11.60287582 3.28262652 18.80395211 52.75829556 99.61675945
147.74124258 187.66178244 215.06160439 234.1907606 229.04221552]
scipy.signal.fftconvolve:
[ -4.88130620e-15 6.74371119e-02 4.91875539e+00 1.94250997e+01
3.88227012e+01 6.70322921e+01 9.78460423e+01 1.08486302e+02
1.17267015e+02 1.15691562e+02]
我认为结果应该是相同的,即使这两个函数使用不同的过程进行卷积?!
我忘记提到,我想要卷积2个二维数组:S 这些数组:
G = array([[1,2],[3,4]])
G_ = array([[5,6],[7,8]])
代码
def getFoldGradientsFFT(G, G_):
X =signal.fftconvolve(G,G_,"same")
X=X.astype("int")
X*=255.0/np.max(X);
return X
def getFoldGradientsNumpy(G, G_):
# convolve via convolve
old_shape = G.shape
G = np.reshape(G, G.size)
G_ = np.reshape(G_, G.size)
Y = np.convolve(G, G_, "same")
Y = np.reshape(Y,old_shape)
Y = Y.astype("int")
Y*=255.0/np.max(Y);
return Y
def getFoldGradientsNDImage(G, G_):
Y = ndimage.convolve(G, G_)
Y = Y.astype("int")
Y *= 255.0/np.max(Y)
return Y
结果:
getFoldGradientsFFT
[[ 21 68]
[ 93 255]]
getFoldGradientsNumpy
[[ 66 142]
[250 255]]
getFoldGradientsNDImage
[[147 181]
[220 255]]