matlab edge（）应该做什么
scipy的LoG过滤器仅执行上述步骤1。我实现了以下代码段来模仿上面的步骤2?4：
import scipy as sp
import numpy as np
import scipy.ndimage as nd
import matplotlib.pyplot as plt
from skimage import data
# lena = sp.misc.lena() this function was deprecated in version 0.17
img = data.camera() # use a standard image from skimage instead
LoG = nd.gaussian_laplace(img , 2)
thres = np.absolute(LoG).mean() * 0.75
output = sp.zeros(LoG.shape)
w = output.shape[1]
h = output.shape[0]
for y in range(1, h - 1):
for x in range(1, w - 1):
patch = LoG[y-1:y+2, x-1:x+2]
p = LoG[y, x]
maxP = patch.max()
minP = patch.min()
if (p > 0):
zeroCross = True if minP < 0 else False
else:
zeroCross = True if maxP > 0 else False
if ((maxP - minP) > thres) and zeroCross:
output[y, x] = 1
plt.imshow(output)
plt.show()
这当然很慢，并且可能不是惯用的，因为我也是Python的新手，但应该可以证明这个想法。也欢迎任何有关如何改进它的建议。
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