高斯边缘检测的拉普拉斯算术的Python实现

matlab edge（）应该做什么

1. 计算LOG
2. 计算LoG上的零交叉
3. 计算本地LoG差异的阈值
4. 边缘像素=零交叉&&局部差异>阈值

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 spimport numpy as npimport scipy.ndimage as ndimport matplotlib.pyplot as pltfrom skimage import data# lena = sp.misc.lena() this function was deprecated in version 0.17img = data.camera()  # use a standard image from skimage insteadLoG = nd.gaussian_laplace(img , 2)thres = np.absolute(LoG).mean() * 0.75output = 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] = 1plt.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。