python边缘检测代码_高斯边缘检测拉普拉斯算子的Python实现

我玩了一点ycyeh的代码(谢谢你提供)。在我的应用程序中，使用与最小-最大范围成比例的输出值比仅使用二进制0和1得到更好的结果(然后我也不再需要阈值，但可以很容易地对结果应用阈值。)此外，我还将循环更改为numpy数组操作，以加快执行速度。import numpy as np

import scipy.misc

import cv2 # using opencv as I am not too familiar w/ scipy yet, sorry

def laplace_of_gaussian(gray_img, sigma=1., kappa=0.75, pad=False):

"""

Applies Laplacian of Gaussians to grayscale image.

:param gray_img: image to apply LoG to

:param sigma: Gauss sigma of Gaussian applied to image, <= 0. for none

:param kappa: difference threshold as factor to mean of image values, <= 0 for none

:param pad: flag to pad output w/ zero border, keeping input image size

"""

assert len(gray_img.shape) == 2

img = cv2.GaussianBlur(gray_img, (0, 0), sigma) if 0. < sigma else gray_img

img = cv2.Laplacian(img, cv2.CV_64F)

rows, cols = img.shape[:2]

# min/max of 3x3-neighbourhoods

min_map = np.minimum.reduce(list(img[r:rows-2+r, c:cols-2+c]

for r in range(3) for c in range(3)))

max_map = np.maximum.reduce(list(img[r:rows-2+r, c:cols-2+c]

for r in range(3) for c in range(3)))

# bool matrix for image value positiv (w/out border pixels)

pos_img = 0 < img[1:rows-1, 1:cols-1]

# bool matrix for min < 0 and 0 < image pixel

neg_min = min_map < 0

neg_min[1 - pos_img] = 0

# bool matrix for 0 < max and image pixel < 0

pos_max = 0 < max_map

pos_max[pos_img] = 0

# sign change at pixel?

zero_cross = neg_min + pos_max

# values: max - min, scaled to 0--255; set to 0 for no sign change

value_scale = 255. / max(1., img.max() - img.min())

values = value_scale * (max_map - min_map)

values[1 - zero_cross] = 0.

# optional thresholding

if 0. <= kappa:

thresh = float(np.absolute(img).mean()) * kappa

values[values < thresh] = 0.

log_img = values.astype(np.uint8)

if pad:

log_img = np.pad(log_img, pad_width=1, mode='constant', constant_values=0)

return log_img

def _main():

"""Test routine"""

# load grayscale image

img = scipy.misc.face() # lena removed from newer scipy versions

img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# apply LoG

log = laplace_of_gaussian(img)

# display

cv2.imshow('LoG', log)

cv2.waitKey(0)

if __name__ == '__main__':

_main()