我想我会用convolution来做这件事。将图像与Gaussian kernel进行卷积是blur an image的常见方法。您可以使用多种方法来完成此操作，但可能最容易使用的是scipy.ndimage.gaussian_filter。这里有一种实现所有这些的方法，看看你是否喜欢结果。

from PIL import Image
from io import BytesIO
import requests
import numpy as np

r = requests.get('https://i.stack.imgur.com/MhUQZ.png')
img = Image.open(BytesIO(r.content))
imarray = np.asarray(img)[..., 0] / 255

这是你的第一张图片，白色长方形。
现在，我将制作这些轮廓，进行模糊处理，创建彩色图像，然后将它们组合在一起：

from scipy.ndimage import binary_erosion
from scipy.ndimage import gaussian_filter

eroded = binary_erosion(imarray, iterations=3)

# Make the outlined rectangles.

outlines = imarray - eroded

# Convolve with a Gaussian to effect a blur.

blur = gaussian_filter(outlines, sigma=11)

# Make binary images into neon green.

neon_green_rgb = [0.224, 1.0, 0.0784]
outlines = outlines[:, :, None] * neon_green_rgb
blur = blur[:, :, None] * neon_green_rgb

# Combine the images and constrain to [0, 1].

blur_strength = 3
glow = np.clip(outlines + blur_strength*blur, 0, 1)

然后看看它：

import matplotlib.pyplot as plt

plt.imshow(glow)

您需要调整高斯的sigma(其宽度)、颜色、模糊强度等。希望能有所帮助。

以下是在Python/OpenCV中执行此操作的一种方法。

• 阅读输入
• 转换为灰度
• 二进制的阈值
• 使用形态渐变获得所需厚度的边缘
• 在白色背景上将边缘反转为黑色
• 进行距离变换
• 拉伸至全动态范围
• 反转
• 通过除以最大值来归一化到0到1的范围
• 使用幂定律控制距离滚落(坡度)进行衰减
• 创建具有输入大小和所需颜色的彩色图像
• 将衰减图像乘以彩色图像
• 保存结果

输入：

import cv2
import numpy as np
import skimage.exposure

# read input

img = cv2.imread('rectangles.png')

# convert to grayscale

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

# threshold

thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

# do morphology gradient to get edges and invert so black edges on white background

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
edges = cv2.morphologyEx(thresh, cv2.MORPH_GRADIENT, kernel)
edges = 255 - edges

# get distance transform

dist = edges.copy()
distance = cv2.distanceTransform(dist, distanceType=cv2.DIST_L2, maskSize=3)
print(np.amin(distance), np.amax(distance))

# stretch to full dynamic range and convert to uint8 as 3 channels

stretch = skimage.exposure.rescale_intensity(distance, in_range=('image'), out_range=(0,255))

# invert

stretch = 255 - stretch
max_stretch = np.amax(stretch)

# normalize to range 0 to 1 by dividing by max_stretch

stretch = (stretch/max_stretch)

# attenuate with power law

pow = 4
attenuate = np.power(stretch, pow)
attenuate = cv2.merge([attenuate,attenuate,attenuate])

# create a green image the size of the input

color_img = np.full_like(img, (0,255,0), dtype=np.float32)

# multiply the color image with the attenuated distance image

glow = (color_img * attenuate).clip(0,255).astype(np.uint8)

# save results

cv2.imwrite('rectangles_edges.png', edges)
cv2.imwrite('rectangles_stretch.png', (255*stretch).clip(0,255).astype(np.uint8))
cv2.imwrite('rectangles_attenuate.png', (255*attenuate).clip(0,255).astype(np.uint8))
cv2.imwrite('rectangles_glow.png', glow)

# view results

cv2.imshow("EDGES", edges)
cv2.imshow("STRETCH", stretch)
cv2.imshow("ATTENUATE", attenuate)
cv2.imshow("RESULT", glow)
cv2.waitKey(0)

边(反转)：

拉伸距离变换：

衰减距离变换：

光晕结果：