id=`convert Arkey.jpg -threshold 50% -type bilevel \
-define connected-components:verbose=true \
-define connected-components:mean-color=true \
-connected-components 4 null: |\
grep "gray(0)" | head -n 1 | sed -n 's/^ *\(.*\):.*$/\1/p'`
convert Arkey.jpg -threshold 50% -type bilevel \
-define connected-components:mean-color=true \
-define connected-components:keep=$id \
-connected-components 4 \
-alpha extract \
-morphology dilate octagon:2 \
mask.png
使用掩码控制,在图像线条上涂白色:convert Arkey.jpg \( -clone 0 -fill white -colorize 100 \) mask.png -compose over -composite result.png
请查看https://imagemagick.org/script/connected-components.php上的-connected-components以了解其工作原理。
您可以尝试使用 cv2.HoughLinesP()
来检测对角线,然后使用掩膜来填充轮廓。
import cv2
import numpy as np
image = cv2.imread('1.jpg')
mask = np.zeros(image.shape, np.uint8)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray,100,200)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
close = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, kernel)
minLineLength = 10
maxLineGap = 350
lines = cv2.HoughLinesP(close,1,np.pi/180,100,minLineLength,maxLineGap)
for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(mask,(x1,y1),(x2,y2),(255,255,255),3)
mask = cv2.cvtColor(mask,cv2.COLOR_BGR2GRAY)
cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
cv2.drawContours(image, [c], -1, (255,255,255), -1)
cv2.imshow('mask', mask)
cv2.imshow('image', image)
cv2.imwrite('image.png', image)
cv2.waitKey()