如何修复不完整的网格单元并修复图像中的缺失部分 [英] How to repair incomplete grid cells and fix missing sections in image

问题描述

我用霍夫线在这张图中得到了水平线和垂直线的交点:

I used Hough lines to get intersection of horizontal and vertical lines in this image:

，但是复杂度随着网格单元数的显着增加而增加.

but the complexity grows with the grid cells count significantly.

是否有任何简单的方法可以在不使用线检测的情况下完成网格?

Is there any simple way to complete the grid without using line detection ?

谢谢.

推荐答案

以下是使用

获取二进制图像.加载灰度图像， 获取水平/垂直线蒙版.创建水平/垂直内核，并使用 cv2.morphologyEx

Obtain horizontal/vertical lines masks. Create horizontal/vertical kernel and isolate horizontal/vertical grid lines with cv2.getStructuringElement and cv2.morphologyEx

组合面具.

Combine masks. Bitwise-and masks together to complete grid

填充单个网格孔.

Fill individual grid holes. Find contours and fix holes by filling in each grid cell

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二进制图片

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Binary image

水平蒙版(左)和垂直蒙版(右)

Horizontal mask (left) and vertical mask (right)

联合口罩

修复单个网格孔

求结果

import cv2

# Load image, grayscale, blur, Otsu's threshold
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Obtain horizontal lines mask
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50,1))
horizontal_mask = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=1)
horizontal_mask = cv2.dilate(horizontal_mask, horizontal_kernel, iterations=9)

# Obtain vertical lines mask
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
vertical_mask = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=1)
vertical_mask= cv2.dilate(vertical_mask, vertical_kernel, iterations=9)

# Bitwise-and masks together
result = 255 - cv2.bitwise_or(vertical_mask, horizontal_mask)

# Fill individual grid holes
cnts = cv2.findContours(result, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(result, (x, y), (x + w, y + h), 255, -1)

cv2.imshow('thresh', thresh)
cv2.imshow('vertical_mask', vertical_mask)
cv2.imshow('horizontal_mask', horizontal_mask)
cv2.imshow('result', result)
cv2.waitKey()

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