How to repair incomplete grid cells and fix missing sections in image

Question

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

but the complexity grows with the grid cells count significantly.

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

Thank you.

Answer 1

Here's a potential solution using morphological operations with OpenCV

1. Obtain binary image. Load image, grayscale, Gaussian blur, Otsu's threshold

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

3. Combine masks. Bitwise-and masks together to complete grid

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

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Binary image

Horizontal mask (left) and vertical mask (right)

Combine masks

Repair individual grid holes

Invert for result

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()

Answer 2

I thought of a creative solution that based on morphological operations.

The final result is not so impressive, and I don't know if it fulfills your goals.

I implemented the code in MATLAB (you didn't add a language tag).

Here is the code (details are in the comments):

I = imread('RedGrid.jpg'); %Read grid image
BW = imbinarize(rgb2gray(I)); %Convert to from RGB to Grayscale, and to binary image.
BW = ~BW; %Inverse polarity

BW = padarray(BW, [1, 1], 1); %Insert frame of white pixels around the image

BW1 = imerode(BW, ones(1, 20)); % Remove vertical lines by erode with short horizontal kernel
BW1 = imdilate(BW1, ones(1, 600)); %Complete horizontal line by dilate with long horizontal kernel
BW1 = imerode(BW1, ones(1, 800)); %Remove some of the horizontal duplications using erode with long horizontal kernel

imwrite(BW1, 'BW1.jpg')

%Repeat the above 3 stages with horizontal lines:
BW2 = imerode(BW, ones(20, 1)); % Remove vertical lines by erode with short horizontal kernel
BW2 = imdilate(BW2, ones(600, 1)); %Complete horizontal line by dilate with long horizontal kernel
BW2 = imerode(BW2, ones(800, 1)); %Remove some of the horizontal duplication using erode with long horizontal kernel

BW = BW1 | BW2; % Merge horizontal and vertical lines.

BW = BW(2:end-1, 2:end-1); %Remove the white frame added at the beginning.

%Paint lines with red color (assume the desired red is RGB = [220, 20, 20])
R = im2uint8(~BW); %Red color channel
G = im2uint8(~BW); %Green color channel
B = im2uint8(~BW); %Blue color channel
R(BW) = 220; %Red color
G(BW) = 20;
B(BW) = 20;

J = cat(3, R, G, B); % Create RGB image from R, G and B.

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Results:

BW1:

BW2:

J: