Question

This is the processed image and I can’t increase the bwareaopen() as it won’t work for my other image.

Anyway I’m trying to find the shortest points in the centre points of the barcode, to get the straight line across the centre points in the barcode.

Example:
After doing a centroid command, the points in the barcode are near to each other. Therefore, I just wanted to get the shortest points(which is the barcode) and draw a straight line across.

All the points need not be join, best fit points will do.

Step 1

Step 2

Step 3

Answer 1

If you dont have the x,y elements Andrey uses, you can find them by segmenting the image and using a naive threshold value on the area to avoid including the number below the bar code.

I’ve hacked out a solution in MATLAB doing the following:

1. Loading the image and making it binary
2. Extracting all connected components using bwlabel().
3. Getting useful information about each of them via regionprops() [.centroid will be a good approximation to the middel point for the lines].
4. Thresholded out small regions (noise and numbers)
5. Extracted x,y coordinates
6. Used Andreys linear fit solution

Code:

set(0,'DefaultFigureWindowStyle','docked');
close all;clear all;clc;
Im = imread('29ekeap.jpg');
Im=rgb2gray(Im);
%%

%Make binary
temp = zeros(size(Im));
temp(Im > mean(Im(:)))=1;
Im = temp;

%Visualize
f1 = figure(1);
imagesc(Im);colormap(gray);

%Find connected components
LabelIm = bwlabel(Im);

RegionInfo = regionprops(LabelIm);

%Remove background region
RegionInfo(1) = [];

%Get average area of regions
AvgArea = mean([RegionInfo(1:end).Area]);

%Vector to keep track of likely "bar elements"
Bar = zeros(length(RegionInfo),1);

%Iterate over regions, plot centroids if area is big enough
for i=1:length(RegionInfo)
   if RegionInfo(i).Area > AvgArea 
       hold on;
       plot(RegionInfo(i).Centroid(1),RegionInfo(i).Centroid(2),'r*')
       Bar(i) = 1;
   end
end

%Extract x,y points for interpolation
X = [RegionInfo(Bar==1).Centroid];
X = reshape(X,2,length(X)/2);

x = X(1,:);
y = X(2,:);

%Plot line according to Andrey
p = polyfit(x,y,1);
xMin = min(x(:));
xMax = max(x(:));
xRange = xMin:0.01:xMax;
yRange = p(1).*xRange + p(2);
plot(xRange,yRange,'LineWidth',2,'Color',[0.9 0.2 0.2]);

The result is a pretty good fitted line. You should be able to extend it to the ends by using the ‘p’ polynomal and evaluate when you dont encounter any more ‘1’s if needed.

Result: