Straighten edges of black rectangle in binary image
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I have a binary image that represents a 'rectangle'. The rectangle is not perfect because a top view of the box was taken (then converted to a binary image). My objective is to find four corner points of the black rectangle. In order for the corner function to work the edges must be completely straight.
clc; clear;
image = imread('0148pm.jpg');
g = rgb2gray(image)
level = graythresh(g);
binary = im2bw(image,level);
imwrite(binary,'imageBinary.jpg');
% Iinv = ~binary; %Invert your binary image
% Iinv = bwareaopen(Iinv,20); %Get rid of small areas (below your size criterion)
% I = ~Inv; %Invert back
imshow(I);
% fill = bwmorph(binary,'hbreak');
%
% f = bwmorph(fill,'majority');
%
% k = bwmorph(f,'close');
%corner algorithm///////////////////////////////////////////////////
% C = corner(k,'MinimumEigenvalue', 4)
% imtool(k);
% hold on
% plot(C(:,1), C(:,2), 'r*');
%end corner algorithm///////////////////////////////////////////////
3 个评论
Image Analyst
2013-4-24
What do you really want to do: find corners or straighten edges? Do you know it's possible to straighten the square without even finding the corners?
Kal
2013-4-24
Kal
2013-4-24
编辑：Image Analyst
2013-4-24
These are the corners being detected currently.
采纳的回答
Image Analyst
2013-4-25
编辑：Image Analyst
2013-4-25
Another way that is pretty easy way, probably the most straightforward is to simply find the centroid of the square and divide it up into quadrants around the centroid. The examine all the points to see which is farthest from the centroid. Like this code:
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
imtool close all; % Close all imtool figures if you have the Image Processing Toolbox.
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
% Read in a standard MATLAB gray scale demo image.
folder = 'C:\Users\User\Documents\Temporary';
baseFileName = 'w0lwro.jpg';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
% File doesn't exist -- didn't find it there. Check the search path for it.
fullFileName = baseFileName; % No path this time.
if ~exist(fullFileName, 'file')
% Still didn't find it. Alert user.
errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
uiwait(warndlg(errorMessage));
return;
end
end
grayImage = imread(fullFileName);
% Get the dimensions of the image.
% numberOfColorBands should be = 1.
[rows, columns, numberOfColorBands] = size(grayImage);
% Display the original gray scale image.
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Give a name to the title bar.
set(gcf,'name','Demo by ImageAnalyst','numbertitle','off')
% Let's compute and display the histogram.
[pixelCount, grayLevels] = imhist(grayImage);
subplot(2, 2, 2);
bar(pixelCount);
grid on;
title('Histogram of original image', 'FontSize', fontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.
binaryImage = grayImage < 128;
% Get rid of small blobs, smaller than two rows of pixels.
binaryImage = bwareaopen(binaryImage, 2*rows);
% Display the original gray scale image.
subplot(2, 2, 3);
imshow(binaryImage, []);
title('Binary Image', 'FontSize', fontSize);
% Label the image.
[labeledImage, numberOfBlobs] = bwlabel(binaryImage);
% Find the centroid
measurements = regionprops(labeledImage, 'Centroid');
% Put a cross at the centroid.
xCentroid = measurements.Centroid(1);
yCentroid = measurements.Centroid(2);
fprintf('X Centroid = %.3f, Y Centroid = %.3f', xCentroid, yCentroid);
hold on;
plot(xCentroid, yCentroid, 'r+', 'MarkerSize', 30);
% Find out how far the centroid is from points in each quadrant
% First get all the points.
[rows columns] = find(binaryImage);
xCorners = [0 0 0 0]; % X coordinate of corners in each quadrant.
yCorners = [0 0 0 0]; % X coordinate of corners in each quadrant.
maxDistance = [0 0 0 0]; % Distance of furthers X coordinate from centroid in each quadrant.
for k = 1 : length(columns)
rowk = rows(k);
colk = columns(k);
distanceSquared = (colk - xCentroid)^2 + (rowk - yCentroid)^2;
if rowk < yCentroid
% It's in the top half
if colk < xCentroid
% It's in the upper left quadrant
if distanceSquared > maxDistance(1)
% Record the new furthest point in quadrant #1.
maxDistance(1) = distanceSquared;
xCorners(1) = colk;
yCorners(1) = rowk;
end
else
% It's in the upper right quadrant
if distanceSquared > maxDistance(2)
% Record the new furthest point in quadrant #2.
maxDistance(2) = distanceSquared;
xCorners(2) = colk;
yCorners(2) = rowk;
end
end
else
% It's in the bottom half.
if colk < xCentroid
% It's in the lower left quadrant
if distanceSquared > maxDistance(3)
% Record the new furthest point in quadrant #3.
maxDistance(3) = distanceSquared;
xCorners(3) = colk;
yCorners(3) = rowk;
end
else
% It's in the lower right quadrant
if distanceSquared > maxDistance(4)
% Record the new furthest point in quadrant #4.
maxDistance(4) = distanceSquared;
xCorners(4) = colk;
yCorners(4) = rowk;
end
end
end
end
% Display in command window.
xCorners
yCorners
figure;
% Display the original gray scale image.
hImage = imshow(binaryImage, []);
title('Binary Image', 'FontSize', fontSize);
hold on;
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Place markers at the corners
plot(xCorners, yCorners, 'rs', 'MarkerSize', 10, 'LineWidth', 3);
impixelinfo(hImage);
% Plot centroid again
plot(xCentroid, yCentroid, 'r+', 'MarkerSize', 30, 'LineWidth', 3);
Results:
6 个评论
Image Analyst
2013-4-25
移动：DGM
2023-2-20
Just get rid of the imhist stuff - it's not really needed. It's just boilerplate demo code. Just for fun to compute a hist - not needed. What are the errors? It's not a color image is it?
Anandakumar Selvaraj
2013-12-7
编辑：Anandakumar Selvaraj
2013-12-7
*hello sir i am a newbie i tried a code working well i have a similar task in my hand i need to do something like the image below were i marked points in orange color...help
i used same code and achieved this marked in blue
i need to achieve something like this image were i marked in orange
sir please say what to do...or show me what to do... *
Image Analyst
2013-12-7
I'm not sure that algorithm is good for irregular blob-shaped regions. Why don't you start a new question and we'll discuss it there. In the mean time, try to use regionprops() on your binary image and then get the orientation and use imrotate() to align it with the axes.
Anandakumar Selvaraj
2013-12-9
i have posted these question regarding my task in here
Image Analyst
2013-12-9
更多回答（2 个）
Image Analyst
2013-4-24
0 个投票
Why not use hough() and look for line intersections? There is an example for it in the help.
Jeff E
2013-4-24
The following may be helpful in validating any solution you do end up finding. If the rectangles in your images are rotated to any significant degree, just change "diamond" into "disk" to get a more robust, but slightly more noisy result.
bw = im2bw(imread('w0lwro.jpg'));
bw = ~bwareaopen(~bw, 5000);
cmask = imclose(bw, strel('diamond', 20));
cornermask = cmask & ~bw;
cornermask = bwareaopen(cornermask, 50);
The result is a binary mask that should contain, or be very close to, your corners, either through corner point detection or hough transform as suggested by Image Analyst.
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