After doing the Hough transform in MATLAB, how do I pick the lines so that I can compare between two or more images?
I followed the example given by Amro and actually what I wanted to detect is the two lines in the first picture. However, what I got is the one in the second picture. How can I do this?
I think you meant the goal to be to detect lines in an image, not comparing two images (?).
Anyway, to find the maximum intensities in the Hough transform matrix generated by the hough function, we use the houghpeaks function, and pass it the desired number of peaks to detect.
EDIT1:
I figured I would add an example to show the procedure:
%# Load image, process it, find edges
I = rgb2gray( imread('pillsetc.png') );
I = imcrop(I, [30 30 450 350]);
J = imfilter(I, fspecial('gaussian', [17 17], 5), 'symmetric');
BW = edge(J, 'canny');
%# Perform Hough transform and show matrix
[H,T,R] = hough(BW);
imshow(imadjust(mat2gray(H)), [], 'XData',T, 'YData',R, ...
'InitialMagnification','fit')
xlabel('\theta (degrees)'), ylabel('\rho')
axis on, axis normal, hold on
colormap(hot), colorbar
%# Detect peaks
P = houghpeaks(H, 4);
plot(T(P(:,2)), R(P(:,1)), 'gs', 'LineWidth',2);
%# Detect lines and overlay on top of image
lines = houghlines(BW, T, R, P);
figure, imshow(I), hold on
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1), xy(:,2), 'g.-', 'LineWidth',2);
end
hold off
EDIT2:
Following your recent update, I managed to detect the lines by only making a few changes to the same above code:
I cropped the region to: [200 70 160 140]
I used an 11x11 Gaussian filter with sigma=3
Note: You will have to add the offset to get the position of the lines in the original image uncropped. Also, if you want more accurate results, you might want to detect four lines and get the lines in the middle as shown below:
Related
I have a segmented region as shown in figure 1. and i want to plot the lower boundary using matlab by connecting the lower edge points as shown in figure 2. I cannot plot like fig 2. So I did some morphological operations like fill,thicken,close but not getting the idea to plot.can you provide the matlab code.
figure 1
figure 2
Here is a solution
Threshold the image so it is just in binary, easy since this image is simple
Identify the position of the lowest pixel in each column
Smooth by just taking the max of every nth column. If you just want the lowest points then you can stop at line 4 of the below code!
Code is commented for more detail:
img = rgb2gray(imread('1.jpg')); % Read image
img = img > 0.5; % Threshold to get binary image
% Get last row where there is a 1 pixel in the image for each column
lastrow = max(repmat((1:size(img,1))', 1, size(img,2)).*img,[],1);
res = 30; % Pixel resolution for line averaging
% Ensure res divides num. columns by padding the end of the vector
lastrowpadded = [lastrow, NaN(1, res - mod(numel(lastrow),res))];
% Reshape into columns of length 'res', then get the max row number
lastrow2 = max(reshape(lastrowpadded,res,[]),[],1);
% Plots
imshow(img);
hold on
plot(1:size(img,2), lastrow, '.')
plot(res/2:res:size(lastrowpadded,2)-res/2, lastrow2, 'linewidth', 1.5)
hold off
legend('lowest points', 'smoothed lowest points')
Result:
Note: because the image is plotted with (0,0) in the upper-left corner, this plot will be upside down if you plotted it without the image. Subtract the lastrow2 or lastrow values from the height of the image to rectify this.
Edit: You might also be interested in convhull for creating a convex hull
[X,Y] = find(img); % After thresholding image as before, get X,Y coords
K = convhull(X,Y); % Get convex hull indices
imshow(img) % Show image
hold on
plot(Y(K),X(K),'linewidth',1.5) % Plot convex hull
Result:
h = vision.GeometricShearer('values' , [0 20]);
The above MATLAB command defines an object for horizontal shearing of an image. Is there a way to define the same object but for up and down shearing?
BTW, you have a small typo in your syntax. values should be capitalized and so it's Values. This apparently is case-sensitive.... which is a bit ridiculous, but that's the way it is.
Back to your post, you need to specify an additional flag to vision.GeometricShearer that determines the direction of where you want to apply the shear. Specifically, you need to set the Direction flag, and you set this to either Horizontal or Vertical. If you omit this, the default is Horizontal. As such, if you want to shear the last column of your image and move it down by 20 pixels, you would do this:
h = vision.GeometricShearer('Values', [0 20], 'Direction', 'Vertical');
If you want to visualize the results, you would use step and apply it to the image. As an example, let's load in the checkerboard image that's part of the MATLAB system path, apply the shear, then show both of the results in the same figure:
%// Define vertical shear
h = vision.GeometricShearer('Values', [0 20], 'Direction', 'Vertical');
img = im2single(checkerboard); %// Read in image
outimg = step(h,img); %// Apply shear
%// Show both results
subplot(2,1,1), imshow(img);
title('Original image');
subplot(2,1,2), imshow(outimg);
title('Output image');
This is what I get:
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
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:
Loading the image and making it binary
Extracting all connected components using bwlabel().
Getting useful information about each of them via regionprops() [.centroid will be a good approximation to the middel point for the lines].
Thresholded out small regions (noise and numbers)
Extracted x,y coordinates
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:
If you already found the x,y of the centers, you should use polyfit function:
You will then find the polynomial coefficients of the best line. In order to draw a segment, you can take the minimal and maximal x
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);
If your ultimate goal is to generate a line perpendicular to the bars in the bar code and passing roughly through the centroids of the bars, then I have another option for you to consider...
A simple solution would be to perform a Hough transform to detect the primary orientation of lines in the bar code. Once you find the angle of the lines in the bar code, all you have to do is rotate that by 90 degrees to get the slope of a perpendicular line. The centroid of the entire bar code can then be used as an intercept for this line. Using the functions HOUGH and HOUGHPEAKS from the Image Processing Toolbox, here's the code starting with a cropped version of your image from step 1:
img = imread('bar_code.jpg'); %# Load the image
img = im2bw(img); %# Convert from RGB to BW
[H, theta, rho] = hough(img); %# Perform the Hough transform
peak = houghpeaks(H); %# Find the peak pt in the Hough transform
barAngle = theta(peak(2)); %# Find the angle of the bars
slope = -tan(pi*(barAngle + 90)/180); %# Compute the perpendicular line slope
[y, x] = find(img); %# Find the coordinates of all the white image points
xMean = mean(x); %# Find the x centroid of the bar code
yMean = mean(y); %# Find the y centroid of the bar code
xLine = 1:size(img,2); %# X points of perpendicular line
yLine = slope.*(xLine - xMean) + yMean; %# Y points of perpendicular line
imshow(img); %# Plot bar code image
hold on; %# Add to the plot
plot(xMean, yMean, 'r*'); %# Plot the bar code centroid
plot(xLine, yLine, 'r'); %# Plot the perpendicular line
And here's the resulting image:
I need to know how to align an image in Matlab for further work.
for example I have the next license plate image and I want to recognize all
the digits.
my program works for straight images so, I need to align the image and then
preform the optical recognition system.
The method should be as much as universal that fits for all kinds of plates and in all kinds of angles.
EDIT: I tried to do this with Hough Transform but I didn't Succeed. anybody can help me do to this?
any help will be greatly appreciated.
The solution was first hinted at by #AruniRC in the comments, then implemented by #belisarius in Mathematica. The following is my interpretation in MATLAB.
The idea is basically the same: detect edges using Canny method, find prominent lines using Hough Transform, compute line angles, finally perform a Shearing Transform to align the image.
%# read and crop image
I = imread('http://i.stack.imgur.com/CJHaA.png');
I = I(:,1:end-3,:); %# remove small white band on the side
%# egde detection
BW = edge(rgb2gray(I), 'canny');
%# hough transform
[H T R] = hough(BW);
P = houghpeaks(H, 4, 'threshold',ceil(0.75*max(H(:))));
lines = houghlines(BW, T, R, P);
%# shearing transforma
slopes = vertcat(lines.point2) - vertcat(lines.point1);
slopes = slopes(:,2) ./ slopes(:,1);
TFORM = maketform('affine', [1 -slopes(1) 0 ; 0 1 0 ; 0 0 1]);
II = imtransform(I, TFORM);
Now lets see the results
%# show edges
figure, imshow(BW)
%# show accumlation matrix and peaks
figure, imshow(imadjust(mat2gray(H)), [], 'XData',T, 'YData',R, 'InitialMagnification','fit')
xlabel('\theta (degrees)'), ylabel('\rho'), colormap(hot), colorbar
hold on, plot(T(P(:,2)), R(P(:,1)), 'gs', 'LineWidth',2), hold off
axis on, axis normal
%# show image with lines overlayed, and the aligned/rotated image
figure
subplot(121), imshow(I), hold on
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1), xy(:,2), 'g.-', 'LineWidth',2);
end, hold off
subplot(122), imshow(II)
In Mathematica, using Edge Detection and Hough Transform:
If you are using some kind of machine learning toolbox for text recognition, try to learn from ALL plates - not only aligned ones. Recognition results should be equally well if you transform the plate or dont, since by transforming, no new informations according to the true number will enhance the image.
If all the images have a dark background like that one, you could binarize the image, fit lines to the top or bottom of the bright area and calculate an affine projection matrix from the line gradient.
I want to plot a line from one well-defined point to another and then turn it into an image matrix to use a Gaussian filter on it for smoothing. For this I use the functions line and getframe to plot a line and capture the figure window in an image, but getframe is very slow and not very reliable. I noticed that it does not capture anything when the computer is locked and I got an out of memory error after 170 executions.
My questions are:
Is there a substitute to getframe that I can use?
Is there a way to create the image matrix and draw the line directly in it?
Here is a minimal code sample:
figure1=line([30 35] ,[200 60]);
F= getframe;
hsize=40; sigma=20;
h = fspecial('gaussian',hsize,sigma);
filteredImg = imfilter(double(F.cdata), h,256);
imshow(uint8(filteredImg));
[update]
High-Performance Mark's idea with linspace looks very promising, but how do I access the matrix coordinates calculated with linspace? I tried the following code, but it does not work as I think it should. I assume it is a very simple and basic MATLAB thing, but I just can not wrap my head around it:
matrix=zeros(200,60);
diagonal=round([linspace(30,200,numSteps); linspace(35,60,numSteps)]);
matrix(diagonal(1,:), diagonal(2,:))=1;
imshow(matrix);
Here's one example of drawing a line directly into a matrix. First, we'll create a matrix of zeros for an empty image:
mat = zeros(250, 250, 'uint8'); % A 250-by-250 matrix of type uint8
Then, let's say we want to draw a line running from (30, 35) to (200, 60). We'll first compute how many pixels long the line will have to be:
x = [30 200]; % x coordinates (running along matrix columns)
y = [35 60]; % y coordinates (running along matrix rows)
nPoints = max(abs(diff(x)), abs(diff(y)))+1; % Number of points in line
Next, we compute row and column indices for the line pixels using linspace, convert them from subscripted indices to linear indices using sub2ind, then use them to modify mat:
rIndex = round(linspace(y(1), y(2), nPoints)); % Row indices
cIndex = round(linspace(x(1), x(2), nPoints)); % Column indices
index = sub2ind(size(mat), rIndex, cIndex); % Linear indices
mat(index) = 255; % Set the line pixels to the max value of 255 for uint8 types
You can then visualize the line and the filtered version with the following:
subplot(1, 2, 1);
image(mat); % Show original line image
colormap(gray); % Change colormap
title('Line');
subplot(1, 2, 2);
h = fspecial('gaussian', 20, 10); % Create filter
filteredImg = imfilter(mat, h); % Filter image
image(filteredImg); % Show filtered line image
title('Filtered line');
If you have Computer Vision System toolbox there is a ShapeInserter object available. This can be used to draw lines, circles, rectangles and polygons on the image.
mat = zeros(250,250,'uint8');
shapeInserter = vision.ShapeInserter('Shape', 'Lines', 'BorderColor', 'White');
y = step(shapeInserter, mat, int32([30 60 180 210]));
imshow(y);
http://www.mathworks.com/help/vision/ref/vision.shapeinserterclass.html
You can check my answer here. It is robust way to achieve what you are asking for. Advantage of my approach is that it doesn't need additional parameters to control density of the line drawn.
Something like this:
[linspace(30,200,numSteps); linspace(35,60,numSteps)]
Does that work for you ?
Mark