I have a set of matrixes with a number of blobs (this changes from matrix to matrix), and I'm looking for a way to find the center of mass of the blobs. To do so I use regionprops with the Centroid option.
B = bwlabel(A);
stat = regionprops(B,'Centroid');
number_centers = numel(stat);
coord_centers = zeros(number_centers,2);
xy_cylinder = zeros(number_centers,2);
for x = 1: number_centers
coord_centers(x,1) = stat(x).Centroid(1); % Angle
coord_centers(x,2) = stat(x).Centroid(2); % Radius
end
If I plot the matrix with the centroids superimposed (code below), I noticed that they slightly offset (they are on the corner of the pixel where the centroid is). Do you think I'm right? And do you know why is that?
axis equal;
pcolor(A), colorbar, hold on;
xlabel('Angle'); ylabel('Radius');
for x = 1: numel(stat)
plot(stat(x).Centroid(1),stat(x).Centroid(2),'r+');
end
Related
I generate a random number between 1 and 2 as a radius for my circle. Then I plot my circle and saves it as a png. Also, several data points are generated both inside and outside the circle to make it noisy.
Then I will use Hough Transform to estimate the radius for the circle but, the number which it returns is more than 100. Although they are the same circles(I plotted to make sure).
I have tried to use polyfit function to map these two numbers but, the estimated radius seems to be smaller than the real one in some examples. After mapping It returns the same number. For example, a random radius is 1.2 and Hough estimates it 110 however it seems that it should be near 1.(Because I plot it and it is clear that is it smaller). Also, for Hough Transform I am using this code https://www.mathworks.com/matlabcentral/fileexchange/35223-circle-detection-using-hough-transforms
I tried the predefined Matlab function (imfindcircles) but it returns null for all my circles.
r1 = 1 + abs((1)*randn(1,1)); %radius of inner circle
r1 = abs(r1);
r2 = (1.2)*r1; %radius of outercircle
k = 500; %number of random numbers
% circle coordinate points
t = 0:2*pi/59:2*pi;
xv = cos(t)';
yv = sin(t)';
%%%%%%%%random points
xq = 2*randn(k,1)-1;
yq = 2*randn(k,1)-1; %distribution 1
xq1 = 2*(rand(k,1))-1; %distribution 2
yq1 = 2*(rand(k,1))-1;
in = inpolygon(xq1,yq1,r1*xv,r1*yv); %number of points inside the geometry
in1= inpolygon(xq,yq,r2*xv,r2*yv); %points inside outer circle
in2 = xor(in,in1); %points between circle
in3= inpolygon(xq1,yq1,r2*xv,r2*yv); %points inside outer circle
fig = figure(22);hold on;
% Random points
plot(xq(in2),yq(in2),'bo','MarkerFaceColor','r','MarkerSize',5,'Marker','o','MarkerEdgeColor','none');
axis equal;
plot(xq1(~in2),yq1(~in2),'bo','MarkerFaceColor','r','MarkerSize',5,'Marker','o','MarkerEdgeColor','none');
axis equal;
img= getframe(fig);
figure;
imshow(img.cdata)
hold on;
[r,c,rad] = circlefinder(img.cdata);
[m I] = max(rad);
%ploting the bigest estimated circle
angle = 2*pi*randn(k,1);
haffpointX = rad(I).*cos(angle)+ c(I);
haffpointY = rad(I).*sin(angle)+ r(I);
scatter(haffpointX,haffpointY,'g');
axis equal
I expect that for different random circles with noisy data Hough Transform estimates its circle with the number between the range of 1 and 2 so, I can use its results.
Thank you in advance
I have a set of data I need to generate two linear best fit lines for (1st order polyfit) but I don't know how to specify which region each line should fit data to. I need one line in the region between minimum x value and 0 and the other one in the region 0.25 < x.
Also, in the second region, there are two clear areas of data, one above the other, and I need the best fit line to be fitted only to the lower one.
I am a complete novice at Matlab so any help would be greatly appreciated
%load data, force and velocity
load ('exp_6_Force');
load ('exp_6_Velocity');
% Give a name to the title bar.
set(gcf,'name','Experiment 6 velocity','numbertitle','off')
%set variables to x and y
x = Force;
y = Velocity;
%plot the graph
plot(x,y);
%add grid and legend
grid on;
legend ('Velocity');
%add labes and title
xlabel ('Force');
ylabel ('Velcoity');
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
%find coordinates of y min point
[value,index1] = min(y);
yminxcoor = x(index1);
yminycoor = y(index1);
Use the logical index to get the x and y data for the two regions:
For region 1:
x_region1 = (x<0).*x
y_region1 = (x<0).*y
For region 2:
x_region2 = (x>0.25).*x
y_region2 = (x>0.25).*y
Then you can polyfit in these regions
For region 1:
p_region1 = polyfit(x_region1, y_region1, 1)
For region 2:
p_region2 = polyfit(x_region2, y_region2, 1)
I have a polygon which intersects itself multiple times. I try to create a mask from this polygon, i.e., to find all points/pixels location within the polygon. I use the Matlab function poly2mask for this. However, due to the multiple self-intersections this is the results I obtain:
Resulting mask from poly2mask for multi-self-intersecting polygon
So, some areas remain unmasked, because of the intersections. I think Matlab sees this as some sort of inclusions. The Matlab help for poly2mask doesn't mention anything about this. Does anyone have an idea how to also include these regions in the mask?
I obtain good results combining a small erosion/dilation step and imfill as follows:
data = load('polygon_edge.mat');
x = data.polygon_edge(:, 1);
y = data.polygon_edge(:, 2);
bw1 = poly2mask(x,y,ceil(max(y)),ceil(max(x)));
se = strel('sphere',1);
bw2 = imerode(imdilate(bw1,se), se);
bw3 = imfill(bw2, 'holes');
figure
imshow(bw3)
hold on
plot(x(:, 1),y(:, 1),'g','LineWidth',2)
The small erosion and dilation step is needed to be sure that all the regions are connected even at places where the polygon is only connected through a single point, otherwise imfill may see some non-existing holes.
you can use inpolygon:
bw1 = poly2mask(x,y,1000,1000);
subplot(131)
imshow(bw1)
hold on
plot(x([1:end 1]),y([1:end 1]),'g','LineWidth',2)
title('using poly2mask')
[xq,yq] = meshgrid(1:1000);
[IN,ON] = inpolygon(xq,yq,x,y);
bw2 = IN | ON;
subplot(132)
imshow(bw2)
hold on
plot(x([1:end 1]),y([1:end 1]),'g','LineWidth',2)
title('using inpolygon')
% boundary - seggested by another answer
k = boundary(x, y, 1); % 1 == tightest single-region boundary
bw3 = poly2mask(x(k), y(k), 1000, 1000);
subplot(133)
imshow(bw3)
hold on
plot(x([1:end 1]),y([1:end 1]),'g','LineWidth',2)
title('using boundary')
Update - I updated my answer to include boundary - it not seems to work well in my case.
You should first calculate the boundary of your polygon and use this to create your mask.
k = boundary(x, y, 0.99); % 1 == tightest single-region boundary
BW = poly2mask(x(k), y(k), m, n)
Using a shrink factor of 0.99 instead of 1 avoids undercutting, but sharp non-convex corners are still not fitted correctly.
To be exact I need the four end points of the road in the image below.
I used find[x y]. It does not provide satisfying result in real time.
I'm assuming the images are already annotated. In this case we just find the marked points and extract coordinates (if you need to find the red points dynamically through code, this won't work at all)
The first thing you have to do is find a good feature to use for segmentation. See my SO answer here what-should-i-use-hsv-hsb-or-rgb-and-why for code and details. That produces the following image:
we can see that saturation (and a few others) are good candidate colors spaces. So now you must transfer your image to the new color space and do thresholding to find your points.
Points are obtained using matlab's region properties looking specifically for the centroid. At that point you are done.
Here is complete code and results
im = imread('http://i.stack.imgur.com/eajRb.jpg');
HUE = 1;
SATURATION = 2;
BRIGHTNESS = 3;
%see https://stackoverflow.com/questions/30022377/what-should-i-use-hsv-hsb-or-rgb-and-why/30036455#30036455
ViewColoredSpaces(im)
%convert image to hsv
him = rgb2hsv(im);
%threshold, all rows, all columns,
my_threshold = 0.8; %determined empirically
thresh_sat = him(:,:,SATURATION) > my_threshold;
%remove small blobs using a 3 pixel disk
se = strel('disk',3');
cleaned_sat = imopen(thresh_sat, se);% imopen = imdilate(imerode(im,se),se)
%find the centroids of the remaining blobs
s = regionprops(cleaned_sat, 'centroid');
centroids = cat(1, s.Centroid);
%plot the results
figure();
subplot(2,2,1) ;imshow(thresh_sat) ;title('Thresholded saturation channel')
subplot(2,2,2) ;imshow(cleaned_sat);title('After morpphological opening')
subplot(2,2,3:4);imshow(im) ;title('Annotated img')
hold on
for (curr_centroid = 1:1:size(centroids,1))
%prints coordinate
x = round(centroids(curr_centroid,1));
y = round(centroids(curr_centroid,2));
text(x,y,sprintf('[%d,%d]',x,y),'Color','y');
end
%plots centroids
scatter(centroids(:,1),centroids(:,2),[],'y')
hold off
%prints out centroids
centroids
centroids =
7.4593 143.0000
383.0000 87.9911
435.3106 355.9255
494.6491 91.1491
Some sample code would make it much easier to tailor a specific solution to your problem.
One solution to this general problem is using impoint.
Something like
h = figure();
ax = gca;
% ... drawing your image
points = {};
points = [points; impoint(ax,initialX,initialY)];
% ... generate more points
indx = 1 % or whatever point you care about
[currentX,currentY] = getPosition(points{indx});
should do the trick.
Edit: First argument of impoint is an axis object, not a figure object.
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: