I am trying to plot x and y velocities using quiver function in MATLAB.
I have x,y,u and v arrays(with their usual meanings) with dimension 100x100
So, the result is my quiver plot is dense and I cannot see the arrows unless I zoom in.
Somewhat like this: quiver not drawing arrows just lots of blue, matlab
Take a look at my plot:
Is there any way to make quiver plot less dense(and with bigger arrows)? I am planning to clip x-axis range to 0-4. But anything apart from that?
I cannot make my mesh less dense for accuracy concerns. I am, however willing to ignore some fine data points if that's required to make the plot look better.
You can plot a reduced number of arrows by plotting, for example, (assuming your data are in arrays)
quiver(x(1:2:end,1:2:end),y(1:2:end,1:2:end),u(1:2:end,1:2:end),v(1:2:end,1:2:end))
where the 2 in this example means we plot only a quarter as many arrows. You can of course change it, as long as you change all of the 2's so that the arrays are all appropriately sized.
If you want to change the length of the arrows there are two options. Firstly, you can use the scale option scale=2 to scale the arrows by the amount specified, or you can normalise the velocities if you want to have all the arrows the same length. You do lose information doing that, because you can't compare the magnitude of the velocity by looking at the arrows, but it may be useful in some situations. You can do this by dividing u and v both by sqrt(u.^2+v.^2) (at the points you wish to plot arrows at.
Hope that helps and sets everything out nicely.
You need to make your interval value a bit larger in order to make your matrix more sparse.
This is very dense:
1:0.0001:100
This is very sparse:
1:1:100
EDIT:
If you have the Image Processing Toolkit you can use the imresize function to reduce the matrix resolution:
newMat = imresize(oldMat, newSize);
And if you don't have the Toolbox then you can resize in a similar manner to this example using interp2 Interpolation:
orgY = 1:size(oldMat,1);
orgX = 1:size(oldMat,2);
[orgX,orgY] = meshgrid(orgX ,orgY);
newY = linspace(1,size(mat,1),newHeight);
newX = linspace(1,size(mat,2),newWidth);
[newX,newY] = meshgrid(newX,newY);
newMat = interp2(orgX,orgY,mat,newX,newY);
And thanks to #David, if you want to just strip out some individual points you can simply do:
xPlot=x(1:2:end)
Related
I would like to draw height lines of a function (represented by matrices, of course), using MATLAB.
I'm familiar with contour, but contour draws lines at even-spaced heights, while I would like to see lines (with height labels), in constant distance from one another when plotted.
This means that if a function grows rapidly in one area, I won't get a plot with dense height lines, but only a few lines, at evenly spaced distances.
I tried to find such an option in the contour help page, but couldn't see anything. Is there a built in function which does it?
There is no built-in function to do this (to my knowledge). You have to realize that in the general case you can't have lines that both represent iso-values and that are spaced with a fixed distance. This is only possible with plots that have special scaling properties, and again, this is not the general case.
This being said, you can imagine to approach your desired plot by using the syntax in which you specify the levels to plots:
...
contour(Z,v) draws a contour plot of matrix Z with contour lines at the data values specified in the monotonically increasing vector v.
...
So all you need is the good vector v of height values. For this we can take the classical Matlab exemple:
[X,Y,Z] = peaks;
contour(X,Y,Z,10);
axis equal
colorbar
and transform it in:
[X,Y,Z] = peaks;
[~, I] = sort(Z(:));
v = Z(I(round(linspace(1, numel(Z),10))));
contour(X,Y,Z,v);
axis equal
colorbar
The result may not be as nice as what you expected, but this is the best I can think of given that what you ask is, again, not possible.
Best,
One thing you could do is, instead of plotting the contours at equally spaces levels (this is what happens when you pass an integer to contour), to plot the contours at fixed percentiles of your data (this requires passing a vector of levels to contour):
Z = peaks(100); % generate some pretty data
nlevel = 30;
subplot(121)
contour(Z, nlevel) % spaced equally between min(Z(:)) and max(Z(:))
title('Contours at fixed height')
subplot(122)
levels = prctile(Z(:), linspace(0, 100, nlevel));
contour(Z, levels); % at given levels
title('Contours at fixed percentiles')
Result:
For the right figure, the lines have somewhat equal spacing for most of the image. Note that the spacing is only approximately equal, and it is impossible to get the equal spacing over the complete image, except in some trivial cases.
I want to detect edges (with sub-pixel accuracy) in images like the one displayed:
The resolution would be around 600 X 1000.
I came across a comment by Mark Ransom here, which mentions about edge detection algorithms for vertical edges. I haven't come across any yet. Will it be useful in my case (since the edge isn't strictly a straight line)? It will always be a vertical edge though. I want it to be accurate till 1/100th of a pixel at least. I also want to have access to these sub-pixel co-ordinate values.
I have tried "Accurate subpixel edge location" by Agustin Trujillo-Pino. But this does not give me a continuous edge.
Are there any other algorithms available? I will be using MATLAB for this.
I have attached another similar image which the algorithm has to work on:
Any inputs will be appreciated.
Thank you.
Edit:
I was wondering if I could do this:
Apply Canny / Sobel in MATLAB and get the edges of this image (note that it won't be a continuous line). Then, somehow interpolate this Sobel edges and get the co-ordinates in subpixel. Is it possible?
A simple approach would be to project your image vertically and fit the projected profile with an appropriate function.
Here is a try, with an atan shape:
% Load image
Img = double(imread('bQsu5.png'));
% Project
x = 1:size(Img,2);
y = mean(Img,1);
% Fit
f = fit(x', y', 'a+b*atan((x0-x)/w)', 'Startpoint', [150 50 10 150])
% Display
figure
hold on
plot(x, y);
plot(f);
legend('Projected profile', 'atan fit');
And the result:
I get x_0 = 149.6 pix for your first image.
However, I doubt you will be able to achieve a subpixel accuracy of 1/100th of pixel with those images, for several reasons:
As you can see on the profile, your whites are saturated (grey levels at 255). As you cut the real atan profile, the fit is biased. If you have control over the experiments, I suggest you do it again again with a smaller exposure time for instance.
There are not so many points on the transition, so there is not so many information on where the transition is. Typically, your resolution will be the square root of the width of the atan (or whatever shape you prefer). In you case this limits the subpixel resolution at 1/5th of a pixel, at best.
Finally, your edges are not stricly vertical, they are slightly titled. If you choose to use this projection method, to increase the accuracy you should look for a way to correct this tilt before projecting. This won't increase your accuracy by several orders of magnitude, though.
Best,
There is a problem with your image. At pixel level, it seems like there are four interlaced subimages (odd and even rows and columns). Look at this zoomed area close to the edge.
In order to avoid this artifact, I just have taken the even rows and columns of your image, and compute subpixel edges. And finally, I look for the best fitting straight line, using the function clsq whose code is in this page:
%load image
url='http://i.stack.imgur.com/bQsu5.png';
image = imread(url);
imageEvenEven = image(1:2:end,1:2:end);
imshow(imageEvenEven, 'InitialMagnification', 'fit');
% subpixel detection
threshold = 25;
edges = subpixelEdges(imageEvenEven, threshold);
visEdges(edges);
% compute fit line
A = [ones(size(edges.x)) edges.x edges.y];
[c n] = clsq(A,2);
y = [1,200];
x = -(n(2)*y+c) / n(1);
hold on;
plot(x,y,'g');
When executing this code, you can see the green line that best aproximate all the edge points. The line is given by the equation c + n(1)*x + n(2)*y = 0
Take into account that this image has been scaled by 1/2 when taking only even rows and columns, so the right coordinates must be scaled.
Besides, you can try with the other tree subimages (imageEvenOdd, imageOddEven and imageOddOdd) and combine the four straigh lines to obtain the best solution.
I am trying to program a matlab code in R2012a that will allow a color representation of temperature change on a surface based on different corresponding height and temperature measurements. Then end goal would be to combine multiple images together to get some sort of a jpeg. The temperatures I recorded were time specific which is why it would be beneficial in the end to have a visual depicting this temperature change over time.
It has been a little bit of time since I have used matlab, and I have never created something This complicated. It has me a little over whelmed and wondering where to start.
Thanks for any and all advice!
You might want to look at the avifile documentation for details on how to create a movie from a series of frames, there is a nice example that should make it clear enough.
Now to generate the frames needed for the avi file. Here, the meshgrid and surf functions come in handy. meshgrid generates two coordinate matrices with x and y coordinates for every point on your plate, surf then allows you to plot a surface with colors given by the temperatures at these positions. For example:
[x,y] = meshgrid(1:5,1:10) % generate a 10*5 plate with step size 1
z = rand(10,5) % height of the plate for a given position
c = rand(10,5) % color values, these should be your (time-dependent) temperatures
surf(x,y,z,c)
It is not clear to me whether you actually have a curved surface (requiring three coordinates to define each position), or just a simple plate. If it is just a simple plate, you can just set z = ones(size(x)) to obtain an uniform height.
I want to assign vector to a contourf graph, in order to show the direction and magnitude of wind.
For this I am using contourf(A) and quiver(x,y), where as A is a matrix 151x401 and x,y are matrices with the same sizes (151x401) with magnitude and direction respectively.
When I am using large maps i get the position of the arrows but they are to densily placed and that makes the graph look bad.
The final graph has the arrows as desired, but they are to many of them and too close, I would like them to be more scarce and distributed with more gap between them, so as to be able to increase their length and at the same time have the components of the contour map visible.
Can anyone help , any pointers would be helpful
i know its been a long time since the question was asked, but i think i found a way to make it work.
I attach the code in case someone encounters the same issues
[nx,ny]= size(A) % A is the matrix used as base
xx=1:1:ny; % set the x-axis to be equal to the y
yy=1:1:nx; % set the y-axis to be equal to the x
contourf(xx,yy,A)
hold on, delta = 8; %delta is the distance between arrows)
quiver(xx(1:delta:end),yy(1:delta:end),B(1:delta:end,1:delta:end),C(1:delta:end,1:delta:end),1) % the 1 at the end is the size of the arrows
set(gca,'fontsize',12);, hold off
A,B,C are the corresponding matrices ones want to use
I want to get a metric of straightness of contour in my binary image (relatively faster). The image looks as follows:
Now, the contours in the red box are the ones which I would like to be removed preferably. Since they are not straight. These are the things I have tried. I am as of now implementing in MATLAB.
1.Collect row and column coordinates of each contour and then take derivative. For straight objects (such as rectangle), derivative will be mostly low with a few spikes (along the corners of the rectangle).
Problem: The coordinates collected are not in order i.e. the order in which the contour will be traversed if we imaging it as a path. Therefore, derivative gives absurdly high values sometimes. Also, the contour is not absolutely straight, its an output of edge detection algorithm, so you can imagine that there might be some discontinuity (see the rectangle at the bottom, human eye can understand that it is a rectangle though it is not absolutely straight).
2.Tried to think about polyfit, but again this contour issue comes up. Since its a rectangle I don't know how to apply polyfit to that point set.
Also, I would like to remove contours which are distributed vertically/horizontally. Basically this is a lane detection algorithm. So lanes cannot be absolutely vertical/horizontal.
Any ideas?
You should look into the features of regionprops more. To be fair I stole the script from this answer, but here it is:
BW = imread('lanes.png');
BW = im2bw(BW);
figure(1),
subplot(1,2,1);
imshow(BW);
cc = bwconncomp(BW);
l = labelmatrix(cc);
a_rp = regionprops(CC,'Area','MajorAxisLength','MinorAxislength','Orientation','PixelList','Eccentricity');
idx = ([a_rp.Eccentricity] > 0.99 & [a_rp.Area] > 100 & [a_rp.Orientation] < 70 & [a_rp.Orientation] > -90);
BW2 = ismember(l,find(idx));
subplot(1,2,2);
imshow(BW2);
You can mess around with the properties. 'Orientation', 'Eccentricity', and 'Area' are probably the parameters you want to mess with. I also messed with the ratios of the major/minor axis lengths but eccentricity basically does this (eccentricity is a measure of how "circular" an ellipse is). Here's the output:
I actually saw a good video specifically from matlab for lane detection using regionprops. I'll try to see if I can find it and link it.
You can segment your image using bwlabel, then work separately on each bwlabel connected object, using find. This should help solve your order problem.
About a metric, the only thing that come to mind at the moment is to fit to an ellipse, and set the a/b (major axis/minor axis) ratio (basically eccentricity) a parameter. For example a straight line (even if not perfect) will be fitted to an ellipse with a very big major axis and a very small minor axis. So say you set a ratio threshold of >10 etc... Fitting to an ellipse can be done using this FEX submission for example.