Is there an easy way to put a bitmap image in the background of a Matlab plot which does not fill the whole available space und keeps its aspect ratio when the figure is resized?
TIA
I'm not quite sure to understand what you mean by
plot which does not fill the whole available space
however the following solution should help you solve your problem (or at least get you started).
Basically read an image (here grayscale) and display it using the imagesc command along with the grayscale colormap, then issue the hold on command and plot the data. Notice that you need to reverse the direction of the x-axis in order to get the right direction for the plot.
Here is the code:
clear
clc
close all
A = imread('cameraman.tif');
x = 1:10;
y = x;
figure
%// Notice the fliplr(A) to reverse the direction of the x data
imagesc([min(x(:)) max(x(:))], [min(y(:)) max(y(:))],fliplr(A));
colormap gray
%// Here reverse the direction of the x axis, otherwise the plot is
%// reversed
set(gca,'XDir','reverse')
hold on
plot(x,y,'--r')
axis off
And the result:
If your background image is RGB, you can use the image function: (modified from answer here): You need to flip the x data from the image for each channel separately, because fliplr only accepts 2D arguments:
DataXImage = linspace(min(x), max(x), size(A, 2));
DataYImage = linspace(min(y), max(y), size(A, 1));
%// flip dimensions for each channel
B = cat(3,fliplr(A(:,:,1)),fliplr(A(:,:,2)),fliplr(A(:,:,3)));
image(DataXImage, DataYImage, B, 'CDataMapping', 'scaled');
which, using the peppers.png image, gives this:
Is this what you had in mind? If not please tell me!
img = imread('myimage.png');
% set the range of the axes
% The image will be stretched to this.
min_x = 0;
max_x = 8;
min_y = 0;
max_y = 6;
% make data to plot - just a line.
x = min_x:max_x;
y = (6/8)*x;
imagesc([min_x max_x], [min_y max_y], img);
hold on;
plot(x,y);
Related
This question already has answers here:
MATLAB: Drawing a line over a black and white image
(5 answers)
Closed 4 years ago.
I have a set of points that I want to connect sequentially. Suppose the points are (A1,A2,A3,...A9); I want to connect A1 to A2, A2 to A3 and so on and finally connect A9 to A1.
All I need is to know a function that would help me connect A1 to A2, I could do the rest using for loops.
I know connecting two points is a question that has been asked here several times before but I couldn't find the answer I required. Several of the solutions suggest using "plot" and "line" but these functions overlay the results on the image and don't actually make any changes to the original image.
I did try them out and managed to save the resulting figure using the "saveas" and "print" functions but the image doesn't get saved in the proper format and there are a lot of problems using the parameters for these functions. Besides, I don't really want to save the image, it's just an unnecessary overhead I was willing to add if I could get the desired image with the lines.
I've also tried "imline" to draw lines but it seems to be interactive.
This particular question reflects my problem perfectly but when I ran the code snippets given as solutions, all of them gave a set of dots in the resulting image.
I tried the above mentioned codes in the link with this image that I found here.
A dotted line was an output for all three code snippets in the link above.
For example, I ran the first code like this:
I = imread('berries_copy.png');
grayImage=rgb2gray(I);
img =false(size(grayImage,1), size(grayImage,2));
I wrote the above piece of code just to get a black image for the following operations:
x = [500 470]; % x coordinates
y = [300 310]; % y coordinates
nPoints = max(abs(diff(x)), abs(diff(y)))+1; % Number of points in line
rIndex = round(linspace(y(1), y(2), nPoints)); % Row indices
cIndex = round(linspace(x(1), x(2), nPoints)); % Column indices
index = sub2ind(size(img), rIndex, cIndex); % Linear indices
img(index) = 255; % Set the line points to white
imshow(img); % Display the image
This is the resulting image for the above code as well as the other two, which as you can see, is just a few points on a black background which isn't the desired output.
I changed the code and used the "plot" function for the same to get this output which is what I want. Is there anyway I can change the dotted output into a solid line?
Or if could anyone suggest a simple function or a method that would draw a line from A1 to A2 and would actually make a change in the input image, I'd be grateful. (I really hope this is just me being a novice rather than Matlab not having a simple function to draw a line in an image.)
P.S. I don't have the Computer Vision toolbox and if possible, I'd like to find a solution that doesn't involve it.
Your first problem is that you are creating a blank image the same size as the image you load with this line:
img =false(size(grayImage,1), size(grayImage,2));
When you add the line to it, you get a black image with a white line on it, as expected.
Your second problem is that you are trying to apply a solution for grayscale intensity images to an RGB (Truecolor) image, which requires you to modify the data at the given indices for all three color planes (red, green, and blue). Here's how you can modify the grayscale solution from my other answer:
img = imread('berries_copy.png'); % Load image
[R, C, D] = size(img); % Get dimension sizes, D should be 3
x = [500 470]; % x coordinates
y = [300 310]; % y coordinates
nPoints = max(abs(diff(x)), abs(diff(y)))+1; % Number of points in line
rIndex = round(linspace(y(1), y(2), nPoints)); % Row indices
cIndex = round(linspace(x(1), x(2), nPoints)); % Column indices
index = sub2ind([R C], rIndex, cIndex); % Linear indices
img(index) = 255; % Modify red plane
img(index+R*C) = 255; % Modify green plane
img(index+2*R*C) = 255; % Modify blue plane
imshow(img); % Display image
imwrite(img, 'berries_line.png'); % Save image, if desired
And the resulting image (note the white line above the berry in the bottom right corner):
You can use the Bresenham Algorithm. Of course it has been implemented and you can find it here: Bresenham optimized for Matlab. This algorithm selects the pixels approximating a line.
A simple example, using your variable name could be:
I = rgb2gray(imread('peppers.png'));
A1 = [1 1];
A2 = [40 40];
[x y] = bresenham(A1(1),A1(2),A2(1),A2(2));
ind = sub2ind(size(I),x,y);
I(ind) = 255;
imshow(I)
You can use imshow to display a image and then use plot to plot lines and save the figure. Check the below code:
I = imread('peppers.png') ;
imshow(I)
hold on
[m,n,p] = size(I) ;
%% Get random points A1, A2,..A10
N = 9 ;
x = (n-1)*rand(1,N)+1 ;
y = (m-1)*rand(1,N)+1 ;
P = [x; y]; % coordinates / points
c = mean(P,2); % mean/ central point
d = P-c ; % vectors connecting the central point and the given points
th = atan2(d(2,:),d(1,:)); % angle above x axis
[th, idx] = sort(th); % sorting the angles
P = P(:,idx); % sorting the given points
P = [P P(:,1)]; % add the first at the end to close the polygon
plot( P(1,:), P(2,:), 'k');
saveas(gcf,'image.png')
Gnoivce and Hartmut helped a lot with this code but it takes a while to run.
The CData property in the bar command doesn't seem to be implemented in the Octave 4.0-4.2.1 version which I'm using. The work around for this was to plot all the single bars individually and set an individual color for each individual bar. People helped me out and got me this far but it takes 5 minutes for the plot to show does anyone know a way of speeding this up?
The following code runs:
marbles.jpg image file used below:
clear all,clf reset,tic,clc
rgbImage = imread('/tmp/marbles.jpg');
hsvImage = rgb2hsv(rgbImage); % Convert the image to HSV space
hPlane = 360.*hsvImage(:, :, 1); % Get the hue plane scaled from 0 to 360
binEdges = 0:360; %# Edges of histogram bins
N = histc(hPlane(:),binEdges); %# Bin the pixel hues from above
C = colormap(hsv(360)); %# create an HSV color map with 360 points
stepsize = 1; % stepsize 1 runs for a while...
for n=binEdges(2:stepsize:end) %# Plot the histogram, one bar each
if (n==1), hold on, end
h=bar(n,N(n));
set(h,'FaceColor',C(n,:)); %# set the bar color individually
end
axis([0 360 0 max(N)]); %# Change the axes limits
set(gca,'Color','k'); %# Change the axes background color
set(gcf,'Pos',[50 400 560 200]); %# Change the figure size
xlabel('HSV hue (in degrees)'); %# Add an x label
ylabel('Bin counts'); %# Add a y label
fprintf('\nfinally Done-elapsed time -%4.4fsec- or -%4.4fmins- or -%4.4fhours-\n',toc,toc/60,toc/3600);
Plot created after 5 mins:
To see original question original question
I'm guessing the loop is the bottleneck in your code that is taking so long? You could remove the loop and create the plot with one call to bar, then call set to modify the hggroup object and its child patch object:
h = bar(binEdges(1:end-1), N(1:end-1), 'histc'); % hggroup object
set(h, 'FaceColor', 'flat', 'EdgeColor', 'none');
hPatch = get(h, 'Children'); % patch object
set(hPatch, 'CData', 1:360, 'CDataMapping', 'direct');
Repeating your code with this fix renders right away for me in Octave 4.0.3:
As I suggested in a comment, I would use image (takes 0.12s on my system for your image).
EDIT: more comments, fix little bug, allow to create bins with stepsize > 1
img_fn = "17S9PUK.jpg";
if (! exist (img_fn, "file"))
disp ("downloading image from imgur.com...");
fflush (stdout);
urlwrite ("http://i.imgur.com/17S9PUK.jpg", "17S9PUK.jpg");
endif
rgbImage = imread (img_fn);
## for debugging so the matrixes fit on screen
if (0)
pkg load image
rgbImage = imresize (rgbImage, [6 8]);
endif
hsvImage = rgb2hsv(rgbImage);
hPlane = 360 .* hsvImage(:, :, 1);
## create bins, I've choosen 2 step to "smooth" the result
binEdges = 1:2:360;
N = histc (hPlane(:), binEdges)';
cm = permute (hsv (numel (binEdges)), [3 1 2]);
## Create an image with x = hue
img = repmat (cm, max(N), 1);
## Create binary mask which is used to black "img" dependent on N
sp = sparse (N(N > 0), (1:360)(N > 0), true, max(N), numel (binEdges));
mask = full (cumsum (flipud (sp)));
## extend mask in depth to suppress RGB
mask = repmat (mask, [1 1 3]);
## use inverted mask to "black out" pixels < N
img(logical (1 - flipud (mask))) = 0;
## show image
image (binEdges, 1:max(N), img)
set (gca, "ydir", "normal");
xlabel('HSV hue (in degrees)');
ylabel('Bin counts');
## print it for stackoverflow
print ("out.png")
Same as above but with bin width 1 (Elapsed time is 0.167423 seconds.)
binEdges = 1:360;
I'm working on an application and I'm at a stage where I'm comparing two images to see if they have any resemblance, with one another. I have managed to do this, an example you can find here.
From the image, it will display white spaces for pixels that are near the same for both images given. What I want to do next is get the coordinates of the white spaces and plot them onto the original image to highlight the strongest features about the coin. However, I'm unsure how to do this as I'm rather new to Matlab.
firstImage = sprintf('M:/Project/MatLab/Coin Image Processing/Image Processing/test-1.jpg');
secondImage = sprintf('M:/Project/MatLab/Coin Image Processing/Image Processing/test-99.jpg');
a = rgb2gray(imread(firstImage));
b = rgb2gray(imread(secondImage));
axes(handles.axes4);
imshow(a==b);
title('Scanning For Strongest Features', 'fontweight', 'bold')
From using disp(a==b), I can see which points of both pictures are the same. So my guess is that I need to do something where I get the coordinates of all the zeroes and then plot them onto the original image in a way that highlights it, similar to using a yellow highlighter, but I just don't know how.
If I got your question, I think you should use find to collect all the coordinates for which a==b:
[X, Y] = find(a == b); % Find coordinates for which the two images are equal
imshow(a), axis image; % Show first image
hold on
plot(Y, X, 'y.'); % Overlay those coordinates
hold off
You can use a transparent overlay to plot the region of interest.
figure
imshow(originalImage); % plot the original image
hold on
% generate a red overlay
overlay(:, :, 1) = ones(size(a)); % red channel
overlay(:, :, 2) = zeros(size(a)); % green channel
overlay(:, :, 3) = zeros(size(a)); % blue channel
h = imshow(overlay); % plot the overlay
set(h, 'AlphaData', (a == b) * 0.5); % set the transparency to 50% if a == b and 0% otherwise
I have a imagesc image in which each pixel represent a data vector. The image itself is from a data cube squeezed into 2D matrix. I can use impixelinfo to navigate through the image and get pixel coordinates when inquiring the image. The code to execute this is below,
load data.mat; % data cube of size 512x256x12000
figure; imagesc(squeeze(mean(abs(data),3)))
axis equal; colormap jet;
impixelinfo
What I want to do is to be able to plot the underlying data vector (laying into the 3rd dimension) in a second figure using the pixel coordinates. This second figure should update automatically when I move the cursor in the image.
Any help is highly appreciated. Thank you in advance.
Thanks guys, I found a solution with ButtonDownFcn and posting it here for folks out there with a similar problem.
x = (-10:10); y = x; z = x;
[mx, my, mz] = ndgrid(x,y,z)
r = sqrt(mx.^2 + my.^2 + mz.^2);
figure;
imagesc(squeeze(r(:,:,1)),'ButtonDownFcn', {#test_func,r});
with the following function
function test_func(hObject, eventdata, r);
P = get(gca,'CurrentPoint');
X = round(P(1,1));Y = round(P(1,2));
figure;plot(squeeze(r(X,Y,:)));
end
I have multiple histograms that I would like to overlay on top of each other but I don't know how to do it. I found the code below but I don't know how to modify it to run on a loop instead of just two histograms.
data1 = randn(100,1); % data of one size
data2 = randn(25, 1); % data of another size!
myBins = linspace(-3,3,10); % pick my own bin locations
% Hists will be the same size because we set the bin locations:
y1 = hist(data1, myBins);
y2 = hist(data2, myBins);
% plot the results:
figure(3);
bar(myBins, [y1;y2]');
title('Mixed size result');
or what is a better way of comparing histograms if they are more than 10 or 20.
Your question is very general. Firstly I do not understand why you insist on a for loop.
Personally I don't like the included bar plot. It quickly gets messy (especially since the bars are not at the "original" location)
If you got a lot of histograms I would consider a stairstep plot as it doesn't fill the plot area so much. Or you could come up with your own - eg using transparent patches.
If it get's lots of curves there are many ways to visualize them google for "multivariate visualization" and be amazed. One of the most amusing ways would be Chernoff faces.
it's much easier now:
histogram(data1, myBins);
hold on;
histogram(data2, myBins);
You could do the following, although it's not the only way:
data = cell(1, N);
y = cell(1, N);
yBar = zeros(N, 10);
for i=1:N
data{1, i} = randn(10*round(rand(1,1)), 1);
y{1, i} = hist(data{1, i}, myBins);
yBar(i, :) = y{1, i};
end
yBar = yBar';
figure(3);
bar(myBins, yBar);
title('Mixed size result');
Using the y cell is not obligatory of course, I left it there to actually show what's happening.
I would suggest this. It's simple and does not require for loops:
bar([y1.' y2.'],'stacked')
Here's a way that was useful to me:
I'm plotting a histogram for each column of the matrix ao.
The code was:
for i = 1:size(ao,2)
[h, y] = hist(ao(:,i), linspace(-5,10,100));
h = i + (0.95./max(h(:))) .* h;
barh(y, h, 'BarWidth', 1, 'BaseValue', i, 'LineStyle', 'none');
hold on;
end
grid;
Note that just changing barh to bar will give the same thing but going up-down instead of left-right (i.e. the figure rotated by 90° anti-clockwise).