I would like to plot the mean and percentile as shown in figure.I tried some code to plot box plot but I'm getting something different
I = propsSynthesizeImage;
imshow(I)
title('Synthetic Image')
% Create a Binary Image
% Segment the grayscale image by creating a binary image containing the objects in the image.
BW = I > 0;
imshow(BW)
title('Binary Image')
s = regionprops(BW, I, {'Centroid','PixelValues','BoundingBox'});
imshow(I);
numObj = numel(s);
title('Standard Deviation of Regions');
hold on
for k = 1 : numObj
s(k).StandardDeviation = std(double(s(k).PixelValues));
text(s(k).Centroid(1),s(k).Centroid(2), ...
sprintf('%2.1f', s(k).StandardDeviation), ...
'EdgeColor','b','Color','r');
end
figure
boxplot(1:numObj,[s.StandardDeviation])
xlabel('Region Label Number');
ylabel('Standard Deviation');
hold off
The boxplot visualizes a distribution. Currently, you're plotting scalars.
To plot the distribution (and have Matlab find median, quantiles, etc), plot
boxplot(1:numObj,{s.PixelValues})
Note that boxplot will not show standard deviations (instead, it shows the inter-quartile range), and that it may give misleading results if the underlying pixel values have a multi-modal distribution.
Related
How can I convert an RGB histogram of an image to create a histogram showing the combined colors along with correct color wavelength range?
Example code:
pkg load image
f=imread('/tmp/marbles.jpg');
f=uint8(f); %need to convert back to uint8 to show picture
%Split into RGB Channels
f_red = f(:,:,1);
f_green = f(:,:,2);
f_blue = f(:,:,3);
%Get histValues for each channel
[y_f_red, x] = imhist(f_red);
[y_f_green, x] = imhist(f_green);
[y_f_blue, x] = imhist(f_blue);
subplot (2,1,1); imshow(f);
subplot (2,1,2); plot(x, y_f_red, 'r', x, y_f_green, 'g', x, y_f_blue, 'b');
Example image along with separate RGB histogram the code produces:
I'm trying to get the histogram to look like the image below but have the colors go from red to blue:
Another image example:
PS: I'm using Octave 4.0 which is very similar to MATLAB.
There's a huge hurdle to converting between standard color representations (like RGB or HSV) and spectral wavelength: many colors can't be represented by a single wavelength of light. Colors such as magenta, pink, brown, or any grayscale color represent mixtures of different wavelengths. Generating an equivalent spectral wavelength is therefore a much more complicated endeavor (you may find some useful ideas and links here and here).
Creating histograms of the colors themselves may be a better way to go (illustrated in one of my other answers), but if you really want to relate color to wavelength in a simple fashion you can try the following...
A first step will be to convert RGB values to HSV values, then create a histogram of the hue channel. I'll adapt part of my answer from here to do that. The next step will be to map hues to wavelengths of light, using some rather gross approximations adapted from this answer:
rgbImage = imread('test_image.png'); % Load image
hsvImage = rgb2hsv(rgbImage); % Convert the image to HSV space
hPlane = 360.*hsvImage(:, :, 1); % Get the hue plane scaled from 0 to 360
binEdges = 0:270; % Edges of histogram bins
N = histc(hPlane(:), binEdges); % Bin the pixel hues from above
wavelength = 620-(170/270).*(0:269); % Approximate wavelength
hBar = bar(wavelength, N(1:end-1), 'histc'); % Plot the histogram
set(hBar, 'CData', 270:-1:1, ... % Change the color of the bars using
'CDataMapping', 'direct', ... % indexed color mapping (360 colors)
'EdgeColor', 'none'); % and remove edge coloring
colormap(hsv(360)); % Change to an HSV color map with 360 points
axis([450 620 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('Wavelength (nm)'); % Add an x label
ylabel('Bin counts'); % Add a y label
NOTE: For the above to work properly in Octave, it may be necessary to change the set(hBar, ... line to the following:
set(hBar, 'FaceColor', 'flat', 'EdgeColor', 'none');
set(get(hBar, 'Children'), 'CData', 270:-1:1, 'CDataMapping', 'direct');
And here's the histogram:
There is, however, one issue with this. If we instead use the code exactly as it is in my other answer to plot the histogram of all the hue values, we would get this:
Note that there is a big cluster of magenta, pink, and reddish pixels that gets excluded when we toss out part of the hue range to convert to wavelengths (they don't correspond to a single wavelength in the light spectrum). Incorporating these into the results would require a more complicated conversion from hue to wavelength.
you can not convert RGB to wavelength unless some physical properties of the image and light is met. Anyway you can fake this by inversing:
RGB values of visible spectrum
if you do not know how look at:
Reverse complex 2D lookup table
But the result will not be the same as physical wavelengths histogram ... For that you would need multi-band image acquisition either by rotating prism optics or by set of bandpass filters ...
PS. HSV is far from accurate ...
Btw. the easiest way to do this is create palette from the spectral colors and convert your input image to it (indexed colors) and then just create histogram sorted by wavelength (and or color index)...
Based on gnovices answer but with an image instead of bar (take 0.12s on my system):
rgbImage = imread ("17S9PUK.jpg");
hsvImage = rgb2hsv(rgbImage);
hPlane = 360 .* hsvImage(:, :, 1);
binEdges = 1:360;
N = histc (hPlane(:), binEdges);
cm = permute (hsv (360), [3 1 2]);
img = repmat (cm, max(N), 1);
row_index = max(N) - N';
sp = sparse (row_index(row_index>0), (1:360)(row_index>0), true);
mask = flipud (cumsum (sp));
img(repmat (logical(1 - full(mask)), [1 1 3])) = 0;
image (img)
set (gca, "ydir", "normal");
xlabel('hue');
ylabel('Bin counts');
I have a d1×d2 array A of integer values and want to color code display them on the xy-plane in a rectangle scaled to d1×d2 using colors that match the magnitude of the value of array at that location. I also want to display the color chart which indicates which color is which magnitude as in the following figure:-
Is there a simple code that can do this?
Or is this kind of charting need special packages?
Will this work ('A' is matrix with non-negative entries)?
function plot2Ddistprf(A, Length, Breadth)
Amax=max(A(:));
A=A/Amax;
G1 = linspace(0,Length,size(A,1));
G2 = linspace(0,Breadth,size(A,2));
[X,Y] = meshgrid(G1,G2);
% plot data
figure; % create a new figure
contourf(X,Y,A); % plot data as surface
caxis([1,100]); % set limits of colormap
colorbar; % display the colorbar
No external library or special package is needed to create such a plot. You can use contourf to plot the data. Then, set the colormap to gray. With caxis you can control the range of colors. colorbar shows the bar on the right side.
The result looks like this:
Here is the code:
% generate sample data
d1 = linspace(-3,3,200);
d2 = linspace(-3,3,200);
[X,Y] = meshgrid(d1,d2);
A = -abs(peaks(X,Y))+100;
% plot data
figure; % create a new figure
contourf(X,Y,A); % plot data as surface
colormap(gray); % use gray colormap
caxis([91,100]); % set limits of colormap
colorbar; % display the colorbar
title('The Title');
xlabel('y');
ylabel('x');
Try the contourf function and then add the colorbar
contourf(A)
colorbar
I want to plot a confusion matrix in MATLAB. Here's my code;
data = rand(3, 3)
imagesc(data)
colormap(gray)
colorbar
When I run this, a confusion matrix with a color bar is shown. But usually, I have seen confusion matrix in MATLAB will give counts as well as probabilities. How can I get them? How can I change the class labels which will be shown as 1,2,3, etc.?
I want a matrix like this:
If you do not have the neural network toolbox, you can use plotConfMat. It gets you the following result.
I have also included an independent example below without the need for the function:
% sample data
confmat = magic(3);
labels = {'Dog', 'Cat', 'Horse'};
numlabels = size(confmat, 1); % number of labels
% calculate the percentage accuracies
confpercent = 100*confmat./repmat(sum(confmat, 1),numlabels,1);
% plotting the colors
imagesc(confpercent);
title('Confusion Matrix');
ylabel('Output Class'); xlabel('Target Class');
% set the colormap
colormap(flipud(gray));
% Create strings from the matrix values and remove spaces
textStrings = num2str([confpercent(:), confmat(:)], '%.1f%%\n%d\n');
textStrings = strtrim(cellstr(textStrings));
% Create x and y coordinates for the strings and plot them
[x,y] = meshgrid(1:numlabels);
hStrings = text(x(:),y(:),textStrings(:), ...
'HorizontalAlignment','center');
% Get the middle value of the color range
midValue = mean(get(gca,'CLim'));
% Choose white or black for the text color of the strings so
% they can be easily seen over the background color
textColors = repmat(confpercent(:) > midValue,1,3);
set(hStrings,{'Color'},num2cell(textColors,2));
% Setting the axis labels
set(gca,'XTick',1:numlabels,...
'XTickLabel',labels,...
'YTick',1:numlabels,...
'YTickLabel',labels,...
'TickLength',[0 0]);
If you have the neural network toolbox you can use the function plotconfusion. You can create a copy and edit it to customise it further, for example to print custom labels.
I would like to plot a 3D surface using the Matlab surf function. The whole surface should be in gray scale, then I need to highlight a specific cut of the surface using a different color.
I thought this code would've worked but it doesn't.
Mat = randi(100); % Matrix to be plotted in gray scale
ind_highlight = 10; % Row of the matrix to be highlighted
Mat2 = Mat;
Mat2([1:ind_highlight-1, ind_highlight+1:end] ,:) = NaN;
figure
surf(X,Y,Mat)
colormap gray
hold on
% Highlight the ind_highlight row
surf(X,Y,Mat2)
colormap hsv
Any help would be highly appreciated!
It seems there is no way to use different colormap to obtain the desired effect since the colormap "belongs" to the figure.
I've found a possible solution which does not use colormap.
It is based on the specifying the color matrix in the call to surf, one for the whole matrix, one for the section to be highlighted, then superimposing the second one to the first one.
Unfortunately, I've not been able to set the first ad gray.
I've used the peaks matrix instead of your "randi" in order to have a more smooth surface to work with and inserted the script in a for loop to highlight different section of the matrix
% Mat = randi(100,100,100); % Matrix to be plotted in gray scale
% Alternative definition of the Matrix to be displayed
n_pt=50;
Mat=peaks(n_pt);
% Generate meshgrid
x=1:n_pt;
y=1:n_pt;
[X,Y]=meshgrid(x,y);
ind_highlight_2 = 5; % Number of rows of the matrix to be highlighted
% Generate two set of color matrix
% The first on for the whole surf
% The second one for the section to be highlighted
a=randi(2,n_pt,n_pt);
b=randi(10,n_pt,n_pt);
for i=1:n_pt-ind_highlight_2
ind_highlight_1 = i; % Starting row of the matrix to be highlighted
Mat2 = Mat;
% Modified set of data (in the original just one row was left
% Mat2([1:ind_highlight-1, ind_highlight+1:end] ,:) = NaN
Mat2(ind_highlight_1:ind_highlight_1+ind_highlight_2,:) = NaN;
COL=a;
COL(ind_highlight_1:ind_highlight_1+ind_highlight_2,:)=b(ind_highlight_1:ind_highlight_1+ind_highlight_2,:);
% Plot the surf specifying the color
s_h=surf(X,Y,Mat,COL);
shading interp
% view([0 90])
% f_name=['jpg_name_' num2str(i)]
% print('-djpeg75',f_name)
pause(.1);
end
Hope this helps.
How can I get contour matrix for a binary image in MATLAB?
See this tutorial Luke Fletcher
In general it says:
%Load the image into im_rgb with
im_rgb = imread('tetx.tiff');
%convert to type double
im_rgb = double(im_rgb);
%convert to grey-scale
im_grey = (im_rgb(:,:,1)+im_rgb(:,:,2)+im_rgb(:,:,3))/3;
%or you can use rgb2gray which gives a slightly different result since it converts
%to a luminance rather than an intensity image, you'll learn about this
%in the lecture on colour imaging.
%convert from [0,255] to [0,1]
im_grey = im_grey/255;
%to view an image use either imshow(im) or imagesc(im), note the difference.
%You can use imagesc and then set the axis for an image with
axis image;
%Axis labelling can be turned on and off by
axis on;
axis off;
etc...