I noticed that sometimes, when I plot the bars using the functions bar() or hist(), the bars plotted have no border. They are a little bit less nice to see, and I would prefer the border or a little bit of space between them.
The figure shows what I got now, plotting three different datasets. The third graph is zoomed in order to show the lack of space between bars.
I get there this has something to do with the 'histc' parameters in bar function. Without the histc parameters the bars have some space between each others, but then the bar will be centered on the edges values, whereas I want the edges values to be, well, EDGES of each bar.
This is (the relevant part of) the code I used:
[...]
if edges==0
%these following lines are used to take the min and max of the three dataset
maxx=max(cellfun(#max, reshape(data,1,size(data,1)*size(data,2))));
minn=min(cellfun(#min, reshape(data,1,size(data,1)*size(data,2))));
edges=minn:binW:maxx+binW;
end
[...]
y{k}=histc(data{r,c}, edges);
bar(edges,y{k} , 'histc');
[...]
I think if you change the color of your bar plots you'll see that there actually is a border it just doesn't show up very well. You can also change the width of the bars so that they are more distinct.
% something to plot
data = 100*rand(1000,1);
edges = 1:100;
hData = histc(data,edges);
figure
subplot(2,1,1)
h1 = bar(edges,hData,'histc');
% change colors
set(h1,'FaceColor','m')
set(h1,'EdgeColor','b')
% Change width
subplot(2,1,2)
h1 = bar(edges,hData,0.4,'histc');
The EdgeColor and LineWidth properties of the Barseries object control the bar outlines. Try using the code and playing with the red, green, blue, and width values to get a better result.
red = 1;
green = 1;
blue = 1;
width = 3;
h = bar(edges,y{k} , 'histc');
set(h,'EdgeColor',[red green blue],'LineWidth',width);
Related
I want to have two transparent bar plots that overlape in one figure. I tried this way:
bar(list2(:,1),list2(:,2),'r','FaceAlpha',0.5)
hold on
bar(list1(:,1),list1(:,2),'g','FaceAlpha',0.5)
but the output is:
Why the second plot is not transparent and has these strange strips?
I use matlab 2016a
your approach is correct, and it works for a few bars:
[list1(:,2),list1(:,1)] = hist(randn(200,1));
[list2(:,2),list2(:,1)] = hist([randn(100,1)-0.5 ; randn(100,1)+0.5]);
bar(list2(:,1),list2(:,2),'r','FaceAlpha',0.5)
hold on
bar(list1(:,1),list1(:,2),'g','FaceAlpha',0.5)
However, you can notice that there are black lines contouring each bar. As the bar number increases, the black contours will cover everything:
[list1(:,2),list1(:,1)] = hist(randn(3000,1), 300);
[list2(:,2),list2(:,1)] = hist([randn(1500,1)-2 ; randn(1500,1)+2], 300);
h1 = bar(list2(:,1),list2(:,2),'r','FaceAlpha',0.5)
hold on
h2 = bar(list1(:,1),list1(:,2),'g','FaceAlpha',0.5)
(The result will depend on the Matlab version. In 2016b it appears that the problem is automatically corrected).
The solution: remove the black contours:
h1.EdgeColor = 'none';
h2.EdgeColor = 'none';
I'm trying to color code some parameter values on a gray image in MATLAB. I'm wondering how to show gray image, make parameter values colored on the gray appearance image and on some pixels, and finally draw a colorbar aside the image just showing parameter values range.
Unsuccessful Code since now:
I = Igray; % gray image
Icc = zeros(size(I,1),size(I,2),3); % color coded image
Icc(:,:,1) = I;
Icc(:,:,2) = I;
Icc(:,:,3) = I;
Icc('some address in the image',3) = 'some number between 0 and 255';
imshow(Icc,[])
colorbar % colorbar showing colored parts spectrum
Result image that I need:
Try something like this:
I = Igray; % gray image
RGB = [1.0,0.7,0.2]; % color for patch
x = 30:50;
y = 70:90;
% If you gray image is in the range [0,255], make it range [0,1]
I = double(I)/255;
Icc = repmat(I,[1,1,3]);
block = I(y,x);
Icc(y,x,1) = 1 - ((1-block) * (1-RGB(1)));
Icc(y,x,2) = 1 - ((1-block) * (1-RGB(2)));
Icc(y,x,3) = 1 - ((1-block) * (1-RGB(3)));
imshow(Icc)
I'm sure there is a prettier way to encode this, but this way it shows the intent.
You're basically multiplying the grey values with the RGB color you want to make the patch. By inverting the patch and the color first, and inverting the result, the multiplication makes the patch brighter, not darker. That way you get the effect you want where the dark parts show the color also. If you multiply directly without inverting first, black stays black and doesn't show the color.
You'll have to figure out how to coordinate with the color bar after that. There are commands in MATLAB to set the limits of the color bar, you can find those reading the documentation.
The color bar you show uses the PARULA color map. You could do this to find the right RGB value to color your patch:
T; % value to color your patch in, in range [0,1]
cm = parula(256);
RGB = interp1(cm,T*255,'linear')
I am doing a research for my higher studies in automation. I have done the automation part of the microscope but I need help in MATLAB. An example of what I would like to segment is shown here:
I need to extract the dark purple pixels from this image and only display that in a figure. It is almost like colour based segmentation but I just want to only take the dark purple pixel from the whole image.
What would I do in this case?
Here's something to get you started. Let's go with the theme of colour segmentation where you only want to extract pixels that are of a deep purple. I would like to point you to the HSV colour space before we get started. The HSV colour space is ideal for representing colours in a way that is most intuitive to humans. We tend to describe colours by their dominant colour, followed by attributes such as how washed out or how pure the colour is, and how bright or dark the colour is. The dominant colour is represented by the Hue, the appearance of how washed out or how pure the colour is is represented by the Saturation and the intensity of the colour is represented by the Value, and hence Hue-Saturation-Value, or the HSV colour space.
We can transform a RGB image so that it becomes HSV by rgb2hsv. This will return a 3D matrix that has the hue, saturation and value as 2D slices in a 3D matrix, much like a RGB image where each slices represents the red, green and blue channels. Let's see what each component looks like once we transform the image into HSV:
im = imread('https://www.cdc.gov/dpdx/images/malaria/ovale/Po_gametocyte_thickB.jpg');
hsv = rgb2hsv(im2double(im));
figure;
for idx = 1 : 3
subplot(1,3,idx);
imshow(hsv(:,:,idx));
end
The first line of code reads in an image from a URL. I'm going to use the one that Hoki referred you to, as it's the most simplest one to deal with. For self-containment, this is what the original image looks like:
Once we do this, we convert the image into the HSV colour space. It is important that you convert the image to double precision and you normalize each component to [0,1], and that is performed by im2double. Next, we spawn a new figure, and place each component in a single row over three columns. The first column represents the hue, next column the saturation and finally the last column being the value. This is the figure that we see:
With the first figure, it looks like the dominant colour is purple, whether it's a light shade or a dark shade of the colour, so the hue won't help us here. If you look at a HSV colour wheel:
(source: hobbitsandhobos.com)
Normalize the wheel so that it falls between [0,1] instead of 0 to 360 degrees. The hue is actually represented as degrees due to the nature of the colour space, but MATLAB normalizes this to [0,1]. You can see that purple falls within a hue of [0.6,0.8], which corresponds to the first figure I showed you that displays the hue for our image. If you examine the pixels around the image, they fluctuate between this range. Therefore, the hue won't help us much here.
What will certainly help us are the saturation and value components. If you take a look, the deep purple pixels have a higher saturation than the rest of the background, which makes sense because the deep purple has a much more pure version of purple than the rest of the background. For the value, you can see that the brightness of the dark purple is darker than the background.
We can use these two points as an exploit to segment out the purple colour in the image. The easiest thing to do would be to threshold the saturation and value planes so that any values that are within a certain range you keep while those that are outside you throw away. Therefore, you can do something like this:
sThresh = hsv(:,:,2) > 0.6 & hsv(:,:,2) < 0.9;
vThresh = hsv(:,:,3) > 0.4 & hsv(:,:,3) < 0.65;
I used impixelinfo and I hovered my mouse over the saturation and value components to examine what the values were for the deep purple regions. It looks like those pixels that are deep purple have a saturation value between 0.6 and 0.9, while the value component has values between 0.4 and 0.65. The above code will create two binary masks where true means that the pixel satisfies our criteria while false means it doesn't. Because I want to combine both things together and not leave any stone unturned, let's logical OR the masks together for the final result:
figure;
result = sThresh | vThresh;
imshow(result);
We will also show the result too. This is what we get:
As you can see, this does a pretty good job, but we have remnants of the red arrow that we don't want in the final result. To do a bit of cleanup, we can use morphology - specifically an opening filter of a small window so that we don't affect the pixels that we want as much. We can use imopen to perform our opening operation for us. A morphological opening removes isolated pixels that appear around your image. You use what is called a structuring element that is used to look at local neighbourhoods of your image. For the basics, any pixel regions that are as small as the shape that is contained within the structuring element get removed. Because we want to preserve the shape of the other objects, we can try using a 5 x 5 disk structuring element to clean these pixels up:
figure;
se = strel('disk', 2, 0);
final = imopen(result, se);
imshow(final);
This is what we get:
Not bad! There are some holes that we need to patch up, so let's fill in those holes with imfill:
figure;
final_noholes = imfill(final, 'holes');
imshow(final_noholes);
This is what we get:
OK! So we have our mask. The last thing we need to do is present the image so that you only show the deep purple colours from the original image, and nothing else. That can easily be achieved with bsxfun:
figure;
out = bsxfun(#times, im, uint8(final_noholes));
imshow(out);
The above operation takes your mask, and multiplies every pixel in your image by this mask. One small thing I'd like to point out is that the mask we found in the previous step needs to be cast to uint8, because bsxfun requires that the multiplication (or whatever operation you perform) need to be the same type. We replicate this mask in 3D so that you mask out the unwanted RGB pixels and only keep the ones you are looking for.
This is what we finally get:
As you can see, it isn't perfect, but it's certainly enough to get you started. Those thresholds are what are important, but with some very simple thresholding, I extracted most of the purple pixels out.
To make it easier for you, here's the code that I wrote above that can easily be copied and pasted into MATLAB for you to run:
clear all; close all; clc;
im = imread('https://www.cdc.gov/dpdx/images/malaria/ovale/Po_gametocyte_thickB.jpg');
hsv = rgb2hsv(im2double(im));
figure;
for idx = 1 : 3
subplot(1,3,idx);
imshow(hsv(:,:,idx));
end
sThresh = hsv(:,:,2) > 0.6 & hsv(:,:,2) < 0.9;
vThresh = hsv(:,:,3) > 0.4 & hsv(:,:,3) < 0.65;
figure;
result = sThresh | vThresh;
imshow(result);
figure;
se = strel('disk', 2, 0);
final = imopen(result, se);
imshow(final);
figure;
final_noholes = imfill(final, 'holes');
imshow(final_noholes);
figure;
out = bsxfun(#times, im, uint8(final_noholes));
imshow(out);
Good luck!
Try this:
function main
clc,clear
A = imread('https://www.cdc.gov/dpdx/images/malaria/ovale/Po_gametocyte_thickB.jpg');
subplot(1,2,1)
imshow(A)
RGB = [230 210 200]; % color you want
e = 40; % color shift
B = pix_in(A,RGB,e);
B = B + 255.*uint8(~B); % choosing white background
subplot(1,2,2)
imshow(B)
end
function B = pix_in(A,RGB,e)
% select specific pixels in image
% A - color image (3D matrix uint8)
% RGB - [R G B] - color to select
% e - color shift/deviation
A = double(A); % for same class operations (RGB - double)
[m, n, ~] = size(A);
RGB = reshape(RGB,1,1,3);
RGB = repmat(RGB,m,n,1); % creating 3D matrix
b = abs(A-RGB) < e; % logical 3D
b = sum(b,3) == 3; % if [R,G,B] of a pixel in range
B = A.*repmat(b,1,1,3); % selecting pixels those in range
B = uint8(B);
end
I generate a transparent chart that lets the background of a web page be seen through it.
So far I've done this (omited the populating of dataset for brevity):
lineChartObject=ChartFactory.createLineChart("Title","Legend","Amount",line_chart_dataset,PlotOrientation.VERTICAL,true,true,false);
CategoryPlot p = lineChartObject.getCategoryPlot();
Color trans = new Color(0xFF, 0xFF, 0xFF, 0);
lineChartObject.setBackgroundPaint(trans);
p.setBackgroundPaint(trans);
for (int i=0;i<=3;i++){
lineChartObject.getCategoryPlot().getRenderer().setSeriesStroke(i, new BasicStroke(3.0f));
lineChartObject.getCategoryPlot().getRenderer().setBaseItemLabelsVisible(false);
}
Which renders this:
I cannot find a way of:
Removing border of plot (1)
Removing border of leyend as well as making it transparent (3)
Making the labels on the X axis (2) to behave intelligently as the labels of Y axis do (A). Labels of Y axis space themselves so as to not clutter the graph, for example if I rendered the graph smaller, it would show fewer labels, like this:
Edit: X label domain is dates.
For (1) try:
plot.setOutlineVisible(false);
For (2), a common reason for having too many categories along the x-axis is that the data is actually numerical, in which case you should be using XYPlot rather than CategoryPlot. With XYPlot, the x-axis scale adjusts in the same way that the y-axis does.
Edit from OP: Using a TimeSeriesChart with a TimeSeriesCollection as XYDataSet did the work! (fotgot to say X domain is dates)
For (3) try:
LegendTitle legend = chart.getLegend();
legend.setFrame(BlockBorder.NONE);
legend.setBackgroundPaint(new Color(0, 0, 0, 0));
I'm trying to make the red bands in the second subplot of the figure below go transparent with an opacity similar to alpha(0.2).
http://i.stack.imgur.com/zJjYk.jpg
However, when I try to call alpha(0.2) after the following code that generates the red bands:
Plotha1 = area([datenum(time(11,1),1,1) datenum(time(15,1),1,1)],[1.3*max(SA(:,2)) 1.3*max(SA(:,2))]);
set(Plotha1,'BaseValue',1.3*min(SA(:,2)),'FaceColor','r','LineStyle', 'none');
Plotha2 = area([datenum(time(4,1),1,1) datenum(time(8,1),1,1)],[1.3*max(SA(:,2)) 1.3*max(SA(:,2))]);
set(Plotha2,'BaseValue',1.3*min(SA(:,2)),'FaceColor','r','LineStyle', 'none');
alpha(0.2);
The third subplot goes blank like in the second figure.
http://i.stack.imgur.com/tjgIT.jpg
The code generating the third subplot comes last in my code:
subplot(3,1,3),
W = zeros(T,1);
PlotZeros = plot(datenum(time,1,1),W);
dateFormat = 'yyyy';
datetick('x',dateFormat);
hold on
PlotResid = plot(datenum(time,1,1),Resid);
set(PlotZeros,'Color',[0.5 0.5 0.5]); %grey
set(PlotResid,'Color','blue');
axis([datenum(time(1,1),1,1) datenum(time(end,1),1,1) 1.3*min(Resid) 1.3*max(Resid)])
hold off
I have tried moving things around so that the code generating the 2nd subplot comes last but it still causes the 3rd subplot to go blank. Does anyone know how to make these areaseries red bands go transparent without disrupting the 3rd subplot?
Thanks!
You could try using fill or patch with the 'facealpha' property instead of the area command:
% x and y vals define the 4 corners of the rectangle
% (slightly different than "area")
x_vals=[datenum(time(4,1),1,1) datenum(time(8,1),1,1) ...
datenum(time(8,1),1,1) datenum(time(4,1),1,1)];
y_vals=[1.3*min(SA(:,2)) 1.3*min(SA(:,2)) ...
1.3*max(SA(:,2)) 1.3*max(SA(:,2))];
Plotha2 = fill(x_vals,y_vals,'red','edgecolor','none','facealpha',0.2);