I have obtained the following figure using a 120x120 matrix and the surf function.
Is there a simple way to make the lines between different colors look smoother?
First of all, surf may not be the best way to display 2D-image - if you don't actually need the height information, imagesc will work just fine. Even better, it won't show the differently colored lines between hexagons, since it's not going through the colormap at intersections.
However, regardless of your approach, a low-resolution bitmap will not be automatically transformed into a "arbitrary"-resolution vector graphics - and you may not want that, anyway, if you use the figure to allow you to inspect at which combination of (x,y) you obtained at a given value.
There are three approaches to make your image prettier - (1) segment the hexagons, and use patch to create a vector-graphics image. (2) upsample the image with imresample. (3) create a RGB image and smoothen each color separately to get softer transitions:
%# assume img is your image
nColors = length(unique(img));
%# transform the image to rgb
rgb = ind2rgb((img+fliplr(img)),jet(nColors)); %# there are much better colormaps than jet
%# filter each color
for i=1:3,rgbf(:,:,i)=imfilter(rgb(:,:,i),fspecial('gaussian',9,3),'replicate');end
Related
I have a series of black and white images (not greyscale, black and white; 2D matrices in Matlab), and I need to randomly scramble the pixels. I found this package in Mathworks File Exchange (https://it.mathworks.com/matlabcentral/fileexchange/66472-image-shuffle); one of the functions, imScrambleRand, does exactly what I need, but it works for RGB images (3D matrices). Is there a way to transform b&w images into 3D matrices so that I can use that function? Or can anyone suggest any other script that does what I need? Keep in mind that I'm not familiar with Matlab, but I'll do my best.
Thank you.
EDIT 1: When I import the BW image I get a 2D matrix of logic values (0 = black, 1 = white). I think the different data format (logic vs integer) is what yields errors when using the function for RGB images.
EDIT 2: I adapted the demo code from the aforementioned package and I used the suggestion by #Jonathan for transforming a 2D matrix into a 3D matrix, and added a loop to transform the logic values into RGB integer values, then use the imScrambleRand function. It works, but what I obtain is the following image: SCRAMBLED IMAGE. This is the BW picture I start with: BW IMAGE. So I checked the scrambled image, and the function from the FEX file actually scrambles within the RGB values, meaning that I found, for instance, a pixel with RGB 0,255,0. So I solved a problem but actually there's a problem within the function: it doesn't scramble pixels, it scrambles values generating colors that were not in the original picture.
EDIT 3: I used the code provided by #nhowe and I obtain exactly what I need, thanks!
EDIT 4: Ok, turns out it's not ok to scramble the pixels since it makes the image too scattered and different from the starting image (you don't say?), but I need to scramble BLOCKS OF PIXELS so that you can't really recognize the image but the black pixels are not too scattered. Is there a way to do that using the code provided by #nhowe?
EDIT 5: It should be ok with this function: https://it.mathworks.com/matlabcentral/fileexchange/56160-hio-been-hb-imagescramble
A simple way to scramble matrix M:
r = rand(size(M));
[~,ri] = sort(r(:));
M(ri) = M;
The simplest solution to go from grayscale to RGB might be this:
rgbImage = cat(3, grayImage, grayImage, grayImage);
Then apply your function from FEX and extract one color channel, assuming that the FEX function will yield three identical color channels.
I am asked to extract three curves from palm images.
original image
expected result.
The curves has lower intensity than neighboring pixels. So I try to morphology to extract the pixels. The matlab code is followed
RGBImage = imread('E:\ImageProcessing\Palm\Matlab\IMG_20140817_144549.jpg');
GrayImage = rgb2gray(RGBImage);
GrayImage = imresize(GrayImage, [768, 1024]);
se = strel('disk', 10);
GrayImage = imbothat(GrayImage, se);
figure; imshow(GrayImage);
The curves still have a little higher intensity values than neighboring pixels. Maybe I need use ridge filter to enhance the curves. I use the filter from matlab file exhange(http://www.mathworks.com/matlabcentral/fileexchange/24409-hessian-based-frangi-vesselness-filter). The filtered result is not satisfactory. I hope to get any advance or links or papers.
This is going to be difficult from the example image you posted, but here is an approach I would suggest that might get you in the right direction:
Use an HSV filter to segment the skin tones from the rest of the image. Now you just have the palm. Mask the rest of the image.
Next, try using an LBP filter to isolate the features you want. The LBP filter is contrast invariant, so changes in lighting levels etc will be irrelevant.
Or, if you wanted a simpler approach, trying using a canny filter, that should probably give you the lines.
Be warned, it's going to be messy.
i am trying to plot the figure of FFT magnitude of an image using the following code in the command window:
a= imread('lena','png')
figure,imshow(a)
ffta=fft2(a)
fftshift1=fftshift(ffta)
magnitude=abs(fftshift1)
figure,imshow(magnitude),title('magnitude')
However, the figure with the title magnitude shows nothing, even though MATLAB shows that it has computed abs() on fftshift. The figure is still empty, and there is no error. Also, why do we need to compute the phase shift before magnitude?
The reason why this is probably happening is because of the following things:
When you take the 2D fft of your image, it will produce a double valued result, even though your image is mostly unsigned 8-bit integer. MATLAB assumes that double formatted images have their intensities / colours between [0,1]. By doing imshow on just the magnitude itself, you will most likely get an entirely white image because I suspect a good majority of the FFT coefficients are bigger than 1. This is probably the blank figure that you're referring to.
Even if you rescale the magnitude so that it is between [0,1], the DC coefficient will be so large that if you try to display the image, you'll only see a white dot in the middle while every other component will be black.
As a side note, the reason why you are doing fftshift is because by default, MATLAB assumes that the origin of the FFT for 2D is located at the top left corner. Doing fftshift will allow the origin to be in the middle, which is what we would intuitively expect of the 2D FFT.
In order to remedy this situation, I would suggest doing a log transformation on the FFT coefficients so you can visually see the results. I would also normalize the coefficients once you log transform it so that they go between [0,1]. Do not actually modify the FFT coefficients as this would be improper. You need to leave them the same way that it is because if you intend to do any processing on the spectrum, you would start by working on the raw image. Doing filter design or anything of that sort will require the raw spectrum, as the final filter will depend on these coefficients untouched. Unless you actually want to do a log operation as part of your pipeline, then leave these coefficients as is. As such, this can be done through the following MATLAB code:
imshow(log(1 + magnitude), []);
I'm going to show an example, using your code that you have provided but using another image as you haven't provided one here. I'm going to use the cameraman.tif image that's part of the MATLAB system path. As such:
a= imread('cameraman.tif');
figure,imshow(a);
ffta=fft2(a);
fftshift1=fftshift(ffta);
magnitude=abs(fftshift1);
figure;
imshow(log(1 + magnitude), []); %// NEW
title('magnitude')
This is what I get:
As you can see, the magnitude is displayed more nicely. Also, the DC coefficient is in the middle of the spectrum thanks to fftshift.
If you want to apply this for colour images, fft2 should still work. It will apply the 2D fft to each colour plane by itself. However, if you want this to work, you'll not only need to take the log transform, but you'll also need to normalize each plane separately. You have to do this because if we tried doing the imshow command we did earlier, it would normalize it so that the greatest value in the spectrum of the colour image gets normalized to 1. This will inevitably produce that same small dot effect that we talked about earlier.
Let's try a colour image that's built-in to MATLAB: onion.png. We will use the same code that you used above, but we need an additional step of normalizing each colour plane by itself. As such:
a = imread('onion.png');
figure,imshow(a);
ffta=fft2(a);
fftshift1=fftshift(ffta);
magnitude=abs(fftshift1);
logMag = log(1 + magnitude); %// New
for c = 1 : size(a,3); %// New - normalize each plane
logMag(:,:,c) = mat2gray(logMag(:,:,c));
end
figure; imshow(logMag); title('magnitude');
Note that I had to loop through each colour plane and use mat2gray to normalize each plane to [0,1]. Also, I had to create a new variable called logMag because I have to modify each colour plane individually, and you can't do this with a single imshow call.
With this, these are the results I get:
What's different with this spectrum is that we are applying the FFT to each colour plane separately, and so you'll see a whole bunch of colour spatters because for each location in this image, we are visualizing a linear combination of components from the red, green and blue channels. For each location, we have a value in between [0,1] for each colour plane, and the combination of these give you a colour at this location. You could say that darker colours are for locations that have a relatively low magnitude for at least one of the colour channels, while locations that are brighter have a relatively high magnitude for at least one of the colour channels.
Hope this helps!
Can't be sure about your version of "lena.png", but if it's a color RGB picture, you need to convert it first to grayscale, or at least select which RGB plane you want to examine.
I.e., the following works for http://optipng.sourceforge.net/pngtech/img/lena.png (color png):
clear; close all;
a = imread('lena','png');
ag = rgb2gray(a);
ag = im2double(ag);
figure(1);
imshow(ag);
F = fftshift( fft2(ag) ); % also try fft2(ag, N, N) where N < image size. Say N=128.
magnitude=abs(F);
figure(2);
imshow(magnitude);
I have a similar question than the one in this post.
I have a grayscale image and I plot points on it. Fro plotting the points I use colormap('jet') but as I want the image to be grayscale, after plotting the points I reset the colormap, colormap('gray').
But I want to show a colorbar! And the colorbar is plotted in grayscale, not 'jet'.
How can I do that?
EDIT:
I want a Colorbar showing the color of the points!
You should convert your image to RGB by putting the same data into R-, G-, and B-channels (this will be grayscale RGB image). Colormap in MatLab is not applied to RGB images, only to indexed ones. Then plot your points over the image with colormap you like.
As discussed here, there's a few ways:
If you have the image processing toolbox, use subimage to create an independent image with a separate colormap. Then plot the image, your points, and join them into one using linkaxes.
Use freezeColors from the file exchange (or multiple colormaps, which I haven't ever tested personally). This is a very easy way to create a larger colormap, and automatically selecting the right portion of the colormap for display of images and colorbars.
As answered by anandr, convert your greyscale image to RGB; Matlab doesn't use colormaps on RGB images, which leaves you freedom to plot your points and show their colorbar independent of the image.
Example code for (3):
I = imread('cameraman.tif');
imshow(cat(3,I,I,I))
hold on
x = #() round(size(I,1) * rand(50,1));
y = #() round(size(I,2) * rand(50,1));
plot(x(), y(), 'r.')
plot(x(), y(), 'g.')
plot(x(), y(), 'b.')
colormap('jet')
colorbar
result:
I would like to crop an image but I want to retain the part of image that is outside of the rectangle. How can this can be done?
It seems that with imcrop only the part within the rectangle can be retained.
An image in Matlab is represented by a matrix, just like any other matrix, you can read more about representation forms here.
It seems that what you want to do is to take the area that you don't want and change the values of the corresponding cells in the matrix to the color that you want to put instead (each cell in the matrix is a pixel in the image). That is if you know the place where your unwanted data is.
If you don't know where it is, and want to use the tool given by imcrop to manually choose the "cropped" area, you can take the resulting matrix, and find the part of the original image which is an exact match with the cropped part, and to color it as you wish.
The code for doing this:
I=imread('img_9.tif');
I2=imcrop(I,[60,50,85,85]);
n_big=size(I);
n_small=size(I2);
for j1=1:(n_big(1)-n_small(1))
for j2=1:(n_big(2)-n_small(2))
Itest=I(j1:j1+n_small(1)-1,j2:j2+n_small(2)-1,:);
if ( Itest == I2)
I(j1:j1+n_small(1)-1,j2:j2+n_small(2)-1,:) = zeros(n_small(1),n_small(2),3);
end
end
end
figure(1);
imshow(I);
figure(2);
imshow(I2);
The results of my test were:
original:
cropped:
resulting image:
maybe what you want to do is first a mask with the inverse area of what you want to crop and save this result.