I don't quite understand what line 5 of the following code is doing.
First 4 lines are importing the image from the folder and then storing it in "image1". Then on line 5 i dont get what is being done.
numFolder=fullfile('NumberZero/','Zero/');
for i=1:10;
numName=sprintf('%d.bmp',i);
image1=imread([numFolder, numName]);
im1(:,:,i)=image1; % what is this line doing?
end
That loop is simply loading all of the image data into a variable called im1. The dimensions of this variable are going to be [nRows, nColumns, nImages]. This assumes that the images coming in are actually grayscale rather than RGB (third dimension == 1)
Once this is loaded in, you can then access the different images via the folling approach.
first_image = im1(:,:,1);
second_image = im1(:,:,2);
As a side note, it is recommended to not use i as a loop index.
I posted this question on the "MATLAB Central" Q&A and "Image Analyst" answered my question very nicely.
The line
im1(:,:,i)=image1;
takes a 2D image called image1 and sticks it into the i'th slice (plane) of a 3D image called im1. If im1 already has i slices, then it just overwrites the i'th slice. If im1 does not yet have i slices, then this code will append a slice to grow the 3D image in the "Z" direction. So it's turning 2D images stored on your disk into a 3D image. The images1's must be grayscale for this code to work.
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 have a medical imaging matrix of size [200x200x200].
In order to display it, I am currently using imshow3D function, which is an excellent tool, built by Maysam Shahedi.
This tool displays the 3D image slice by slice, with mouse based slice browsing
In my current project, I generate an RGB image for each z-layer from the original input image. The output is a 3D color image of size [200x200x200x3] (each layer is now represented by 3 channels).
The imshow3D function works great on grayscale images. Is it possible to use it to display RGB images?
I took a look at this nice imshow3D function from Matlab FileExchange, and it is quite straight-forward to change it to allow working with a stack of RGB images.
The magic part of the function is
imshow(Img(:,:,S))
which displays the slice S of the image Img. We can simply change it to show all 3 channels of image S by changing this to Img(:,:,S,:). The result will be of size 200-by-200-by-1-by-3, while MATLAB expects RGB images to be of size 200-by-200-by-3. Simply squeeze this image to get the correct dimension. This results in:
imshow(squeeze(Img(:,:,S,:))
So to show RGB images, do a search-and-replace inside the function imshow3D, to replace all occurrences of Img(:,:,S) with squeeze(Img(:,:,S,:)) and it works!
I am new to matlab. I have few binary images, as shown below, which I have to display with isosurfaces as a 3d object. I could not understand what input I need to pass to this function, when I have some images. I am referring this documentation.
I have total 22 images which are slightly diffrent from this image:
If your 22 images are all images from the same volume, you can concatenate them into a 3D array with the dimensions of [nRows, nCols, 22]. How you do this really depends upon the format your data is currently in.
You can then pass this 3D matrix (images) directly to isosurface along with an isovalue (I'll assume 1 for demonstration purposes);
FV = isosurface(images, 1);
I am struggling to find a good contour detection function that would count the number of contour in bw images that I have processed using some previous tools. As you can see, my profile picture is an example of such images,
,
In this image, ideally, I wish to have a function which counts four closed contour.
I don't mind if it also detects the really tiny contours in between, or the entire shape itself as extra contours. As long as it counts the medium sized ones, I can fix the rest by applying area threshold. My problem is that any function I have tried detects only one contour - the entire shape, as it cannot separate it to the su-conours which are connected to one another.
Any suggestions?
Here is my shot at this, although your question might get closed because it's off-topic, too broad or a possible duplicate. Anyhow I propose another way to count the number of contours. You could also do it using bwboundaries as was demonstrated in the link provided by #knedlsepp in the possible duplicate. Just for the sake of it here is another way.
The idea is to apply a morphological closure of your image and actually count the number of enclosed surfaces instead instead of contours. That might end up being the same thing but I think it's easier to visualize surfaces.
Since the shapes in your image look like circle (kind of...) the structuring element used to close the image is a disk. The size (here 5) is up to you but for the image you provided its fine. After that, use regionprops to locate image regions (here the blobs) and count them, which comes back to counting contours I guess. You can provide the Area parameter to filter out shapes based on their area. Here I ask the function to provide centroids to plot them.
Whole code:
clear
clc
close all
%// Read, threshold and clean up the image
Im = im2bw(imread('ImContour.png'));
Im = imclearborder(Im);
%// Apply disk structuring element to morphologically close the image.
%// Play around with the size to alter the output.
se = strel('disk',5);
Im_closed = imclose(Im,se);
%// Find centroids of circle-ish shapes. Youcan also get the area to filter
%// out those you don't want.
S = regionprops(~Im_closed,'Centroid','Area');
%// remove the outer border of the image (1st output of regioprops).
S(1) = [];
%// Make array with centroids and show them.
Centro = vertcat(S.Centroid);
imshow(Im)
hold on
scatter(Centro(:,1),Centro(:,2),40,'filled')
And the output:
So as you see the algorithm detected 5 regions, but try playing a bit with the parameters and you will see which ones to change to get the desired output of 4.
Have fun!
I already had some figure that I saved before, Like this:
Now after some days, I need the original size of these images for copy these in my paper. I want to extract the main matrix of these three image for save these again with imwrite function.
I searched this problem on the internet but the people says I have to use getframe and frame2im functions. But how? I want the original matrix. Can anyone tell me how to extract the main matrix from the figured image in matlab??
Try using the following code:
imgs = findobj(gcf,'Type','image');
images = cell(1,numel(imgs));
for i = 1:numel(imgs)
images = get(imgs(i),'CData');
end
The image matrices should now be stored in the separate cells of images.