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.
Related
Can someone help me in placing a logo (for example, the MATLAB logo) on an image?
First I read an image in using the imread command, and then on the top left corner, I need to add the MATLAB logo on that image. How would I do it?
Here's a reproducible example for you. #liangbright is on the right track, but I want to produce an example that works. Let's say I want to embed the MATLAB logo in the top left corner of a snapshot showing fictional federal agent, turned rogue: Jack Bauer. Here's an example MATLAB logo from the Wikimedia Commons media archive:
Source: Wikimedia Commons
Here's the picture of Jack Bauer I'm going to embed the MATLAB logo in:
Source: The Mirror
The MATLAB logo is quite large, so we're going to resize it so that it's a small icon. Let's resize this down to 10% of the original size. Once we do this, we simply have to replace those pixels that are in the top left corner with the MATLAB logo. Keep in mind that this MATLAB logo is in PNG format, which means that it will have a transparency / alpha channel. This is great because we will need the alpha channel so that we can place the MATLAB logo while making it look natural. That's the whole point of transparency. You can use imread as you have said, but we need to specify additional output parameters in order to grab the alpha channel. Specifically, we need the third parameter. The second parameter is the colour map, but let's ignore that as we don't need it for what you want to do. Now, the alpha channel is only a single 2D matrix, while the MATLAB logo and Jack Bauer are colour images. As such, we want to mix all of the colours together, and so we need to make the alpha channel a 3D matrix. This can be done by simply replicating the alpha map 3 times, and stacking it into a 3D matrix. We can do this by using repmat.
Once we have this, we can finally mix in the logo with the image. #liangbright has the equation right. If you want to mix two images together, given an alpha channel, you do it this way:
out = alpha*im1 + (1-alpha)*im2;
im1 is the image you want to mix in, while im2 is the image where im1 will be placed on top of this image. In our case, im1 is our MATLAB logo, while im2 is Jack Bauer. Before we can even do this, the alpha channel returned from imread actually (it's usually...) an unsigned 8-bit integer type image which has its alpha values span from [0-255]. As such, we need to transform the alpha map so that it spans between [0-1]. You can use im2double to do this for us.
Finally, we can use imresize to scale the image down so that it is 10% original size. We will also need to know the dimensions of this resized image so that we can properly put this into our bigger image.
One final note: We need to temporally convert the type of each image to double as the alpha maps are now double. We need to make sure that when you are multiplying two matrices together, they must be the same type. Once we finish mixing the stuff together, we then cast this part of the image back to uint8, as that is what the original type of bigger image was.
Without further ado, here's the code you should use. Note that I have saved the images to my computer before running this:
%// Load in MATLAB logo
[logo, map, alpha] = imread('Matlab_Logo.png');
%// Load in Jack Bauer
jack = imread('Kiefer-Sutherland-in-24.jpg');
%// Resize the MATLAB logo
logoResize = imresize(logo, 0.1, 'bilinear');
%// Make sure you do the same for the alpha map
alphaResize = imresize(alpha, 0.1, 'bilinear');
%// Duplicate the alpha map to make this three channels - This is a colour image
alphaResize = repmat(alphaResize, [1 1 3]);
%// Make double to ensure mixing
alphaResize = im2double(alphaResize);
%// Get the size of the resized logo - we need this
%// to properly mix the stuff in
rows = size(logoResize, 1);
cols = size(logoResize, 2);
%// Mix in the logo with the image
jack(1:rows,1:cols,:) = uint8(alphaResize.*double(logoResize) + ...
(1-alphaResize).*double(jack(1:rows,1:cols,:)));
figure;
imshow(jack);
This is the image I get:
Now, supposing you want to change the location of the logo so that it appears in either the top right, bottom left, or bottom right. As such, you simply need to change the last statement of the code before you show the final image. The last statement basically controls where you want the logo to go in the bigger image.
Specifically, you have to change the indexes of where we want to assign to the output. As such, let's do the other three cases where I'll show you each statement, and then the resulting image after.
Top Right
jack(1:rows,end-cols+1:end,:) = uint8(alphaResize.*double(logoResize) + ...
(1-alphaResize).*double(jack(1:rows,end-cols+1:end,:)));
Bottom Left
jack(end-rows+1:end,1:cols,:) = uint8(alphaResize.*double(logoResize) + ...
(1-alphaResize).*double(jack(end-rows+1:end,1:cols,:)));
Bottom Right
jack(end-rows+1:end,end-cols+1:end,:) = uint8(alphaResize.*double(logoResize) + ...
(1-alphaResize).*double(jack(end-rows+1:end,end-cols+1:end,:)));
Minor Note
The images that you may want to mix into your bigger image may not have an alpha channel. Examples of this are JPEG images. If you don't have access to the alpha channel, then you can do what #liangbright suggests and simply specify a constant for the alpha channel. As such, simply do the following. Most of the code that I showed above would disappear as there is no alpha channel. The code would thus simplify to:
%// Load in MATLAB logo without alpha channel
logo = imread('Matlab_Logo.png');
%// Load in Jack Bauer
jack = imread('Kiefer-Sutherland-in-24.jpg');
%// Resize the MATLAB logo
logoResize = imresize(logo, 0.1, 'bilinear');
%// Get the size of the resized logo - we need this
%// to properly mix the stuff in
rows = size(logoResize, 1);
cols = size(logoResize, 2);
%// Specify alpha here
alpha = 0.9;
%// Mix in the logo with the image
jack(1:rows,1:cols,:) = uint8(alpha.*double(logoResize) + ...
(1-alpha).*double(jack(1:rows,1:cols,:)));
figure;
imshow(jack);
... and this is the image I get:
Take note that putting in the MATLAB logo like this doesn't make the logo look like it's naturally there. This is because we assumed that all of the alpha values for the logo are the same, where the alphas should be zero except along the edges of the logo. Also, the actual logo pixels (not along the edges or the background) should have an alpha value of 1, as you want this to appear on top of the bigger image. With all of this, this roughly defines how the alpha channel should behave and thus make the transition between the logo and the bigger image more natural.
I've also decided not to show you what the logo looks like in the other corner positions with the simplified alpha, as the code is basically the same as the top left case. Just specify alpha to be a constant, then modify the last statement of your code before you show the image to be whichever case you want (top right, bottom left, bottom right).
Hope this helps!
I : the image
M : the logo
I(a:b, c:d) = (1-Alpha)*I(a:b, c:d)+ Alpha*M
(set Alpha = 0.9)
a:b c:d is related to the top left corner
then you show the image I
you can not just plot two images, one by one, because the second will cover the first
I have this image (8 bit, pseudo-colored, gray-scale):
And I want to create an intensity band of a specific measure around it's border.
I tried erosion and other mathematical operations, including filtering to achieve the desired band but the actual image intensity changes as soon as I use erosion to cut part of the border.
My code so far looks like:
clear all
clc
x=imread('8-BIT COPY OF EGFP001.tif');
imshow(x);
y = imerode(x,strel('disk',2));
y1=imerode(y,strel('disk',7));
z=y-y1;
figure
z(z<30)=0
imshow(z)
The main problem I am encountering using this is that it somewhat changes the intensity of the original images as follows:
So my question is, how do I create such a band across image border without changing any other attribute of the original image?
Going with what beaker was talking about and what you would like done, I would personally convert your image into binary where false represents the background and true represents the foreground. When you're done, you then erode this image using a good structuring element that preserves the roundness of the contours of your objects (disk in your example).
The output of this would be the interior of the large object that is in the image. What you can do is use this mask and set these locations in the image to black so that you can preserve the outer band. As such, try doing something like this:
%// Read in image (directly from StackOverflow) and pseudo-colour the image
[im,map] = imread('http://i.stack.imgur.com/OxFwB.png');
out = ind2rgb(im, map);
%// Threshold the grayscale version
im_b = im > 10;
%// Create structuring element that removes border
se = strel('disk',7);
%// Erode thresholded image to get final mask
erode_b = imerode(im_b, se);
%// Duplicate mask in 3D
mask_3D = cat(3, erode_b, erode_b, erode_b);
%// Find indices that are true and black out result
final = out;
final(mask_3D) = 0;
figure;
imshow(final);
Let's go through the code slowly. The first two lines take your PNG image, which contains a grayscale image and a colour map and we read both of these into MATLAB. Next, we use ind2rgb to convert the image into its pseudo-coloured version. Once we do this, we use the grayscale image and threshold the image so that we capture all of the object pixels. I threshold the image with a value of 10 to escape some quantization noise that is seen in the image. This binary image is what we will operate on to determine those pixels we want to set to 0 to get the outer border.
Next, we declare a structuring element that is a disk of a radius of 7, then erode the mask. Once I'm done, I duplicate this mask in 3D so that it has the same number of channels as the pseudo-coloured image, then use the locations of the mask to set the values that are internal to the object to 0. The result would be the original image, but having the outer contours of all of the objects remain.
The result I get is:
I was working on my image processing problem with detecting coins.
I have some images like this one here:
and wanted to separate the falsely connected coins.
We already tried the watershed method as stated on the MATLAB-Homepage:
the-watershed-transform-strategies-for-image-segmentation.html
especially since the first example is exactly our problem.
But instead we get a somehow very messed up separation as you can see here:
We already extracted the area of the coin using the regionprops Extrema parameter and casting the watershed only on the needed area.
I'd appreciate any help with the problem or even another method of getting it separated.
If you have the Image Processing Toolbox, I can also suggest the Circular Hough Transform through imfindcircles. However, this requires at least version R2012a, so if you don't have it, this won't work.
For the sake of completeness, I'll assume you have it. This is a good method if you want to leave the image untouched. If you don't know what the Hough Transform is, it is a method for finding straight lines in an image. The circular Hough Transform is a special case that aims to find circles in the image.
The added advantage of the circular Hough Transform is that it is able to detect partial circles in an image. This means that those regions in your image that are connected, we can detect them as separate circles. How you'd call imfindcircles is in the following fashion:
[centers,radii] = imfindcircles(A, radiusRange);
A would be your binary image of objects, and radiusRange is a two-element array that specifies the minimum and maximum radii of the circles you want to detect in your image. The outputs are:
centers: A N x 2 array that tells you the (x,y) co-ordinates of each centre of a circle that is detected in the image - x being the column and y being the row.
radii: For each corresponding centre detected, this also gives the radius of each circle detected. This is a N x 1 array.
There are additional parameters to imfindcircles that you may find useful, such as the Sensitivity. A higher sensitivity means that it is able to detect circular shapes that are more non-uniform, such as what you are showing in your image. They aren't perfect circles, but they are round shapes. The default sensitivity is 0.85. I set it to 0.9 to get good results. Also, playing around with your image, I found that the radii ranged from 50 pixels to 150 pixels. Therefore, I did this:
im = im2bw(imread('http://dennlinger.bplaced.net/t06-4.jpg'));
[centers,radii] = imfindcircles(im, [50 150], 'Sensitivity', 0.9);
The first line of code reads in your image directly from StackOverflow. I also convert this to logical or true black and white as the image you uploaded is of type uint8. This image is stored in im. Next, we call imfindcircles in the method that we described.
Now, if we want to visualize the detected circles, simply use imshow to show your image, then use the viscircles to draw the circles in the image.
imshow(im);
viscircles(centers, radii, 'DrawBackgroundCircle', false);
viscircles by default draws the circles with a white background over the contour. I want to disable this because your image has white circles and I don't want to show false contouring. This is what I get with the above code:
Therefore, what you can take away from this is the centers and radii variables. centers will give you the centre of each detected circle while radii will tell you what the radii is for each circle.
Now, if you want to simulate what regionprops is doing, we can iterate through all of the detected circles and physically draw them onto a 2D map where each circle would be labeled by an ID number. As such, we can do something like this:
[X,Y] = meshgrid(1:size(im,2), 1:size(im,1));
IDs = zeros(size(im));
for idx = 1 : numel(radii)
r = radii(idx);
cen = centers(idx,:);
loc = (X - cen(1)).^2 + (Y - cen(2)).^2 <= r^2;
IDs(loc) = idx;
end
We first define a rectangular grid of points using meshgrid and initialize an IDs array of all zeroes that is the same size as the image. Next, for each pair of radii and centres for each circle, we define a circle that is centered at this point that extends out for the given radius. We then use these as locations into the IDs array and set it to a unique ID for that particular circle. The result of IDs will be that which resembles the output of bwlabel. As such, if you want to extract the locations of where the idx circle is, you would do:
cir = IDs == idx;
For demonstration purposes, this is what the IDs array looks like once we scale the IDs such that it fits within a [0-255] range for visibility:
imshow(IDs, []);
Therefore, each shaded circle of a different shade of gray denotes a unique circle that was detected with imfindcircles.
However, the shades of gray are probably a bit ambiguous for certain coins as this blends into the background. Another way that we could visualize this is to apply a different colour map to the IDs array. We can try using the cool colour map, with the total number of colours to be the number of unique circles + 1 for the background. Therefore, we can do something like this:
cmap = cool(numel(radii) + 1);
RGB = ind2rgb(IDs, cmap);
imshow(RGB);
The above code will create a colour map such that each circle gets mapped to a unique colour in the cool colour map. The next line applies a mapping where each ID gets associated with a colour with ind2rgb and we finally show the image.
This is what we get:
Edit: the following solution is more adequate to scenarios where one does not require fitting the exact circumferences, although simple heuristics could be used to approximate the radii of the coins in the original image based on the centers found in the eroded one.
Assuming you have access to the Image Processing toolbox, try imerode on your original black and white image. It will apply an erosion morphological operator to your image. In fact, the Matlab webpage with the documentation of that function has an example strikingly similar to your problem/image and they use a disk structure.
Run the following code (based on the example linked above) assuming the image you submitted is called ima.jpg and is local to the code:
ima=imread('ima.jpg');
se = strel('disk',50);
eroded = imerode(ima,se);
imshow(eroded)
and you will see the image that follows as output. After you do this, you can use bwlabel to label the connected components and compute whatever properties you may want, for example, count the number of coins or detect their centers.
How do I separate the two connected circles in the image below, using MATLAB? I have tried using imerode, but this does not give good results. Eroding does not work, because in order to erode enough to separate the circles, the lines disappear or become mangled. In other starting pictures, a circle and a line overlap, so isolating the overlapping objects won't work either.
The image shows objects identified by bwboundaries, each object painted a different color. As you can see, the two light blue circles are joined, and I want to disjoin them, producing two separate circles. Thanks
I would recommend you use the Circular Hough Transform through imfindcircles. However, you need version 8 of the Image Processing Toolbox, which was available from version R2012a and onwards. If you don't have this, then unfortunately this won't work :(... but let's go with the assumption that you do have it. However, if you are using something older than R2012a, Dev-iL in his/her comment above linked to some code on MATLAB's File Exchange on an implementation of this, most likely created before the Circular Hough Transform was available: http://www.mathworks.com/matlabcentral/fileexchange/9168-detect-circles-with-various-radii-in-grayscale-image-via-hough-transform/
This is a special case of the Hough Transform where you are trying to find circles in your image rather than lines. The beauty with this is that you are able to find circles even when the circle is partially completed or overlapping.
I'm going to take the image that you provided above and do some post-processing on it. I'm going to convert the image to binary, and remove the border, which is white and contains the title. I'm also going to fill in any holes that result so that all of the objects are filled in with solid white. There is also some residual quantization noise after I do this step, so I'm going to a small opening with a 3 x 3 square element. After, I'm going to close the shapes with a 3 x 3 square element, as I see that there are noticeable gaps in the shapes. Therefore:
Therefore, directly reading in your image from where you've posted it:
im = imread('http://s29.postimg.org/spkab8oef/image.jpg'); %// Read in the image
im_gray = im2double(rgb2gray(im)); %// Convert to grayscale, then [0,1]
out = imclearborder(im_gray > 0.6); %// Threshold using 0.6, then clear the border
out = imfill(out, 'holes'); %// Fill in the holes
out = imopen(out, strel('square', 3));
out = imclose(out, strel('square', 3));
This is the image I get:
Now, apply the Circular Hough Transform. The general syntax for this is:
[centres, radii, metric] = imfindcircles(img, [start_radius, end_radius]);
img would be the binary image that contains your shapes, start_radius and end_radius would be the smallest and largest radius of the circles you want to find. The Circular Hough Transform is performed such that it will find any circles that are within this range (in pixels). The outputs are:
centres: Which returns the (x,y) positions of the centres of each circle detected
radii: The radius of each circle
metric: A measure of purity of the circle. Higher values mean that the shape is more probable to be a circle and vice-versa.
I searched for circles having a radius between 30 and 60 pixels. Therefore:
[centres, radii, metric] = imfindcircles(out, [30, 60]);
We can then demonstrate the detected circles, as well as the radii by a combination of plot and viscircles. Therefore:
imshow(out);
hold on;
plot(centres(:,1), centres(:,2), 'r*'); %// Plot centres
viscircles(centres, radii, 'EdgeColor', 'b'); %// Plot circles - Make edge blue
Here's the result:
As you can see, even with the overlapping circles towards the top, the Circular Hough Transform was able to detect two distinct circles in that shape.
Edit - November 16th, 2014
You wish to ensure that the objects are separated before you do bwboundaries. This is a bit tricky to do. The only way I can see you do this is if you don't even use bwboundaries at all and do this yourself. I'm assuming you'll want to analyze each shape's properties by themselves after all of this, so what I suggest you do is iterate through every circle you have, then place each circle on a new blank image, do a regionprops call on that shape, then append it to a separate array. You can also keep track of all of the circles by having a separate array that adds the circles one at a time to this array.
Once you've finished with all of the circles, you'll have a structure array that contains all of the measured properties for all of the measured circles you have found. You would use the array that contains only the circles from above, then use these and remove them from the original image so you get just the lines. You'd then call one more regionprops on this image to get the information for the lines and append this to your final structure array.
Here's the first part of the procedure I outlined above:
num_circles = numel(radii); %// Get number of circles
struct_reg = []; %// Save the shape analysis per circle / line here
%// For creating our circle in the temporary image
[X,Y] = meshgrid(1:size(out,2), 1:size(out,1));
%// Storing all of our circles in this image
circles_img = false(size(out));
for idx = 1 : num_circles %// For each circle we have...
%// Place our circle inside a temporary image
r = radii(idx);
cx = centres(idx,1); cy = centres(idx,2);
tmp = (X - cx).^2 + (Y - cy).^2 <= r^2;
% // Save in master circle image
circles_img(tmp) = true;
%// Do regionprops on this image and save
struct_reg = [struct_reg; regionprops(tmp)];
end
The above code may be a bit hard to swallow, but let's go through it slowly. I first figure out how many circles we have, which is simply looking at how many radii we have detected. I keep a separate array called struct_reg that will append a regionprops struct for each circle and line we have in our image. I use meshgrid to determine the (X,Y) co-ordinates with respect to the image containing our shapes so that I can draw one circle onto a blank image at each iteration. To do this, you simply need to find the Euclidean distance with respect to the centre of each circle, and set the pixels to true only if that location has its distance less than r. After doing this operation, you will have created only one circle and filtered all of them out. You would then use regionprops on this circle, add it to our circles_img array, which will only contain the circles, then continue with the rest of the circles.
At this point, we will have saved all of our circles. This is what circles_img looks like so far:
You'll notice that the circles drawn are clean, but the actual circles in the original image are a bit jagged. If we tried to remove the circles with this clean image, you will get some residual pixels along the border and you won't completely remove the circles themselves. To illustrate what I mean, this is what your image looks like if I tried to remove the circles with circles_img by itself:
... not good, right?
If you want to completely remove the circles, then do a morphological reconstruction through imreconstruct where you can use this image as the seed image, and specify the original image to be what we're working on. The job of morphological reconstruction is essentially a flood fill. You specify seed pixels, and an image you want to work on, and the job of imreconstruct is from these seeds, flood fill with white until we reach the boundaries of the objects that the seed pixels resided in. Therefore:
out_circles = imreconstruct(circles_img, out);
Therefore, we get this for our final reconstructed circles image:
Great! Now, use this and remove the circles from the original image. Once you do this, run regionprops again on this final image and append to your struct_reg variable. Obviously, save a copy of the original image before doing this:
out_copy = out;
out_copy(out_circles) = false;
struct_reg = [struct_reg; regionprops(out_copy)];
Just for sake of argument, this is what the image looks like with the circles removed:
Now, we have analyzed all of our shapes. Bear in mind I did the full regionprops call because I don't know exactly what you want in your analysis... so I just decided to give you everything.
Hope this helps!
erosion is the way to go. You should probably use a larger structuring element.
How about
1 erode
2 detect your objects
3 dilate each object for itself using the same structuring element
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