I have two images and have to divide that image. Due to manual error the area in the image is not as same. How can i correct it automatically using matlab so as to perform divison pixel by pixel accurately?
Once you've read both image files into the variables, say A and B, assuming A contains the image of the size you actually want and B contains the image of the size you want to change, you could use:
[numrows numcols] = size(A);
A = imresize(A, [numrows numcols]);
Related
I'm trying to segment an image with Color-Based Segmentation Using K-Means Clustering. I already created 3 clusters, and the cluster number 3 is like this image:
This cluster has 3 different colors. And I want to only display the black spots of this image. How can I do that?
The image is 500x500x3 uint8.
Those "holes" look like they are well defined with the RGB values all being set to 0. To make things easy, convert the image to grayscale, then threshold the image so that any intensities less than 5 set the output to white. I use a threshold of 5 instead to ensure that we capture object pixels in their entirety taking variations into account.
Once that's done, you can use the function bwlabel from the image processing toolbox (I'm assuming you have it as you're dealing with images) where the second output tells you how many distinct white objects there are.
Something like this could work:
im = imread('http://i.stack.imgur.com/buW8C.png');
im_gray = rgb2gray(im);
holes = im_gray < 5;
[~,count] = bwlabel(holes);
I read in the image directly from StackOverflow, convert the image to grayscale, then determine a binary mask where any intensity that is less than 5, set the output to white or true. Once we have this image, we can use bwlabel's second output to determine how many objects there are.
I get this:
>> count
count =
78
As an illustration, if we show the image where the holes appear, I get this:
>> imshow(holes);
The amount of "holes" is a bit misleading though. If you specifically take a look at the bottom right of the image, there are some noisy pixels that don't belong to any of the "holes" so we should probably filter that out. As such, a simple morphological opening filter with a suitable sized structure will help remove spurious noisy islands. As such, use imopen combined with strel to define the structuring element (I'll choose a square) as well as a suitable size of the structuring element. After, use the structuring element and filter the resulting image and you can use this image to count the number of objects.
Something like this:
%// Code the same as before
im = imread('http://i.stack.imgur.com/buW8C.png');
im_gray = rgb2gray(im);
holes = im_gray < 5;
%// Further processing
se = strel('square', 5);
holes_process = imopen(holes, se);
%// Back to where we started
[~,count] = bwlabel(holes_process);
We get the following count of objects:
>> count
count =
62
This seems a bit more realistic. I get this image now instead:
>> imshow(holes_process);
Good day,
In MATLAB, I have multiple image-pairs of various samples. The images in a pair are taken by different cameras. The images are in differing orientations, though I have created transforms (for each image-pair) that can be applied to correct that. Their bounds contain the same physical area, but one image has smaller dimensions (ie. 50x50 against 250x250). Additionally, the smaller image is not in a consistent location within the larger image. However, the smaller image is within the borders of the larger image.
What I'd like to do is as follows: after applying my pre-determined transform to the larger image, I want to crop the part of the larger image that is of the same as the smaller image.
I know I can specify XData and YData when applying my transforms to output a subset of the transformed image, but I don't know how to relate that to the location of the smaller image. (Note: Transforms were created from control-point structures)
Please let me know if anything is unclear.
Any help is much appreciated.
Seeing how you are specifying control points to get the transformation from one image to another, I'm assuming this is a registration problem. As such, I'm also assuming you are using imtransform to warp one image to another.
imtransform allows you to specify two additional output parameters:
[out, xdata, ydata] = imtransform(in, tform);
Here, in would be the smaller image and tform would be the transformation you created to register the smaller image to warp into the larger image. You don't need to specify the XData and YData inputs here. The inputs of XData and YData will bound where you want to do the transformation. Usually people specify the dimensions of the image to ensure that the output image is always contained within the borders of the image. However in your case, I don't believe this is necessary.
The output variable out is the warped and transformed image that is dictated by your tform object. The other two output variables xdata and ydata are the minimum and maximum x and y values within your co-ordinate system that will encompass the transformed image fully. As such, you can use these variables to help you locate where exactly in the larger image the transformed smaller image appears. If you want to do a comparison, you can use these to crop out the larger image and see how well the transformation worked.
NB: Sometimes the limits of xdata and ydata will go beyond the dimensions of your image. However, because you said that the smaller image will always be contained within the larger image (I'm assuming fully contained), then this shouldn't be a problem. Also, the limits may also be floating point so you'll need to be careful here if you want to use these co-ordinates to crop a minimum spanning bounding box.
I have a original gray scale image(I m using mammogram image with labels outside image).
I need to remove some objects(Labels) in that image, So i converted that grayscale image to a binary image. Then i followed the answer method provided in
How to Select Object with Largest area
Finally i extracted an Object with largest area as binary image. I want that region in gray scale for accessing and segmenting small objects within that. For example. Minor tissues in region and also should detect its edge.
**
How can i get that separated object region as grayscale image or
anyway to get the largest object region from gray scale directly
without converting to binary or any other way.?
**
(I am new to matlab. I dono whether i explained it correctly or not. If u cant get, I ll provide more detail)
If I understood you correctly, you are looking to have a gray image with only the biggest blob being highlighted.
Code
img = imread(IMAGE_FILEPATH);
BW = im2bw(img,0.2); %%// 0.2 worked to get a good area for the biggest blob
%%// Biggest blob
[L, num] = bwlabel(BW);
counts = sum(bsxfun(#eq,L(:),1:num));
[~,ind] = max(counts);
BW = (L==ind);
%%// Close the biggest blob
[L,num] = bwlabel( ~BW );
counts = sum(bsxfun(#eq,L(:),1:num));
[~,ind] = max(counts);
BW = ~(L==ind);
%%// Original image with only the biggest blob highlighted
img1 = uint8(255.*bsxfun(#times,im2double(img),BW));
%%// Display input and output images
figure,
subplot(121),imshow(img)
subplot(122),imshow(img1)
Output
If I understand your question correctly, you want to use the binary map and access the corresponding pixel intensities in those regions.
If that's the case, then it's very simple. You can use the binary map to identify the spatial co-ordinates of where you want to access the intensities in the original image. Create a blank image, then copy over these intensities over to the blank image using those spatial co-ordinates.
Here's some sample code that you can play around with.
% Assumptions:
% im - Original image
% bmap - Binary image
% Where the output image will be stored
outImg = uint8(zeros(size(im)));
% Find locations in the binary image that are white
locWhite = find(bmap == 1);
% Copy over the intensity values from these locations from
% the original image to the output image.
% The output image will only contain those pixels that were white
% in the binary image
outImg(locWhite) = im(locWhite);
% Show the original and the result side by side
figure;
subplot(1,2,1);
imshow(im); title('Original Image');
subplot(1,2,2);
imshow(outImg); title('Extracted Result');
Let me know if this is what you're looking for.
Method #2
As suggested by Rafael in his comments, you can skip using find all together and use logical statements:
outImg = img;
outImg(~bmap) = 0;
I decided to use find as it less obfuscated for a beginner, even though it is less efficient to do so. Either method will give you the correct result.
Some food for thought
The extracted region that you have in your binary image has several holes. I suspect you would want to grab the entire region without any holes. As such, I would recommend that you fill in these holes before you use the above code. The imfill function from MATLAB works nicely and it accepts binary images as input.
Check out the documentation here: http://www.mathworks.com/help/images/ref/imfill.html
As such, apply imfill on your binary image first, then go ahead and use the above code to do your extraction.
I have two images with same dimensions but with different sizes. I want the bigger image's size to be changed to match the smaller image's size.
For example, I have an image A with size 300x400x3 and image B with size 600x800x3. I want to change the size of image B to 300x400x3. Thanks for any help.
Just one-liner as suggested by Andrey -
B = imresize( B,[size(A,1) size(A,2)]);
For accessing more options with resizing, use this resource.
I have an Image :
I want to manually divide the image into the parts as show below:
The division of the image should be user controlled. I should be able to take the user input of the rectangular squares in which I am trying to divide the Image.
The output is shown below :
How can I do this in matlab?
After operating on the individual images can I join them back together to make the image as one ?
Use imrect to create an interactive rectangular selection tool on top of the input image. Look closely at the second example.
Once the user has selected a rectangel, you can use imcrop to get the corresponding part.
Saving the relative position of the selected rectangle (i.e., the position vector [x y w h]) you can then "re-embbed" the part into the original image at the same location.
I finally got it . Thanks !
Img = imread('cameraman.tif');
figure();
imshow(Img);
h = imrect();
crop_area = wait(h);
cropped = imcrop(Img, crop_area);
imshow(cropped);
This works well.