How to view the pixel intensities of an image in matlab by moving the mouse pointer over the image?
I used:
imshow(imread('image.jpg'));
But there is no option to view the pixel intensities of each pixel in the image.
For example,
In MS-paint, while moving the mouse pointer
over the image we can see the location of
the pixel such as (20, 117) at the status bar.
But I need to see the pixel intensity instead.
Is there any other option to view it. Or I need to code to view it?
One other option which is more interactive is
imtool(imread('image.jpg')); % For GrayScale Image
imtool(rgb2gray(imread('image.jpg'))); % For RGB Image
If you want to create an intensity map, you can use MATLAB's rgb2gray. This will convert the n-by-m-by-3 RGB array you got from imread to an n-by-m matrix which contains the pixel intensities.
You can then point the interactive data cursor to this intensity matrix for the current mouse coordinates.
you have impixelinfo and impixelinfoval for showing interactive information.
Related
I have a simple green fluorescent image. I want to find the total number of pixels that are above a specific value using MATLAB. I don't know where the pixel values are stored in an image.
Here is the green fluorescent image. I want to know which percentage of the pixels have value of more than a specific threshold. For example in this image, if the pixel value in the cells are around X, then I want to find the total number of pixels that are above X.
If you read a colored image using imread, you get a 3D matrix in which the first two indices are the image coordinates; (row, columns); and the last index represents the color channels. For the typical use case of an RGB image, the color channels are:
1 = red
2 = green
3 = blue.
Other possibilities are grayscale, CMYK and indexed images. Please check the official documentation for more information.
I have an image with 6 colors each indicating a value. I had obtained an image as shown below.
I need to smoothen the edges and then find out the area as well as the surface area of that region. The second image shows a black line drawn in the edges which indicates that I need to smoothen the edges in such a way.
I had used segmentation to create a mask as shown in the third image, and then obtain a segmented image using the code following the image.
I have used the following code for generating till the masked image.
Source : How to segment
imshow(Out1)
str = 'Click to select initial contour location. Double-click to confirm and proceed.';
title(str,'Color','b','FontSize',12);
disp(sprintf('\nNote: Click close to object boundaries for more accurate result.'));
mask = roipoly;
figure, imshow(mask)
title('Initial MASK');
maxIterations = 3000;
bw = activecontour(Out1, mask, maxIterations, 'Chan-Vese');
% Display segmented image
figure, imshow(bw)
title('Segmented Image');
In order to use the 'activecontour' function my image needs to be a grey-scale image, which I'm not being able to convert to greyscale and back. Also to find out surface area/ area of the region is there any inbuilt function. Please help thanks.
use im2double, im2uint8, etc. to convert binary image to grayscale.
use bwarea or regionprops to find the region area.
I am working with RGB images that contain a single object against a monochrome background.
My goal is to isolate the object in the image and resize the image to contain only the object.
I have successfully been able to detect the object by converting the image to a binary image using an appropriate threshold. Then, in order to isolate the object in the original RGB image I use the binary image as a mask with the original RGB image.
maskedImage = bsxfun(#times,originalimage, cast(binaryimage,class(originalimage)));
This leaves me with a image only containing the object surrounded by a black background. This is due to the fact that the binary image mask I used contained the object in white pixels and the background in black pixels and since possess intensity values of 0 the masking process converted all pixels that didn't belong to the object to black pixels. I've attached an example below.
I would now like to draw a bounding box around the object and resize the image to the size of the bounding box, so that I can get rid as much of the surrounding black pixels as possible. Is there any way of doing this? Any help would be appreciated.
Given the segmented image, you want to crop out all of the black pixels and provide the closest bounding box that fully encapsulates the object. That's very simple.
You already have a binary mask that determines what is an object and what's background. You simply need to find the minimum spanning bounding box. You can find the top-left and bottom right corner by obtaining all of the pixel locations that are non-zero in the mask, and finding the minimum and maximum row and column coordinates. You'd then just use these to crop out the segmented image.
As such:
%// Find all non-zero locations in the mask
[row,col] = find(binaryImage);
%// Find the top left corner of the mask
topLeftRow = min(row);
topLeftCol = min(col);
%// Find the bottom right corner of the mask
bottomRightRow = max(row);
bottomRightCol = max(col);
%// Extract the object
extracted = maskedImage(topLeftRow:bottomRightRow, topLeftCol:bottomRightCol, :);
The words of the day are Bounding boxes !
If you want the minimum-area rectangle to crop only the nonzero values, you want the bounding box of your region, then set your phasers to stun and you're all set !
See this Matlab help forum question for more implementation details in Matlab.
I wish to find the number of black pixels in a color image using MATLAB, how can I do it?
Thanks.
Use nnz like this -
nnz(all(im==0,3))
where im is your image data.
Alternatives - sum(reshape(all(im==0,3),[],1)) and sum(sum(all(im==0,3))).
The assumption here is that black pixels are triplets (0,0,0).
Instead if you define black pixels as pixels that have values in the interval [0 th] for the same pixel location across all channels, use this -
nnz(all(im<=th,3))
I have original RGB image.
then, I convert it to gray scale.
then, I assign new threhold value into some pixel.
then, I need to convert the gray scale image from step#3 to RGB image.
I am trying on the step#4. Could you help to advise?
Check out rgb2hsv and hsv2rgb in matlab. Then threshold on the value matrix. This makes more sense to me.
Another possibility is to convert to grey scale but keep the original colour image. Then create a mask by thresholding the grey scale image and use that mask to discard the pixels above/below your threshold on the colour matrix.