I would like to know how can ı achieve thresholding based on grayscale intensity by not converting it to binary image. As an example, below 50 intensity will be 0 while 50-255 intensity values remain the same(in Matlab).
Check the following...
Read sample image:
I = imread('cameraman.tif');
Set all values below 50 to zero.
I(I < 50) = 0;
Related
Let say we have grayscale image im:
And use imagesc(im) to get:
With code:
im = rgb2gray(im2single(imread('tesla.jpg'))); % get image
h = imagesc(log(abs(fftshift(fft2(im))))); % imagesc handle
How can one convert the intensity graphic h (2nd image) to a standard RGB image (2x2 float matrix that one can manipulate, crop, etc) in matlab?
I don't need the axes, numbers or tics of the intensity image, I only need to maintain the color.
Thank you.
%turn off the axes
axis off
%save the image
saveas(h,'test.png')
%read the saved image
im_fft = imread('test.png');
%remove white border
sum_img = sum(im_fft,3); sum_img(sum_img(:) ~= 255*3) = 0; sum_img = logical(sum_img);
im_fft = im_fft(~all(sum_img,2), ~all(sum_img,1),:);
%Done!
figure, imshow(im_fft)
The resulting image can be used only for presentations/illustrations, not for analysis - quantization and sampling corrupts it significantly
I'm using the MATLAB function
imagesc(my_gray_valued_image)
to visualize my_gray_valued_image: [1024x1024] double array with values from 0.0 - 1.0 (gray values) using colormaps like jet.
I want to store the output as a RGB image ([1024x1024x3] double array). However the output of the function is a Image object (matlab.graphics.primitive.Image) that contains the original array (Image.CData) but doesn't allow to extract the colorscaled image.
Following a similar (although confusingly cluttered) question (How to convert an indexed image to rgb image in MATLAB?) I tried the following, but that gave me a plain blue image:
RGB = ind2rgb(my_gray_valued_image, jet);
imshow(RGB);
here for arbitrary colormap:
im = rand(5); % input [0-1] image
figure;
h = imagesc(im); % imagesc handle
title('imagesc output')
cdata = h.CData; % get image data (if you don't have variable 'im')
cm = colormap(h.Parent); % get axes colormap
n = size(cm,1); % number of colors in colormap
c = linspace(h.Parent.CLim(1),h.Parent.CLim(2),n); % intensity range
ind = reshape(interp1(c,1:n,im(:),'nearest'),size(im)); % indexed image
rgb = ind2rgb(ind,cm); % rgb image
figure;
imshow(rgb,'InitialMagnification','fit');
title('rgb image')
You can use ind2rgb to convert an intensity image into RGB using a colormap of your choice; but make sure that the range of the input is from 1 to the number of colors in the colormap. This is because ind2rgb maps value 1 to the first color, 2 to the second etc.
im = rand(5,5); % example intensity image
cmap = jet(256); % desired colormap
result = ind2rgb(ceil(size(cmap,1)*im), cmap);
The reason why you are getting a blue image is that ind2rgb clips the values of the input image to the range from 1 to the number of colors in the colormap. So, if the input image has values between 0 and 1 they are all mapped to 1, that is, to the first color in the colormap.
I want to create an HSV image (or maybe a coordinate map) which shows that coordinates accurately.
I am using the following code and get an image like this the result of the following code which is not what I want.
img = rand(200,200);
[ind_x, ind_y] = ind2sub(size(img),find(isfinite(img)));
ind_x = reshape(ind_x,size(img));
ind_y = reshape(ind_y,size(img));
ind = ind_x.*ind_y;
figure, imagesc(ind); axis equal tight xy
Lets say you quantize the HSV space (0-1) into 256 bins. There will be 256*256*256 possible colors. We could fix a dimension (say saturation) and generate the matrix. Then there will be 256*256 colors.
[x1,x2]=meshgrid(linspace(0,1,256),linspace(0,1,256));
img(:,:,1)=x1;
img(:,:,2)=1; %fully saturated colors
img(:,:,3)=x2;
imgRGB=hsv2rgb(img); %for display purposes
imshow(imgRGB,[])
It will look different in RGB (that's where you would visualize). It looks similar to your image if you visualize HSV matrix (i.e. without converting it to RGB, but MATLAB doesn't know that its HSV)
imshow(img,[]);
The second image you have posted can be obtained with:
[x1,x2]=meshgrid(linspace(0,1,256),linspace(0,1,256));
img(:,:,1)=x1;
img(:,:,2)=0;
img(:,:,3)=x2;
imshow(img,[]) %visualizing HSV
I have a RGB image which has only black and white squares. I want to count number to non gray pixels in this image. I am new to matlab. I want to check the quality of image as it should only contain black and white pixels.Actually I have undistorted this image due that some colored fringes are appeared.I want to know the how many color are introduced to check the quality of the image.
using matlab to get counts of specific pixel values in an image.
Images are RGBA <512x512x4 uint8> when read into matlab (although we can disregard the alpha channel).
Something like this
count = sum(im(:, :, 1) == 255 & im(:, :, 3) == 255 & im(:, :, 3) == 255);
will give you the count of such pixels. Replace sum with find to get the indices of those pixels if you need that.
A pixel is said to be gray if its R,G,B components are all same.
Using this logic
%// checking for equality of R,G,B values
B = any(diff(im,[],3),3); %// selecting only non-gray pixels
count = sum(B(:)); %// Number of non-gray pixels
PS: This answer is tailored from this and this answer.
I want to convert 2 RGB images to HSV images then calculate the difference between the two images saturation and output this resulting image as a uint8 image. Here is the code I've tried but uint8 is converting the intensities to 1 or 0 resulting in a binary image essentially.
inputImage = rgb2hsv(inputImage);
background = rgb2hsv(background);
sDiff = imabsdiff(background(:,:,2), inputImage(:,:,2));
sDiff = uint8(sDiff);
figure, imshow(sDiff, []);
Its outputting a binary image though. I tried:
gDiff = double(sDiff) * 255;
But the resulting intensities are either 255 or 0.
Use sDiff = uint8(sDiff.*256); to convert it to uint8 format