I use following example codes to overlay an index image on a background image (in the example below it is RGB but for my problem the background is a gray sacle image). Then my questions are:
How to display the colorbar on the side of the overlaid image? The colorbar should be the colorbar of the overlaid index image NOT the colorbar of the background image.
How to fix the color map range of the overlaid index image? I have several pairs of gray scale background + overlaid index image (using 'jet' color map). I need them to be shown in the same scale. I try to use set(iim2,'caxis', [0 1]); but the 'image' in Matlab hasn't got the property of 'caxis'.
Please help and thanks very much!
% Create the background
% This example uses a blend of colors from left to right, converted to a TrueColor image
% Use repmat to replicate the pattern in the matrix
% Use the "jet" colormap to specify the color space
bg = ind2rgb(repmat(1:64,64,1),jet(64));
% Create an image and its corresponding transparency data
% This example uses a random set of pixels to create a TrueColor image
im = rand(100,100,3);
% Make the image fade in from left to right by designing its alphadata
% Use repmat to replicate the pattern in the transparency fading
imAlphaData = repmat(0:1/size(im,2):1-1/size(im,2),size(im,1),1);
% Display the images created in subplots
hf = figure('units','normalized','position',[.2 .2 .6 .6]);
ax1 = subplot(2,3,1);
ibg = image(bg);
axis off
title('Background')
ax2 = subplot(2,3,4);
iim = image(im);
axis off
title('Image without transparency yet')
% Now set up axes that overlay the background with the image
% Notice how the image is resized from specifying the spatial
% coordinates to locate it in the axes.
ax3 = subplot(2,3,[2:3, 5:6]);
ibg2 = image(bg);
axis off
hold on
% Overlay the image, and set the transparency previously calculated
iim2 = image(im,'XData',[30 50],'YData',[10 30]);
set(iim2,'AlphaData',imAlphaData);
title(sprintf('Using transparency while overlaying images:\nresult is multiple image objects.'))
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 am having issue with cropping of image. My task includes an image. I have to crop the image at x,y coordinates, which I have tried and got success.
Now I want to show/save both images, the cropped one and also the image which is being cropped (which will have a subtracted area of cropped part just like subtracting the small cavity from the image).
My Code:
B = imread('B1.jpg');
% figure,imshow(B)
GimageB = rgb2gray(B);
% figure, imshow(GimageB)
J = imcrop(B,[284 235 95 80]);
figure, imshow(J)
To show the image without the "extracted" area, fill that area with zero!
img=rgb2gray(imread('http://weknowyourdreams.com/images/cat/cat-03.jpg'));
img2 = imcrop(img,[500 600 700 800]);
img3=img;
% fill area with zero (note the numbers, compare to imcrop)
img3(500:500+700, 600:600+800)=0;
figure()
imshow(img3)
I am trying to display a lung CT image as a background image and its ROI mask over the first image with transparency. The following image
Until now I have developed the following code:
load example.mat
fig = figure;
him = imagesc(img);
colormap(gray)
set(him, 'AlphaData', 0.3 * mask)
But it only display with transparency the region covered by the Mask. What I really want is the whole lung displayed with the Mask over it in some color like green.
This image is an example of what I am trying to reach.
This is the link for the file contaning the CT lung matrix and the Mask matrix.
Link for the .mat file with the images
You can just overlay another image:
hold on
ih = image(cmask, 'AlphaData', mask.*.3, 'CDataMapping', 'direct');
Here, cmask is obtained for example by
cmask = zeros(size(mask, 1), size(mask, 2), 3);
cmask(:, :, 2) = mask.*255;
to get a green mask.
I'd like to take in an RGB image, find the points in the image that are white, and get the cartesian coordinates of those points in the image. I've gotten most of the way there, but when I try to plot the cartesian coordinates, I get a vertically tiled image (i.e. 5 overlapped copies of what I should see). Anyone know what could be causing this?
,
Code: (JPG comes in as 2448 x x3264 x 3 uint8)
I = imread('IMG_0245.JPG');
imshow(I); % display unaltered image
% Convert image to grayscale
I = rgb2gray(I);
% Convert image to binary (black/white)
I = im2bw(I, 0.9);
% Generate cartesian coordinates of image
imageSize = size(I);
[x, y] = meshgrid( 1:imageSize(1), 1:imageSize(2) );
PerspectiveImage = [x(:), y(:), I(:)];
% Get indices of white points only
whiteIndices = find(PerspectiveImage(:,3));
figure; plot( PerspectiveImage(whiteIndices, 1), PerspectiveImage(whiteIndices, 2),'.');
% Flip vertically to correct indexing vs. plotting issue
axis ij
Very simple. You're declaring your meshgrid wrong. It should be:
[x, y] = meshgrid( 1:imageSize(2), 1:imageSize(1) );
The first parameter denotes the horizontal extents of the 2D grid, and so you want to make this vary for as many columns as you have. Similarly, the second parameter denotes the vertical extents of the 2D grid, and so you want to make this for as many rows as you have.
I had to pre-process some of your image to get some good results because your original image had a large white border surrounding the image. I had to remove this border by removing all pure white pixels. I also read in the image directly from StackOverflow:
I = imread('http://s7.postimg.org/ovb53w4ff/Track_example.jpg');
mask = all(I == 255, 3);
I = bsxfun(#times, I, uint8(~mask));
This was the image I get after doing my pre-processing:
Once I do this and change your meshgrid call, I get this:
I have an image, in that image all red objects are detected.
Here's an example with two images:
http://img.weiku.com/waterpicture/2011/10/30/18/road_Traffic_signs_634577283637977297_4.jpg
But when i proceed that image for edge detection method i got the output as only black color. However, I want to detect the edges in that red object.
r=im(:,:,1); g=im(:,:,2); b=im(:,:,3);
diff=imsubtract(r,rgb2gray(im));
bw=im2bw(diff,0.18);
area=bwareaopen(bw,300);
rm=immultiply(area,r); gm=g.*0; bm=b.*0;
image=cat(3,rm,gm,bm);
axes(handles.Image);
imshow(image);
I=image;
Thresholding=im2bw(I);
axes(handles.Image);
imshow(Thresholding)
fontSize=20;
edgeimage=Thresholding;
BW = edge(edgeimage,'canny');
axes(handles.Image);
imshow(BW);
When you apply im2bw you want to use only the red channel of I(i.e the 1st channel). Therefore using this command:
Thresholding =im2bw(I(:,:,1));
for example yields this output:
Just FYI for anyone else that manages to stumble here. The HSV colorspace is better suited for detecting colors over the RGB colorspace. A good example is in gnovice's answer. The main reason for this is that there are colors which can contain full 255 red values but aren't actually red (yellow can be formed from (255,255,0), white from (255,255,255), magenta from (255,0,255), etc).
I modified his code for your purpose below:
cdata = imread('roadsign.jpg');
hsvImage = rgb2hsv(cdata); %# Convert the image to HSV space
hPlane = 360.*hsvImage(:,:,1); %# Get the hue plane scaled from 0 to 360
sPlane = hsvImage(:,:,2); %# Get the saturation plane
bPlane = hsvImage(:,:,3); %# Get the brightness plane
% Must get colors with high brightness and saturation of the red color
redIndex = ((hPlane <= 20) | (hPlane >= 340)) & sPlane >= 0.7 & bPlane >= 0.7;
% Show edges
imshow(edge(redIndex));
Output: