i'm working in matlab and i wanted to apply the Contrast Stretching for grey scale image and also RGB image ,
so for the grey scale i've tried this one and it worked
clear all;
clc;
itemp = imread('cameraman.tif'); %read the image
i = itemp(:,:,1);
rtemp = min(i); % find the min. value of pixels in all the
columns (row vector)
rmin = min(rtemp); % find the min. value of pixel in the image
rtemp = max(i); % find the max. value of pixels in all the
columns (row vector)
rmax = max(rtemp); % find the max. value of pixel in the image
m = 255/(rmax - rmin); % find the slope of line joining point
(0,255) to (rmin,rmax)
c = 255 - m*rmax; % find the intercept of the straight line
with the axis
i_new = m*i + c; % transform the image according to new slope
figure,imshow(i); % display original image
figure,imshow(i_new); % display transformed image
this is for greyscale image ,
the problem is that that i don't know how to do for the RGB image
any idea? how to implement that?
thank you :)
Could the function stretchlim (reference) be useful for your purpose?
Find limits to contrast stretch image.
Low_High = stretchlim(RGB,Tol) returns Low_High, a two-element vector
of pixel values that specify lower and upper limits that can be used
for contrast stretching truecolor image RGB.
img = imread('myimg.png');
lohi = stretchlim(img,[0.2 0.8]);
If you write
rmin = min(i(:));
Then it computes the minimum over all values in i. This will work for RGB images also, which simply are 3D matrices with 3 values along the 3rd dimension.
The rest of your code also applies directly to such images.
Related
This question already has an answer here:
How to find and highlight the brightest region an image in Matlab?
(1 answer)
Closed 1 year ago.
Dears,
I would like to ask you for help with the code. My goal is to find the brightest point an image and mark it.
I used the following code to get the Image in Grey Values -> How to find and highlight the brightest region an image in Matlab? and tried to extend it based on my intention.
Then I found the max value of the matrix and go with the for loop function to highlight the coordinates of the max points. Unfortunately, here I have started struggle.
rgbImage = imread( 'Zoom1_WhiteImage.png' );
%imshow(rgbImage);
[rows, columns, numberOfColorChannels] = size(rgbImage)
if numberOfColorChannels == 1
brightness = rgbImage; % For a 1 channel image, brightness is the same as the original pixel value.
else
% For an RGB image, the brightness can be estimated in various ways, here's one standard formula:
brightness = (0.2126*rgbImage(:, :, 1) + 0.7152*rgbImage(:, :, 2) + 0.0722*rgbImage(:, :, 3));
end
% Establish a brightness threshold - pixels brighter than this value will
% be highlighted
threshold = 105;
% Create a zero-filled mask of equal size to the image to hold the
% information of which pixels met the brightness criterion:
mask = zeros(rows, columns, 'logical');
% Assign "1" to any mask pixels whose corresponding image pixel met the
% brightness criterion:
mask(brightness > threshold) = 1;
[a,b] = size(brightness)
maxgrey = max(max(brightness));
aux=0;
for i=1:1:a;
for j=1:1:b;
if brightness(a,b)>brightness(a,b+1)
aux=brightness(a,b+1)
else aux
end
end
end
Could you help me to finish the code, please?
You can achieve your goal with the function find() or with the function ind2sub():
% Random 2D matrix
I = rand(10);
% First option with find
[x,y] = find(I == max(I(:)))
% Second option using ind2sub, a bit more efficient since we only read I once.
[~,ind] = max(I(:))
[x,y] = ind2sub(size(I),ind)
I trying to create my own nearest neighbor interpolation algorithm in Matlab to enlarge an image of 556×612 to 1668×1836.
This is homework!!!
I have attempted this already but encounter the error where the values inside M gets (not all but most) transformed to 255 (White Space), and I cannot get my head around why. Any help would be appreciated! The picture is a picture of a zebra.
%Take in image and convert to greyscale
I = imread('Zebra.jpg');
Igray = rgb2gray(I);
% Step-3: Resize the image to enlarged 1668x1836 by interpolation
% Step-3(a) : Using nearest neighbour
%First we will need to work out the dimension of the image
[j , k] = size(Igray);
%Now we need to set the size of the image we want
NewX = 1836;
NewY = 1668;
% Work out ratio of old to new
ScaleX = NewX./(j-1);
ScaleY = NewY./(k-1);
%Image Buffer
M = zeros(NewX, NewY);
%Create output image
for count1 = 1:NewX
for count2 = 1:NewY
M(count1,count2) = Igray(1+round(count1./ScaleX),1+round(count2./ScaleY));
end
end
%Show Images
imshow(M);
title('Scaled Image NN');
try imshow(M,[]). You created M without specifying type, which makes it double. double images are [0-1], so imshow by default makes white everything with higher value than 1.
Alternatively, create M to be uint8 as the original image
M = zeros(NewX, NewY,'uint8');
even better code would be:
M = zeros(NewX, NewY,class(Igray));
I'm using the watershed algorithm to try and segment touching nuclei. A typical image may look like:
or this:
I'm trying to apply the watershed algorithm with this code:
show(RGB_img)
%Convert to grayscale image
I = rgb2gray(RGB_img);
%Take structuring element of a disk of size 10, for the morphological transformations
%Attempt to subtract the background from the image: top hat is the
%subtraction of the open image from the original
%Morphological transformation to subtract background noise from the image
%Tophat is the subtraction of an opened image from the original. Remove all
%images smaller than the structuring element of 10
I1 = imtophat(I, strel('disk', 10));
%Increases contrast
I2 = imadjust(I1);
%show(I2,'contrast')
%Assume we have background and foreground and assess thresh as such
level = graythresh(I2);
%Convert to binary image based on graythreshold
BW = im2bw(I2,level);
show(BW,'C');
BW = bwareaopen(BW,8);
show(BW,'C2');
BW = bwdist(BW) <= 1;
show(BW,'joined');
%Complement because we want image to be black and background white
C = ~BW;
%Use distance tranform to find nearest nonzero values from every pixel
D = -bwdist(C);
%Assign Minus infinity values to the values of C inside of the D image
% Modify the image so that the background pixels and the extended maxima
% pixels are forced to be the only local minima in the image (So you could
% hypothetically fill in water on the image
D(C) = -Inf;
%Gets 0 for all watershed lines and integers for each object (basins)
L = watershed(D);
show(L,'L');
%Takes the labels and converts to an RGB (Using hot colormap)
fin = label2rgb(L,'hot','w');
% show(fin,'fin');
im = I;
%Superimpose ridgelines,L has all of them as 0 -> so mark these as 0(black)
im(L==0)=0;
clean_img = L;
show(clean_img)
After C = ~BW; the whole image goes dark. I believe this is because the image pixels are all -inf or some smaller negative number. This is there a way around this and if so what could I change in my code to get this algorithm working? I've experimented a ton and I don't really know what's happening. Any help would be great!
The problem is with your show command. As you said in the comments this uses imshow under the hood. If you try imshow directly you'll see you also get a black image. However, if you call it with appropriate limits:
imshow(clean_img,[min(clean_img(:)), max(clean_img(:))])
you'll see everything you expect to see.
In general I usually prefer imagesc for that reason. imshow makes arbitrary judgements as to what range to represent, and I usually can't be bothered to keep up with it. I think in your case, your end image is uint16 so imshow chooses to represent the range [1, 65025]. Since all your pixel values are below 400, they look black to the naked eye for that range.
I am trying to automatically crop the image below to a bounding box. The background will always be the same colour. I have tried the answers at
Find the edges of image and crop it in MATLAB
and various applications and examples on Mathworks' file exchange but I get stuck at getting a proper boundingbox.
I was thinking to convert the image to black and white, converting it to binary and removing everything that's closer to white than black, but I'm not sure how to go about it.
Here's a nice way
img = im2double(imread('http://i.stack.imgur.com/ZuiEt.jpg')); % read image and convert it to double in range [0..1]
b = sum( (1-img).^2, 3 ); % check how far each pixel from "white"
% display
figure; imshow( b > .5 ); title('non background pixels');
% use regionprops to get the bounding box
st = regionprops( double( b > .5 ), 'BoundingBox' ); % convert to double to avoid bwlabel of logical input
rect = st.BoundingBox; % get the bounding box
% display
figure; imshow( img );
hold on; rectangle('Position', rect );
Following Jak's request, here's the second line explained
after converting img to double type (using im2double), the image is stored in memory as h-by-w-by-3 matrix of type double. Each pixel has 3 values between 0 and 1 (not 255!), representing its RGB values 0 being dark and 1 being bright.
Thus (1-img).^2 checks, for each pixel and each channel (RGB) how far it is from 1 - bright. The darker the pixel - the larger this distance.
Next, we sum the distance per channel to a single value per pixel using sum( . ,3 ) command leaving us with h-by-w 2D matrix of distances of each pixels from white.
Finally, assuming background is bright white we select all pixels that are significantly far from birght b > .5. This threshold is not perfect, but it captures well the boundary of the object.
Following the answer of Shai, I present a way to circumvent regionprops (image processing toolbox) just based on find on the black-white image.
% load
img = im2double(imread('http://i.stack.imgur.com/ZuiEt.jpg'));
% black-white image by threshold on check how far each pixel from "white"
bw = sum((1-img).^2, 3) > .5;
% show bw image
figure; imshow(bw); title('bw image');
% get bounding box (first row, first column, number rows, number columns)
[row, col] = find(bw);
bounding_box = [min(row), min(col), max(row) - min(row) + 1, max(col) - min(col) + 1];
% display with rectangle
rect = bounding_box([2,1,4,3]); % rectangle wants x,y,w,h we have rows, columns, ... need to convert
figure; imshow(img); hold on; rectangle('Position', rect);
to crop an image
first create boundry box where you want to crop.
crp = imcrop(original_image_name,boundry_box);
I have done this in my assignment. This really works!!!!!!
can any one please help me in filling these black holes by values taken from neighboring non-zero pixels.
thanks
One nice way to do this is to is to solve the linear heat equation. What you do is fix the "temperature" (intensity) of the pixels in the good area and let the heat flow into the bad pixels. A passable, but somewhat slow, was to do this is repeatedly average the image then set the good pixels back to their original value with newImage(~badPixels) = myData(~badPixels);.
I do the following steps:
Find the bad pixels where the image is zero, then dilate to be sure we get everything
Apply a big blur to get us started faster
Average the image, then set the good pixels back to their original
Repeat step 3
Display
You could repeat averaging until the image stops changing, and you could use a smaller averaging kernel for higher precision---but this gives good results:
The code is as follows:
numIterations = 30;
avgPrecisionSize = 16; % smaller is better, but takes longer
% Read in the image grayscale:
originalImage = double(rgb2gray(imread('c:\temp\testimage.jpg')));
% get the bad pixels where = 0 and dilate to make sure they get everything:
badPixels = (originalImage == 0);
badPixels = imdilate(badPixels, ones(12));
%# Create a big gaussian and an averaging kernel to use:
G = fspecial('gaussian',[1 1]*100,50);
H = fspecial('average', [1,1]*avgPrecisionSize);
%# User a big filter to get started:
newImage = imfilter(originalImage,G,'same');
newImage(~badPixels) = originalImage(~badPixels);
% Now average to
for count = 1:numIterations
newImage = imfilter(newImage, H, 'same');
newImage(~badPixels) = originalImage(~badPixels);
end
%% Plot the results
figure(123);
clf;
% Display the mask:
subplot(1,2,1);
imagesc(badPixels);
axis image
title('Region Of the Bad Pixels');
% Display the result:
subplot(1,2,2);
imagesc(newImage);
axis image
set(gca,'clim', [0 255])
title('Infilled Image');
colormap gray
But you can get a similar solution using roifill from the image processing toolbox like so:
newImage2 = roifill(originalImage, badPixels);
figure(44);
clf;
imagesc(newImage2);
colormap gray
notice I'm using the same badPixels defined from before.
There is a file on Matlab file exchange, - inpaint_nans that does exactly what you want. The author explains why and in which cases it is better than Delaunay triangulation.
To fill one black area, do the following:
1) Identify a sub-region containing the black area, the smaller the better. The best case is just the boundary points of the black hole.
2) Create a Delaunay triangulation of the non-black points in inside the sub-region by:
tri = DelaunayTri(x,y); %# x, y (column vectors) are coordinates of the non-black points.
3) Determine the black points in which Delaunay triangle by:
[t, bc] = pointLocation(tri, [x_b, y_b]); %# x_b, y_b (column vectors) are coordinates of the black points
tri = tri(t,:);
4) Interpolate:
v_b = sum(v(tri).*bc,2); %# v contains the pixel values at the non-black points, and v_b are the interpolated values at the black points.