I followed the MathWorks tutorial on wavedec2 and am unable to properly display any of the coefficient or approximation subplots.
Can anyone suggest how I fix this so it displays the approximation and detail subplots correctly?
This is what I have so far:
% Load image ‘Sample.tif’ and convert it into a grayscale image, denoted with ‘img1’
I = imread('Sample.tif');
img1 = rgb2gray(I);
% Decompose img1 with wavelet transform using function wavedec2
% Perform decomposition at level 2
% of X using haar.
[C,S] = wavedec2(I,2,'haar');
[H1,V1,D1] = detcoef2('all',C,S,1);
A1 = appcoef2(C,S,'haar',1);
V1img = wcodemat(V1,255,'mat',1);
H1img = wcodemat(H1,255,'mat',1);
D1img = wcodemat(D1,255,'mat',1);
A1img = wcodemat(A1,255,'mat',1);
[H2,V2,D2] = detcoef2('all',C,S,2);
A2 = appcoef2(C,S,'haar',2);
V2img = wcodemat(V2,255,'mat',1);
H2img = wcodemat(H2,255,'mat',1);
D2img = wcodemat(D2,255,'mat',1);
A2img = wcodemat(A2,255,'mat',1);
subplot(2,2,1);
imagesc(A1img);
colormap red(255);
title('Approximation Coef. of Level 1');
subplot(2,2,2);
imagesc(H1img);
title('Horizontal detail Coef. of Level 1');
subplot(2,2,3);
imagesc(V1img);
title('Vertical detail Coef. of Level 1');
subplot(2,2,4);
imagesc(D1img);
title('Diagonal detail Coef. of Level 1');
The output is here, and all subplots are completely white:
Sample.tif is here:
The datatype of your images is double but the range of value of your images is [0 255] so you have to cast your picture into the good datatype. [0 255] correspond to the range of the uint8 datatype, so you can simply write:
imagesc(uint8(A1img));
or
A1img = uint8(A1img);
Related
I am trying to do a translation, euclidian, similarity, affine and projective transformation on an image pixel by pixel. the input for my program is the image name and the transformation matrix.
This is my code
function imagetrans(name, m)
Image = imread(name);
[rows, cols] = size(Image);
newImage(1:rows,1:cols) = 1;
for row = 1 : rows
for col = 1 : cols
if(Image(row,col) == 0)
point = [row;col;1];
answer = m * point;
answer = int8(answer);
newx = answer(1,1);
newy = answer(2,1);
newImage(newx,newy) = 0;
end
end
end
imshow(newImage);
end
This is the image
Right now I am testing just a translation matrix.
matrix =
1 0 7
0 1 2
0 0 1
when I pass the image and matrix through the function my result is just a little black line
What am I doing wrong?
Using the matlab debugger, I noticed that that you are casting to int8, which is too small too represent all indices. So, you should use int32/int64 or uint32/uint64 instead, i.e.
answer = uint8(answer);
should be
answer = uint32(answer);
Please try to use the Matlab debugger before asking the question: why does it not work?
I'm trying to color only a segment of an image in Matlab. For example, I load an RGB image, then I obtain a mask with Otsu's method (graythresh). I want to keep the color only in the pixels that have value of 1 after applying im2bw with graythresh as the threshold. For example:
image = imread('peppers.png');
thr = graythresh(image);
bw = im2bw(image, thr);
With this code I obtain the following binary image:
My goal is to keep the color in the white pixels.
Thanks!
I have another suggestion on how to replace the pixels we don't care about. This works by creating linear indices for each of the slices where black pixels exist in the bw image. The summation with the result of find is done because bw is the size of just one "slice" of image and this is how we get the indices for the other 2 slices.
Starting MATLAB 2016b:
image(find(~bw)+[0 numel(bw)*[1 2]]) = NaN;
In older versions:
image(bsxfun(#plus,find(~bw),[0 numel(bw)*[1 2]])) = NaN;
Then imshow(image) gives:
Note that NaN gets converted to 0 for integer classes.
Following the clarification that the other pixels should be kept in their gray version, see the below code:
% Load image:
img = imread('peppers.png');
% Create a grayscale version:
grayimg = rgb2gray(img);
% Segment image:
if ~verLessThan('matlab','9.0') && exist('imbinarize.m','file') == 2
% R2016a onward:
bw = imbinarize(grayimg);
% Alternatively, work on just one of the color channels, e.g. red:
% bw = imbinarize(img(:,:,1));
else
% Before R2016a:
thr = graythresh(grayimg);
bw = im2bw(grayimg, thr);
end
output_img = repmat(grayimg,[1 1 3]);
colorpix = bsxfun(#plus,find(bw),[0 numel(bw)*[1 2]]);
output_img(colorpix) = img(colorpix);
figure; imshow(output_img);
The result when binarizing using only the red channel:
Your question misses "and replace the rest with black". here are two ways:
A compact solution: use bsxfun:
newImage = bsxfun(#times, Image, cast(bw, 'like', Image));
Although I am glad with the previous one, you can also take a look at this step-by-step approach:
% separate the RGB layers:
R = image(:,:,1);
G = image(:,:,2);
B = image(:,:,3);
% change the values to zero or your desired color wherever bw is false:
R(~bw) = 0;
G(~bw) = 0;
B(~bw) = 0;
% concatenate the results:
newImage = cat(3, R, G, B);
Which can give you different replacements for the black region:
UPDATE:
According to the comments, the false area of bw should be replaced with grayscale image of the same input. This is how to achieve it:
image = imread('peppers.png');
thr = graythresh(image);
bw = im2bw(image, thr);
gr = rgb2gray(image); % generate grayscale image from RGB
newImage(repmat(~bw, 1, 1, 3)) = repmat(gr(~bw), 1, 1, 3); % substitude values
% figure; imshow(newImage)
With this result:
What I would like to do is plot an image of a graph (from say a pdf file or a scanned image). Next, I would like to overlay an axis on the graph in the image, and then plot data on that axis (over the image).
Using imtool, I know the coordinates of the graph in the image (x range = ~52-355 pixels, and y range = 23(top) - 262(bottom) pixels in this case).
This is what I have tried:
I = imread('C:\MATLAB\R2014a\help\images\ref\ftrans2_fig.png');
I = squeeze(uint8(mean(I,3)));
figure, imshow(I)
[rows, cols] = size(I);
x_data = (-1 : .01 : +1)';
y_data = 1 - x_data.^2;
h1 = axes('Position',([52, 23, 355-52, 262-23] ./ [cols, rows, cols, rows] ));
set(h1, 'Color', 'none')
hold on
plot(x_data, y_data, '-rx')
Question: Knowing the pixel coordinates of the graph in the image, how do I determine the proper position of the axis in the figure, (my code fails to account for the actual size of the figure box, the gray border around the image). I have to do this for several images and sets of data, so I would like an automated method, assuming I find the coordinates of the graphs in the image ahead of time.
Thanks for your reply! (1st time posting, please be kind)
You may be able to solve your problem by forcing the image onto the same axis as the plot. Try this:
I = imread('C:\MATLAB\R2014a\help\images\ref\ftrans2_fig.png');
I = squeeze(uint8(mean(I,3)));
[rows, cols] = size(I);
x_data = (-1 : .01 : +1)';
y_data = 1 - x_data.^2;
h1 = axes('Position',([52, 23, 355-52, 262-23] ./ [cols, rows, cols, rows] ));
set(h1, 'Color', 'none')
hold on
image(I, 'Parent', h1);
plot(h1, x_data, y_data, '-rx')
That should at ensure that the plot axis and the image axis have the same origin, as they will be one and the same. You may need to adjust your sizing code. Let me know if that doesn't do it for you.
Good Luck!
I think I have it figured out.
It would have been easier if I could use:
figure, h1=imshow(I)
get(h1,'Position')
but that results in "The name 'Position' is not an accessible property for an instance of class 'image'."
Instead, this appears to work:
I = imread('C:\MATLAB\R2014a\help\images\ref\ftrans2_fig.png');
I = squeeze(uint8(mean(I,3)));
in_mag = 300;
figure, imshow(I, 'Border', 'tight', 'InitialMagnification', in_mag)
[rows, cols] = size(I);
x_data = (-1 : .01 : +1)';
y_data = 1 - x_data.^2;
% Coord of graph in image pixels
x_0 = 50; x_max = 354; y_0 = 262; y_max = 23;
h1 = axes('Position',([x_0, rows-y_0, x_max-x_0, y_0-y_max] ...
./ [cols, rows, cols, rows] ));
set(h1,'Color','none')
hold on
plot(x_data, y_data, '-rx')
ylim([0,1.4])
set(gca,'YColor', [0 0 1], 'XColor', [0 0 1])
However, if anybody has a better idea, I would be very happy to explore it!
Thanks
I am trying to read an image using imread then, save the RGB values of all of the pixels in an array. And finally, be able to recreate this image using only the RGB values.
This is the for loop that saves all of the RGB values of each pixel.
A=imread('image.jpg');
N=2500; %the image dimensions are 50x50
i=1;
rgbValues = zeros(N, 3);
for x = 1:50
for y = 1:50
rgbValues(i,:) = A(x,y,:);
i=i+1;
end
end
Now, how am I able to recreate this image if I have all of the rgb values saved.
A direct way to do this is:
ny = 50;
nx = 50;
recreatedImage = zeros(ny,nx,3, 'uint8');
for ind = 1:3
recreatedImage(:,:, ind) = ( reshape(rgbValues(:, ind), nx, ny))';
end
As Natan indicated, reshape will work also but you have to do this:
recreatedImage=reshape(rgbValues,[ny,nx,3]);
Which is, unfortunately, transposed so you will need to work it to get it rotated back up.
You might consider swapping your x and y values in your for loop so you iterate over all y and then all x values---because this is how MATLAB stores the data and you can change the above code to:
for ind = 1:3
recreatedImage(:,:, ind) = ( reshape(rgbValues(:, ind), ny, nx));
end
(edit) and then the direct reshape works as well:
rgbValuesBacktoShape=reshape(rgbValues,[50,50,3]);
I am using Octave 3.6.4 to process an image and store it afterwards. The image I read is grayscale, and after calculations the matrix should be of the same type. However, if I open the stored image, there are no gray pixels. There are only black and white ones and the gray ones got lost. They are essentially all white.
Here is the processing code:
function aufgabe13()
[img, map, alpha] = imread("Buche.png");
[imax, jmax] = size(img);
a = 0.7;
M = 255 * ones(imax,jmax + round(imax * a));
for i = 1:imax
begin = round((imax-i)*a);
M(i,begin +1 : begin + jmax) = img(i,:);
end
imwrite(M, 'BucheScherung.png', 'png');
end
So what am I doing wrong?
The reason why is because M is a double matrix so the values are expected to be between [0,1] when representing an image. Because your values in your image are between [0,255] when read in (type uint8), a lot of the values are white because they're beyond the value of 1. What you should do is convert the image so that it is double precision and normalized between [0,1], then proceed as normal. This can be done with the im2double function.
In other words, do this:
function aufgabe13()
[img, map, alpha] = imread("Buche.png");
img = im2double(img); % Edit
[imax, jmax] = size(img);
a = 0.7;
M = ones(imax,jmax + round(imax * a)); % Edit
for i = 1:imax
begin = round((imax-i)*a);
M(i,begin +1 : begin + jmax) = img(i,:);
end
imwrite(M, 'BucheScherung.png', 'png');
end