I write following code in MATLAB to apply int2int lifting wavelet transform to the lena.jpg image.
I=imread('lena.jpg');
im=double(I);
lshaarInt = liftwave('haar','int2int');
els = {'p',[-0.125 0.125],0};
lsnewInt = addlift(lshaarInt,els);
[LL1,HL1,LH1,HH1] = lwt2(im,lsnewInt)
LL1=uint8(LL1);LH1=uint8(LH1);HL1=uint8(HL1);HH1=uint8(HH1);
figure()
subplot(2,2,1);imshow(LL1);
subplot(2,2,2);imshow(LH1);
subplot(2,2,3);imshow(HL1);
subplot(2,2,4);imshow(HH1);
Now I apply inverse LWT using following code without applying any other operations on sub-bands:
LL1=double(LL1);LH1=double(LH1);HL1=double(HL1);HH1=double(HH1);
wmd1= (ilwt2(LL1,HL1,LH1,HH1,lsnewInt));
wmd=uint8(wmd1);
I compare two images 'I' and 'wmd' using Normalized Co-relation Coefficient (NCC)
In output, in place of 1 as a answer I got 0.9994.
When I compare the intensity values of two images then the values are not same.
LWT is the lossless data technique but i found that the data loss.
Please suggest me where I am wrong.
I found the solution
The problem is with following code:
LL1=uint8(LL1);LH1=uint8(LH1);HL1=uint8(HL1);HH1=uint8(HH1);
No need to convert all the above values from double to int.
I got NCC value as 1 when I gave comment to above statement.
Thank you for response.
Related
I am working on pedestrian step detection (acceleration). I want to calculate statistical features from my filtered signal. I have already calculated some and now I want to calculate gradient.
My data is of 1x37205 double. I calculated features using for loop with moving window size=2samples and 50% overlap of previous window. Below I am attaching the code I tried to calculate the gradient.
I am not sure if it is the right way to calculate or not? In addition, I am also unable to understand that what is the purpose to use gradient, how it can be useful for step detection and how to work with gradient? Could some one guide me or provide any code help in matlab?
%%Here M is mean and V is variance i already calculated from filtered data
G = zeros(length(window:length(M)), 2);
for i = window:length(M)
temp = gradient(M(i+1-window:i),V(i+1-window:i));
G(i, 1) = temp(2, 1); % "c1"
G(i, 2) = temp(2, 1); % "c2"
end
One of the best features of Matlab is its documentation. If you are unfamiliar on how to get specific function documentation, enter the following in the command line:
doc functionName
Alternatively, for 'brief' documentation that displays in the command line, you can enter:
help functionName
Also see the documentation link here.
Your question is worded poorly, so I will summarize what I understand and answer accordingly:
You have a step detection data (1*37205) double, let us call it stepSignal
stepSignal is position data
You want your window to be 2 steps with 50% overlap. This is the default behavior for the gradient function.
You do not need a "for" loop to achieve your goal. According to the documentation, "gradient" can take one input.
See the code below, and if need be add clarifications to the original question.
%% Assume that stepSignal is already imported into the workspace
velocity = gradient(stepSignal);
One last note, when you give "gradient" two inputs, it automatically assumes the second input is a uniform spacing value.
I have a very huge 3D matrix, the data was written into disk for future use. Writing the matrix into a bin is easy, reading it back however have some issue.
Write to bin:
z=repmat(complex(rand(5),rand(5)),[1 1 5])
z_imag = imag(z);
z_real = real(z);
adjacent = [z_real z_imag];
fileID = fopen('complex.bin','w');
fwrite(fileID,adjacent,'double')
And now, I try to read it back using memmapfile:
m = memmapfile('complex.bin', 'Offset', 0, 'Format', {'double' [5,5,5] 'x'});
complexValues = complex(m.Data(:).x(1,:), m.Data(:).x(2,:)); %this line doesn't work though, just for explanation's sake
It gave me an error saying that
Error using memmapfile/subsref (line 764) A subscripting operation on
the Data field attempted to create a comma-separated list. The
memmapfile class does not support the use of comma-separated lists
when subscripting.
I was referring to the solution here, the suggested solution used the reshape to shape the matrix beforehand (as contrast to my method above). I try to avoid using reshape in my code as I'm dealing with very huge data and that might computationally expensive and takes a long time. Is there an alternative/better way to do this?
Thanks in advance!
How does Matlab calculate immse? I want to find the mse between two images. According to how to measure he similarity between two 2D complex fields in matlab?, immse is the same as MSE=mean((abs(Y(:))-abs(Y1(:))).^2) for reference image Y1 and comparison image Y. Likewise, I could calculate MSE as the summed square errors divided by the number of row*cols. When I run on one of the demo images, these different approaches don't give the same answer as immse.
Here are two MSE approaches in the sample code below. The image is from the Matlab immse demo and immse gives around an MSE=340. The other two codes give around an MSE=2.5.
Note: The code is example code, I did not use the same function name twice in the same script. And I understand if you want to complain about using size(image) but that is a detail. I am more worried about the basic flaw in my understanding that is giving me orders of magnitude differences. Thank you so much.
n01 = imread('pout.tif');
n02 = imnoise(n01,'salt & pepper', 0.02);
mse = mymse(n02,n01);
mlmse = immse(n02,n01);
function this = mymse(icomp, ibase)
[X Y nchan] = size(ibase);
diff = (icomp - ibase);
this = sum(sum(diff.*diff))/(X*Y*nchan);
end
function this = mymse(icomp, ibase)
this = mean ((abs(ibase(:)) - abs(icomp(:))).^2);
end
You can check the underlying code to many matlab functions by simply doing
open <func>
in the Matlab command window.
In this case you can see that immse is doing the norm of the differences, scaled by number of points.
function this = mymse(icomp, ibase)
this = sum((ibase(:) - icomp(:)).^2) / numel(ibase);
end
What is the difference between hist and imhist functions in Matlab? I have a matrix of color levels values loaded from image with imread and need to count entropy value of the image using histogram.
When using imhist the resulting matrix contains zeros in all places except the last one (lower-right) which contains some high value number (few thousands or so).
Because that output seems to be wrong, I have tried to use hist instead of imhist and the resulting values are much better, the matrix is fulfilled with correct-looking values instead of zeros.
However, according to the docs, imhist should be better in this case and hist should give weird results..
Unfortunately I am not good at Matlab, so I can not provide you with better problem description. I can add some other information in the future, though.
So I will try to better explain my problem..I have an image, for which I should count entropy and few other values (how much bytes it will take to save that image,..). I wrote this function and it works pretty well
function [entropy, bytes_image, bytes_coding] = entropy_single_pixels(im)
im = double(im);
histg = hist(im);
histg(histg==0) = [];
nzhist = histg ./ numel(im);
entropy = -sum(nzhist.*log2(nzhist));
bytes_image = (entropy*(numel(im))/8);
bytes_coding = 2*numel(unique(im));
fprintf('ENTROPY_VALUE:%s\n',num2str(entropy));
fprintf('BYTES_IMAGE:%s\n',num2str(bytes_image));
fprintf('BYTES_CODING:%s\n',num2str(bytes_coding));
end
Then I have to count the same, but I have to make "pairs" from pixels which are below each other. So I have only half the rows and the same count of columns. I need to express every unique pixel pair as a different number, so I multiplied the first one by 1000 and added the second one to it... Subsequently I need to actually apply the same function as in the first example, but that is the time, when I am getting weird numbers from the imhist function. When using hist, it seems to be OK, but I really don't think that behavior is correct, so that must be my error somewhere. I actually understand pretty good, to what I want to do, or at least I hope so, but unfortunately Matlab makes all that kind of hard for me :)
hist- compute histogram(count number of occurance of each pixel) in color image.........
imhist- compute histogram in two dimensional image.
Use im2double instead of double if you want to use imhist. The imhist function expects double or single-precision data to be in the [0,1] data range, which is why you see everything in the last bin of the histogram.
I need to show 1st 10 eigenfaces using PCA for a image feature vector matrix.
I am using following matlab code to create 1st eigenface but I am getting very dark and not so correct eigenfaces.
eFea is a matrix of 240x4096 where each row represents an image of 64x64
newData = eFea';
data = newData;
[M,N] = size(data);
mn = mean(data,2);
data = double(data) - repmat(mn,1,N);
% construct the matrix Y
Y = data' / sqrt(N-1);
% SVD
[u,S,PC] = svd(Y,0);
imshow(reshape(PC(1,:),64,64))
any hints regarding the error in code will be helpful.
IMSHOW does not automatically scale the image. Thus, if you only have values from, say, 0 to 0.3 in the eigenface, everything will be really dark. Try imshow(reshape(PC(1,:),64,64),[]) instead.
This is a really old topic but I want to answer something anyway.
Honestly, I think the error is somewhere else, although what Jonas said might give good-looking results.
You need to add the mean of the data again in the end. I just had the same problem with the dark principal components, that's why I found this question. But then I realized, that when you do PCA, you substract the mean first. That means that in the end, you need to add it again.