Related
I'm working on this function which gets axis handler and data, and is supposed to plot it correctly in the axis. The function is called in for loop. It's supposed to draw the multiple data in one figure. My resulted figure is shown below.
There are only two correctly plotted graphs (those with four colors). Others miss areas plotted before the final area (red area is the last plotted area in each graph). But the script is same for every axis. So where can be the mistake? The whole function is written below.
function [] = powerSpectrumSmooth(axis,signal,fs)
N= length(signal);
samplesPer1Hz = N/fs;
delta = int16(3.5*samplesPer1Hz); %last sample of delta frequncies
theta = int16(7.5*samplesPer1Hz); %last sample of theta frequncies
alpha = int16(13*samplesPer1Hz); %last sample of alpha frequncies
beta = int16(30*samplesPer1Hz); %last sample of beta frequncies
x=fft(double(signal));
powerSpectrum = 20*log10(abs(real(x)));
smoothPS=smooth(powerSpectrum,51);
PSmin=min(powerSpectrum(1:beta));
y1=[(smoothPS(1:delta)); zeros(beta-delta,1)+PSmin];
y2=[zeros(delta-1,1)+PSmin; (smoothPS(delta:theta)); zeros(beta-theta,1)+PSmin];
y3=[zeros(theta-1,1)+PSmin; (smoothPS(theta:alpha)); zeros(beta-alpha,1)+PSmin];
y4=[zeros(alpha-1,1)+PSmin; (smoothPS(alpha:beta))];
a1=area(axis,1:beta,y1);
set(a1,'FaceColor','yellow')
hold on
a2=area(axis,1:beta,y2);
set(a2,'FaceColor','blue')
a3=area(axis,1:beta,y3);
set(a3,'FaceColor','green')
a4=area(axis,1:beta,y4);
set(a4,'FaceColor','red')
ADDED
And here is the function which calls the function above.
function [] = drawPowerSpectrum(axesContainer,dataContainer,fs)
size = length(axesContainer);
for l=1:size
powerSpectrumSmooth(axesContainer{l},dataContainer{l},fs)
set(axesContainer{l},'XTickLabel','')
set(axesContainer{l},'YTickLabel','')
uistack(axesContainer{l}, 'top');
end
ADDED 29th July
Here is a script which reproduces the error, so you can run it in your computer. Before running it again you might need to clear variables.
len = 9;
axesContainer = cell(len,1);
x = [0.1,0.4,0.7,0.1,0.4,0.7,0.1,0.4,0.7];
y = [0.1,0.1,0.1,0.4,0.4,0.4,0.7,0.7,0.7];
figure(1)
for i=1:len
axesContainer{i} = axes('Position',[x(i),y(i),0.2,0.2]);
end
dataContainer = cell(len,1);
N = 1500;
for i=1:len
dataContainer{i} = rand(1,N)*100;
end
for l=1:len
y1=[(dataContainer{l}(1:N/4)) zeros(1,3*N/4)];
y2=[zeros(1,N/4) (dataContainer{l}(N/4+1:(2*N/4))) zeros(1,2*N/4)];
y3=[zeros(1,2*N/4) (dataContainer{l}(2*N/4+1:3*N/4)) zeros(1,N/4)];
y4=[zeros(1,3*N/4) (dataContainer{l}(3*N/4+1:N))];
axes=axesContainer{l};
a1=area(axes,1:N,y1);
set(a1,'FaceColor','yellow')
hold on
a2=area(axes,1:N,y2);
set(a2,'FaceColor','blue')
hold on
a3=area(axes,1:N,y3);
set(a3,'FaceColor','green')
hold on
a4=area(axes,1:N,y4);
set(a4,'FaceColor','red')
set(axes,'XTickLabel','')
set(axes,'YTickLabel','')
end
My result of this script is plotted below:
Again only one picture contains all areas.
It looks like that every call to plot(axes,data) deletes whatever was written in axes.
Important note: Do not use a variable name the same as a function. Do not call something sin ,plot or axes!! I changed it to axs.
To solve the problem I just used the classic subplot instead of creating the axes as you did:
len = 9;
axesContainer = cell(len,1);
x = [0.1,0.4,0.7,0.1,0.4,0.7,0.1,0.4,0.7];
y = [0.1,0.1,0.1,0.4,0.4,0.4,0.7,0.7,0.7];
figure(1)
dataContainer = cell(len,1);
N = 1500;
for i=1:len
dataContainer{i} = rand(1,N)*100;
end
for l=1:len
y1=[(dataContainer{l}(1:N/4)) zeros(1,3*N/4)];
y2=[zeros(1,N/4) (dataContainer{l}(N/4+1:(2*N/4))) zeros(1,2*N/4)];
y3=[zeros(1,2*N/4) (dataContainer{l}(2*N/4+1:3*N/4)) zeros(1,N/4)];
y4=[zeros(1,3*N/4) (dataContainer{l}(3*N/4+1:N))];
axs=subplot(3,3,l);
a1=area(axs,1:N,y1);
set(a1,'FaceColor','yellow')
hold on
a2=area(axs,1:N,y2);
set(a2,'FaceColor','blue')
hold on
a3=area(axs,1:N,y3);
set(a3,'FaceColor','green')
hold on
a4=area(axs,1:N,y4);
set(a4,'FaceColor','red')
set(axs,'XTickLabel','')
set(axs,'YTickLabel','')
axis tight % this is to beautify it.
end
As far as I know, you can still save the axs variable in an axescontainer and then modify the properties you want (like location).
I found out how to do what I needed.
len = 8;
axesContainer = cell(len,1);
x = [0.1,0.4,0.7,0.1,0.4,0.7,0.1,0.4];
y = [0.1,0.1,0.1,0.4,0.4,0.4,0.7,0.7];
figure(1)
for i=1:len
axesContainer{i} = axes('Position',[x(i),y(i),0.2,0.2]);
end
dataContainer = cell(len,1);
N = 1500;
for i=1:len
dataContainer{i} = rand(1,N)*100;
end
for l=1:len
y1=[(dataContainer{l}(1:N/4)) zeros(1,3*N/4)];
y2=[zeros(1,N/4) (dataContainer{l}(N/4+1:(2*N/4))) zeros(1,2*N/4)];
y3=[zeros(1,2*N/4) (dataContainer{l}(2*N/4+1:3*N/4)) zeros(1,N/4)];
y4=[zeros(1,3*N/4) (dataContainer{l}(3*N/4+1:N))];
axes=axesContainer{l};
Y=[y1',y2',y3',y4'];
a=area(axes,Y);
set(axes,'XTickLabel','')
set(axes,'YTickLabel','')
end
The area is supposed to work with matrices like this. The tricky part is, that the signal in every next column is not plotted absolutely, but relatively to the data in previous column. That means, if at time 1 the data in first column has value 1 and data in second column has value 4, the second column data is ploted at value 5. Source: http://www.mathworks.com/help/matlab/ref/area.html
I want to find the local maximum and the minimum values between this 2 local maximums in every column of the image img_gauss. And then put them minimum values at 1 (white). Anyone knows how to do this in a easy way?
Below I have my code. But I'm having some trouble, I try in every iteration (for each column) take the localization (locs) and then put them in the array peaks_column, to have the peaks localization by column, but this error apears:
Subscripted assignment dimension mismatch.
Error in cropping_image_long (line 136)
peaks_column(1:size(Intens_graph,1),x)=pks(:,1);
pks= [];
locs_column=zeros(20,size(img_gauss,2));
locs= [];
pks_column=zeros(20,size(img_gauss,2));
for x = 1:size(img_gauss,2) % 2 = colunas x(colunas)
% make a row wise intensity distribution graphic for each column
Intens_graph(:,x)=img_gauss(size(img_gauss,1):-1:1,x);
[pks,locs] = findpeaks(Intens_graph(:,x));%find the local maximum
peaks_column(1:size(Intens_graph,1),x)=pks(:,1); %associate to each column
locs_column(1:size(Intens_graph,1),x)=locs(:,1);
BW = imregionalmin(Intens_graph);
end
Not exactly sure what you mean with the minimums. Between two maximas there will always be one minimum. So i will find minimas with findpeaks.
% test data
data = rand(100);
% for saving minima and maxima positions
minimas = zeros(size(data));
for i = 1:size(data,2)
column = data(:,i);
[~,minis] = findpeaks(-column);
% save the positions
minimas(sub2ind(size(minimas),minis,repmat(i,length(minis),1))) = 1;
end
%generate result, paint all minimas with 1
result = data;
result(minimas==1) = 1;
I was trying to loop over an image in MATLAB but my code is running to slow. I am fairly knew to MATLAB but I suspect that it's because it's making a copy of my randomly selected image. My code is:
function patches = sampleIMAGES()
load IMAGES; % load images from disk
patchsize = 8; % we'll use 8x8 patches
numpatches = 10000;
patches = zeros(patchsize*patchsize, numpatches);
size_img = size(IMAGES);
num_rows_img = size_img(1);
num_cols_img = size_img(2);
num_images = size_img(3);
for i=1:numpatches,
%get random image
rand_img_number = randi(num_images);
rand_img = IMAGES(:, :, rand_img_number);
%get random patch patchsizexpatchsize
rand_row = randi(num_rows_img - patchsize);
rand_col = randi(num_cols_img - patchsize);
rand_patch = rand_img(rand_row:rand_row+patchsize-1, rand_col:rand_col+patchsize-1);
patches(:, i) = rand_patch(:)';
end
end
How is it possible to loop over this without making a copy if MATLAB does not allow to index twice into a matrix/array?
Approach #1 - im2col based
numpatches = 10000; %//Number of patches
blksz = 8; %// Blocksize
[m,n,r] = size(IMAGES); %// Get sizes
%// Store blocks from IMAGES as columns, so that they could be processed in
%// a vectorized fashion later on
blks_col(blksz*blksz,(m-blksz+1)*(n-blksz+1),r)=0; %// Pre-allocate
for k1=1:r
blks_col(:,:,k1) = im2col(IMAGES(:,:,k1),[blksz blksz],'sliding');
end
blks_col = reshape(blks_col,size(blks_col,1),[]);
%// Get rand row, column and dimension-3 indices to be used for indexing
%// into blks_col in one go
rand_row = randi(size(IMAGES,1)-blksz+1,numpatches,1);
rand_col = randi(size(IMAGES,2)-blksz+1,numpatches,1);
rand_dim3 = randi(size(IMAGES,3),numpatches,1);
%// Select the specific column from blks_col that represents the
%// [blksz x blksz] used to make a single patch in each iteration from
%// original code
num_cols_im2col = (m-blksz+1)*(n-blksz+1);
col_ind = (rand_dim3-1)*num_cols_im2col + (rand_col-1)*(m-blksz+1) + rand_row;
patches = blks_col(:,col_ind);
Example
As an example I assumed IMAGES as the 3D data obtained from reading one of the images provided in the image gallery of Image Processing Toolbox and increased the number of patches to 100000, i.e. -
IMAGES = imread('peppers.png');
numpatches = 100000;
The runtime with original code - 22.376446 seconds.
The runtime with im2col based code - 2.237993 seconds
Then, I doubled the number of patches to 200000, for which the runtime with original code literally doubled and im2col based approach's runtime stayed around that ~2.3 sec mark.
Thus, this im2col based approach would make sense when you are working with lots of patches as opposed to when working with lots of images (that are put in the third dimension of IMAGES).
Approach #2 - Indexing based
Being a purely indexing based approach, this is expected to be memory-efficient and good with performance too.
numpatches = 10000; %//Number of patches
blksz = 8; %// Blocksize
[m,n,r] = size(IMAGES); %// Get sizes
%// Get rand row, column and dimension-3 indices to be used for indexing
rand_row = randi(size(IMAGES,1)-blksz+1,numpatches,1);
rand_col = randi(size(IMAGES,2)-blksz+1,numpatches,1);
rand_dim3 = randi(size(IMAGES,3),numpatches,1);
%// Starting indices for each patch
start_ind = (rand_dim3-1)*m*n + (rand_col-1)*m + rand_row;
%// Row indices for each patch
lin_row = permute(bsxfun(#plus,start_ind,[0:blksz-1])',[1 3 2]); %//'
%// Get linear indices based on row and col indices
lin_rowcol = reshape(bsxfun(#plus,lin_row,[0:blksz-1]*m),blksz*blksz,[]);
%// Finally get the patches
patches = IMAGES(lin_rowcol);
To no longer copy the images, instead of these two lines:
rand_img = IMAGES(:, :, rand_img_number);
rand_patch = rand_img(rand_row:rand_row+patchsize-1, rand_col:rand_col+patchsize-1);
combine both to one line:
rand_patch = IMAGES(rand_row:rand_row+patchsize-1, rand_col:rand_col+patchsize-1, rand_img_number);
Another way to improve the performance: Generating 100 random numbers at onece is faster than generating 1 number 100 times. Generate all numbers you need outside the loop:
rand_img_number = randi(num_images,numpatches,1);
Then use rand_img_number(i) instead of rand_img_number inside the loop. Do the same for the two other random numbers.
Finding maximum values of wave heights and wave lengths
dwcL01 though dwcL10 is arrays of <3001x2 double> with output from a numerical wave model.
Part of my script:
%% Plotting results from SWASH
% Examination of phase velocity on deep water with different number of layers
% Wave height 3 meters, wave peroid 8 sec on a depth of 30 meters
clear all; close all; clc;
T=8;
L0=1.56*T^2;
%% Loading results tabels.
load dwcL01.tbl; load dwcL02.tbl; load dwcL03.tbl; load dwcL04.tbl;
load dwcL05.tbl; load dwcL06.tbl; load dwcL07.tbl; load dwcL08.tbl;
load dwcL09.tbl; load dwcL10.tbl;
M(:,:,1) = dwcL01; M(:,:,2) = dwcL02; M(:,:,3) = dwcL03; M(:,:,4) = dwcL04;
M(:,:,5) = dwcL05; M(:,:,6) = dwcL06; M(:,:,7) = dwcL07; M(:,:,8) = dwcL08;
M(:,:,9) = dwcL09; M(:,:,10) = dwcL10;
%% Finding position of wave crest using diff and sign.
for ii=1:10
Tp(:,1,ii) = diff(sign(diff([M(1,2,ii);M(:,2,ii)]))) < 0;
Wc(:,:,ii) = M(Tp,1,ii);
L(:,ii) = diff(Wc(:,1,ii))
end
The loop
for ii=1:10
Tp(:,1,ii) = diff(sign(diff([M(1,2,ii);M(:,2,ii)]))) < 0;
Wc(:,:,ii) = M(Tp,1,ii);
L(:,ii) = diff(Wc(:,1,ii))
end
Works fine for ii = 1 Getting the following error for ii = 2
Index exceeds matrix dimensions.
Error in mkPlot (line 19)
Wc(:,:,i) = M(Tp,:,i);
Don't have the same number of wave crests for the different set ups, naturally M(Tp,1,ii) will have different dimensions. How do I work around this issue? Can it be done in a for loop? please feel free to email me or other wise ask for further information.
The problem is that Tp is a three dimensional array. I need to call the Tp(:,:,ii) corresponding to the present scenario. Together with this and defining Wc as a cell I solve my issue.
for ii = 1:10
Tp(:,1,ii) = diff(sign(diff([M(1,2,ii);M(:,2,ii)]))) < 0;
Wc{:,:,ii} = M(Tp(:,:,ii),1,ii);
L{:,ii} = diff(cell2mat(Wc(ii)));
end
I have the following code, pasted below. I would like to change it to only average the 10 most recently filtered images and not the entire group of filtered images. The line I think I need to change is: Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;, but how do I do it?
j=1;
K = 1:3600;
window = zeros(1,10);
Yout = zeros(10,column,row);
figure;
y = 0; %# Preallocate memory for output
%Load one image
for i = 1:length(K)
disp(i)
str = int2str(i);
str1 = strcat(str,'.mat');
load(str1);
D{i}(:,:) = A(:,:);
%Go through the columns and rows
for p = 1:column
for q = 1:row
if(mean2(D{i}(p,q))==0)
x = 0;
else
if(i == 1)
meanvalue = mean2(D{i}(p,q));
end
%Calculate the temporal mean value based on previous ones.
meanvalue = (meanvalue+D{i}(p,q))/2;
x = double(D{i}(p,q)/meanvalue);
end
%Filtering for 10 bands, based on the previous state
for k = 1:10
[y, ZState{k}] = filter(bCoeff{k},aCoeff{k},x,ZState{k});
Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;
end
end
end
% for k = 2:10
% subplot(5,2,k)
% subimage(Yout(k)*5000, [0 100]);
% colormap jet
% end
% pause(0.01);
end
disp('Done Loading...')
The best way to do this (in my opinion) would be to use a circular-buffer to store your images. In a circular-, or ring-buffer, the oldest data element in the array is overwritten by the newest element pushed in to the array. The basics of making such a structure are described in the short Mathworks video Implementing a simple circular buffer.
For each iteration of you main loop that deals with a single image, just load a new image into the circular-buffer and then use MATLAB's built in mean function to take the average efficiently.
If you need to apply a window function to the data, then make a temporary copy of the frames multiplied by the window function and take the average of the copy at each iteration of the loop.
The line
Yout(k,p,q) = (Yout(k,p,q) + (y.^2))/2;
calculates a kind of Moving Average for each of the 10 bands over all your images.
This line calculates a moving average of meanvalue over your images:
meanvalue=(meanvalue+D{i}(p,q))/2;
For both you will want to add a buffer structure that keeps only the last 10 images.
To simplify it, you can also just keep all in memory. Here is an example for Yout:
Change this line: (Add one dimension)
Yout = zeros(3600,10,column,row);
And change this:
for q = 1:row
[...]
%filtering for 10 bands, based on the previous state
for k = 1:10
[y, ZState{k}] = filter(bCoeff{k},aCoeff{k},x,ZState{k});
Yout(i,k,p,q) = y.^2;
end
YoutAvg = zeros(10,column,row);
start = max(0, i-10+1);
for avgImg = start:i
YoutAvg(k,p,q) = (YoutAvg(k,p,q) + Yout(avgImg,k,p,q))/2;
end
end
Then to display use
subimage(Yout(k)*5000, [0 100]);
You would do sth. similar for meanvalue