I have data in an array in matlab. I want to find peaks, but faced the following problem shown in the picture below.
To generate peaks and plot them I used the following code:
gyryMF = medfilt1(gyry, 3);
[pks, gyryPeaks] = findpeaks(gyryMF);
%%
plot(gyryMF);
text(gyryPeaks+.02,pks,num2str((1:numel(pks))'));
As you see from picture, some peaks not found, because there is a flat region. I wonder if somehow I can find and include them as well?
How about writing your own peaks function with your own criteria?
peak_no = []
ind_peak_no = []
if Data(x) < Data(x+1) && Data(x+1) > Data(x+2) && Data(x+1)> Peak_min
peak_no = [peak_no;Data(x+1)];
ind_peak_no = [ind_peak_no; x+1];
end
Related
Hoping you may be able to assist me with this error. I am running some code to fit curves to ages using a cross validation regime. I iterate the curve fitting 1000 times to assess the best fit.
I define my models as:
linear_ft = fittype({'x', '1'});
monotonic_ft= fittype({'-1/x', '1'});
quadratic_ft = fittype('poly2');
I then run the following to iterate through different selections of data splitting, recording the residuals following the curve fit...
Data = randn(4,300,10,10);
Ages = randn(300,1);
for thisDim1 = 1:4
for thisDim2 = 1:10
for thisDim3 = 1:10
for nIts = 1:1000
RandomOrder = randperm(300,300);
Fit_Subs = RandomOrder(1:length(Ages)/2); % Take random subs to fit to
Test_Subs = RandomOrder(length(Ages)/2+1:300); % Take random subs to test fit to
Fit_Data = squeeze(Data(thisDim1,Fit_Subs,thisDim2,thisDim3)); % Take data to fit to
Test_Data = squeeze(Data(thisDim1,Test_Subs,thisDim2,thisDim3)); % Take data to test fit
Fit_Ages = Ages;
Fit_Ages(Fit_Subs) = []; %Take ages of Fit Subs only
Test_Ages = Ages;
Test_Ages(Test_Subs) = []; % Take ages of Test Subs only
Nsubs = (length(Ages)/2);
% Model Data using Curves
fFit_Lin = fit(Fit_Ages,Fit_Data',linear_ft);
fFit_Mon = fit(Fit_Ages,Fit_Data',monotonic_ft);
fFit_Quad = fit(Fit_Ages,Fit_Data',quadratic_ft);
% Fit Modelled Data to Test Data
tFit_Lin = fFit_Lin(Test_Ages);
tFit_Mon = fFit_Mon(Test_Ages);
tFit_Quad = fFit_Quad(Test_Ages);
% Calculate Median Residual
Lin_Med_Resid(nIts) = median(tFit_Lin - Test_Data');
Mon_Med_Resid(nIts) = median(tFit_Mon - Test_Data');
Quad_Med_Resid(nIts) = median(tFit_Quad - Test_Data');
end
end
end
end
If you run this with the fourth loop (nIts) as a for-loop it will run. If you run it as a parfor-loop it won't stating the error:
Error using fit>iFit (line 264)
The name 'lower' is not an accessible property for an instance of class
'llsqoptions'.
Error in fit (line 108) [fitobj, goodness, output, convmsg] = iFit(
xdatain, ydatain, fittypeobj, ...
Does anyone have any idea how to fix this? I would be most grateful for any advice!!
Thanks,
Ben
Try restarting MATLAB or typing clear all to see if it clears things up for you.
Your code works for me, but the parallel toolbox can be a bit finicky in my experience.
I've created some Matlab code which anyone helping can run and see the problem.
When I run the following code, for each data point on my plot I seem to get all 15 labels instead of only 1 specific label.
So how do I get the Matlab data point labels correct for the following code?
Based on the the suggestions, I did the following:
I replaced these two lines of code:
labels = num2str(test_vector_label,'F%d');
labels_cell = cellstr(labels);
With this line of code as suggested:
labels_cell = strread(num2str(test_vector_label),'%s');
Now there are two follow-up questions:
1) A warning appears stating that I should use textscan instead of strread:
labels_cell = textscan(num2str(test_vector_label),'%s');
Then when I use textscan as in the above line of code above, I get an error?
"Error using text
Cell array of strings may only contain string and numeric
matrices"
"Error in Code_Test (line 46)
text(x_val,y_val,labels_cell,'horizontal','left',
'vertical','bottom')"
2) How do I put a letter in front of the number labels? For example, in the original code I had put letter F followed by a number?
%--------------Randomly select training and testing data.-----------
num_data = 35;
data_idx = 1:35;
train_data_idx_tmp = randsample(num_data,20)
train_dataRand_idx = sort(train_data_idx_tmp)
% Lia = ismember(A,B) returns an array the same size as A, containing 1 (true)
% where the elements of A are found in B, and 0 (false) elsewhere.
test_data_idx_tmp = ismember(data_idx,train_dataRand_idx)
test_dataRand_idx = data_idx(~test_data_idx_tmp)'
% Check to see if training and test data index are exclusive.
check_train_test_idx = ismember(train_dataRand_idx,test_dataRand_idx)
%--------------------------------------------------------------------------
% Testing stage.
test_vector = test_dataRand_idx; %Select randomly obtained testing data.
% Training stage.
train_vector = train_dataRand_idx; %Select randomly obtained training
x_val = [1:15];
y_val = 2*[1:15];
plot(x_val,y_val,'or','MarkerFaceColor','r')
grid on
%Put specific data point labels on plots.
test_vector_label = test_vector';
labels = num2str(test_vector_label,'F%d');
labels_cell = cellstr(labels);
text(x_val,y_val,labels_cell,'horizontal','left', 'vertical','bottom')
Your variable labels_cell is a 1x1 string cell not an array of strings. Replace
labels = num2str(test_vector_label,'F%d');
labels_cell = cellstr(labels);
with
labels_cell = strread(num2str(test_vector_label),'%s');
I know basic commands in order to identify objects in a picture like:
level = graythresh(bw);
bw = im2bw(bw,level);
cc = bwconncomp(bw, 4);
cc.NumObjects;
graindata = regionprops(cc, 'basic');
perimeter = regionprops(cc, 'perimeter');
Those codes above is the code I am using.
In the picture attached, I can get the number to be 4. So the code identify that there is in total 4 objects.
However, this picture actually contains two objects. If we replicate this picture and move the replicate to the up, down, left and right, we can see that there is only two objects. But they are "separated" by the boundary.
It is not doable to change the way of making the image so the only way I can think of is to use some function or codes in matlab.
I will really appreciate it if someone can provide some matlab function to solve this problem.
All you need to do is loop over the border rows and columns and merge any regions that line up on opposite sides. The following code will produce an image with the regions labelled by number in the way you want.
cc=bwconncomp(bw);
[rows,cols] = size(reg);
% matrix of region labels
regions = uint8(zeros(rows,cols));
% label each pixel with an integer for its region number
for i = 1:length(cc.PixelIdxList)
region(cc.PixelIdxList{i}) = i;
end
% loop over rows, merge the regions if pixels line up
for i = 1:rows
left = region(i,1);
right = region(i,end);
if (left>0) && (right>0) && (left~=right)
region(region==right) = left;
end
end
% loop over columns, merge the regions if pixels line up
for j = 1:cols
top = region(1,j);
bottom = region(end,j);
if (top>0) && (bottom>0) && (top~=bottom)
region(region==bottom) = top;
end
end
I am pretty new to Matlab and encountered a problem when working with images.
I want to get a pixel that is in a specific colour (blue) in the following image:
image
My current code looks something like this:
function p = mark(image)
%// display image I in figure
imshow(image);
%// first detect all blue values higher 60
high_blue = find(image(:,:,3)>60);
%cross elements is needed as an array later on, have to initialize it with 0
cross_elements = 0;
%// in this iteration the marked values are reduced to the ones
%where the statement R+G < B+70 applies
for i = 1:length(high_blue)
%// my image has the size 1024*768, so to access the red/green/blue values
%// i have to call the i-th, i+1024*768-th or i+1024*768*2-th position of the "array"
if ((image(high_blue(i))+image(high_blue(i)+768*1024))<...
image(high_blue(i)+2*768*1024)+70)
%add it to the array
cross_elements(end+1) = high_blue(i);
end
end
%// delete the zero element, it was only needed as a filler
cross_elements = cross_elements(cross_elements~=0);
high_vector = zeros(length(cross_elements),2);
for i = 1:length(cross_elements)
high_vector(i,1) = ceil(cross_elements(i)/768);
high_vector(i,2) = mod(cross_elements(i), 768);
end
black = zeros(768 ,1024);
for i = 1:length(high_vector)
black(high_vector(i,2), high_vector(i,1)) = 1;
end
cc = bwconncomp(black);
a = regionprops(cc, 'Centroid');
p = cat(1, a.Centroid);
%// considering the detection of the crosses:
%// RGB with B>100, R+G < 100 for B<150
%// consider detection in HSV?
%// close the figure
%// find(I(:,:,3)>150)
close;
end
but it is not optimized for Matlab, obviously.
So i was wondering if there was a way to search for pixels with specific values,
where the blue value is larger than 60 (not hard with the find command,
but at the same time the values in the red and green area not too high.
Is there a command I am missing?
Since English isn't my native language, it might even help if you gave me some suitable keywords for googling ;)
Thanks in advance
Based on your question at the end of the code, you could get what you want in a single line:
NewImage = OldImage(:,:,1) < SomeValue & OldImage(:,:,2) < SomeValue & OldImage(:,:,3) > 60;
imshow(NewImage);
for example, where as you see you provide a restriction for each channel using logical operators, that you can customize of course (eg. using | as logical OR). Is this what you are looking for? According to your code you seem to be looking for specific regions in the image like crosses or coins is that the case? Please provide more details if the code I gave you is completely off the track :)
Simple example:
A = imread('peppers.png');
B = A(:,:,3)>60 & A(:,:,2)<150 & A(:,:,1) < 100;
figure;
subplot(1,2,1);
imshow(A);
subplot(1,2,2)
imshow(B);
Giving this:
I am on a project thumb recognition system on matlab. I implemented Kmean Algorithm and I got results as well. Actually now I want to plot the results like here they done. I am trying but couldn't be able to do so. I am using the following code.
load training.mat; % loaded just to get trainingData variable
labelData = zeros(200,1);
labelData(1:100,:) = 0;
labelData(101:200,:) = 1;
k=2;
[trainCtr, traina] = kmeans(trainingData,k);
trainingResult1=[];
for i=1:k
trainingResult1 = [trainingResult1 sum(trainCtr(1:100)==i)];
end
trainingResult2=[];
for i=1:k
trainingResult2 = [trainingResult2 sum(trainCtr(101:200)==i)];
end
load testing.mat; % loaded just to get testingData variable
c1 = zeros(k,1054);
c1 = traina;
cluster = zeros(200,1);
for j=1:200
testTemp = repmat(testingData(j,1:1054),k,1);
difference = sum((c1 - testTemp).^2, 2);
[value index] = min(difference);
cluster(j,1) = index;
end
testingResult1 = [];
for i=1:k
testingResult1 = [testingResult1 sum(cluster(1:100)==i)];
end
testingResult2 = [];
for i=1:k
testingResult2 = [testingResult2 sum(cluster(101:200)==i)];
end
in above code trainingData is matrix of 200 X 1054 in which 200 are images of thumbs and 1054 are columns. actually each image is of 25 X 42. I reshaped each image in to row matrix (1 X 1050) and 4 other (some features) columns so total of 1054 columns are in each image. Similarly testingData I made it in the similar manner as I made testingData It is also the order of 200 X 1054. Now my Problem is just to plot the results as they did in here.
After selecting 2 features, you can just follow the example. Start a figure, use hold on, and use plot or scatter to plot the centroids and the data points. E.g.
selectedFeatures = [42,43];
plot(trainingData(trainCtr==1,selectedFeatures(1)),
trainingData(trainCtr==1,selectedFeatures(2)),
'r.','MarkerSize',12)
Would plot the selected feature values of the data points in cluster 1.