I have solved one image recognition problem of total 32 categories. I got the result and calculated the average precision of it. I need to draw the confusion matrix.
I am working on YOLOv2 and have created a confusion matrix for detection network. Hope this helps :)
testObjects are true labels, predLabels are predicted labels
TestData is imageDatastore() of imdsTest
testObjects = testData.UnderlyingDatastores{1, 1}.Files ; %'C:\Users\admin\Desktop\Img_Data\Flower1\Flower101.jpg'
testObjects = erase(testObjects,fullfile(pwd,imgFolderName)); %'\Flower1\Flower101.jpg'
testObjects = categorical(extractBetween(testObjects, "\","\")); % Flower1 - array
predLabels = zeros(2,1);
predLabels = categorical(predLabels); % Prelocation
for iPred = 1:length(testObjects)
[~, idxx] = max(cell2mat(detectionResults.Scores(iPred))); % max of all the bounding box scores
multiLabels = detectionResults.Labels{iPred,1}; % find label of max score
if isempty(multiLabels) == 1
predLabels(iPred,1) = {'NaN'};
predLabels(iPred,1) = standardizeMissing(predLabels(iPred,1),{'NaN'});
else
predLabels(iPred,1) = (multiLabels(idxx,1));
end
end
predLabels = removecats(predLabels);
plotconfusion (testObjects,predLabels) %confusionchart(testAsts,predLabels)
Related
With using the PCA technique and the Yale database, I'm trying to work on face recognition within Matlab by randomly splitting the training process to 20% and the testing process to 80%. It is given an
Index in position 2 exceeds array bounds (must not exceed 29)
error. The following is the code, hoping to get help:
dataset = load('yale_FaceDataset.mat');
trainSz = round(dataset.samples*0.2);
testSz = round(dataset.samples*0.8);
trainSetCell = cell(1,trainSz*dataset.classes);
testSetCell = cell(1,testSz*dataset.classes);
j = 1;
k = 1;
m = 1;
for i = 1:dataset.classes
% training set
trainSetCell(k:k+trainSz-1) = dataset.images(j:j+trainSz-1);
trainLabels(k:k+trainSz-1) = dataset.labels(j:j+trainSz-1);
k = k+trainSz;
% test set
testSetCell(m:m+testSz-1) = dataset.images(j+trainSz:j+dataset.samples-1);
testLabels(m:m+testSz-1) = dataset.labels(j+trainSz:j+dataset.samples-1);
m = m+testSz;
j = j+dataset.samples;
end
% convert the data from a cell into a matrix format
numImgs = length(trainSetCell);
trainSet = zeros(numImgs,numel(trainSetCell{1}));
for i = 1:numImgs
trainSet(i,:) = reshape(trainSetCell{i},[],1);
end
numImgs = length(testSetCell);
testSet = zeros(numImgs,numel(testSetCell{1}));
for i = 1:numImgs
testSet(i,:) = reshape(testSetCell{i},[],1);
end
%% applying PCA
% compute the mean face
mu = mean(trainSet)';
% centre the training data
trainSet = trainSet - (repmat(mu,1,size(trainSet,1)))';
% generate the eigenfaces(features of the training set)
eigenfaces = pca(trainSet);
% set the number of principal components
Ncomponents = 100;
% Out of the generated components, we keep "Ncomponents"
eigenfaces = eigenfaces(:,1:Ncomponents);
% generate training features
trainFeatures = eigenfaces' * trainSet';
% Subspace projection
% centre features
testSet = testSet - (repmat(mu,1,size(testSet,1)))';
% subspace projection
testFeatures = inv(eigenfaces'*eigenfaces) * eigenfaces' * testSet';
mdl = fitcdiscr(trainFeatures',trainLabels);
labels = predict(mdl,testFeatures');
% find the images that were recognised and their respect. labels
correctRec = find(testLabels == labels');
correctLabels = labels(correctRec);
% find the images that were NOT recognised and their respect. labels
falseRec = find(testLabels ~= labels');
falseLabels = labels(falseRec);
% compute and display the recognition rate
result = length(correctRec)/length(testLabels)*100;
fprintf('The recognition rate is: %0.3f \n',result);
% divide the images into : recognised and unrecognised
correctTest = testSetCell(correctRec);
falseTest = testSetCell(falseRec);
% display some recognised samples and their respective labels
imgshow(correctTest(1:8),correctLabels(1:8));
% display all unrecognised samples and their respective labels
imgshow(falseTest(1:length(falseTest)), falseLabels(1:length(falseTest)));
it would be nice, if you provide also the line-number and the full message of the error and if you would strip your code to the essential. I guess, the PCA-stuff is not necessary here, as the error is raised probably in your loop. That is because you are incrementing j by j = j+dataset.samples; and take this in the next loop-set for indexing j:j+trainSz-1, which now must exceed dataset.samples...
Nevertheless, there is no randomness in the indexing. It is easiest if you use the built-in cvpartition-function:
% split data
cvp = cvpartition(Lbl,'HoldOut',.2);
lgTrn = cvp.training;
lgTst = cvp.test;
You may provide the number of classes as first input (Lbl in this case) or the actual class vector to let cvpartition pick random subsets that reflect the original distribution of the individual classes.
bet{j,3} = react{j};
numBins = {};
edges = linspace(min(bet{j,3}), max(bet{j,3}), numBins(bet{j,3}));
[N, whichBin] = histc(bet{j,3}, edges);
binsize = NaN*zeros(size(bins));
for k = 1:numBins
bin = find(whichBin == k);
binMembers = bet{j,3}(bin);
if (~isempty(bin))
mu(k) = mean(y(bin));
end
end
error on
edges = linspace(min(bet{j,3}), max(bet{j,3}), numBins(bet{j,3})); that says it exceeds matrix dimensions
Any suggestions to what could be the problem, as well as suggestions if this is code might work for binning data (e.g., reaction time)?
Your line numBins = {}; creates an empty cell array. But in numBins(bet{j,3})); you are trying to access an element. As there is none it fails on index exceeds matrix dimension.
I need to create a GUI in Matlab. It requires me to identify the spots for two images, and calculate the distance between them.
I have obtained the code for finding and encircling a single spot. It is as follows:
function [meanx,meany] = centroid(pic)
[x,y,z] = size(pic);
if(z==1)
;
else
pic = rgb2gray(pic);
end
% N=2;
% image = interp2(double(pic),N,'spline');
image = sort(sort(pic,1),2);
image =reshape(image,1,numel(image));
i=0;
while(i<3)
if(image(end)==image(end-1))
image(end)=[];
else
image(end)=[];
i=i+1;
end
end
threshold = image(end);
pic2 =(pic>=threshold);
pic=(pic-threshold).*uint8(pic2);
% % image=(pic-threshold+1).*uint8(image); %minus threshold
[rows,cols] = size(pic);
x = ones(rows,1)*[1:cols];
y = [1:rows]'*ones(1,cols);
area = sum(sum(pic));
if area ~= 0
meanx = sum(sum(double(pic).*x))/area;
meany = sum(sum(double(pic).*y))/area;
else
meanx = cols/2;
meany = rows/2;
end
However, I need it to work for multiple spots as shown below :
http://imgur.com/oEe0mRV,UAnbH5y#0
http://imgur.com/oEe0mRV,UAnbH5y#1
So far, I have come up with this, but it only circles separate spots and not all together.
PLEASE HELP - I need to encircle at least 10X10 spots and store their values, and do this for two images as shown above, and find the distance between them!
img1 = imread('r0.bmp');
centroidmat=zeros(10,10,2);
for numx=1:2
for numy=1:2
single_spot=img1((numx*220+780):((numx+1)*220+780),(numy*220+1272):((numy+1)*220+1272),:);
figure
imshow(single_spot);
figure
[cx,cy] = centroid(single_spot);
centroidmat(numx,numy,1)=cx;
centroidmat(numx,numy,2)=cy;
imshow(single_spot);
hold on;
plot(cx,cy,'og')
end
end
Please HELP GUYS! Any help is appreciated!
Would this work ? -
centroidmat=zeros(10,10,2);
for numx=1:2
for numy=1:2
single_spot=img1((numx*220+780):((numx+1)*220+780),(numy*220+1272):((numy+1)*220+1272),:);
[cx,cy] = centroid(single_spot);
centroidmat(numx,numy,1)=cx;
centroidmat(numx,numy,2)=cy;
figure,
imshow(single_spot);
hold on;
plot(cx,cy,'og')
end
end
I have only removed the redundant figure and imshow(single_spot); at the start of the loop, as they appear again later on inside the same loop.
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 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.