how to plot confusionmat for this code?

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susanta reang
susanta reang 2018-6-18
回答： Yuvaraj Venkataswamy 2018-6-18
close all clear all clc delete('Frames\*.jpg'); [filename pathname] = uigetfile({'*.avi'},'Select A Video File'); I = VideoReader([pathname,filename]); implay([pathname,filename]); pause(3); nFrames = I.numberofFrames; vidHeight = I.Height; vidWidth = I.Width; mov(1:nFrames) = ... struct('cdata', zeros(vidHeight, vidWidth, 3, 'uint8'),... 'colormap', []); WantedFrames = 50; for k = 1:WantedFrames mov(k).cdata = read( I, k); mov(k).cdata = imresize(mov(k).cdata,[256,256]); imwrite(mov(k).cdata,['Frames\',num2str(k),'.jpg']); end
for I = 1:WantedFrames im=imread(['Frames\',num2str(I),'.jpg']); figure(1),subplot(5,10,I),imshow(im); end clc for i=1:WantedFrames disp(['Processing frame no.',num2str(i)]); img=imread(['Frames\',num2str(i),'.jpg']); f1=il_rgb2gray(double(img)); [ysize,xsize]=size(f1); nptsmax=40; kparam=0.04; pointtype=1; sxl2=4; sxi2=2*sxl2; % detect points [posinit,valinit]=STIP(f1,kparam,sxl2,sxi2,pointtype,nptsmax); Test_Feat(i,1:40)=valinit; %imshow(f1,[]), hold on % axis off; % showellipticfeatures(posinit,[1 1 0]); % title('Feature Points','fontsize',12,'fontname','Times New Roman','color','Black') end
% Use KNN To classify the videos load('TrainFeature.mat') X = meas; Y = New_Label; Z = Test_Feat; % Now Classify
%ens = fitensemble(X,Y,'Subspace',300,'KNN'); %class = predict(ens,Z(1,:)) md1 = ClassificationKNN.fit(X,Y); Type = predict(md1,Z); if (Type == 1) disp('Boxing'); helpdlg(' Boxing '); elseif (Type == 2) disp('Hand Clapping'); helpdlg('Hand Clapping'); elseif (Type == 3) disp('Hand Waving'); helpdlg('Hand Waving'); elseif (Type == 4) disp('Jogging'); helpdlg('Jogging'); elseif (Type == 5) disp('Running'); helpdlg('Running'); elseif (Type == 6) disp('Walking'); helpdlg('Walking'); elseif (Type == 7) disp('Cycling'); helpdlg('Cycling'); elseif (Type == 8) disp('Surfing'); helpdlg('Surfing'); end

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回答（1 个）

Yuvaraj Venkataswamy
if true
plotconfusion(actual_labels,Predicted_labels)
end
In this, Predicted_labels are which you have classified through KNN and actual_labels are the true labels.

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