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106 lines
2.9 KiB
Matlab
106 lines
2.9 KiB
Matlab
function stats=stats_ism_carsScale
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thresholds=[0.25:0.1:2.25];
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method{1}='Hough';method{2}='PC/BC-DIM';
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spread=2
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numTest=108;
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scales=1.2.^[-1:4];
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%DEFINE CODEBOOK
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load('ISM_codebook_carsScale.mat');
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Locations=postprocess_codebook(Locations,patchClassList,spread,scales);
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[~,data_dir]=load_image_car(1,4);
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%FOR EACH TEST IMAGE PERFORM ISM TO LOCATE CARS
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disp(['Processing Images (',int2str(numTest),')'])
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param{numTest}{length(method)}=[];%pre-allocate memory
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for i=1:numTest
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fprintf(1,'.%i.',i);
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%load image and display
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I=load_image_car(i-1,4);
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figured(2),clf, imagesc(I), hold on, axis('equal','tight'); title(' ');
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colormap('gray');
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%APPLY METHODS
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[param{i}{1},param{i}{2}]=ism_compare_methods(I,patches,patchClassList,Locations,similarityThres,scales);
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end
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%FOR EACH METHOD AND THRESHOLD TEST ACCURACY WITH WHICH CARS ARE LOCATED
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filename='estimated_car_locations.txt';
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stats{length(method)}=[];f1score{length(method)}=[];%pre-allocate memory
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for m=1:length(method)
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disp([method{m}]);
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t=0;
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for thres=thresholds.*m %*m to make thresholds larger for DIM
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t=t+1;
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disp(['***Testing Accuracy for threshold=',num2str(thres)])
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%record results for current threshold into a file
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file=fopen([data_dir,filename],'w');
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for i=1:numTest
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%find coordinates for which the votes exceed the threshold
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index=find(param{i}{m}{1}(4,:)>thres);
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coords=param{i}{m}{1}(1:2,index);
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scale=param{i}{m}{1}(3,index);
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[coords,widths]=convert_car_coordinates(coords,scale);
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write_result(i,[coords;widths],file);
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end
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fclose(file);
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%evaluate the results using the java code supplied with the UIUC Cars dataset
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dir=pwd;
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cd(data_dir);
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[status,result]=system(['java Evaluator_Scale trueLocations_Scale.txt ',filename])
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stats{m}(t,:)=parse_car_evaluation(result);
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cd(dir)
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f1score{m}(t)=calc_f1score(stats{m}(t,:));
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end
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end
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%PLOT THE RESULTS
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max_error=0;
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for m=1:length(method)
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max_error=max(max_error,max(sum(stats{m}(:,2:3),2)));
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disp(['f1score ',method{m}]);
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disp(f1score{m})
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end
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lineStyle{1}='b-s';lineStyle{2}='r-d';lineStyle{3}='g-o';
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figured(11),clf
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for m=1:length(method)
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subplot(1,length(method),m),plot_errors(thresholds.*m,stats{m},max_error);title(method{m})
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end
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figured(12),clf
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for m=1:length(method)
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plot_precision_recall(stats{m},lineStyle{m}), hold on
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end
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legend(method,'Location','southeast')
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figured(13),clf
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for m=1:length(method)
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plot_RFPPI(stats{m},lineStyle{m},numTest), hold on
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end
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legend(method,'Location','southeast')
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function write_result(i,pos,file)
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pos=pos(:);
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fprintf(file,'%i: ',i-1);
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if ~isnan(pos), fprintf(file,'(%i,%i,%i) ',round(pos)); end
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fprintf(file,'\n');
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function stats=parse_car_evaluation(result)
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vals=sscanf(result,'%*cCorrect detections :%u out of%u%');
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TP=vals(1);
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FN=vals(2)-vals(1);
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ind=strfind(result,'False detections :');
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FP=sscanf(result(ind:end),'False detections :%u');
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stats=[TP,FP,FN]; |