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Updated MATLAB scripts for showing logs
git-svn-id: http://rtabmap.googlecode.com/svn/trunk/rtabmap@222 f169173b-cf89-36c8-b27e-44dbe73f0c83
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matlabbe
363
Matlab/ShowLogs/showlogs.m
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363
Matlab/ShowLogs/showlogs.m
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function showlogs(GT_file, PathPrefix)
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% SHOWLOGS Plot a RTAB-Map results (LogI.txt, LogF.txt). Just put this
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% file in the same directory as LogF.txt and LogI.txt files
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% generated by RTAB-Map (RTAB-Map's working directory). The files
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% must have the same number of lines.
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%
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% SHOWLOGS(GT_file)
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% GT_file (optional) is the Ground Truth file. The Ground Truth is a
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% squared bmp file (size must match the log files length) where
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% white dots mean loop closures. Grey dots mean 'loop closures to
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% ignore', this happens when the rehearsal doesn't match consecutive
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% images together.
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% PathPrefix (optional) is a path prefix to put before the loaded
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% files (LogI.txt, LogF.txt and GT_File).
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%
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% Dependency : importfile.m
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%--------------------
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% Parameters
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%--------------------
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set(0,'defaultAxesFontName', 'Times')
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set(0,'defaultTextFontName', 'Times')
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if nargin < 2, PathPrefix = '.'; end
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if nargin < 1, GT_file = ''; end
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%---------------------------------------------------------
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display(' ');
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display('Loading log files...');
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LogF = importfile([PathPrefix '/' 'LogF.txt']);
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% COLUMN HEADERS :
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% 1 totalTime
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% 2 timeMemoryUpdate,
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% 3 timeReactivations,
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% 4 timeLikelihoodCalculation,
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% 5 timePosteriorCalculation,
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% 6 timeHypothesesCreation,
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% 7 timeHypothesesValidation,
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% 8 timeRealTimeLimitReachedProcess,
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% 9 timeStatsCreation
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% 10 highestHypothesisValue
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% 11 vpLikelihood
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% 12 maxLikelihood
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% 13 sumLikelihoods
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% 14 mean likelihood
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% 15 stddev likelihood
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% 16 vp hypothesis
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% 17 timeEmtyingTrash
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LogI = importfile([PathPrefix '/' 'LogI.txt']);
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% COLUMN HEADERS :
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% 1 lcHypothesisId,
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% 2 mostLikelihoodId,
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% 3 signaturesRemoved,
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% 4 hessianThr,
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% 5 wordsNewSign,
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% 6 dictionarySize,
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% 7 this->getSTMem().size(),
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% 8 rejectedHypothesis?,
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% 9 processMemoryUsed,
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% 10 databaseMemoryUsed
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% 11 signaturesReactivated
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% 12 lcHypothesisReactivated
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% 13 refUniqueWordsCount
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% 14 reactivateId
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% 15 non nulls count
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if isempty(LogI) || isempty(LogF)
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error('Log files are empty')
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end
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if size(LogI, 1) ~= size(LogF, 1)
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error('Log files are not the same size')
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end
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% figure
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% subplot(211)
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% H1 = plot(LogF(:,1)*1000);
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% hold on
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% % H2 = plot(1:length(LogF(:,1)), ones(length(LogF(:,1)),1).*mean(LogF(:,1))*1000, 'r-')
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% %title('Total process time / Location')
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% ylabel('Time (ms)')
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% xlabel('Location indexes')
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% meanTime = mean(LogF(:,1))*1000
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% %plot([1 length(LogF(:,1))], [800 800], 'r')
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% %plot([1 length(LogF(:,1))], [1000 1000], 'k')
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% subplot(212)
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% plot(sum(LogF(:,2:7),2)*1000);
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% ylabel('Time (ms)')
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% xlabel('Location indexes')
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figure
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plot(sum(LogF(:,2:7),2)*1000);
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hold on
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ylabel('Time (ms)')
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xlabel('Location indexes')
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%meanTime = mean(LogF(:,1))*1000
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%plot([1 length(LogF(:,1))], [700 700], 'r')
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%plot([1 length(LogF(:,1))], [1000 1000], 'k')
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maxTime = max(sum(LogF(:,2:7),2))
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maxDict = max(LogI(:, 6))
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maxWM = max(LogI(:,7))
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%%
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% -------------------------
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% Time details
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figure
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subplot(8,1,1)
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plot(LogF(:,2)*1000)
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title('timeMemoryUpdate (ms)')
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subplot(8,1,2)
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plot(LogF(:,3)*1000)
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title('timeReactivations (ms)')
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subplot(8,1,3)
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plot(LogF(:,4)*1000)
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title('timeLikelihoodCalculation (ms)')
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subplot(8,1,4)
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plot(LogF(:,5)*1000)
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title('timePosteriorCalculation (ms)')
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subplot(8,1,5)
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plot(LogF(:,6)*1000)
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title('timeHypothesesCreation (ms)')
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subplot(8,1,6)
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plot(LogF(:,7)*1000)
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title('timeHypothesesValidation (ms)')
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subplot(8,1,7)
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plot(LogF(:,8)*1000)
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title('timeStatsCreation (ms)')
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if size(LogF, 2) > 16
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subplot(8,1,8)
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plot(LogF(:,17)*1000)
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title('timeEmptyingTrash (ms)')
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end
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xlabel('Location indexes')
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% -------------------------
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figure
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plot([LogF(:,2) sum(LogF(:,2:3),2) sum(LogF(:,2:4),2) sum(LogF(:,2:5),2) sum(LogF(:,2:6),2) sum(LogF(:,2:7),2) sum(LogF(:,2:8),2)]);
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legend('timeMemoryUpdate', 'timeReactivations', 'timeLikelihoodCalculation', 'timePosteriorCalculation', 'timeHypothesesCreation', 'timeHypothesesValidation', 'timeRealTimeLimitReachedProcess')
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title('Process time details')
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ylabel('s')
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xlabel('Location indexes')
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figure
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subplot(211)
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plot(LogF(:,3));
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title('Reactivation time (s)')
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ylabel('s')
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subplot(212)
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plot(LogI(:,11),'.')
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ylabel('Locations reactivated')
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xlabel('Location indexes')
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% -------------------------
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figure
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subplot(211)
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plot(LogI(:, 6));
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title('dictionary size')
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ylabel('words')
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subplot(212)
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plot([LogI(:, 9)/1000000 LogI(:, 10)/1000000]);
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title('Memory usage (in MB)')
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legend('Process', 'Database')
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ylabel('MB')
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xlabel('Location indexes')
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% -------------------------
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figure
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% subplot(211)
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H1 = plot(LogI(:,7));
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% hold on
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% H2 = plot(1:length(LogI(:,7)), ones(length(LogI(:,7)),1).*mean(LogI(:,7)), 'r--')
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title('Working memory size')
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meanWM = mean(LogI(:,7))
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ylabel('WM size (locations)')
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xlabel('Location indexes')
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% set(H1,'color',[0.3 0.3 0.3])
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% set(H2,'color',[0 0 0])
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% subplot(212)
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% plot(LogI(:,6));
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meanDict = mean(LogI(:,6))
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% ylabel('Dictionary size')
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% xlabel('Location indexes')
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meanWordsPerSign = mean(LogI(:,5))
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% -------------------------
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% Detected/Accepted/Rejected loop closures
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figure;
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subplot(311)
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plot(LogF(:,10), '.')
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title('Highest posterior, green=accepted, red=rejected, blue=under T_{Loop}')
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hold on
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ylabel('p')
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%rejected (by T_loop) hypotheses
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y = LogF(:,10);
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x = 1:length(y);
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y(LogI(:, 1) == 0 & LogI(:, 8) ~= 0) = [];
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x(LogI(:, 1) == 0 & LogI(:, 8) ~= 0) = [];
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plot(x,y, 'b.')
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%rejected (by ratio) hypotheses
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y = LogF(:,10);
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x = 1:length(y);
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y(LogI(:, 8) == 0 | LogI(:, 1) > 0) = [];
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x(LogI(:, 8) == 0 | LogI(:, 1) > 0) = [];
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plot(x,y, 'r.')
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%Accepted hypotheses
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y = LogF(:,10);
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x = 1:length(y);
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y(LogI(:, 1) == 0) = [];
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x(LogI(:, 1) == 0) = [];
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plot(x,y, 'g.')
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subplot(312)
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plot(LogI(:,2), '.')
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title('Id corresponding to highest posterior + lc accepted and rejected')
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hold on
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ylabel('Matched location index')
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%rejected hypotheses
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y = LogI(:,2);
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x = 1:length(y);
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y(LogI(:, 1) == 0 & LogI(:, 8) ~= 0) = [];
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x(LogI(:, 1) == 0 & LogI(:, 8) ~= 0) = [];
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plot(x,y, 'b.')
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%rejected (by ratio) hypotheses
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y = LogI(:,2);
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x = 1:length(y);
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y(LogI(:, 8) == 0 | LogI(:, 1) > 0) = [];
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x(LogI(:, 8) == 0 | LogI(:, 1) > 0) = [];
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plot(x,y, 'r.')
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%Accepted hypotheses
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y = LogI(:,2);
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x = 1:length(y);
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y(LogI(:, 1) == 0) = [];
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x(LogI(:, 1) == 0) = [];
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plot(x,y, 'g.')
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subplot(313)
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plot(LogI(:,5),'.')
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title('wordsNewSign')
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hold on
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ylabel('words')
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xlabel('Location indexes')
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%rejected hypotheses
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y = LogI(:,5);
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x = 1:length(y);
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y(LogI(:, 1) == 0 & LogI(:, 8) ~= 0) = [];
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x(LogI(:, 1) == 0 & LogI(:, 8) ~= 0) = [];
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plot(x,y, 'b.')
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%rejected (by ratio) hypotheses
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y = LogI(:,5);
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x = 1:length(y);
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y(LogI(:, 8) == 0 | LogI(:, 1) > 0) = [];
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x(LogI(:, 8) == 0 | LogI(:, 1) > 0) = [];
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plot(x,y, 'r.')
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%Accepted hypotheses
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y = LogI(:,5);
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x = 1:length(y);
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y(LogI(:, 1) == 0) = [];
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x(LogI(:, 1) == 0) = [];
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plot(x,y, 'g.')
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set(datacursormode,'UpdateFcn',@(Y,X){sprintf('X: %0.2f',X.Position(1)),sprintf('Y: %0.2f',X.Position(2))})
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% %matched sign words
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% y = LogI(:,2);
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% x = 1:length(y);
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% mask = zeros(1,length(y));
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% y(LogI(:, 8) ~= 11) = [];
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% for i=1:length(y)
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% mask(y(i)) = 1;
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% end
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% y = LogI(:,5);
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% y(~mask) = [];
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% x(~mask) = [];
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% plot(x,y, 'c.')
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% %matched sign words for rejected
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% y = LogI(:,2);
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% x = 1:length(y);
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% mask = zeros(1,length(y));
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% y(LogI(:, 8) < 12) = [];
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% for i=1:length(y)
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% mask(y(i)) = 1;
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% end
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% y = LogI(:,5);
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% y(~mask) = [];
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% x(~mask) = [];
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% plot(x,y, 'm.')
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lcAccepted = sum(LogI(:, 1) > 0)
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lcReactivated = sum(LogI(:, 12) == 1)
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lcIgnored = sum(LogI(:, 1) == 0 & LogI(:, 8) == 0)
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lcRejected = sum(LogI(:, 8) == 1)
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%figure;
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%plot([1.0 * (LogI(:, 8) == 10) ...
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% 1.01 * (LogI(:, 8) == 11) ...
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% 1.02 * (LogI(:, 8) == 14) ...
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% 1.03 * (LogI(:, 8) == 15)], '.');
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%title('Reject loop reason')
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%legend('UNDEFINED', 'ACCEPTED', 'NOT ENOUGH MATCHING PAIRS', 'EPIPOLAR CONSTRAINT FAILED')
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% -----------------
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% Squared matrix
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%%
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%Precision-Recall graph
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GroundTruthFile = [PathPrefix '/' GT_file];
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if exist(GroundTruthFile, 'file')
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PR = getPrecisionRecall(LogI, LogF, GroundTruthFile, 1.0);
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Precision = PR(:,1);
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Recall = PR(:,2);
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PrecisionVerified = PR(:,3);
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RecallVerified = PR(:,4);
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%plot the Precision-Recall
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figure
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plot(Recall*100, Precision*100)
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%plot([Recall RecallVerified], [Precision PrecisionVerified])
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%legend('Without verification', 'With verification')
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title('Precision-Recall curve')
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xlabel('Recall (%)')
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ylabel('Precision (%)')
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else
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display('Precision-recall curve is not computed...');
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end
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%%
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% count = 0;
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% for i=2:length(LogF(:,10))
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% if(LogF(i,10) >= 0.03723 && LogF(i,10) < LogF(i-1,10)*0.90)
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% display(['i=' num2str(i) ' with=' num2str(LogI(i,2)) ' ratio=' num2str(LogF(i,10)/LogF(i-1,10))])
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% count = count +1;
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% end
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% end
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% count
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%%
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% figure
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% hold on
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% K=100;
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% %plot(1./(K*LogF(:,15)), 'r')
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% %plot(log10(1./(LogF(:,15))), 'c')
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% scale=1;
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% %plot(log10(1./(LogF(:,15))).^2 ./ ((LogF(:,12)-LogF(:,15))./LogF(:,14)), 'k')
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% %plot(log10(1./(LogF(:,15))), 'm')
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% plot((LogF(:,12)-LogF(:,15))./LogF(:,14), 'g')
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% plot([0, length(LogF(:,15))], [1 1], 'k:')
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% plot(LogF(:,11), 'b')
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% %legend(['K=' num2str(K)], 'ln', 'ln scaled', 'log10', 'max sim', '1', 'Vp likelihood')
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