mirror of
https://github.com/introlab/rtabmap.git
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Changing/updating name of matlab scripts
git-svn-id: http://rtabmap.googlecode.com/svn/trunk/rtabmap@221 f169173b-cf89-36c8-b27e-44dbe73f0c83
This commit is contained in:
matlabbe
@@ -1,340 +0,0 @@
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%---------------------------------------------------------
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% MatLab script.
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% This shows some informations logged by the application.
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% This script may work directly with octave.
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%---------------------------------------------------------
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% Just put along LogF.txt and LogI.txt files generated
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% (look in the application working directory).
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% The files must have the same number of lines.
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%
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% Dependency : importfile.m
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%---------------------------------------------------------
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%--------------------
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% Parameters
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%--------------------
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close all
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clear all
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Prefix = '.';
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%Prefix = './Results';
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DataSet = '';
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%DataSet = 'NewCollege';
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%DataSet = 'CityCentre';
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%DataSet = 'Lip6Indoor';
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%DataSet = 'Lip6Outdoor';
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%DataSet = 'Lip6Outdoor_1Hz';
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%DataSet = 'UdeS_1Hz';
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% The Ground Truth is a squared bmp file (size must match the log files
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% length) where white dots mean loop closures.
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% Grey dots mean 'loop closures to ignore', this happens when the rehearsal
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% doesn't match consecutive images together.
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PrefixGT = '.';
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%PrefixGT = './GT';
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GroundTruthFile = [PrefixGT '/' '090206-3_GT.bmp'];
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%GroundTruthFile = [PrefixGT '/' DataSet '.bmp'];
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%---------------------------------------------------------
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display(' ');
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display('Loading log files...');
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importfile([Prefix '/' DataSet '/' '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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importfile([Prefix '/' DataSet '/' '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 rejectLoopReason,
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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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startAt = 1;
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% endAt = 1175;
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endAt = length(LogF(:,1));
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figure
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H1 = plot(LogF(startAt:endAt,1)*1000);
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hold on
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% H2 = plot(1:length(LogF(startAt:endAt,1)), ones(length(LogF(startAt:endAt,1)),1).*mean(LogF(startAt:endAt,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(startAt:endAt,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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% -------------------------
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% Time details
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figure
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subplot(7,1,1)
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plot(LogF(startAt:endAt,2)*1000)
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title('timeMemoryUpdate (ms)')
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subplot(7,1,2)
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plot(LogF(startAt:endAt,3)*1000)
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title('timeReactivations (ms)')
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subplot(7,1,3)
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plot(LogF(startAt:endAt,4)*1000)
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title('timeLikelihoodCalculation (ms)')
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subplot(7,1,4)
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plot(LogF(startAt:endAt,5)*1000)
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title('timePosteriorCalculation (ms)')
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subplot(7,1,5)
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plot(LogF(startAt:endAt,6)*1000)
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title('timeHypothesesCreation (ms)')
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subplot(7,1,6)
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plot(LogF(startAt:endAt,7)*1000)
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title('timeHypothesesValidation (ms)')
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subplot(7,1,7)
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plot(LogF(startAt:endAt,8)*1000)
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title('timeStatsCreation (ms)')
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% -------------------------
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figure
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plot([LogF(startAt:endAt,2) sum(LogF(startAt:endAt,2:3),2) sum(LogF(startAt:endAt,2:4),2) sum(LogF(startAt:endAt,2:5),2) sum(LogF(startAt:endAt,2:6),2) sum(LogF(startAt:endAt,2:7),2) sum(LogF(startAt:endAt,2:8),2)]);
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legend('timeMemoryUpdate', 'timeReactivations', 'timeLikelihoodCalculation', 'timePosteriorCalculation', 'timeHypothesesCreation', 'timeHypothesesValidation', 'timeRealTimeLimitReachedProcess', 'timeStatsCreation')
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title('Process time details')
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figure
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subplot(211)
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plot(LogF(startAt:endAt,3));
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title('Reactivation time')
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subplot(212)
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plot(LogI(:,11),'.')
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% -------------------------
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figure
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subplot(211)
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plot(LogI(startAt:endAt, 6));
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title('dictionary size')
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subplot(212)
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plot([LogI(startAt:endAt, 9)/1000000 LogI(startAt:endAt, 10)/1000000]);
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title('Memory usage (in MB)')
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legend('Process', 'Database')
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% -------------------------
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figure
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% subplot(211)
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H1 = plot(LogI(startAt:endAt,7));
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% hold on
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% H2 = plot(1:length(LogI(startAt:endAt,7)), ones(length(LogI(startAt:endAt,7)),1).*mean(LogI(startAt:endAt,7)), 'r--')
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%title('Working memory size')
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meanWM = mean(LogI(startAt:endAt,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(startAt:endAt,6));
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meanDict = mean(LogI(startAt:endAt,6))
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% ylabel('Dictionary size')
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% xlabel('Location indexes')
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meanWordsPerSign = mean(LogI(startAt:endAt,5))
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% -------------------------
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% Detected/Accepted/Rejected loop closures
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% from VerifyEpipolarGeometry.h
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% UNDEFINED, 10
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% ACCEPTED, 11
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% NO_HYPOTHESIS, 12
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% MEMORY_IS_NULL, 13
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% NOT_ENOUGH_MATCHING_PAIRS, 14
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% EPIPOLAR_CONSTRAINT_FAILED, 15
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% NULL_MATCHING_SURF_SIGNATURES 16
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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 + lc accepted and rejected')
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hold on
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%rejected hypotheses
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y = LogF(:,10);
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x = 1:length(y);
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y(LogI(startAt:endAt, 8) >= 10 & LogI(startAt:endAt, 8) <= 11) = [];
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x(LogI(startAt:endAt, 8) >= 10 & LogI(startAt:endAt, 8) <= 11) = [];
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plot(x,y, 'r.')
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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(startAt:endAt, 8) ~= 3) = [];
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x(LogI(startAt:endAt, 8) ~= 3) = [];
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plot(x,y, 'b.')
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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(startAt:endAt, 8) < 10 | LogI(startAt:endAt, 8) > 11) = [];
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x(LogI(startAt:endAt, 8) < 10 | LogI(startAt:endAt, 8) > 11) = [];
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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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%rejected hypotheses
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y = LogI(:,2);
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x = 1:length(y);
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y(LogI(startAt:endAt, 8) >= 10 & LogI(startAt:endAt, 8) <= 11) = [];
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x(LogI(startAt:endAt, 8) >= 10 & LogI(startAt:endAt, 8) <= 11) = [];
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plot(x,y, 'r.')
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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(startAt:endAt, 8) ~= 3) = [];
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x(LogI(startAt:endAt, 8) ~= 3) = [];
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plot(x,y, 'b.')
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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(startAt:endAt, 8) < 10 | LogI(startAt:endAt, 8) > 11) = [];
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x(LogI(startAt:endAt, 8) < 10 | LogI(startAt:endAt, 8) > 11) = [];
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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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%rejected hypotheses
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y = LogI(:,5);
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x = 1:length(y);
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y(LogI(startAt:endAt, 8) >= 10 & LogI(startAt:endAt, 8) <= 11) = [];
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x(LogI(startAt:endAt, 8) >= 10 & LogI(startAt:endAt, 8) <= 11) = [];
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plot(x,y, 'r.')
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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(startAt:endAt, 8) ~= 3) = [];
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x(LogI(startAt:endAt, 8) ~= 3) = [];
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plot(x,y, 'b.')
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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(startAt:endAt, 8) < 10 | LogI(startAt:endAt, 8) > 11) = [];
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x(LogI(startAt:endAt, 8) < 10 | LogI(startAt:endAt, 8) > 11) = [];
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plot(x,y, 'g.')
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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(startAt:endAt, 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(startAt:endAt, 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(startAt:endAt, 8) >= 10 & LogI(startAt:endAt, 8) <= 11)
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lcReactivated = sum(LogI(startAt:endAt, 12) == 1)
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lcRejected = sum(LogI(startAt:endAt, 8) > 11 | LogI(startAt:endAt, 8) == 3)
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lcRejectedNotEnoughPairs = sum(LogI(startAt:endAt, 8) == 14)
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lcRejectedEpipolarGeo = sum(LogI(startAt:endAt, 8) > 14)
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%figure;
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%plot([1.0 * (LogI(startAt:endAt, 8) == 10) ...
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% 1.01 * (LogI(startAt:endAt, 8) == 11) ...
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% 1.02 * (LogI(startAt:endAt, 8) == 14) ...
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% 1.03 * (LogI(startAt:endAt, 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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GroundTruth = [];
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if exist(GroundTruthFile, 'file')
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PR = getPrecisionRecall(LogI, LogF, GroundTruthFile, 0.03);
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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 RecallVerified], [Precision PrecisionVerified])
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legend('Without verification', 'With verification')
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title('Precision - Recall')
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xlabel('Recall (%)')
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ylabel('Precision (%)')
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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) > 0.45 && LogF(i,10) < LogF(i-1,10)*0.9)
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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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@@ -1,109 +0,0 @@
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function [ PR ] = getPrecisionRecall( LogI, LogF, GT_file, LoopThr )
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%GETPRECISIONRECALL Calculate the precision-recall results from the log
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%files of RTAB-Map and a Ground Truth file (a bmp).
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% PR(:,1) = Precision
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% PR(:,2) = Recall
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% PR(:,3) = Precision with verification
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% PR(:,4) = Recall with verification
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%
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% LogI: The 'LogI.txt' generated file
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% LogF: The 'LogF.txt' generated file
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% GT_file: The related Ground truth file of the dataset ('GT.bmp')
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% LoopThr: Display false positives over the loop thr (>=0.0 && < 1.0)
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GroundTruth = [];
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if exist(GT_file, 'file')
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display('--- getPrecisionRecall ---');
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display(['Loading GroundTruth ''' GT_file ''' ...']);
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GroundTruth = imread(GT_file);
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else
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error(['The ground truth ''' GT_file '''doesn''t exist.'])
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end
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if ~isempty(GroundTruth)
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%display('Calculating Precision-Recall graph')
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%figure
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%imshow(GroundTruth)
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%title('GroundTruth')
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if size(GroundTruth, 1) ~= length(LogF(:,1)) || size(GroundTruth, 1) ~= length(LogI(:,1))
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error(['The ground truth size doesn''t match the log files (LogI=' num2str(length(LogI(:,1))) ', LogF=' num2str(length(LogF(:,1))) ', GT=' num2str(size(GroundTruth, 1)) ')'])
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end
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%[highestHypot, CorrespondingID, GT, Accepted, Good, Index, UnderLoopRatio] descending order
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lc = [LogF(:,10) LogI(:,2) sum(GroundTruth == 255, 2)>0 (LogI(:, 8) == 10 | LogI(:, 8) == 11) zeros(length(LogI(:,1)),1) (1:length(LogF(:,10)))' LogI(:, 8) == 3];
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%eliminate loops on diagonal
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ignored = 0;
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for i=1:length(lc)
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index = find(GroundTruth(:,i) > 0 & GroundTruth(:,i) < 255);
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if ~isempty(index)
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row = GroundTruth(index(1), :);
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if lc(i,2) >= min(index) && lc(i,2) <= max(index)
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display(['i=' NUM2STR(i) ' loop=' NUM2STR(LogI(i,2)) ' min(index)=' NUM2STR(min(index)) ' max(index)' NUM2STR(max(index))])
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lc(i,1) = 0;
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ignored = ignored + 1;
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end
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end
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end
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lc = sortrows(lc, -1);
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GT_total_positives = sum(sum(GroundTruth == 255, 2) > 0)
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%figure
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%plot(sum(GroundTruth > 0, 2)>0)
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%title('Ground truth (timeline)')
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sizeNonZero = sum(lc(:,1) > 0);
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PR = zeros(sizeNonZero, 4);
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for i=1:length(lc)
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if lc(i,1) == 0
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break;
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end
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id = lc(i,2);
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if id && sum(GroundTruth(lc(i,6), id)) > 0
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lc(i,5) = 1;
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end
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PR(i,2) = sum(lc(1:i,5) & ~lc(1:i,7) & lc(1:i,2))/GT_total_positives;
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PR(i,1) = sum(lc(1:i,5) & ~lc(1:i,7) & lc(1:i,2)) / sum(~lc(1:i, 7) & lc(1:i,2));
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%PR(i,2) = sum(lc(1:i,5))/GT_total_positives;
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%PR(i,1) = sum(lc(1:i,5)) / i;
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|
||||
PR(i,4) = sum(lc(1:i,4) & lc(1:i,5))/GT_total_positives;
|
||||
PR(i,3) = sum(lc(1:i,4) & lc(1:i,5)) / sum(lc(1:i,4));
|
||||
|
||||
if ~lc(i,5) && ~lc(i,7) && id && lc(i,1) >= LoopThr
|
||||
display(['False positive! id=' num2str(lc(i,6)) ' with old=' num2str(id) ' (p=' num2str(lc(i,1)) ')'] )
|
||||
end
|
||||
|
||||
if lc(i,4) ~= lc(i,5) && lc(i,4)
|
||||
display(['False positive! (v) id=' num2str(lc(i,6)) ' with old=' num2str(id) ' (p=' num2str(lc(i,1)) ')'] )
|
||||
end
|
||||
end
|
||||
|
||||
index = find(PR(:,1) == 1);
|
||||
if ~isempty(index)
|
||||
maxRecall = PR(index(end),2) * 100;
|
||||
display(['Recall max (Precision=100%) = ' num2str(maxRecall) '% (p=' num2str(lc(index(end),1)) '), accepted=' num2str(sum(lc(1:index(end),5) & ~lc(1:index(end),7) & lc(1:index(end),2)))])
|
||||
else
|
||||
display('Recall max (Precision=100%) = 0')
|
||||
end
|
||||
indexV = find(PR(:,3) == 1);
|
||||
if ~isempty(indexV)
|
||||
maxRecallVerified = PR(indexV(end),4) * 100;
|
||||
display(['Recall max (Precision=100%, with verification) = ' num2str(maxRecallVerified) '% (p=' num2str(lc(indexV(end),1)) ')'])
|
||||
else
|
||||
display('Recall max (Precision=100%, with verification) = 0')
|
||||
end
|
||||
display(['ignored = ' num2str(ignored)])
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
Reference in New Issue
Block a user