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view scripts/compute-clearmot.py @ 593:e2a873e08568
non-working clear mot metrics
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Sat, 06 Dec 2014 22:15:56 -0500 |
parents | 985a3021cff2 |
children | 9e39cd95e017 |
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#! /usr/bin/env python import sys, argparse from numpy import loadtxt import moving, storage # TODO: need to trim objects to same mask ? # pass frame interval where matching is done? parser = argparse.ArgumentParser(description='The program computes the CLEAR MOT metrics between ground truth and tracker output (in Polytrack format)', epilog='''CLEAR MOT metrics information: Keni, Bernardin, and Stiefelhagen Rainer. "Evaluating multiple object tracking performance: the CLEAR MOT metrics." EURASIP Journal on Image and Video Processing 2008 (2008) Polytrack format: JP. Jodoin\'s MSc thesis (in french) see examples on http://www.jpjodoin.com/urbantracker/dataset.html''', formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('-d', dest = 'trackerDatabaseFilename', help = 'name of the Sqlite database containing the tracker output', required = True) parser.add_argument('-g', dest = 'groundTruthDatabaseFilename', help = 'name of the Sqlite database containing the ground truth', required = True) parser.add_argument('-o', dest = 'homographyFilename', help = 'name of the filename for the homography (if tracking was done using the homography)') parser.add_argument('-m', dest = 'matchingDistance', help = 'matching distance between tracker and ground truth trajectories', required = True) parser.add_argument('-f', dest = 'firstInstant', help = 'first instant for measurement', required = True) parser.add_argument('-l', dest = 'lastInstant', help = 'last instant for measurement', required = True) args = parser.parse_args() # args.homographyFilename is None if nothing as argument if args.homographyFilename != None: homography = loadtxt(args.homographyFilename) else: homography = None firstInstant = int(args.firstInstant) lastInstant = int(args.lastInstant) objects = storage.loadTrajectoriesFromSqlite(args.trackerDatabaseFilename, 'object') annotations = storage.loadGroundTruthFromSqlite(args.groundTruthDatabaseFilename) for a in annotations: a.computeCentroidTrajectory(homography) matchTable = moving.matchingGroundTruthToTracker(objects, annotations, args.matchingDistance, firstInstant, lastInstant) # number of frames of existence of all objects within [firstInstant, lastInstant] nTrackFrames = sum([min(o.getLastInstant(),lastInstant)-max(o.getFirstInstant(),firstInstant)+1 for o in objects]) print moving.computeClearMOT(matchTable, nTrackFrames)