Mercurial Hosting > traffic-intelligence
view scripts/compute-clearmot.py @ 776:84420159c5f4 dev
added __eq__ functions for Point and Trajectory
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 05 Feb 2016 17:17:12 -0500 |
parents | f8e0a8ea8402 |
children | 3aa6102ccc12 |
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#! /usr/bin/env python import sys, argparse from numpy import loadtxt from numpy.linalg import inv import moving, storage, cvutils # TODO: need to trim objects to same mask ? 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, type = float) parser.add_argument('--mask', dest = 'maskFilename', help = 'filename of the mask file used to define the where objects were tracked') parser.add_argument('-f', dest = 'firstInstant', help = 'first instant for measurement', required = True, type = int) parser.add_argument('-l', dest = 'lastInstant', help = 'last instant for measurement', required = True, type = int) parser.add_argument('--display', dest = 'display', help = 'display the ground truth to object matches (graphically)', action = 'store_true') parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (for display)') args = parser.parse_args() if args.homographyFilename is not None: homography = loadtxt(args.homographyFilename) else: homography = None objects = storage.loadTrajectoriesFromSqlite(args.trackerDatabaseFilename, 'object') if args.maskFilename is not None: maskObjects = [] from matplotlib.pyplot import imread mask = imread(args.maskFilename) if len(mask) > 1: mask = mask[:,:,0] for obj in objects: maskObjects += obj.getObjectsInMask(mask, inv(homography), 2) # TODO add option to keep object if at least one feature in mask objects = maskObjects annotations = storage.loadBBMovingObjectsFromSqlite(args.groundTruthDatabaseFilename) for a in annotations: a.computeCentroidTrajectory(homography) if args.display: motp, mota, mt, mme, fpt, gt, gtMatches, toMatches = moving.computeClearMOT(annotations, objects, args.matchingDistance, args.firstInstant, args.lastInstant, True) else: motp, mota, mt, mme, fpt, gt = moving.computeClearMOT(annotations, objects, args.matchingDistance, args.firstInstant, args.lastInstant) print 'MOTP: {}'.format(motp) print 'MOTA: {}'.format(mota) print 'Number of missed objects.frames: {}'.format(mt) print 'Number of mismatches: {}'.format(mme) print 'Number of false alarms.frames: {}'.format(fpt) if args.display: cvutils.displayTrajectories(args.videoFilename, objects, {}, inv(homography), args.firstInstant, args.lastInstant, annotations = annotations, gtMatches = gtMatches, toMatches = toMatches)#, rescale = args.rescale, nFramesStep = args.nFramesStep, saveAllImages = args.saveAllImages, undistort = (undistort or args.undistort), intrinsicCameraMatrix = intrinsicCameraMatrix, distortionCoefficients = distortionCoefficients, undistortedImageMultiplication = undistortedImageMultiplication) #print('Ground truth matches') #print(gtMatches) #print('Object matches') #rint toMatches