Mercurial Hosting > traffic-intelligence

comparison scripts/classify-objects.py @ 1092:49c0bb6eacc2

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minor modification
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 30 Jan 2019 16:51:52 -0500
parents cc5cb04b04b0
children 9f7a4a026dab
comparison
equal deleted inserted replaced
1091:3945d239634e 1092:49c0bb6eacc2
42 'bicycle': lambda s: bicLogNorm.pdf(s)} 42 'bicycle': lambda s: bicLogNorm.pdf(s)}
43 43
44 if args.plotSpeedDistribution: 44 if args.plotSpeedDistribution:
45 import matplotlib.pyplot as plt 45 import matplotlib.pyplot as plt
46 plt.figure() 46 plt.figure()
47 speeds = np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)
47 for k in speedProbabilities: 48 for k in speedProbabilities:
48 plt.plot(np.arange(0.1, args.maxSpeedDistributionPlot, 0.1), [speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)], label = k) 49 plt.plot(speeds, [speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds], label = k) # the distribution parameters are in video intrinsic units, unit of distance per frame
49 maxProb = -1. 50 maxProb = -1.
50 for k in speedProbabilities: 51 for k in speedProbabilities:
51 maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)])) 52 maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds]))
52 plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-') 53 plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-')
53 plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class') 54 plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class')
54 plt.xlabel('Speed (km/h)') 55 plt.xlabel('Speed (km/h)')
55 plt.ylabel('Probability') 56 plt.ylabel('Probability density function')
56 plt.legend() 57 plt.legend()
57 plt.title('Probability Density Function') 58 #plt.title('Probability Density Function')
58 plt.show() 59 plt.show()
59 sys.exit() 60 sys.exit()
60 61
61 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True) 62 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
62 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects]) 63 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])