Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/classify-objects.py @ 1228:5654c9173548

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merged (bicycle)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 12 Jul 2023 13:21:08 -0400
parents d478d3122804
children bb14f919d1cb
comparison
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1227:eb3936809ea5 1228:5654c9173548
35 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.) 35 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
36 carNorm = norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed) 36 carNorm = norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed)
37 pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed) 37 pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed)
38 # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is exp(location) (loc=mean of the normal) 38 # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is exp(location) (loc=mean of the normal)
39 bicLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed)) 39 bicLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed))
40 speedProbabilities = {'car': lambda s: carNorm.pdf(s), 40 speedProbabilities = {moving.userTypeNames[1]: lambda s: carNorm.pdf(s),
41 'pedestrian': lambda s: pedNorm.pdf(s), 41 moving.userTypeNames[2]: lambda s: pedNorm.pdf(s),
42 'bicycle': lambda s: bicLogNorm.pdf(s)} 42 moving.userTypeNames[4]: 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 speeds = np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)