Mercurial Hosting > traffic-intelligence
changeset 1092:49c0bb6eacc2
minor modification
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
---|---|
date | Wed, 30 Jan 2019 16:51:52 -0500 |
parents | 3945d239634e |
children | 05ccd8ef150c |
files | scripts/classify-objects.py |
diffstat | 1 files changed, 5 insertions(+), 4 deletions(-) [+] |
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diff -r 3945d239634e -r 49c0bb6eacc2 scripts/classify-objects.py --- a/scripts/classify-objects.py Tue Jan 22 09:57:26 2019 -0500 +++ b/scripts/classify-objects.py Wed Jan 30 16:51:52 2019 -0500 @@ -44,17 +44,18 @@ if args.plotSpeedDistribution: import matplotlib.pyplot as plt plt.figure() + speeds = np.arange(0.1, args.maxSpeedDistributionPlot, 0.1) for k in speedProbabilities: - 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) + 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 maxProb = -1. for k in speedProbabilities: - maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)])) + maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds])) plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-') plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class') plt.xlabel('Speed (km/h)') - plt.ylabel('Probability') + plt.ylabel('Probability density function') plt.legend() - plt.title('Probability Density Function') + #plt.title('Probability Density Function') plt.show() sys.exit()