Mercurial Hosting > traffic-intelligence
changeset 854:33d296984dd8
rework and more info on speed probabilities for classification
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Thu, 22 Sep 2016 17:50:35 -0400 |
| parents | 95e7622b11be |
| children | 2277ab1a8141 |
| files | classifier.cfg python/storage.py python/utils.py scripts/classify-objects.py |
| diffstat | 4 files changed, 21 insertions(+), 9 deletions(-) [+] |
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--- a/classifier.cfg Thu Sep 22 16:49:43 2016 -0400 +++ b/classifier.cfg Thu Sep 22 17:50:35 2016 -0400 @@ -30,6 +30,7 @@ mean-ped-speed = 4.91 std-ped-speed = 0.88 # mean cyclist speed and standard deviation (in a log-normal distribution) 11.+-4.83 km/h +# to multiply the parameters by a, loc = loc+ln(a) cyc-speed-loc = 2.31 cyc-speed-scale = 0.42 # mean vehicle speed and standard deviation (in a normal distribution) 18.45+-7.6 km/h
--- a/python/storage.py Thu Sep 22 16:49:43 2016 -0400 +++ b/python/storage.py Thu Sep 22 17:50:35 2016 -0400 @@ -1244,7 +1244,7 @@ self.stdVehicleSpeed = self.stdVehicleSpeed/denominator # special case for the lognormal distribution self.locationCyclistSpeed = self.locationCyclistSpeed-log(denominator) - #self.scaleCyclistSpeed = self.scaleCyclistSpeed + #self.scaleCyclistSpeed = self.scaleCyclistSpeed # no modification of scale class ProcessParameters(VideoFilenameAddable):
--- a/python/utils.py Thu Sep 22 16:49:43 2016 -0400 +++ b/python/utils.py Thu Sep 22 17:50:35 2016 -0400 @@ -39,9 +39,11 @@ def logNormalMeanVar(loc, scale): '''location and scale are respectively the mean and standard deviation of the normal in the log-normal distribution - https://en.wikipedia.org/wiki/Log-normal_distribution''' + https://en.wikipedia.org/wiki/Log-normal_distribution + + same as lognorm.stats(scale, 0, exp(loc))''' mean = exp(loc+(scale**2)/2) - var = (exp(loc**2)-1)*exp(2*loc+scale**2) + var = (exp(scale**2)-1)*exp(2*loc+scale**2) return mean, var def sampleSize(stdev, tolerance, percentConfidence, printLatex = False):
--- a/scripts/classify-objects.py Thu Sep 22 16:49:43 2016 -0400 +++ b/scripts/classify-objects.py Thu Sep 22 17:50:35 2016 -0400 @@ -16,11 +16,12 @@ parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)') parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None) parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true') -parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display', type = float, default = 50.) +parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display (km/h)', type = float, default = 50.) args = parser.parse_args() params = storage.ProcessParameters(args.configFilename) classifierParams = storage.ClassifierParameters(params.classifierFilename) +classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame if args.videoFilename is not None: videoFilename = args.videoFilename @@ -31,7 +32,6 @@ else: databaseFilename = params.databaseFilename -classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/s if params.homography is not None: invHomography = np.linalg.inv(params.homography) else: @@ -53,15 +53,24 @@ bikeCarSVM.load(classifierParams.bikeCarSVMFilename) # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.) -speedProbabilities = {'car': lambda s: norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed).pdf(s), - 'pedestrian': lambda s: norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed).pdf(s), - 'bicycle': lambda s: lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed)).pdf(s)} # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is location (mean of the normal) +carNorm = norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed) +pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed) +# 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) +bicLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed)) +speedProbabilities = {'car': lambda s: carNorm.pdf(s), + 'pedestrian': lambda s: pedNorm.pdf(s), + 'bicycle': lambda s: bicLogNorm.pdf(s)} if args.plotSpeedDistribution: import matplotlib.pyplot as plt plt.figure() for k in speedProbabilities: - plt.plot(np.arange(0.1, args.maxSpeedDistributionPlot, 0.1), [speedProbabilities[k](s/3.6/25) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)], label = k) + 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) + 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)])) + 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.legend()