Mercurial Hosting > traffic-intelligence
changeset 683:f3a0b652b590 dev
added function to display the speed distributions and optimize a little going through objects
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Fri, 05 Jun 2015 11:04:06 +0200 |
| parents | fbe29be25501 |
| children | 6d2ece4aed7c |
| files | Makefile scripts/classify-objects.py scripts/uninstall-scripts.sh |
| diffstat | 3 files changed, 36 insertions(+), 11 deletions(-) [+] |
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--- a/Makefile Fri Jun 05 02:35:29 2015 +0200 +++ b/Makefile Fri Jun 05 11:04:06 2015 +0200 @@ -1,3 +1,4 @@ +INSTALL_DIR = /usr/local/bin cexe: @cd c && make feature-based-tracking @@ -17,9 +18,9 @@ @cp bin/feature-based-tracking /usr/local/bin @echo "=========================================" @echo "Copying Python scripts" - @cp scripts/* /usr/local/bin + @cp scripts/* $(INSTALL_DIR) uninstall: @echo "Uninstalling for Linux" - rm /usr/local/bin/feature-based-tracking - @cd scripts && ./uninstall-scripts.sh \ No newline at end of file + rm $(INSTALL_DIR)/feature-based-tracking + @cd scripts && ./uninstall-scripts.sh $(INSTALL_DIR)
--- a/scripts/classify-objects.py Fri Jun 05 02:35:29 2015 +0200 +++ b/scripts/classify-objects.py Fri Jun 05 11:04:06 2015 +0200 @@ -12,6 +12,8 @@ parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) +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('-u', dest = 'undistort', help = 'undistort the video (because features have been extracted that way)', action = 'store_true') #parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to display', type = int) #parser.add_argument('--last-frame', dest = 'lastFrameNum', help = 'number of last frame number to save (for image saving, no display is made)', type = int) @@ -23,7 +25,8 @@ params = storage.ProcessParameters(args.configFilename) params.convertToFrames(3.6) -invHomography = np.linalg.inv(params.homography) +if params.homography is not None: + invHomography = np.linalg.inv(params.homography) if params.speedAggregationMethod == 'median': speedAggregationFunc = np.median @@ -33,8 +36,7 @@ speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile) else: print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod)) - from sys import exit - exit() + sys.exit() pedBikeCarSVM = ml.SVM() pedBikeCarSVM.load(params.pedBikeCarSVMFilename) @@ -46,9 +48,17 @@ 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s), 'bicycle': lambda s: lognorm(params.scaleCyclistSpeed, loc = 0., scale = np.exp(params.locationCyclistSpeed)).pdf(s)} # lognorm shape, loc, scale -def plotSpeedProbabilities(): +if args.plotSpeedDistribution: + import matplotlib.pyplot as plt + plt.figure() for k in speedProbabilities: - plot(arange(0.1, 50, 0.1), [speedProbabilities[k](s/3.6/25) for s in arange(0.1, 50, 0.1)]) + 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.xlabel('Speed (km/h)') + plt.ylabel('Probability') + plt.legend() + plt.title('Probability Density Function') + plt.show() + sys.exit() objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'object') features = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'feature') @@ -75,12 +85,18 @@ if ret: if frameNum%50 == 0: print('frame number: {}'.format(frameNum)) + currentObjects = [] + for obj in objects: + if obj.getLastFrameNum() < frameNum: + obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities) + else: + currentObjects.append(obj) + objects = currentObjects if params.undistort: img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) for obj in objects: if obj.existsAtInstant(frameNum): if obj.getFirstInstant() == frameNum: - print 'first frame for obj {}'.format(obj.getNum()) obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, params.maxPedestrianSpeed, params.maxCyclistSpeed, params.nFramesIgnoreAtEnds) obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, 0.2, 0.2, 800) # px, py, pixelThreshold frameNum += 1
--- a/scripts/uninstall-scripts.sh Fri Jun 05 02:35:29 2015 +0200 +++ b/scripts/uninstall-scripts.sh Fri Jun 05 11:04:06 2015 +0200 @@ -1,7 +1,15 @@ #!/bin/sh -installDirname='/usr/local/bin' +if [ $# -ge 1 ]; +then + installDirname=$1 + echo 'Removing from directory '$installDirname +else + installDirname='/usr/local/bin' + echo 'Removing from default directory '$installDirname +fi + for filename in * do echo 'rm '$installDirname/$filename rm $installDirname/$filename -done \ No newline at end of file +done