Mercurial Hosting > traffic-intelligence
changeset 946:e5970606066f
bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Fri, 21 Jul 2017 11:25:20 -0400 |
| parents | 05d4302bf67e |
| children | 053484e08947 |
| files | python/cvutils.py python/prediction.py scripts/classify-objects.py scripts/extract-appearance-images.py scripts/safety-analysis.py |
| diffstat | 5 files changed, 18 insertions(+), 10 deletions(-) [+] |
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--- a/python/cvutils.py Thu Jul 20 14:29:46 2017 -0400 +++ b/python/cvutils.py Fri Jul 21 11:25:20 2017 -0400 @@ -344,8 +344,7 @@ if frameNum not in toMatches[obj.getNum()]: objDescription += " FA" cv2.putText(img, objDescription, obj.projectedPositions[frameNum-obj.getFirstInstant()].asint().astuple(), cv2.FONT_HERSHEY_PLAIN, 1, cvColors[colorType][obj.getNum()]) - if obj.getLastInstant() == frameNum: - objects.remove(obj) + objects[:] = [obj for obj in objects if obj.getLastInstant() != frameNum] # plot object bounding boxes if frameNum in boundingBoxes.keys(): for rect in boundingBoxes[frameNum]:
--- a/python/prediction.py Thu Jul 20 14:29:46 2017 -0400 +++ b/python/prediction.py Fri Jul 21 11:25:20 2017 -0400 @@ -575,10 +575,16 @@ predictedTrajectories = [] if instant-obj.getFirstInstant()+1 >= self.minFeatureTime: if self.useFeatures and obj.hasFeatures(): - # get current features existing for the most time, sort on first instant of feature and take n first - firstInstants = [(f,f.getFirstInstant()) for f in obj.getFeatures() if f.existsAtInstant(instant)] + if not hasattr(obj, 'currentPredictionFeatures'): + obj.currentPredictionFeatures = [] + else: + obj.currentPredictionFeatures[:] = [f for f in obj.currentPredictionFeatures if f.existsAtInstant(instant)] + firstInstants = [(f,f.getFirstInstant()) for f in obj.getFeatures() if f.existsAtInstant(instant) and f not in obj.currentPredictionFeatures] firstInstants.sort(key = lambda t: t[1]) - for f,t1 in firstInstants[:min(self.nPredictedTrajectories, len(firstInstants))]: + for f,t1 in firstInstants[:min(self.nPredictedTrajectories, len(firstInstants), self.nPredictedTrajectories-len(obj.currentPredictionFeatures))]: + obj.currentPredictionFeatures.append(f) + print len(obj.currentPredictionFeatures), self.nPredictedTrajectories + for f in obj.currentPredictionFeatures: self.addPredictedTrajectories(predictedTrajectories, f, instant) else: self.addPredictedTrajectories(predictedTrajectories, obj, instant)
--- a/scripts/classify-objects.py Thu Jul 20 14:29:46 2017 -0400 +++ b/scripts/classify-objects.py Fri Jul 21 11:25:20 2017 -0400 @@ -102,15 +102,15 @@ if obj.getFirstInstant() <= frameNum: # if images are skipped obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds, invHomography, intrinsicCameraMatrix, distortionCoefficients) currentObjects.append(obj) - objects.remove(obj) + objects[:] = [obj for obj in objects if obj.getFirstInstant() > frameNum] for obj in currentObjects: if obj.getLastInstant() <= frameNum: # if images are skipped obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown) pastObjects.append(obj) - currentObjects.remove(obj) else: obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm) + currentObjects[:] = [obj for obj in objects if obj.getLastInstant() > frameNum] frameNum += 1 for obj in currentObjects:
--- a/scripts/extract-appearance-images.py Thu Jul 20 14:29:46 2017 -0400 +++ b/scripts/extract-appearance-images.py Fri Jul 21 11:25:20 2017 -0400 @@ -80,8 +80,9 @@ croppedImg = cvutils.imageBox(img, obj, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels) if croppedImg is not None: cv2.imwrite(args.directoryName+os.sep+moving.userTypeNames[obj.getUserType()]+os.sep+args.imagePrefix+'-{}-{}.png'.format(obj.getNum(), frameNum), croppedImg) - elif obj.getLastInstant() == frameNum: - objects.remove(obj) +# elif obj.getLastInstant() == frameNum: +# objects.remove(obj) + objects[:] = [obj for obj in objects if obj.getLastInstant() != frameNum] frameNum += 1 # todo speed info: distributions AND min speed equiprobable
--- a/scripts/safety-analysis.py Thu Jul 20 14:29:46 2017 -0400 +++ b/scripts/safety-analysis.py Fri Jul 21 11:25:20 2017 -0400 @@ -16,6 +16,7 @@ # TODO analyze only parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp']) parser.add_argument('--prototypeDatabaseFilename', dest = 'prototypeDatabaseFilename', help = 'name of the database containing the prototypes') +parser.add_argument('--no-motion-prediction', dest = 'noMotionPrediction', help = 'does not compute indicators like TTC depending on motion prediction', action = 'store_true') parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true') parser.add_argument('--display-cp', dest = 'displayCollisionPoints', help = 'display collision points', action = 'store_true') parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1) @@ -75,7 +76,8 @@ interactions = events.createInteractions(objects) for inter in interactions: inter.computeIndicators() -# inter.computeCrossingsCollisions(predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, nProcesses = args.nProcesses, debug = True) + if not args.noMotionPrediction: + inter.computeCrossingsCollisions(predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, nProcesses = args.nProcesses, debug = True) if args.computePET: for inter in interactions: