Mercurial Hosting > traffic-intelligence
diff scripts/train-object-classification.py @ 998:933670761a57
updated code to python 3 (tests pass and scripts run, but non-executed parts of code are probably still not correct)
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Sun, 27 May 2018 23:22:48 -0400 |
| parents | e8eabef7857c |
| children | d478d3122804 |
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--- a/scripts/train-object-classification.py Fri May 25 18:15:18 2018 -0400 +++ b/scripts/train-object-classification.py Sun May 27 23:22:48 2018 -0400 @@ -1,4 +1,4 @@ -#! /usr/bin/env python +#! /usr/bin/env python3 import numpy as np import argparse @@ -35,7 +35,7 @@ trainingSamplesPV = {} trainingLabelsPV = {} -for k, v in imageDirectories.iteritems(): +for k, v in imageDirectories.items(): print('Loading {} samples'.format(k)) trainingSamples, trainingLabels = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogBlockNorm, classifierParams.hogNCellsPerBlock) trainingSamplesPBV[k], trainingLabelsPBV[k] = trainingSamples, trainingLabels @@ -47,30 +47,30 @@ trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels # Training the Support Vector Machine -print "Training Pedestrian-Cyclist-Vehicle Model" +print("Training Pedestrian-Cyclist-Vehicle Model") model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) -classifications = model.train(np.concatenate(trainingSamplesPBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPBV.values()), True) +classifications = model.train(np.concatenate(list(trainingSamplesPBV.values())), ROW_SAMPLE, np.concatenate(list(trainingLabelsPBV.values())), True) if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPBV.xml") -print "Training Cyclist-Vehicle Model" +print("Training Cyclist-Vehicle Model") model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) -classifications = model.train(np.concatenate(trainingSamplesBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsBV.values()), True) +classifications = model.train(np.concatenate(list(trainingSamplesBV.values())), ROW_SAMPLE, np.concatenate(list(trainingLabelsBV.values())), True) if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelBV.xml") -print "Training Pedestrian-Cyclist Model" +print("Training Pedestrian-Cyclist Model") model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) -classifications = model.train(np.concatenate(trainingSamplesPB.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPB.values()), True) +classifications = model.train(np.concatenate(list(trainingSamplesPB.values())), ROW_SAMPLE, np.concatenate(list(trainingLabelsPB.values())), True) if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPB.xml") -print "Training Pedestrian-Vehicle Model" +print("Training Pedestrian-Vehicle Model") model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) -classifications = model.train(np.concatenate(trainingSamplesPV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPV.values()), True) +classifications = model.train(np.concatenate(list(trainingSamplesPV.values())), ROW_SAMPLE, np.concatenate(list(trainingLabelsPV.values())), True) if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPV.xml")