Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/train-object-classification.py @ 807:52aa03260f03 opencv3

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reversed all code to OpenCV 2.4.13
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 10 Jun 2016 15:26:19 -0400
parents 5b970a5bc233
children 21f10332c72b
comparison
equal deleted inserted replaced
806:c6f497291fd8 807:52aa03260f03
1 #! /usr/bin/env python 1 #! /usr/bin/env python
2 2
3 import numpy as np 3 import numpy as np
4 import sys, argparse 4 import sys, argparse
5 from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE 5 from cv2 import SVM_RBF, SVM_C_SVC
6 #from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE # row_sample for layout in cv2.ml.SVM_load
7
6 8
7 import cvutils, moving, ml 9 import cvutils, moving, ml
8 10
9 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') 11 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
10 parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True) 12 parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True)
46 trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels 48 trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels
47 49
48 # Training the Support Vector Machine 50 # Training the Support Vector Machine
49 print "Training Pedestrian-Cyclist-Vehicle Model" 51 print "Training Pedestrian-Cyclist-Vehicle Model"
50 model = ml.SVM(args.svmType, args.kernelType) 52 model = ml.SVM(args.svmType, args.kernelType)
51 model.train(np.concatenate(trainingSamplesPBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPBV.values())) 53 model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()))
52 model.save(args.directoryName + "/modelPBV.xml") 54 model.save(args.directoryName + "/modelPBV.xml")
53 55
54 print "Training Cyclist-Vehicle Model" 56 print "Training Cyclist-Vehicle Model"
55 model = ml.SVM(args.svmType, args.kernelType) 57 model = ml.SVM(args.svmType, args.kernelType)
56 model.train(np.concatenate(trainingSamplesBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsBV.values())) 58 model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()))
57 model.save(args.directoryName + "/modelBV.xml") 59 model.save(args.directoryName + "/modelBV.xml")
58 60
59 print "Training Pedestrian-Cyclist Model" 61 print "Training Pedestrian-Cyclist Model"
60 model = ml.SVM(args.svmType, args.kernelType) 62 model = ml.SVM(args.svmType, args.kernelType)
61 model.train(np.concatenate(trainingSamplesPB.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPB.values())) 63 model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()))
62 model.save(args.directoryName + "/modelPB.xml") 64 model.save(args.directoryName + "/modelPB.xml")
63 65
64 print "Training Pedestrian-Vehicle Model" 66 print "Training Pedestrian-Vehicle Model"
65 model = ml.SVM(args.svmType, args.kernelType) 67 model = ml.SVM(args.svmType, args.kernelType)
66 model.train(np.concatenate(trainingSamplesPV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPV.values())) 68 model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()))
67 model.save(args.directoryName + "/modelPV.xml") 69 model.save(args.directoryName + "/modelPV.xml")