Mercurial Hosting > traffic-intelligence

comparison scripts/train-object-classification.py @ 963:2757efeabbb4

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minor renaming
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 06 Nov 2017 23:04:03 -0500
parents ec1682ed999f
children e8eabef7857c
comparison
equal deleted inserted replaced
962:64259b9885bf 963:2757efeabbb4
16 parser.add_argument('--coef0', dest = 'coef0', help = 'SVM coef0', default = 0, type = int) 16 parser.add_argument('--coef0', dest = 'coef0', help = 'SVM coef0', default = 0, type = int)
17 parser.add_argument('--cvalue', dest = 'cvalue', help = 'SVM Cvalue', default = 1, type = int) 17 parser.add_argument('--cvalue', dest = 'cvalue', help = 'SVM Cvalue', default = 1, type = int)
18 parser.add_argument('--nu', dest = 'nu', help = 'SVM nu', default = 0, type = int) 18 parser.add_argument('--nu', dest = 'nu', help = 'SVM nu', default = 0, type = int)
19 parser.add_argument('--svmp', dest = 'svmP', help = 'SVM p', default = 0, type = int) 19 parser.add_argument('--svmp', dest = 'svmP', help = 'SVM p', default = 0, type = int)
20 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the classifier configuration file', required = True) 20 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the classifier configuration file', required = True)
21 parser.add_argument('--compute-classifications', dest = 'computeClassifications', help = 'compute the confusion matrix on the training data', action = 'store_true') 21 parser.add_argument('--confusion-matrix', dest = 'computeConfusionMatrix', help = 'compute the confusion matrix on the training data', action = 'store_true')
22 22
23 args = parser.parse_args() 23 args = parser.parse_args()
24 classifierParams = storage.ClassifierParameters(args.configFilename) 24 classifierParams = storage.ClassifierParameters(args.configFilename)
25 25
26 imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/", 26 imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/",
49 49
50 # Training the Support Vector Machine 50 # Training the Support Vector Machine
51 print "Training Pedestrian-Cyclist-Vehicle Model" 51 print "Training Pedestrian-Cyclist-Vehicle Model"
52 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) 52 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP)
53 classifications = model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()), True) 53 classifications = model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()), True)
54 if args.computeClassifications: 54 if args.computeConfusionMatrix:
55 print(classifications) 55 print(classifications)
56 model.save(args.directoryName + "/modelPBV.xml") 56 model.save(args.directoryName + "/modelPBV.xml")
57 57
58 print "Training Cyclist-Vehicle Model" 58 print "Training Cyclist-Vehicle Model"
59 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) 59 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP)
60 classifications = model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()), True) 60 classifications = model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()), True)
61 if args.computeClassifications: 61 if args.computeConfusionMatrix:
62 print(classifications) 62 print(classifications)
63 model.save(args.directoryName + "/modelBV.xml") 63 model.save(args.directoryName + "/modelBV.xml")
64 64
65 print "Training Pedestrian-Cyclist Model" 65 print "Training Pedestrian-Cyclist Model"
66 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) 66 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP)
67 classifications = model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()), True) 67 classifications = model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()), True)
68 if args.computeClassifications: 68 if args.computeConfusionMatrix:
69 print(classifications) 69 print(classifications)
70 model.save(args.directoryName + "/modelPB.xml") 70 model.save(args.directoryName + "/modelPB.xml")
71 71
72 print "Training Pedestrian-Vehicle Model" 72 print "Training Pedestrian-Vehicle Model"
73 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) 73 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP)
74 classifications = model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()), True) 74 classifications = model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()), True)
75 if args.computeClassifications: 75 if args.computeConfusionMatrix:
76 print(classifications) 76 print(classifications)
77 model.save(args.directoryName + "/modelPV.xml") 77 model.save(args.directoryName + "/modelPV.xml")