Mercurial Hosting > traffic-intelligence
comparison scripts/train-object-classification.py @ 963:2757efeabbb4
minor renaming
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 06 Nov 2017 23:04:03 -0500 |
parents | ec1682ed999f |
children | e8eabef7857c |
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962:64259b9885bf | 963:2757efeabbb4 |
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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") |