Mercurial Hosting > traffic-intelligence
comparison scripts/train-object-classification.py @ 961:ec1682ed999f
added computation of confusion matrix and improved default parameter for block normalization for SVM classification
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Sun, 05 Nov 2017 23:45:47 -0500 |
parents | 238008f81c16 |
children | 2757efeabbb4 |
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960:0c1d1eeef544 | 961:ec1682ed999f |
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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('-s', dest = 'rescaleSize', help = 'rescale size of image samples', default = 64, type = int) | 21 parser.add_argument('--compute-classifications', dest = 'computeClassifications', help = 'compute the confusion matrix on the training data', action = 'store_true') |
22 # parser.add_argument('-o', dest = 'nOrientations', help = 'number of orientations in HoG', default = 9, type = int) | |
23 # parser.add_argument('-p', dest = 'nPixelsPerCell', help = 'number of pixels per cell', default = 8, type = int) | |
24 # parser.add_argument('-c', dest = 'nCellsPerBlock', help = 'number of cells per block', default = 2, type = int) | |
25 | 22 |
26 args = parser.parse_args() | 23 args = parser.parse_args() |
27 classifierParams = storage.ClassifierParameters(args.configFilename) | 24 classifierParams = storage.ClassifierParameters(args.configFilename) |
28 | |
29 # rescaleSize = (args.rescaleSize, args.rescaleSize) | |
30 # nPixelsPerCell = (args.nPixelsPerCell, args.nPixelsPerCell) | |
31 # nCellsPerBlock = (args.nCellsPerBlock, args.nCellsPerBlock) | |
32 | 25 |
33 imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/", | 26 imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/", |
34 'bicycle': args.directoryName + "/Cyclists/", | 27 'bicycle': args.directoryName + "/Cyclists/", |
35 'car': args.directoryName + "/Vehicles/"} | 28 'car': args.directoryName + "/Vehicles/"} |
36 | 29 |
55 trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels | 48 trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels |
56 | 49 |
57 # Training the Support Vector Machine | 50 # Training the Support Vector Machine |
58 print "Training Pedestrian-Cyclist-Vehicle Model" | 51 print "Training Pedestrian-Cyclist-Vehicle Model" |
59 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) |
60 model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values())) | 53 classifications = model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()), True) |
54 if args.computeClassifications: | |
55 print(classifications) | |
61 model.save(args.directoryName + "/modelPBV.xml") | 56 model.save(args.directoryName + "/modelPBV.xml") |
62 | 57 |
63 print "Training Cyclist-Vehicle Model" | 58 print "Training Cyclist-Vehicle Model" |
64 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) |
65 model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values())) | 60 classifications = model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()), True) |
61 if args.computeClassifications: | |
62 print(classifications) | |
66 model.save(args.directoryName + "/modelBV.xml") | 63 model.save(args.directoryName + "/modelBV.xml") |
67 | 64 |
68 print "Training Pedestrian-Cyclist Model" | 65 print "Training Pedestrian-Cyclist Model" |
69 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) |
70 model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values())) | 67 classifications = model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()), True) |
68 if args.computeClassifications: | |
69 print(classifications) | |
71 model.save(args.directoryName + "/modelPB.xml") | 70 model.save(args.directoryName + "/modelPB.xml") |
72 | 71 |
73 print "Training Pedestrian-Vehicle Model" | 72 print "Training Pedestrian-Vehicle Model" |
74 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) |
75 model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values())) | 74 classifications = model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()), True) |
75 if args.computeClassifications: | |
76 print(classifications) | |
76 model.save(args.directoryName + "/modelPV.xml") | 77 model.save(args.directoryName + "/modelPV.xml") |