Mercurial Hosting > traffic-intelligence
comparison scripts/train-object-classification.py @ 807:52aa03260f03 opencv3
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") |