Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/train-object-classification.py @ 614:5e09583275a4

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Merged Nicolas/trafficintelligence into default
author Mohamed Gomaa <eng.m.gom3a@gmail.com>
date Fri, 05 Dec 2014 12:13:53 -0500
parents ce40a89bd6ae
children da1352b89d02
comparison
equal deleted inserted replaced
598:11f96bd08552 614:5e09583275a4
1 #! /usr/bin/env python
2
3 import numpy as np
4 import sys, argparse
5 from cv2 import SVM_RBF, SVM_C_SVC
6
7 import cvutils, moving, ml
8
9 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)
11 parser.add_argument('--kernel', dest = 'kernelType', help = 'kernel type for the support vector machine (SVM)', default = SVM_RBF, type = long)
12 parser.add_argument('--svm', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long)
13 parser.add_argument('-s', dest = 'rescaleSize', help = 'rescale size of image samples', default = 64, type = int)
14 parser.add_argument('-o', dest = 'nOrientations', help = 'number of orientations in HoG', default = 9, type = int)
15 parser.add_argument('-p', dest = 'nPixelsPerCell', help = 'number of pixels per cell', default = 8, type = int)
16 parser.add_argument('-c', dest = 'nCellsPerBlock', help = 'number of cells per block', default = 2, type = int)
17 args = parser.parse_args()
18
19 rescaleSize = (args.rescaleSize, args.rescaleSize)
20 nPixelsPerCell = (args.nPixelsPerCell, args.nPixelsPerCell)
21 nCellsPerBlock = (args.nCellsPerBlock, args.nCellsPerBlock)
22
23 imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/",
24 'bicycle': args.directoryName + "/Cyclists/",
25 'car': args.directoryName + "/Vehicles/"}
26
27 #directory_model = args.directoryName
28 trainingSamplesPBV = {}
29 trainingLabelsPBV = {}
30 trainingSamplesBV = {}
31 trainingLabelsBV = {}
32 trainingSamplesPB = {}
33 trainingLabelsPB = {}
34 trainingSamplesPV = {}
35 trainingLabelsPV = {}
36
37 for k, v in imageDirectories.iteritems():
38 print('Loading {} samples'.format(k))
39 trainingSamplesPBV[k], trainingLabelsPBV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
40 if k != 'pedestrian':
41 trainingSamplesBV[k], trainingLabelsBV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
42 if k != 'car':
43 trainingSamplesPB[k], trainingLabelsPB[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
44 if k != 'bicycle':
45 trainingSamplesPV[k], trainingLabelsPV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
46
47 # Training the Support Vector Machine
48 print "Training Pedestrian-Cyclist-Vehicle Model"
49 model = ml.SVM(args.svmType, args.kernelType)
50 model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()))
51 model.save(args.directoryName + "/modelPBV.xml")
52
53 print "Training Cyclist-Vehicle Model"
54 model = ml.SVM(args.svmType, args.kernelType)
55 model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()))
56 model.save(args.directoryName + "/modelBV.xml")
57
58 print "Training Pedestrian-Cyclist Model"
59 model = ml.SVM(args.svmType, args.kernelType)
60 model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()))
61 model.save(args.directoryName + "/modelPB.xml")
62
63 print "Training Pedestrian-Vehicle Model"
64 model = ml.SVM(args.svmType, args.kernelType)
65 model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()))
66 model.save(args.directoryName + "/modelPV.xml")