Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/train-object-classification.py @ 788:5b970a5bc233 dev

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updated classifying code to OpenCV 3.x (bug in function to load classification models)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 24 Mar 2016 16:37:37 -0400
parents da1352b89d02
children 52aa03260f03
comparison
equal deleted inserted replaced
787:0a428b449b80 788:5b970a5bc233
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 import SVM_RBF, SVM_C_SVC 5 from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE
6 6
7 import cvutils, moving, ml 7 import cvutils, moving, ml
8 8
9 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') 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) 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) 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) 12 parser.add_argument('--svm', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long)
13 # TODO make other SVM parameters apparent: C, C0, Nu, etc.
13 parser.add_argument('-s', dest = 'rescaleSize', help = 'rescale size of image samples', default = 64, type = int) 14 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('-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('-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 parser.add_argument('-c', dest = 'nCellsPerBlock', help = 'number of cells per block', default = 2, type = int)
17 args = parser.parse_args() 18 args = parser.parse_args()
22 23
23 imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/", 24 imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/",
24 'bicycle': args.directoryName + "/Cyclists/", 25 'bicycle': args.directoryName + "/Cyclists/",
25 'car': args.directoryName + "/Vehicles/"} 26 'car': args.directoryName + "/Vehicles/"}
26 27
27 #directory_model = args.directoryName
28 trainingSamplesPBV = {} 28 trainingSamplesPBV = {}
29 trainingLabelsPBV = {} 29 trainingLabelsPBV = {}
30 trainingSamplesBV = {} 30 trainingSamplesBV = {}
31 trainingLabelsBV = {} 31 trainingLabelsBV = {}
32 trainingSamplesPB = {} 32 trainingSamplesPB = {}
45 if k != 'bicycle': 45 if k != 'bicycle':
46 trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels 46 trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels
47 47
48 # Training the Support Vector Machine 48 # Training the Support Vector Machine
49 print "Training Pedestrian-Cyclist-Vehicle Model" 49 print "Training Pedestrian-Cyclist-Vehicle Model"
50 model = ml.SVM() 50 model = ml.SVM(args.svmType, args.kernelType)
51 model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()), args.svmType, args.kernelType) 51 model.train(np.concatenate(trainingSamplesPBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPBV.values()))
52 model.save(args.directoryName + "/modelPBV.xml") 52 model.save(args.directoryName + "/modelPBV.xml")
53 53
54 print "Training Cyclist-Vehicle Model" 54 print "Training Cyclist-Vehicle Model"
55 model = ml.SVM() 55 model = ml.SVM(args.svmType, args.kernelType)
56 model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()), args.svmType, args.kernelType) 56 model.train(np.concatenate(trainingSamplesBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsBV.values()))
57 model.save(args.directoryName + "/modelBV.xml") 57 model.save(args.directoryName + "/modelBV.xml")
58 58
59 print "Training Pedestrian-Cyclist Model" 59 print "Training Pedestrian-Cyclist Model"
60 model = ml.SVM() 60 model = ml.SVM(args.svmType, args.kernelType)
61 model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()), args.svmType, args.kernelType) 61 model.train(np.concatenate(trainingSamplesPB.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPB.values()))
62 model.save(args.directoryName + "/modelPB.xml") 62 model.save(args.directoryName + "/modelPB.xml")
63 63
64 print "Training Pedestrian-Vehicle Model" 64 print "Training Pedestrian-Vehicle Model"
65 model = ml.SVM() 65 model = ml.SVM(args.svmType, args.kernelType)
66 model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()), args.svmType, args.kernelType) 66 model.train(np.concatenate(trainingSamplesPV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPV.values()))
67 model.save(args.directoryName + "/modelPV.xml") 67 model.save(args.directoryName + "/modelPV.xml")