Mercurial Hosting > traffic-intelligence
comparison scripts/train-object-classification.py @ 788:5b970a5bc233 dev
updated classifying code to OpenCV 3.x (bug in function to load classification models)
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 24 Mar 2016 16:37:37 -0400 |
parents | da1352b89d02 |
children | 52aa03260f03 |
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787:0a428b449b80 | 788:5b970a5bc233 |
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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") |