Mercurial Hosting > traffic-intelligence
view scripts/train-object-classification.py @ 776:84420159c5f4 dev
added __eq__ functions for Point and Trajectory
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 05 Feb 2016 17:17:12 -0500 |
parents | da1352b89d02 |
children | 5b970a5bc233 |
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#! /usr/bin/env python import numpy as np import sys, argparse from cv2 import SVM_RBF, SVM_C_SVC import cvutils, moving, ml parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True) parser.add_argument('--kernel', dest = 'kernelType', help = 'kernel type for the support vector machine (SVM)', default = SVM_RBF, type = long) parser.add_argument('--svm', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long) parser.add_argument('-s', dest = 'rescaleSize', help = 'rescale size of image samples', default = 64, type = int) parser.add_argument('-o', dest = 'nOrientations', help = 'number of orientations in HoG', default = 9, type = int) parser.add_argument('-p', dest = 'nPixelsPerCell', help = 'number of pixels per cell', default = 8, type = int) parser.add_argument('-c', dest = 'nCellsPerBlock', help = 'number of cells per block', default = 2, type = int) args = parser.parse_args() rescaleSize = (args.rescaleSize, args.rescaleSize) nPixelsPerCell = (args.nPixelsPerCell, args.nPixelsPerCell) nCellsPerBlock = (args.nCellsPerBlock, args.nCellsPerBlock) imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/", 'bicycle': args.directoryName + "/Cyclists/", 'car': args.directoryName + "/Vehicles/"} #directory_model = args.directoryName trainingSamplesPBV = {} trainingLabelsPBV = {} trainingSamplesBV = {} trainingLabelsBV = {} trainingSamplesPB = {} trainingLabelsPB = {} trainingSamplesPV = {} trainingLabelsPV = {} for k, v in imageDirectories.iteritems(): print('Loading {} samples'.format(k)) trainingSamples, trainingLabels = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock) trainingSamplesPBV[k], trainingLabelsPBV[k] = trainingSamples, trainingLabels if k != 'pedestrian': trainingSamplesBV[k], trainingLabelsBV[k] = trainingSamples, trainingLabels if k != 'car': trainingSamplesPB[k], trainingLabelsPB[k] = trainingSamples, trainingLabels if k != 'bicycle': trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels # Training the Support Vector Machine print "Training Pedestrian-Cyclist-Vehicle Model" model = ml.SVM() model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()), args.svmType, args.kernelType) model.save(args.directoryName + "/modelPBV.xml") print "Training Cyclist-Vehicle Model" model = ml.SVM() model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()), args.svmType, args.kernelType) model.save(args.directoryName + "/modelBV.xml") print "Training Pedestrian-Cyclist Model" model = ml.SVM() model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()), args.svmType, args.kernelType) model.save(args.directoryName + "/modelPB.xml") print "Training Pedestrian-Vehicle Model" model = ml.SVM() model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()), args.svmType, args.kernelType) model.save(args.directoryName + "/modelPV.xml")