--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/train-object-classification.py	Wed Jun 18 00:53:39 2014 -0400
@@ -0,0 +1,68 @@
+#! /usr/bin/env python
+
+import numpy as np
+import sys, argparse
+from cv2 import SVM_RBF, SVM_C_SVC
+
+import cvutils, moving, ml
+
+
+# todo update with argparse
+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))
+    trainingSamplesPBV[k], trainingLabelsPBV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
+    if k != 'pedestrian':
+	trainingSamplesBV[k], trainingLabelsBV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
+    if k != 'car':
+	trainingSamplesPB[k], trainingLabelsPB[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
+    if k != 'bicycle':
+	trainingSamplesPV[k], trainingLabelsPV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
+
+# Training the Support Vector Machine
+print "Training Pedestrian-Cyclist-Vehicle Model"
+model = ml.SVM(args.svmType, args.kernelType)
+model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()))
+model.save(args.directoryName + "/modelPBV.xml")
+
+print "Training Cyclist-Vehicle Model"
+model = ml.SVM(args.svmType, args.kernelType)
+model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()))
+model.save(args.directoryName + "/modelBV.xml")
+
+print "Training Pedestrian-Cyclist Model"
+model = ml.SVM(args.svmType, args.kernelType)
+model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()))
+model.save(args.directoryName + "/modelPB.xml")
+
+print "Training Pedestrian-Vehicle Model"
+model = ml.SVM(args.svmType, args.kernelType)
+model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()))
+model.save(args.directoryName + "/modelPV.xml")