--- a/scripts/train-object-classification.py	Thu Mar 17 16:01:19 2016 -0400
+++ b/scripts/train-object-classification.py	Thu Mar 24 16:37:37 2016 -0400
@@ -2,7 +2,7 @@
 
 import numpy as np
 import sys, argparse
-from cv2 import SVM_RBF, SVM_C_SVC
+from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE
 
 import cvutils, moving, ml
 
@@ -10,6 +10,7 @@
 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)
+# TODO make other SVM parameters apparent: C, C0, Nu, etc.
 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)
@@ -24,7 +25,6 @@
                     'bicycle': args.directoryName + "/Cyclists/",
                     'car': args.directoryName + "/Vehicles/"}
 
-#directory_model = args.directoryName
 trainingSamplesPBV = {}
 trainingLabelsPBV = {}
 trainingSamplesBV = {}
@@ -47,21 +47,21 @@
 
 # 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 = ml.SVM(args.svmType, args.kernelType)
+model.train(np.concatenate(trainingSamplesPBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPBV.values()))
 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 = ml.SVM(args.svmType, args.kernelType)
+model.train(np.concatenate(trainingSamplesBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsBV.values()))
 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 = ml.SVM(args.svmType, args.kernelType)
+model.train(np.concatenate(trainingSamplesPB.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPB.values()))
 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 = ml.SVM(args.svmType, args.kernelType)
+model.train(np.concatenate(trainingSamplesPV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPV.values()))
 model.save(args.directoryName + "/modelPV.xml")