--- a/scripts/train-object-classification.py	Fri Jun 10 12:29:58 2016 -0400
+++ b/scripts/train-object-classification.py	Fri Jun 10 15:26:19 2016 -0400
@@ -2,7 +2,9 @@
 
 import numpy as np
 import sys, argparse
-from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE
+from cv2 import SVM_RBF, SVM_C_SVC
+#from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE # row_sample for layout in cv2.ml.SVM_load
+
 
 import cvutils, moving, ml
 
@@ -48,20 +50,20 @@
 # Training the Support Vector Machine
 print "Training Pedestrian-Cyclist-Vehicle Model"
 model = ml.SVM(args.svmType, args.kernelType)
-model.train(np.concatenate(trainingSamplesPBV.values()), ROW_SAMPLE, np.concatenate(trainingLabelsPBV.values()))
+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()), ROW_SAMPLE, np.concatenate(trainingLabelsBV.values()))
+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()), ROW_SAMPLE, np.concatenate(trainingLabelsPB.values()))
+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()), ROW_SAMPLE, np.concatenate(trainingLabelsPV.values()))
+model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()))
 model.save(args.directoryName + "/modelPV.xml")