--- a/scripts/train-object-classification.py	Fri Oct 20 16:59:32 2017 -0400
+++ b/scripts/train-object-classification.py	Sun Nov 05 23:45:47 2017 -0500
@@ -18,18 +18,11 @@
 parser.add_argument('--nu', dest = 'nu', help = 'SVM nu', default = 0, type = int)
 parser.add_argument('--svmp', dest = 'svmP', help = 'SVM p', default = 0, type = int)
 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the classifier configuration file', required = True)
-# 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)
+parser.add_argument('--compute-classifications', dest = 'computeClassifications', help = 'compute the confusion matrix on the training data', action = 'store_true')
 
 args = parser.parse_args()
 classifierParams = storage.ClassifierParameters(args.configFilename)
 
-# 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/"}
@@ -57,20 +50,28 @@
 # Training the Support Vector Machine
 print "Training Pedestrian-Cyclist-Vehicle Model"
 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP)
-model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()))
+classifications = model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()), True)
+if args.computeClassifications:
+    print(classifications)
 model.save(args.directoryName + "/modelPBV.xml")
 
 print "Training Cyclist-Vehicle Model"
 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP)
-model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()))
+classifications = model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()), True)
+if args.computeClassifications:
+    print(classifications)
 model.save(args.directoryName + "/modelBV.xml")
 
 print "Training Pedestrian-Cyclist Model"
 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP)
-model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()))
+classifications = model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()), True)
+if args.computeClassifications:
+    print(classifications)
 model.save(args.directoryName + "/modelPB.xml")
 
 print "Training Pedestrian-Vehicle Model"
 model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP)
-model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()))
+classifications = model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()), True)
+if args.computeClassifications:
+    print(classifications)
 model.save(args.directoryName + "/modelPV.xml")