--- a/scripts/train-object-classification.py	Fri Jun 10 15:44:08 2016 -0400
+++ b/scripts/train-object-classification.py	Fri Jun 10 17:07:36 2016 -0400
@@ -5,23 +5,30 @@
 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
+import cvutils, moving, ml, storage
 
 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)
-# 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)
-parser.add_argument('-c', dest = 'nCellsPerBlock', help = 'number of cells per block', default = 2, type = int)
+parser.add_argument('--deg', dest = 'degree', help = 'SVM degree', default = 0, type = int)
+parser.add_argument('--gamma', dest = 'gamma', help = 'SVM gamma', default = 1, type = int)
+parser.add_argument('--coef0', dest = 'coef0', help = 'SVM coef0', default = 0, type = int)
+parser.add_argument('--cvalue', dest = 'cvalue', help = 'SVM Cvalue', default = 1, type = int)
+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)
+
 args = parser.parse_args()
+classifierParams = storage.ClassifierParameters(args.configFilename)
 
-rescaleSize = (args.rescaleSize, args.rescaleSize)
-nPixelsPerCell = (args.nPixelsPerCell, args.nPixelsPerCell)
-nCellsPerBlock = (args.nCellsPerBlock, args.nCellsPerBlock)
+# rescaleSize = (args.rescaleSize, args.rescaleSize)
+# nPixelsPerCell = (args.nPixelsPerCell, args.nPixelsPerCell)
+# nCellsPerBlock = (args.nCellsPerBlock, args.nCellsPerBlock)
 
 imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/",
                     'bicycle': args.directoryName + "/Cyclists/",
@@ -38,7 +45,7 @@
 
 for k, v in imageDirectories.iteritems():
     print('Loading {} samples'.format(k))
-    trainingSamples, trainingLabels = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock)
+    trainingSamples, trainingLabels = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock)
     trainingSamplesPBV[k], trainingLabelsPBV[k] = trainingSamples, trainingLabels
     if k != 'pedestrian':
 	trainingSamplesBV[k], trainingLabelsBV[k] = trainingSamples, trainingLabels
@@ -49,21 +56,21 @@
 
 # Training the Support Vector Machine
 print "Training Pedestrian-Cyclist-Vehicle Model"
-model = ml.SVM(args.svmType, args.kernelType)
+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()))
 model.save(args.directoryName + "/modelPBV.xml")
 
 print "Training Cyclist-Vehicle Model"
-model = ml.SVM(args.svmType, args.kernelType)
+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()))
 model.save(args.directoryName + "/modelBV.xml")
 
 print "Training Pedestrian-Cyclist Model"
-model = ml.SVM(args.svmType, args.kernelType)
+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()))
 model.save(args.directoryName + "/modelPB.xml")
 
 print "Training Pedestrian-Vehicle Model"
-model = ml.SVM(args.svmType, args.kernelType)
+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()))
 model.save(args.directoryName + "/modelPV.xml")