--- a/python/cvutils.py	Thu Mar 17 16:01:19 2016 -0400
+++ b/python/cvutils.py	Thu Mar 24 16:37:37 2016 -0400
@@ -602,6 +602,6 @@
             inputData.append(features)
 
         nImages = len(inputData)
-        return array(inputData, dtype = float32), array([classLabel]*nImages, dtype = float32)
+        return array(inputData, dtype = float32), array([classLabel]*nImages)
 
         

--- a/python/ml.py	Thu Mar 17 16:01:19 2016 -0400
+++ b/python/ml.py	Thu Mar 24 16:37:37 2016 -0400
@@ -11,6 +11,7 @@
 import matplotlib.pyplot as plt
 from scipy.cluster.vq import kmeans, whiten, vq
 from sklearn import mixture
+import cv2
 
 import utils
 
@@ -18,7 +19,7 @@
 # OpenCV ML models
 #####################
 
-class Model(object):
+class StatModel(object):
     '''Abstract class for loading/saving model'''    
     def load(self, filename):
         if path.exists(filename):
@@ -29,16 +30,21 @@
     def save(self, filename):
         self.model.save(filename)
 
-class SVM(Model):
+class SVM(StatModel):
     '''wrapper for OpenCV SimpleVectorMachine algorithm'''
+    def __init__(self, svmType = cv2.ml.SVM_C_SVC, kernelType = cv2.ml.SVM_RBF, degree = 0, gamma = 1, coef0 = 0, Cvalue = 1, nu = 0, p = 0):
+        self.model = cv2.ml.SVM_create()
+        self.model.setType(svmType)
+        self.model.setKernel(kernelType)
+        self.model.setDegree(degree)
+        self.model.setGamma(gamma)
+        self.model.setCoef0(coef0)
+        self.model.setC(Cvalue)
+        self.model.setNu(nu)
+        self.model.setP(p)
 
-    def __init__(self):
-        import cv2
-        self.model = cv2.SVM()
-
-    def train(self, samples, responses, svm_type, kernel_type, degree = 0, gamma = 1, coef0 = 0, Cvalue = 1, nu = 0, p = 0):
-        self.params = dict(svm_type = svm_type, kernel_type = kernel_type, degree = degree, gamma = gamma, coef0 = coef0, Cvalue = Cvalue, nu = nu, p = p)
-        self.model.train(samples, responses, params = self.params)
+    def train(self, samples, layout, responses):
+        self.model.train(samples, layout, responses)
 
     def predict(self, hog):
         return self.model.predict(hog)

--- a/scripts/classify-objects.py	Thu Mar 17 16:01:19 2016 -0400
+++ b/scripts/classify-objects.py	Thu Mar 24 16:37:37 2016 -0400
@@ -4,14 +4,16 @@
 
 import numpy as np
 import sys, argparse
-#from cv2 import SVM_RBF, SVM_C_SVC
+from cv2 import SVM_RBF, SVM_C_SVC
 import cv2
 from scipy.stats import norm, lognorm
 
-# TODO add mode detection live
+# TODO add mode detection live, add choice of kernel and svm type (to be saved in future classifier format)
 
 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', 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('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None)
@@ -44,9 +46,9 @@
     print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod))
     sys.exit()
 
-pedBikeCarSVM = ml.SVM()
+pedBikeCarSVM = ml.SVM(args.svmType, args.kernelType)
 pedBikeCarSVM.load(params.pedBikeCarSVMFilename)
-bikeCarSVM = ml.SVM()
+bikeCarSVM = ml.SVM(args.svmType, args.kernelType)
 bikeCarSVM.load(params.bikeCarSVMFilename)
 
 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
@@ -75,8 +77,8 @@
 timeInterval = moving.unionIntervals(intervals)
 
 capture = cv2.VideoCapture(videoFilename)
-width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
-height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
+width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
+height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
 
 pastObjects = []
 if params.undistort: # setup undistortion
@@ -84,7 +86,7 @@
 if capture.isOpened():
     ret = True
     frameNum = timeInterval.first
-    capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
+    capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
     lastFrameNum = timeInterval.last
 
     while ret and frameNum <= lastFrameNum:

--- 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")