#! /usr/bin/env python

import numpy as np
import sys, argparse
from cv2 import SVM_RBF, SVM_C_SVC

import cvutils, moving, ml

parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
parser.add_argument('-d', dest = 'directoryName', help = 'name of the parent directory containing the videos and extracted trajectories to process', 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('-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()

print('TODO') # add all parameters for classification (distribution parameters)
