#! /usr/bin/env python

import numpy as np
import argparse
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, storage

parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information')
parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
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('--gt', dest = 'classificationAnnotationFilename', help = 'name of the file containing the correct classes (user types)', required = True)
parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int)
parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None)
parser.add_argument('--compute-speed-distributions', dest = 'computeSpeedDistribution', help = 'computes the distribution of the road users of each type and fits parameters to each', action = 'store_true')


#parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True)

args = parser.parse_args()
params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
classifierParams = storage.ClassifierParameters(params.classifierFilename)

objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])

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))

if undistort: # setup undistortion
    [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
if capture.isOpened():
    ret = True
    frameNum = timeInterval.first
    capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
    lastFrameNum = timeInterval.last
    while ret and frameNum <= lastFrameNum:
        ret, img = capture.read()
        if ret:
            if frameNum%50 == 0:
                print('frame number: {}'.format(frameNum))
            if undistort:
                img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)


        frameNum += 1



# todo speed info: distributions AND min speed equiprobable

# provide csv delimiter for the classification file as arg