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annotate scripts/extract-appearance-images.py @ 902:c69a8defe5c3

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changed workflow of classify objects
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 22 Jun 2017 16:57:34 -0400
parents 753a081989e2
children 8f60ecfc2f06
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900
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1 #! /usr/bin/env python
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2
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3 import numpy as np
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4 import argparse
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5 from cv2 import SVM_RBF, SVM_C_SVC
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6 #from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE # row_sample for layout in cv2.ml.SVM_load
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7
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8 import cvutils, moving, ml, storage
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9
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10 parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information')
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11 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
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12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
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13 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
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14 parser.add_argument('--gt', dest = 'classificationAnnotationFilename', help = 'name of the file containing the correct classes (user types)', required = True)
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15 parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int)
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16 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None)
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17 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')
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20 #parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True)
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21
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22 args = parser.parse_args()
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23 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
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24 classifierParams = storage.ClassifierParameters(params.classifierFilename)
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25
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26 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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27 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
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28
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29 capture = cv2.VideoCapture(videoFilename)
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30 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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31 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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32
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33 if undistort: # setup undistortion
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34 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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35 if capture.isOpened():
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36 ret = True
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37 frameNum = timeInterval.first
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38 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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39 lastFrameNum = timeInterval.last
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40 while ret and frameNum <= lastFrameNum:
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41 ret, img = capture.read()
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42 if ret:
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43 if frameNum%50 == 0:
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44 print('frame number: {}'.format(frameNum))
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45 if undistort:
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46 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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49 frameNum += 1
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53 # todo speed info: distributions AND min speed equiprobable
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55 # provide csv delimiter for the classification file as arg
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