Mercurial > hg > nsaunier > traffic-intelligence

comparison 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
comparison
equal deleted inserted replaced
901:753a081989e2 902:c69a8defe5c3
7 7
8 import cvutils, moving, ml, storage 8 import cvutils, moving, ml, storage
9 9
10 parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information') 10 parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information')
11 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) 11 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
13 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
14 parser.add_argument('--gt', dest = 'classificationAnnotationFilename', help = 'name of the file containing the correct classes (user types)', required = True)
15 parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int)
16 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None)
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')
18
12 19
13 #parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True) 20 #parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True)
14 21
15 args = parser.parse_args() 22 args = parser.parse_args()
16 params = storage.ProcessParameters(args.configFilename) 23 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
17 classifierParams = storage.ClassifierParameters(params.classifierFilename) 24 classifierParams = storage.ClassifierParameters(params.classifierFilename)
18 25
19 # need all info as for classification (image info) 26 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
27 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
28
29 capture = cv2.VideoCapture(videoFilename)
30 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
31 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
32
33 if undistort: # setup undistortion
34 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
35 if capture.isOpened():
36 ret = True
37 frameNum = timeInterval.first
38 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
39 lastFrameNum = timeInterval.last
40 while ret and frameNum <= lastFrameNum:
41 ret, img = capture.read()
42 if ret:
43 if frameNum%50 == 0:
44 print('frame number: {}'.format(frameNum))
45 if undistort:
46 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
47
48
49 frameNum += 1
50
51
20 52
21 # todo speed info: distributions AND min speed equiprobable 53 # todo speed info: distributions AND min speed equiprobable
22 54
23 # provide csv delimiter for the classification file as arg 55 # provide csv delimiter for the classification file as arg
24 56