Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/classify-objects.py @ 911:3dd5acfa1899

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corrected potential issues with videos where one cannot reach a give frame from its number
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 28 Jun 2017 16:46:45 -0400
parents 0e017178f7ab
children fd057a6b04db
comparison
equal deleted inserted replaced
910:b58a1061a717 911:3dd5acfa1899
13 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') 13 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
14 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) 14 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
15 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)') 15 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
16 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)') 16 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None) 17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None)
18 parser.add_argument('--start-frame0', dest = 'startFrame0', help = 'starts with first frame for videos with index problem where frames cannot be reached', action = 'store_true')
18 parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true') 19 parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true')
19 parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display (km/h)', type = float, default = 50.) 20 parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display (km/h)', type = float, default = 50.)
20 21
21 args = parser.parse_args() 22 args = parser.parse_args()
22 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args) 23 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
64 plt.show() 65 plt.show()
65 sys.exit() 66 sys.exit()
66 67
67 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True) 68 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
68 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects]) 69 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
70 if args.startFrame0:
71 timeInterval.first = 0
69 72
70 capture = cv2.VideoCapture(videoFilename) 73 capture = cv2.VideoCapture(videoFilename)
71 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) 74 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
72 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) 75 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
73 76
78 pastObjects = [] 81 pastObjects = []
79 currentObjects = [] 82 currentObjects = []
80 if capture.isOpened(): 83 if capture.isOpened():
81 ret = True 84 ret = True
82 frameNum = timeInterval.first 85 frameNum = timeInterval.first
83 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum) 86 if not args.startFrame0:
87 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
84 lastFrameNum = timeInterval.last 88 lastFrameNum = timeInterval.last
85 89
86 while ret and frameNum <= lastFrameNum: 90 while ret and frameNum <= lastFrameNum:
87 ret, img = capture.read() 91 ret, img = capture.read()
88 if ret: 92 if ret:
89 if frameNum%50 == 0: 93 if frameNum%50 == 0:
90 print('frame number: {}'.format(frameNum)) 94 print('frame number: {}'.format(frameNum))
91 if undistort: 95 if undistort:
92 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) 96 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
93 for obj in objects: 97 for obj in objects:
94 if obj.getFirstInstant() == frameNum: 98 if obj.getFirstInstant() >= frameNum: # if images are skipped
95 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds) 99 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds)
96 currentObjects.append(obj) 100 currentObjects.append(obj)
97 objects.remove(obj) 101 objects.remove(obj)
98 102
99 for obj in currentObjects: 103 for obj in currentObjects:
100 if obj.getLastInstant() == frameNum: 104 if obj.getLastInstant() <= frameNum: # if images are skipped
101 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown) 105 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
102 pastObjects.append(obj) 106 pastObjects.append(obj)
103 currentObjects.remove(obj) 107 currentObjects.remove(obj)
104 else: 108 else:
105 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm) 109 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)