Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/classify-objects.py @ 962:64259b9885bf

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
verbose option to print classification information (more to add)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 06 Nov 2017 21:25:41 -0500
parents 053484e08947
children e8eabef7857c
comparison
equal deleted inserted replaced
961:ec1682ed999f 962:64259b9885bf
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('--start-frame0', dest = 'startFrame0', help = 'starts with first frame for videos with index problem where frames cannot be reached', 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('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true')
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 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.)
21 parser.add_argument('--verbose', dest = 'verbose', help = 'verbose information', action = 'store_true')
21 22
22 args = parser.parse_args() 23 args = parser.parse_args()
23 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args) 24 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
24 classifierParams = storage.ClassifierParameters(params.classifierFilename) 25 classifierParams = storage.ClassifierParameters(params.classifierFilename)
25 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame 26 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame
109 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown) 110 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
110 pastObjects.append(obj) 111 pastObjects.append(obj)
111 currentObjects.remove(obj) 112 currentObjects.remove(obj)
112 else: 113 else:
113 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm) 114 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
115 if args.verbose:
116 print('obj {}@{}: {}'.format(obj.getNum(), frameNum, moving.userTypeNames[obj.userTypes[frameNum]]))
114 frameNum += 1 117 frameNum += 1
115 118
116 for obj in currentObjects: 119 for obj in currentObjects:
117 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown) 120 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
118 pastObjects.append(obj) 121 pastObjects.append(obj)