Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/classify-objects.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
18 parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', 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('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display (km/h)', type = float, default = 50.) 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 20
21 args = parser.parse_args() 21 args = parser.parse_args()
22 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args) 22 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
23
24 classifierParams = storage.ClassifierParameters(params.classifierFilename) 23 classifierParams = storage.ClassifierParameters(params.classifierFilename)
25 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame 24 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame
26 25
27 if classifierParams.speedAggregationMethod == 'median': 26 if classifierParams.speedAggregationMethod == 'median':
28 speedAggregationFunc = np.median 27 speedAggregationFunc = np.median
64 plt.title('Probability Density Function') 63 plt.title('Probability Density Function')
65 plt.show() 64 plt.show()
66 sys.exit() 65 sys.exit()
67 66
68 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True) 67 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
69 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, 'feature') 68 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
70 intervals = []
71 for obj in objects:
72 #obj.setFeatures(features)
73 intervals.append(obj.getTimeInterval())
74 timeInterval = moving.TimeInterval.unionIntervals(intervals)
75 69
76 capture = cv2.VideoCapture(videoFilename) 70 capture = cv2.VideoCapture(videoFilename)
77 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) 71 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
78 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) 72 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
79 73
80 pastObjects = [] 74 pastObjects = []
75 currentObjects = []
81 if undistort: # setup undistortion 76 if undistort: # setup undistortion
82 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) 77 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
83 if capture.isOpened(): 78 if capture.isOpened():
84 ret = True 79 ret = True
85 frameNum = timeInterval.first 80 frameNum = timeInterval.first
90 ret, img = capture.read() 85 ret, img = capture.read()
91 if ret: 86 if ret:
92 if frameNum%50 == 0: 87 if frameNum%50 == 0:
93 print('frame number: {}'.format(frameNum)) 88 print('frame number: {}'.format(frameNum))
94 if undistort: 89 if undistort:
95 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) 90 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
96 currentObjects = []
97 for obj in objects: 91 for obj in objects:
98 inter = obj.getTimeInterval() 92 if obj.getFirstInstant() == frameNum:
99 if inter.contains(frameNum): 93 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds)
100 if inter.first == frameNum: 94 currentObjects.append(obj)
101 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds) 95 objects.remove(obj)
102 currentObjects.append(obj) 96
103 elif inter.last == frameNum: 97 for obj in currentObjects:
104 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown) 98 if obj.getLastInstant() == frameNum:
105 pastObjects.append(obj) 99 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
106 else: 100 pastObjects.append(obj)
107 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm) 101 currentObjects.remove(obj)
108 currentObjects.append(obj)
109 else: 102 else:
110 currentObjects.append(obj) 103 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
111 objects = currentObjects
112 frameNum += 1 104 frameNum += 1
113 105
114 for obj in objects: 106 for obj in currentObjects:
115 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown) 107 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
116 pastObjects.append(obj) 108 pastObjects.append(obj)
117 print('Saving user types') 109 print('Saving user types')
118 storage.setRoadUserTypes(databaseFilename, pastObjects) 110 storage.setRoadUserTypes(databaseFilename, pastObjects)