Mercurial Hosting > traffic-intelligence
comparison scripts/classify-objects.py @ 911:3dd5acfa1899
corrected potential issues with videos where one cannot reach a give frame from its number
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 28 Jun 2017 16:46:45 -0400 |
parents | 0e017178f7ab |
children | fd057a6b04db |
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910:b58a1061a717 | 911:3dd5acfa1899 |
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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) |