Mercurial Hosting > traffic-intelligence
comparison scripts/classify-objects.py @ 812:21f10332c72b
moved the classification parameters from tracking.cfg to a new classifier.cfg and made all classification parameters apparent
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 10 Jun 2016 17:07:36 -0400 |
parents | 52aa03260f03 |
children | b9ec0cc2677d |
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810:082a5c2685f4 | 812:21f10332c72b |
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10 | 10 |
11 # TODO add mode detection live, add choice of kernel and svm type (to be saved in future classifier format) | 11 # TODO add mode detection live, add choice of kernel and svm type (to be saved in future classifier format) |
12 | 12 |
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('--kernel', dest = 'kernelType', help = 'kernel type for the support vector machine (SVM)', default = cv2.SVM_RBF, type = long) | |
16 parser.add_argument('--svm', dest = 'svmType', help = 'SVM type', default = cv2.SVM_C_SVC, type = long) | |
17 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)') |
18 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)') |
19 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) |
20 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') |
21 parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display', 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', type = float, default = 50.) |
22 | 20 |
23 args = parser.parse_args() | 21 args = parser.parse_args() |
24 params = storage.ProcessParameters(args.configFilename) | 22 params = storage.ProcessParameters(args.configFilename) |
23 classifierParams = storage.ClassifierParameters(params.classifierFilename) | |
25 | 24 |
26 if args.videoFilename is not None: | 25 if args.videoFilename is not None: |
27 videoFilename = args.videoFilename | 26 videoFilename = args.videoFilename |
28 else: | 27 else: |
29 videoFilename = params.videoFilename | 28 videoFilename = params.videoFilename |
30 if args.databaseFilename is not None: | 29 if args.databaseFilename is not None: |
31 databaseFilename = args.databaseFilename | 30 databaseFilename = args.databaseFilename |
32 else: | 31 else: |
33 databaseFilename = params.databaseFilename | 32 databaseFilename = params.databaseFilename |
34 | 33 |
35 params.convertToFrames(3.6) | 34 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/s |
36 if params.homography is not None: | 35 if params.homography is not None: |
37 invHomography = np.linalg.inv(params.homography) | 36 invHomography = np.linalg.inv(params.homography) |
38 else: | 37 else: |
39 invHomography = None | 38 invHomography = None |
40 | 39 |
41 if params.speedAggregationMethod == 'median': | 40 if classifierParams.speedAggregationMethod == 'median': |
42 speedAggregationFunc = np.median | 41 speedAggregationFunc = np.median |
43 elif params.speedAggregationMethod == 'mean': | 42 elif classifierParams.speedAggregationMethod == 'mean': |
44 speedAggregationFunc = np.mean | 43 speedAggregationFunc = np.mean |
45 elif params.speedAggregationMethod == 'quantile': | 44 elif classifierParams.speedAggregationMethod == 'quantile': |
46 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile) | 45 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile) |
47 else: | 46 else: |
48 print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod)) | 47 print('Unknown speed aggregation method: {}. Exiting'.format(classifierParams.speedAggregationMethod)) |
49 sys.exit() | 48 sys.exit() |
50 | 49 |
51 pedBikeCarSVM = ml.SVM(args.svmType, args.kernelType) | 50 pedBikeCarSVM = ml.SVM() |
52 pedBikeCarSVM.load(params.pedBikeCarSVMFilename) | 51 pedBikeCarSVM.load(classifierParams.pedBikeCarSVMFilename) |
53 bikeCarSVM = ml.SVM(args.svmType, args.kernelType) | 52 bikeCarSVM = ml.SVM() |
54 bikeCarSVM.load(params.bikeCarSVMFilename) | 53 bikeCarSVM.load(classifierParams.bikeCarSVMFilename) |
55 | 54 |
56 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.) | 55 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.) |
57 speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s), | 56 speedProbabilities = {'car': lambda s: norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed).pdf(s), |
58 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s), | 57 'pedestrian': lambda s: norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed).pdf(s), |
59 'bicycle': lambda s: lognorm(params.scaleCyclistSpeed, loc = 0., scale = np.exp(params.locationCyclistSpeed)).pdf(s)} # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is location (mean of the normal) | 58 'bicycle': lambda s: lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed)).pdf(s)} # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is location (mean of the normal) |
60 | 59 |
61 if args.plotSpeedDistribution: | 60 if args.plotSpeedDistribution: |
62 import matplotlib.pyplot as plt | 61 import matplotlib.pyplot as plt |
63 plt.figure() | 62 plt.figure() |
64 for k in speedProbabilities: | 63 for k in speedProbabilities: |
94 while ret and frameNum <= lastFrameNum: | 93 while ret and frameNum <= lastFrameNum: |
95 ret, img = capture.read() | 94 ret, img = capture.read() |
96 if ret: | 95 if ret: |
97 if frameNum%50 == 0: | 96 if frameNum%50 == 0: |
98 print('frame number: {}'.format(frameNum)) | 97 print('frame number: {}'.format(frameNum)) |
99 currentObjects = [] | 98 if params.undistort: |
100 for obj in objects: | 99 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) |
101 if obj.getLastInstant() < frameNum: | 100 currentObjects = [] |
102 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities) | 101 for obj in objects: |
102 inter = obj.getTimeInterval() | |
103 if inter.contains(frameNum): | |
104 if inter.first == frameNum: | |
105 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds) | |
106 currentObjects.append(obj) | |
107 elif inter.last == frameNum: | |
108 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities) | |
103 pastObjects.append(obj) | 109 pastObjects.append(obj) |
104 else: | 110 else: |
111 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock) | |
105 currentObjects.append(obj) | 112 currentObjects.append(obj) |
106 objects = currentObjects | 113 objects = currentObjects |
107 if params.undistort: | |
108 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) | |
109 for obj in objects: | |
110 if obj.existsAtInstant(frameNum): | |
111 if obj.getFirstInstant() == frameNum: | |
112 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, params.maxPedestrianSpeed, params.maxCyclistSpeed, params.nFramesIgnoreAtEnds) | |
113 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, 0.2, 0.2, 800) # px, py, pixelThreshold | |
114 frameNum += 1 | 114 frameNum += 1 |
115 | 115 |
116 for obj in objects: | 116 for obj in objects: |
117 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities) | 117 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities) |
118 pastObjects.append(obj) | 118 pastObjects.append(obj) |
119 print('Saving user types') | 119 print('Saving user types') |
120 storage.setRoadUserTypes(databaseFilename, pastObjects) | 120 storage.setRoadUserTypes(databaseFilename, pastObjects) |