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annotate scripts/classify-objects.py @ 812:21f10332c72b

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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>
date Fri, 10 Jun 2016 17:07:36 -0400
parents 52aa03260f03
children b9ec0cc2677d
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1 #! /usr/bin/env python
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3 import cvutils, moving, ml, storage
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5 import numpy as np
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6 import sys, argparse
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7 #from cv2.ml import SVM_RBF, SVM_C_SVC
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8 import cv2
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9 from scipy.stats import norm, lognorm
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11 # TODO add mode detection live, add choice of kernel and svm type (to be saved in future classifier format)
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13 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
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14 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
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15 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
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16 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
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17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None)
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18 parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true')
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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.)
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21 args = parser.parse_args()
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22 params = storage.ProcessParameters(args.configFilename)
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23 classifierParams = storage.ClassifierParameters(params.classifierFilename)
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25 if args.videoFilename is not None:
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26 videoFilename = args.videoFilename
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27 else:
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28 videoFilename = params.videoFilename
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29 if args.databaseFilename is not None:
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30 databaseFilename = args.databaseFilename
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31 else:
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32 databaseFilename = params.databaseFilename
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33
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34 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/s
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35 if params.homography is not None:
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36 invHomography = np.linalg.inv(params.homography)
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37 else:
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38 invHomography = None
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40 if classifierParams.speedAggregationMethod == 'median':
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41 speedAggregationFunc = np.median
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42 elif classifierParams.speedAggregationMethod == 'mean':
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43 speedAggregationFunc = np.mean
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44 elif classifierParams.speedAggregationMethod == 'quantile':
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45 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile)
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46 else:
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47 print('Unknown speed aggregation method: {}. Exiting'.format(classifierParams.speedAggregationMethod))
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48 sys.exit()
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50 pedBikeCarSVM = ml.SVM()
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51 pedBikeCarSVM.load(classifierParams.pedBikeCarSVMFilename)
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52 bikeCarSVM = ml.SVM()
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53 bikeCarSVM.load(classifierParams.bikeCarSVMFilename)
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54
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55 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
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56 speedProbabilities = {'car': lambda s: norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed).pdf(s),
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57 'pedestrian': lambda s: norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed).pdf(s),
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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)
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60 if args.plotSpeedDistribution:
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61 import matplotlib.pyplot as plt
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62 plt.figure()
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63 for k in speedProbabilities:
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64 plt.plot(np.arange(0.1, args.maxSpeedDistributionPlot, 0.1), [speedProbabilities[k](s/3.6/25) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)], label = k)
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65 plt.xlabel('Speed (km/h)')
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66 plt.ylabel('Probability')
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67 plt.legend()
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68 plt.title('Probability Density Function')
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69 plt.show()
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70 sys.exit()
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71
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72 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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73 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, 'feature')
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74 intervals = []
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75 for obj in objects:
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76 #obj.setFeatures(features)
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77 intervals.append(obj.getTimeInterval())
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78 timeInterval = moving.TimeInterval.unionIntervals(intervals)
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79
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80 capture = cv2.VideoCapture(videoFilename)
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81 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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82 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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83
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84 pastObjects = []
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85 if params.undistort: # setup undistortion
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86 [map1, map2] = cvutils.computeUndistortMaps(width, height, params.undistortedImageMultiplication, params.intrinsicCameraMatrix, params.distortionCoefficients)
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87 if capture.isOpened():
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88 ret = True
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89 frameNum = timeInterval.first
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90 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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91 lastFrameNum = timeInterval.last
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92
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93 while ret and frameNum <= lastFrameNum:
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94 ret, img = capture.read()
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95 if ret:
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96 if frameNum%50 == 0:
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97 print('frame number: {}'.format(frameNum))
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98 if params.undistort:
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99 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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100 currentObjects = []
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101 for obj in objects:
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102 inter = obj.getTimeInterval()
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103 if inter.contains(frameNum):
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104 if inter.first == frameNum:
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105 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds)
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106 currentObjects.append(obj)
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107 elif inter.last == frameNum:
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108 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
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109 pastObjects.append(obj)
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110 else:
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111 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock)
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112 currentObjects.append(obj)
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 807
diff changeset
113 objects = currentObjects
680
da1352b89d02 classification is working
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 536
diff changeset
114 frameNum += 1
da1352b89d02 classification is working
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 536
diff changeset
115
da1352b89d02 classification is working
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 536
diff changeset
116 for obj in objects:
812
21f10332c72b moved the classification parameters from tracking.cfg to a new classifier.cfg and made all classification parameters apparent
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 807
diff changeset
117 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
684
6d2ece4aed7c bug forgot to svae objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 683
diff changeset
118 pastObjects.append(obj)
6d2ece4aed7c bug forgot to svae objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 683
diff changeset
119 print('Saving user types')
686
cdee6a3a47b4 allowing alternate database and filename for classify-objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 685
diff changeset
120 storage.setRoadUserTypes(databaseFilename, pastObjects)