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annotate scripts/classify-objects.py @ 1028:cc5cb04b04b0

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major update using the trafficintelligence package name and install through pip
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 15 Jun 2018 11:19:10 -0400
parents a13f47c8931d
children 49c0bb6eacc2
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933670761a57 updated code to python 3 (tests pass and scripts run, but non-executed parts of code are probably still not correct)
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1 #! /usr/bin/env python3
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2
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3 import sys, argparse
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4
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5 import numpy as np
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6 import cv2
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7 from scipy.stats import norm, lognorm
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8
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9 from trafficintelligence import cvutils, moving, ml, storage, utils
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10
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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('--start-frame0', dest = 'startFrame0', help = 'starts with first frame for videos with index problem where frames cannot be reached', action = 'store_true')
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19 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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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.)
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21 parser.add_argument('--verbose', dest = 'verbose', help = 'verbose information', action = 'store_true')
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22
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23 args = parser.parse_args()
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24 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
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25 classifierParams = storage.ClassifierParameters(params.classifierFilename)
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26 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame
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27
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28 speedAggregationFunc = utils.aggregationFunction(classifierParams.speedAggregationMethod)
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29 if speedAggregationFunc is None:
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30 sys.exit()
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31
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32 pedBikeCarSVM = ml.SVM_load(classifierParams.pedBikeCarSVMFilename)
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33 bikeCarSVM = ml.SVM_load(classifierParams.bikeCarSVMFilename)
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34
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35 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
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36 carNorm = norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed)
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37 pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed)
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38 # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is exp(location) (loc=mean of the normal)
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39 bicLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed))
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40 speedProbabilities = {'car': lambda s: carNorm.pdf(s),
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41 'pedestrian': lambda s: pedNorm.pdf(s),
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42 'bicycle': lambda s: bicLogNorm.pdf(s)}
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43
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44 if args.plotSpeedDistribution:
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45 import matplotlib.pyplot as plt
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46 plt.figure()
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47 for k in speedProbabilities:
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48 plt.plot(np.arange(0.1, args.maxSpeedDistributionPlot, 0.1), [speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)], label = k)
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49 maxProb = -1.
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50 for k in speedProbabilities:
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51 maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)]))
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52 plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-')
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53 plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class')
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54 plt.xlabel('Speed (km/h)')
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55 plt.ylabel('Probability')
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56 plt.legend()
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57 plt.title('Probability Density Function')
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58 plt.show()
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59 sys.exit()
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60
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61 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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62 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
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63 if args.startFrame0:
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64 timeInterval.first = 0
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65
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66 capture = cv2.VideoCapture(videoFilename)
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67 width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
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68 height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
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69
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70 #if undistort: # setup undistortion
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71 # [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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72 # height, width = map1.shape
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73 # newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication)))
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74 # newCameraMatrix = cv2.getDefaultNewCameraMatrix(intrinsicCameraMatrix, newImgSize, True)
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75 #else:
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76 # newCameraMatrix = None
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77
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78 pastObjects = []
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79 currentObjects = []
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80 if capture.isOpened():
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81 ret = True
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82 frameNum = timeInterval.first
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83 if not args.startFrame0:
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84 capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
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85 lastFrameNum = timeInterval.last
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86
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87 while ret and frameNum <= lastFrameNum:
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88 ret, img = capture.read()
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89 if ret:
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90 if frameNum%50 == 0:
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91 print('frame number: {}'.format(frameNum))
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92 #if undistort:
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93 # img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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94 for obj in objects[:]:
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95 if obj.getFirstInstant() <= frameNum: # if images are skipped
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96 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds, invHomography, intrinsicCameraMatrix, distortionCoefficients)
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97 currentObjects.append(obj)
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98 objects.remove(obj)
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99
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100 for obj in currentObjects[:]:
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101 if obj.getLastInstant() <= frameNum: # if images are skipped
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102 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
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103 pastObjects.append(obj)
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104 currentObjects.remove(obj)
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105 else:
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106 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
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107 if args.verbose:
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108 print('obj {}@{}: {}'.format(obj.getNum(), frameNum, moving.userTypeNames[obj.userTypes[frameNum]]))
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109 frameNum += 1
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110
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111 for obj in currentObjects:
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112 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
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113 pastObjects.append(obj)
6d2ece4aed7c bug forgot to svae objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 683
diff changeset
114 print('Saving user types')
686
cdee6a3a47b4 allowing alternate database and filename for classify-objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 685
diff changeset
115 storage.setRoadUserTypes(databaseFilename, pastObjects)