Mercurial > hg > nsaunier > traffic-intelligence

annotate scripts/classify-objects.py @ 904:8f60ecfc2f06

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work in progress, almost ready
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 22 Jun 2017 18:08:46 -0400
parents c69a8defe5c3
children 0e017178f7ab
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1 #! /usr/bin/env python
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2
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3 import cvutils, moving, ml, storage
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4
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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 (km/h)', type = float, default = 50.)
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21 args = parser.parse_args()
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22 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
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23 classifierParams = storage.ClassifierParameters(params.classifierFilename)
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24 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame
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26 if classifierParams.speedAggregationMethod == 'median':
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27 speedAggregationFunc = np.median
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28 elif classifierParams.speedAggregationMethod == 'mean':
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29 speedAggregationFunc = np.mean
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30 elif classifierParams.speedAggregationMethod == 'quantile':
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31 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile)
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32 else:
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33 print('Unknown speed aggregation method: {}. Exiting'.format(classifierParams.speedAggregationMethod))
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34 sys.exit()
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36 pedBikeCarSVM = ml.SVM()
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37 pedBikeCarSVM.load(classifierParams.pedBikeCarSVMFilename)
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38 bikeCarSVM = ml.SVM()
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39 bikeCarSVM.load(classifierParams.bikeCarSVMFilename)
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41 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
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42 carNorm = norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed)
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43 pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed)
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44 # 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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45 bicLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed))
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46 speedProbabilities = {'car': lambda s: carNorm.pdf(s),
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47 'pedestrian': lambda s: pedNorm.pdf(s),
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48 'bicycle': lambda s: bicLogNorm.pdf(s)}
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49
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50 if args.plotSpeedDistribution:
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51 import matplotlib.pyplot as plt
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52 plt.figure()
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53 for k in speedProbabilities:
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54 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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55 maxProb = -1.
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56 for k in speedProbabilities:
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57 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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58 plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-')
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59 plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class')
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60 plt.xlabel('Speed (km/h)')
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61 plt.ylabel('Probability')
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62 plt.legend()
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63 plt.title('Probability Density Function')
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64 plt.show()
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65 sys.exit()
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66
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67 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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68 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
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69
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70 capture = cv2.VideoCapture(videoFilename)
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71 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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72 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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73
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74 if undistort: # setup undistortion
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75 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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76
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77 pastObjects = []
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78 currentObjects = []
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79 if capture.isOpened():
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80 ret = True
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81 frameNum = timeInterval.first
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82 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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83 lastFrameNum = timeInterval.last
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84
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85 while ret and frameNum <= lastFrameNum:
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86 ret, img = capture.read()
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87 if ret:
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88 if frameNum%50 == 0:
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89 print('frame number: {}'.format(frameNum))
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90 if undistort:
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91 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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92 for obj in objects:
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93 if obj.getFirstInstant() == frameNum:
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94 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds)
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95 currentObjects.append(obj)
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96 objects.remove(obj)
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97
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98 for obj in currentObjects:
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99 if obj.getLastInstant() == frameNum:
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100 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
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101 pastObjects.append(obj)
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102 currentObjects.remove(obj)
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103 else:
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104 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
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105 frameNum += 1
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106
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107 for obj in currentObjects:
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108 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
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109 pastObjects.append(obj)
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110 print('Saving user types')
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111 storage.setRoadUserTypes(databaseFilename, pastObjects)