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annotate scripts/classify-objects.py @ 788:5b970a5bc233 dev

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updated classifying code to OpenCV 3.x (bug in function to load classification models)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 24 Mar 2016 16:37:37 -0400
parents de278c5e65f6
children 1158a6e2d28e
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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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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5 import numpy as np
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6 import sys, argparse
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7 from cv2 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('--kernel', dest = 'kernelType', help = 'kernel type for the support vector machine (SVM)', default = SVM_RBF, type = long)
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16 parser.add_argument('--svm', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long)
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17 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
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18 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
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19 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None)
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20 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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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.)
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23 args = parser.parse_args()
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24 params = storage.ProcessParameters(args.configFilename)
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26 if args.videoFilename is not None:
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27 videoFilename = args.videoFilename
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28 else:
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29 videoFilename = params.videoFilename
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30 if args.databaseFilename is not None:
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31 databaseFilename = args.databaseFilename
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32 else:
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33 databaseFilename = params.databaseFilename
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34
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35 params.convertToFrames(3.6)
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36 if params.homography is not None:
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37 invHomography = np.linalg.inv(params.homography)
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38
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39 if params.speedAggregationMethod == 'median':
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40 speedAggregationFunc = np.median
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41 elif params.speedAggregationMethod == 'mean':
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42 speedAggregationFunc = np.mean
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43 elif params.speedAggregationMethod == 'quantile':
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44 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile)
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45 else:
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46 print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod))
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47 sys.exit()
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49 pedBikeCarSVM = ml.SVM(args.svmType, args.kernelType)
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50 pedBikeCarSVM.load(params.pedBikeCarSVMFilename)
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51 bikeCarSVM = ml.SVM(args.svmType, args.kernelType)
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52 bikeCarSVM.load(params.bikeCarSVMFilename)
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53
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54 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
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55 speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s),
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56 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s),
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57 '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)
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58
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59 if args.plotSpeedDistribution:
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60 import matplotlib.pyplot as plt
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61 plt.figure()
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62 for k in speedProbabilities:
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63 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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64 plt.xlabel('Speed (km/h)')
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65 plt.ylabel('Probability')
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66 plt.legend()
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67 plt.title('Probability Density Function')
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68 plt.show()
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69 sys.exit()
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70
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71 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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72 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, 'feature')
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73 intervals = []
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74 for obj in objects:
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75 #obj.setFeatures(features)
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76 intervals.append(obj.getTimeInterval())
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77 timeInterval = moving.unionIntervals(intervals)
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78
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79 capture = cv2.VideoCapture(videoFilename)
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80 width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
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81 height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
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82
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83 pastObjects = []
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84 if params.undistort: # setup undistortion
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85 [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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86 if capture.isOpened():
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87 ret = True
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88 frameNum = timeInterval.first
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89 capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
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90 lastFrameNum = timeInterval.last
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91
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92 while ret and frameNum <= lastFrameNum:
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93 ret, img = capture.read()
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94 if ret:
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95 if frameNum%50 == 0:
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96 print('frame number: {}'.format(frameNum))
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97 currentObjects = []
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98 for obj in objects:
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99 if obj.getLastInstant() < frameNum:
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100 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
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101 pastObjects.append(obj)
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102 else:
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103 currentObjects.append(obj)
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104 objects = currentObjects
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105 if params.undistort:
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106 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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107 for obj in objects:
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108 if obj.existsAtInstant(frameNum):
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109 if obj.getFirstInstant() == frameNum:
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110 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, params.maxPedestrianSpeed, params.maxCyclistSpeed, params.nFramesIgnoreAtEnds)
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111 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, 0.2, 0.2, 800) # px, py, pixelThreshold
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112 frameNum += 1
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113
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114 for obj in objects:
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115 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
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116 pastObjects.append(obj)
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117 print('Saving user types')
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118 storage.setRoadUserTypes(databaseFilename, pastObjects)