Mercurial > hg > nsaunier > traffic-intelligence

annotate scripts/classify-objects.py @ 741:5b91b8d97cf3 dev

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corrected bug
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 28 Aug 2015 10:38:08 -0400
parents de278c5e65f6
children 5b970a5bc233
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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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 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
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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
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24 if args.videoFilename is not None:
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25 videoFilename = args.videoFilename
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26 else:
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27 videoFilename = params.videoFilename
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28 if args.databaseFilename is not None:
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29 databaseFilename = args.databaseFilename
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30 else:
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31 databaseFilename = params.databaseFilename
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32
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33 params.convertToFrames(3.6)
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34 if params.homography is not None:
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35 invHomography = np.linalg.inv(params.homography)
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36
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37 if params.speedAggregationMethod == 'median':
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38 speedAggregationFunc = np.median
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39 elif params.speedAggregationMethod == 'mean':
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40 speedAggregationFunc = np.mean
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41 elif params.speedAggregationMethod == 'quantile':
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42 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile)
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43 else:
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44 print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod))
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45 sys.exit()
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46
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47 pedBikeCarSVM = ml.SVM()
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48 pedBikeCarSVM.load(params.pedBikeCarSVMFilename)
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49 bikeCarSVM = ml.SVM()
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50 bikeCarSVM.load(params.bikeCarSVMFilename)
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51
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52 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
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53 speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s),
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54 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s),
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55 '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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56
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57 if args.plotSpeedDistribution:
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58 import matplotlib.pyplot as plt
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59 plt.figure()
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60 for k in speedProbabilities:
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61 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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62 plt.xlabel('Speed (km/h)')
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63 plt.ylabel('Probability')
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64 plt.legend()
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65 plt.title('Probability Density Function')
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66 plt.show()
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67 sys.exit()
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68
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69 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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70 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, 'feature')
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71 intervals = []
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72 for obj in objects:
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73 #obj.setFeatures(features)
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74 intervals.append(obj.getTimeInterval())
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75 timeInterval = moving.unionIntervals(intervals)
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76
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77 capture = cv2.VideoCapture(videoFilename)
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78 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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79 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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80
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81 pastObjects = []
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82 if params.undistort: # setup undistortion
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83 [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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84 if capture.isOpened():
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85 ret = True
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86 frameNum = timeInterval.first
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87 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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88 lastFrameNum = timeInterval.last
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89
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90 while ret and frameNum <= lastFrameNum:
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91 ret, img = capture.read()
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92 if ret:
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93 if frameNum%50 == 0:
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94 print('frame number: {}'.format(frameNum))
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95 currentObjects = []
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96 for obj in objects:
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97 if obj.getLastInstant() < frameNum:
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98 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
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99 pastObjects.append(obj)
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100 else:
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101 currentObjects.append(obj)
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102 objects = currentObjects
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103 if params.undistort:
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104 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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105 for obj in objects:
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106 if obj.existsAtInstant(frameNum):
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107 if obj.getFirstInstant() == frameNum:
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108 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, params.maxPedestrianSpeed, params.maxCyclistSpeed, params.nFramesIgnoreAtEnds)
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109 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, 0.2, 0.2, 800) # px, py, pixelThreshold
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110 frameNum += 1
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111
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112 for obj in objects:
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113 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
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114 pastObjects.append(obj)
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115 print('Saving user types')
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116 storage.setRoadUserTypes(databaseFilename, pastObjects)