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annotate scripts/classify-objects.py @ 683:f3a0b652b590 dev

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added function to display the speed distributions and optimize a little going through objects
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 05 Jun 2015 11:04:06 +0200
parents fbe29be25501
children 6d2ece4aed7c
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
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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
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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('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true')
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16 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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17 #parser.add_argument('-u', dest = 'undistort', help = 'undistort the video (because features have been extracted that way)', action = 'store_true')
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18 #parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to display', type = int)
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19 #parser.add_argument('--last-frame', dest = 'lastFrameNum', help = 'number of last frame number to save (for image saving, no display is made)', type = int)
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20 # parser.add_argument('--min-speed-equiprobable', dest = 'minSpeedEquiprobable', help = 'speed value below which all classes are equiprobable (distributions give odd values there) (km/h)', type = float, default = 3.33)
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21 # parser.add_argument('--speed-aggregation', dest = 'speedAggregationMethod', help = 'method to aggregate road user speed', type = str, choices = ['median', 'mean', 'quantile'], default = 'median')
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22 # parser.add_argument('--speed-aggregation-quantile', dest = 'speedAggregationQuantile', help = 'quantile for the speed aggregation, if quantile is chosen', type = int, default = 50)
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24 args = parser.parse_args()
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25 params = storage.ProcessParameters(args.configFilename)
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26
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27 params.convertToFrames(3.6)
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28 if params.homography is not None:
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29 invHomography = np.linalg.inv(params.homography)
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30
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31 if params.speedAggregationMethod == 'median':
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32 speedAggregationFunc = np.median
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33 elif params.speedAggregationMethod == 'mean':
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34 speedAggregationFunc = np.mean
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35 elif params.speedAggregationMethod == 'quantile':
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36 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile)
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37 else:
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38 print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod))
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39 sys.exit()
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40
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41 pedBikeCarSVM = ml.SVM()
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42 pedBikeCarSVM.load(params.pedBikeCarSVMFilename)
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43 bikeCarSVM = ml.SVM()
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44 bikeCarSVM.load(params.bikeCarSVMFilename)
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45
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46 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
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47 speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s),
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48 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s),
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49 'bicycle': lambda s: lognorm(params.scaleCyclistSpeed, loc = 0., scale = np.exp(params.locationCyclistSpeed)).pdf(s)} # lognorm shape, loc, scale
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50
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51 if args.plotSpeedDistribution:
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52 import matplotlib.pyplot as plt
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53 plt.figure()
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54 for k in speedProbabilities:
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55 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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56 plt.xlabel('Speed (km/h)')
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57 plt.ylabel('Probability')
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58 plt.legend()
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59 plt.title('Probability Density Function')
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60 plt.show()
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61 sys.exit()
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62
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63 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'object')
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64 features = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'feature')
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65 intervals = []
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66 for obj in objects:
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67 obj.setFeatures(features)
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68 intervals.append(obj.getTimeInterval())
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69 timeInterval = moving.unionIntervals(intervals)
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70
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71 capture = cv2.VideoCapture(params.videoFilename)
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72 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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73 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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74
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75 if params.undistort: # setup undistortion
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76 [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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77 if capture.isOpened():
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78 ret = True
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79 frameNum = timeInterval.first
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80 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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81 lastFrameNum = timeInterval.last
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82
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83 while ret and frameNum <= lastFrameNum:
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84 ret, img = capture.read()
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85 if ret:
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86 if frameNum%50 == 0:
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87 print('frame number: {}'.format(frameNum))
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88 currentObjects = []
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89 for obj in objects:
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90 if obj.getLastFrameNum() < frameNum:
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91 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
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92 else:
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93 currentObjects.append(obj)
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94 objects = currentObjects
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95 if params.undistort:
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96 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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97 for obj in objects:
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98 if obj.existsAtInstant(frameNum):
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99 if obj.getFirstInstant() == frameNum:
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100 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, params.maxPedestrianSpeed, params.maxCyclistSpeed, params.nFramesIgnoreAtEnds)
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101 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, 0.2, 0.2, 800) # px, py, pixelThreshold
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102 frameNum += 1
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103
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104 for obj in objects:
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105 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities)