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annotate scripts/classify-objects.py @ 901:753a081989e2

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factorized some argument handling code
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 22 Jun 2017 12:02:34 -0400
parents 1466a63dd1cf
children c69a8defe5c3
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1 #! /usr/bin/env python
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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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24 classifierParams = storage.ClassifierParameters(params.classifierFilename)
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25 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame
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27 if classifierParams.speedAggregationMethod == 'median':
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28 speedAggregationFunc = np.median
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29 elif classifierParams.speedAggregationMethod == 'mean':
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30 speedAggregationFunc = np.mean
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31 elif classifierParams.speedAggregationMethod == 'quantile':
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32 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile)
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33 else:
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34 print('Unknown speed aggregation method: {}. Exiting'.format(classifierParams.speedAggregationMethod))
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35 sys.exit()
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37 pedBikeCarSVM = ml.SVM()
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38 pedBikeCarSVM.load(classifierParams.pedBikeCarSVMFilename)
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39 bikeCarSVM = ml.SVM()
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40 bikeCarSVM.load(classifierParams.bikeCarSVMFilename)
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41
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42 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
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43 carNorm = norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed)
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44 pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed)
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45 # 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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46 bicLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed))
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47 speedProbabilities = {'car': lambda s: carNorm.pdf(s),
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48 'pedestrian': lambda s: pedNorm.pdf(s),
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49 'bicycle': lambda s: bicLogNorm.pdf(s)}
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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*params.videoFrameRate)) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)], label = k)
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56 maxProb = -1.
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57 for k in speedProbabilities:
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58 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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59 plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-')
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60 plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class')
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61 plt.xlabel('Speed (km/h)')
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62 plt.ylabel('Probability')
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63 plt.legend()
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64 plt.title('Probability Density Function')
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65 plt.show()
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66 sys.exit()
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67
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68 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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69 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, 'feature')
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70 intervals = []
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71 for obj in objects:
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72 #obj.setFeatures(features)
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73 intervals.append(obj.getTimeInterval())
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74 timeInterval = moving.TimeInterval.unionIntervals(intervals)
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75
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76 capture = cv2.VideoCapture(videoFilename)
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77 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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78 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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79
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80 pastObjects = []
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81 if undistort: # setup undistortion
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82 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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83 if capture.isOpened():
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84 ret = True
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85 frameNum = timeInterval.first
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86 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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87 lastFrameNum = timeInterval.last
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88
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89 while ret and frameNum <= lastFrameNum:
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90 ret, img = capture.read()
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91 if ret:
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92 if frameNum%50 == 0:
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93 print('frame number: {}'.format(frameNum))
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94 if undistort:
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95 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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96 currentObjects = []
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97 for obj in objects:
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98 inter = obj.getTimeInterval()
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99 if inter.contains(frameNum):
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100 if inter.first == frameNum:
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101 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds)
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102 currentObjects.append(obj)
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103 elif inter.last == frameNum:
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104 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
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105 pastObjects.append(obj)
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106 else:
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107 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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108 currentObjects.append(obj)
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109 else:
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110 currentObjects.append(obj)
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111 objects = currentObjects
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112 frameNum += 1
da1352b89d02 classification is working
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 536
diff changeset
113
da1352b89d02 classification is working
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 536
diff changeset
114 for obj in objects:
899
1466a63dd1cf added a new classification parameter
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 898
diff changeset
115 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
684
6d2ece4aed7c bug forgot to svae objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 683
diff changeset
116 pastObjects.append(obj)
6d2ece4aed7c bug forgot to svae objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 683
diff changeset
117 print('Saving user types')
686
cdee6a3a47b4 allowing alternate database and filename for classify-objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 685
diff changeset
118 storage.setRoadUserTypes(databaseFilename, pastObjects)