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annotate scripts/classify-objects.py @ 946:e5970606066f

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bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 21 Jul 2017 11:25:20 -0400
parents 0e63a918a1ca
children 053484e08947
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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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('--start-frame0', dest = 'startFrame0', help = 'starts with first frame for videos with index problem where frames cannot be reached', action = 'store_true')
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19 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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20 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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22 args = parser.parse_args()
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23 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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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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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 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
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70 if args.startFrame0:
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71 timeInterval.first = 0
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72
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73 capture = cv2.VideoCapture(videoFilename)
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74 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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75 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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76
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77 #if undistort: # setup undistortion
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78 # [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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79 # height, width = map1.shape
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80 # newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication)))
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81 # newCameraMatrix = cv2.getDefaultNewCameraMatrix(intrinsicCameraMatrix, newImgSize, True)
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82 #else:
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83 # newCameraMatrix = None
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84
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85 pastObjects = []
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86 currentObjects = []
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87 if capture.isOpened():
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88 ret = True
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89 frameNum = timeInterval.first
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90 if not args.startFrame0:
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91 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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92 lastFrameNum = timeInterval.last
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93
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94 while ret and frameNum <= lastFrameNum:
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95 ret, img = capture.read()
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96 if ret:
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97 if frameNum%50 == 0:
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98 print('frame number: {}'.format(frameNum))
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99 #if undistort:
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100 # img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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101 for obj in objects:
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102 if obj.getFirstInstant() <= frameNum: # if images are skipped
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103 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds, invHomography, intrinsicCameraMatrix, distortionCoefficients)
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104 currentObjects.append(obj)
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105 objects[:] = [obj for obj in objects if obj.getFirstInstant() > frameNum]
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106
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107 for obj in currentObjects:
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108 if obj.getLastInstant() <= frameNum: # if images are skipped
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109 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
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110 pastObjects.append(obj)
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111 else:
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parents: 912
diff changeset
112 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
946
e5970606066f bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 935
diff changeset
113 currentObjects[:] = [obj for obj in objects if obj.getLastInstant() > frameNum]
680
da1352b89d02 classification is working
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 536
diff changeset
114 frameNum += 1
da1352b89d02 classification is working
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 536
diff changeset
115
902
c69a8defe5c3 changed workflow of classify objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 901
diff changeset
116 for obj in currentObjects:
899
1466a63dd1cf added a new classification parameter
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 898
diff changeset
117 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
118 pastObjects.append(obj)
6d2ece4aed7c bug forgot to svae objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 683
diff changeset
119 print('Saving user types')
686
cdee6a3a47b4 allowing alternate database and filename for classify-objects
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 685
diff changeset
120 storage.setRoadUserTypes(databaseFilename, pastObjects)