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annotate scripts/classify-objects.py @ 1242:4cd8ace3552f

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major update for classification, allowing the use of neural network classification
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 07 Feb 2024 11:43:03 -0500
parents ab4c72b9475c
children 88eedf79f16a
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
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933670761a57 updated code to python 3 (tests pass and scripts run, but non-executed parts of code are probably still not correct)
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1 #! /usr/bin/env python3
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3 import sys, argparse
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4
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5 import numpy as np
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6 import cv2
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7 from scipy.stats import norm, lognorm
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8 from pathlib import Path
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9
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10 try:
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11 from ultralytics import YOLO
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12 ultralyticsAvailable = True
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13 except ImportError:
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14 #print('OpenCV library could not be loaded (video replay functions will not be available)') # TODO change to logging module
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15 ultralyticsAvailable = False
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16
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18 from trafficintelligence import cvutils, moving, ml, storage, utils
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19
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20 # TODO add mode detection live, add choice of kernel and svm type (to be saved in future classifier format)
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21
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22 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene', epilog='The integer ids for the categories are stored in the moving module:\n{}'.format(moving.userType2Num))
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23 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
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24 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
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25 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
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26 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None)
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27 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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28 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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29 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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30 parser.add_argument('--verbose', dest = 'verbose', help = 'verbose information', action = 'store_true')
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31
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32 args = parser.parse_args()
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33 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
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34 classifierParams = storage.ClassifierParameters(params.classifierFilename)
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35 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame
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36
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37 speedAggregationFunc = utils.aggregationFunction(classifierParams.speedAggregationMethod, classifierParams.speedAggregationCentile)
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38 if speedAggregationFunc is None:
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39 sys.exit()
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40
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41 if ultralyticsAvailable and Path(classifierParams.dlFilename).is_file(): # use Yolo
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42 pedBikeCarSVM = None
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43 bikeCarSVM = None
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44 yolo = YOLO(classifierParams.dlFilename, task='detect')
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45 useYolo = True
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46 else:
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47 useYolo = False
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48 pedBikeCarSVM = ml.SVM_load(classifierParams.pedBikeCarSVMFilename)
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49 bikeCarSVM = ml.SVM_load(classifierParams.bikeCarSVMFilename)
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50
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51 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
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52 carNorm = norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed)
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53 pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed)
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54 # 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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55 cycLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed))
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56 speedProbabilities = {moving.userType2Num['car']: lambda s: carNorm.pdf(s),
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57 moving.userType2Num['pedestrian']: lambda s: pedNorm.pdf(s),
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58 moving.userType2Num['cyclist']: lambda s: cycLogNorm.pdf(s)}
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59 if useYolo: # consider other user types
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60 for i in [3, 5, 6, 7]:
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61 speedProbabilities[i] = speedProbabilities[moving.userType2Num['car']]
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62
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63 if args.plotSpeedDistribution:
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64 import matplotlib.pyplot as plt
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65 plt.figure()
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66 speeds = np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)
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67 for k in speedProbabilities:
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68 plt.plot(speeds, [speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds], label = k) # the distribution parameters are in video intrinsic units, unit of distance per frame
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69 maxProb = -1.
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70 for k in speedProbabilities:
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71 maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds]))
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72 plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-')
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73 plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class')
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74 plt.xlabel('Speed (km/h)')
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75 plt.ylabel('Probability density function')
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76 plt.legend()
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77 #plt.title('Probability Density Function')
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78 plt.show()
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79 sys.exit()
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80
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81 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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82 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
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83
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84 capture = cv2.VideoCapture(videoFilename)
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85 width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
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86 height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
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87
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88 #if undistort: # setup undistortion
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89 # [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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90 # height, width = map1.shape
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91 # newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication)))
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92 # newCameraMatrix = cv2.getDefaultNewCameraMatrix(intrinsicCameraMatrix, newImgSize, True)
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93 #else:
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94 # newCameraMatrix = None
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95
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96 pastObjects = []
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97 currentObjects = []
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98 if capture.isOpened():
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99 ret = True
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100 frameNum = timeInterval.first
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101 capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
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102 lastFrameNum = timeInterval.last
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103
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104 while ret and frameNum <= lastFrameNum:
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105 ret, img = capture.read()
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106 if ret:
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107 if frameNum%50 == 0:
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108 print('frame number: {}'.format(frameNum))
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109 #if undistort:
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110 # img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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111 if useYolo:
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112 results = yolo.predict(img, conf = classifierParams.confidence, classes=list(moving.cocoTypeNames.keys()), verbose=False)
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113
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114 for obj in objects[:]:
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115 if obj.getFirstInstant() <= frameNum: # if images are skipped
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116 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds, invHomography, intrinsicCameraMatrix, distortionCoefficients)
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117 currentObjects.append(obj)
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118 objects.remove(obj)
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119
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120 for obj in currentObjects[:]:
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121 if obj.getLastInstant() <= frameNum:
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122 obj.classifyUserTypeHoGSVM(classifierParams.minSpeedEquiprobable, speedProbabilities, classifierParams.maxPercentUnknown)
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123 pastObjects.append(obj)
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124 currentObjects.remove(obj)
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125 else:
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126 if useYolo:
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parents: 1240
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127 # if one feature falls in bike, it's a bike
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128 # could one count all hits in various objects, or one takes majority at the instant?
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129 #print('yolo', len(results[0].boxes))
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130 obj.classifyUserTypeYoloAtInstant(frameNum, results[0].boxes)
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131 else:
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parents: 1240
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132 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
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133 if args.verbose:
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134 print('obj {}@{}: {}'.format(obj.getNum(), frameNum, moving.userTypeNames[obj.userTypes[frameNum]]))
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da1352b89d02 classification is working
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135 frameNum += 1
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parents: 536
diff changeset
136
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diff changeset
137 for obj in currentObjects:
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parents: 1241
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138 obj.classifyUserTypeHoGSVM(classifierParams.minSpeedEquiprobable, speedProbabilities, classifierParams.maxPercentUnknown)
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139 pastObjects.append(obj)
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diff changeset
140 print('Saving user types')
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cdee6a3a47b4 allowing alternate database and filename for classify-objects
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parents: 685
diff changeset
141 storage.setRoadUserTypes(databaseFilename, pastObjects)