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correct bug to take into account first frame num in config, and other related bugs in dltrack.py
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 27 Jun 2024 15:31:36 -0400
parents b2f90cada58f
children
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1 #! /usr/bin/env python3
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2 # from https://docs.ultralytics.com/modes/track/
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3 import sys, argparse
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4 from math import inf
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5 from copy import copy
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6 from collections import Counter
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7 import numpy as np
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8 from scipy.optimize import linear_sum_assignment
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9 from ultralytics import YOLO
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10 from torch import cat
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11 from torchvision.ops import box_iou
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12 import cv2
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13
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14 from trafficintelligence import cvutils, moving, storage, utils
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15
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16 parser = argparse.ArgumentParser(description='The program tracks objects using the ultralytics models and trackers.',
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17 epilog= '''The models can be found in the Ultralytics model zoo,
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18 eg YOLOv8 (https://docs.ultralytics.com/models/yolov8/).
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19 The tracking models can be found also online
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20 (https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers).
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21 The choice is to project the middle of the bottom line for persons,
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22 and the bounding box center otherwise.''')
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23 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
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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('-m', dest = 'detectorFilename', help = 'name of the detection model file', required = True)
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27 parser.add_argument('-t', dest = 'trackerFilename', help = 'name of the tracker file', required = True)
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28 parser.add_argument('-o', dest = 'homographyFilename', help = 'filename of the homography matrix')
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29 parser.add_argument('-k', dest = 'maskFilename', help = 'name of the mask file')
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30 parser.add_argument('--undistort', dest = 'undistort', help = 'undistort the video', action = 'store_true')
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31 parser.add_argument('--intrinsic', dest = 'intrinsicCameraMatrixFilename', help = 'name of the intrinsic camera file')
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32 parser.add_argument('--distortion-coefficients', dest = 'distortionCoefficients', help = 'distortion coefficients', nargs = '*', type = float)
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33 parser.add_argument('--display', dest = 'display', help = 'show the raw detection and tracking results', action = 'store_true')
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34 parser.add_argument('--no-image-coordinates', dest = 'notSavingImageCoordinates', help = 'not saving the raw detection and tracking results', action = 'store_true')
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35 parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to process', type = int)
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36 parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to process', type = int, default = inf)
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37 parser.add_argument('--conf', dest = 'confidence', help = 'object confidence threshold for detection', type = float, default = 0.25)
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38 parser.add_argument('--bike-prop', dest = 'bikeProportion', help = 'minimum proportion of time a person classified as bike or motorbike to be classified as cyclist', type = float, default = 0.2)
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39 parser.add_argument('--cyclist-iou', dest = 'cyclistIou', help = 'IoU threshold to associate a bike and ped bounding box', type = float, default = 0.15)
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40 parser.add_argument('--cyclist-match-prop', dest = 'cyclistMatchingProportion', help = 'minimum proportion of time a bike exists and is associated with a pedestrian to be merged as cyclist', type = float, default = 0.3)
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41 #parser.add_argument('--max-temp-overal', dest = 'maxTemporalOverlap', help = 'maximum proportion of time to merge 2 bikes associated with same pedestrian', type = float, default = 0.05)
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42
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43 args = parser.parse_args()
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44 params, videoFilename, databaseFilename, homography, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
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46 print(params, videoFilename, databaseFilename, homography)
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47
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48 if args.homographyFilename is not None:
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49 homography = np.loadtxt(args.homographyFilename)
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50 if args.intrinsicCameraMatrixFilename is not None:
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51 intrinsicCameraMatrix = np.loadtxt(args.intrinsicCameraMatrixFilename)
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52 if args.distortionCoefficients is not None:
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53 distortionCoefficients = np.array(args.distortionCoefficients)
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54 if args.firstFrameNum is not None:
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55 firstFrameNum = args.firstFrameNum
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56 if args.lastFrameNum is not None:
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57 lastFrameNum = args.lastFrameNum
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58 elif args.configFilename is not None:
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59 lastFrameNum = params.lastFrameNum
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60 else:
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61 lastFrameNum = np.inf
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62 if args.maskFilename is not None:
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63 mask = cv2.imread(args.maskFilename, cv2.IMREAD_GRAYSCALE)
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64 elif params is not None and params.maskFilename is not None:
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65 mask = cv2.imread(params.maskFilename, cv2.IMREAD_GRAYSCALE)
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66 else:
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67 mask = None
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68 if params is not None:
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69 smoothingHalfWidth = params.smoothingHalfWidth
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70 minObjectDuration = params.minFeatureTime
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71 else:
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72 smoothingHalfWidth = None
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73 minObjectDuration = 3
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74
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75 # TODO use mask, remove short objects, smooth
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76
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77 # TODO add option to refine position with mask for vehicles, to save different positions
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78 # TODO work with optical flow (farneback or RAFT) https://pytorch.org/vision/main/models/raft.html
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79
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80 # use 2 x bytetrack track buffer to remove objects from existing ones
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81
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82 # Load a model
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83 model = YOLO(args.detectorFilename) # seg yolov8x-seg.pt
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84 # seg could be used on cropped image... if can be loaded and kept in memory
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85 # model = YOLOX('/home/nicolas/Research/Data/classification-models/yolo_nas_l.pt ') # AttributeError: 'YoloNAS_L' object has no attribute 'get'
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86
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87 # Track with the model
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88 if args.display:
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89 windowName = 'frame'
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90 cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
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91
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92 capture = cv2.VideoCapture(videoFilename)
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93 objects = {}
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94 featureNum = 1
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95 frameNum = firstFrameNum
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96 capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
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97
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98 success, frame = capture.read()
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99 if not success:
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100 print('Input {} could not be read. Exiting'.format(videoFilename))
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101 import sys; sys.exit()
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102
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103 results = model.track(source=frame, tracker=args.trackerFilename, classes=list(moving.cocoTypeNames.keys()), conf=args.confidence, persist=True, verbose=False)
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104 while capture.isOpened() and success and frameNum <= lastFrameNum:
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105 result = results[0]
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106 if frameNum %10 == 0:
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107 print(frameNum, len(result.boxes), 'objects')
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108 for box in result.boxes:
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109 if box.id is not None:# None are objects with low confidence
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110 xyxy = copy(box.xyxy)
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111 minPoint = moving.Point(xyxy[0,0].item(), xyxy[0,1].item())
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112 maxPoint = moving.Point(xyxy[0,2].item(), xyxy[0,3].item())
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113 center = (minPoint+maxPoint).divide(2.).asint()
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114 if mask is None or mask[center.y, center.x] > 0:
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115 num = int(box.id.item())
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116 if num in objects:
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117 objects[num].timeInterval.last = frameNum
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diff changeset
118 objects[num].features[0].timeInterval.last = frameNum
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119 objects[num].features[1].timeInterval.last = frameNum
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diff changeset
120 objects[num].bboxes[frameNum] = xyxy
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diff changeset
121 objects[num].userTypes.append(moving.coco2Types[int(box.cls.item())])
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diff changeset
122 objects[num].features[0].tmpPositions[frameNum] = minPoint # min
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123 objects[num].features[1].tmpPositions[frameNum] = maxPoint # max
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diff changeset
124 else:
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diff changeset
125 inter = moving.TimeInterval(frameNum, frameNum)
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diff changeset
126 objects[num] = moving.MovingObject(num, inter)
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diff changeset
127 objects[num].bboxes = {frameNum: copy(xyxy)}
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128 objects[num].userTypes = [moving.coco2Types[int(box.cls.item())]]
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129 objects[num].features = [moving.MovingObject(featureNum, copy(inter)), moving.MovingObject(featureNum+1, copy(inter))]
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130 objects[num].featureNumbers = [featureNum, featureNum+1]
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diff changeset
131 objects[num].features[0].tmpPositions = {frameNum: minPoint}
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132 objects[num].features[1].tmpPositions = {frameNum: maxPoint}
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133 featureNum += 2
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134 if args.display:
1231
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diff changeset
135 cvutils.cvImshow(windowName, result.plot()) # original image in orig_img
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136 key = cv2.waitKey()
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137 if cvutils.quitKey(key):
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138 break
1234
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139 frameNum += 1
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diff changeset
140 success, frame = capture.read()
1268
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diff changeset
141 if success:
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142 results = model.track(source=frame, persist=True)
1249
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diff changeset
143 capture.release()
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diff changeset
144 cv2.destroyAllWindows()
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145
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146 # classification
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147 shortObjectNumbers = []
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diff changeset
148 for num, obj in objects.items():
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149 if obj.length() < minObjectDuration:
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diff changeset
150 shortObjectNumbers.append(num)
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151 else:
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152 obj.setUserType(utils.mostCommon(obj.userTypes)) # improve? mix with speed?
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153 for num in shortObjectNumbers:
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154 del objects[num]
1246
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diff changeset
155 # TODO add quality control: avoid U-turns
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156
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157 # merge bikes and people
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diff changeset
158 twowheels = [num for num, obj in objects.items() if obj.getUserType() in (moving.userType2Num['motorcyclist'],moving.userType2Num['cyclist'])]
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diff changeset
159 pedestrians = [num for num, obj in objects.items() if obj.getUserType() == moving.userType2Num['pedestrian']]
1237
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diff changeset
160
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diff changeset
161 def mergeObjects(obj1, obj2):
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diff changeset
162 obj1.features = obj1.features+obj2.features
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163 obj1.featureNumbers = obj1.featureNumbers+obj2.featureNumbers
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diff changeset
164 obj1.timeInterval = moving.TimeInterval(min(obj1.getFirstInstant(), obj2.getFirstInstant()), max(obj1.getLastInstant(), obj2.getLastInstant()))
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165
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diff changeset
166 costs = []
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diff changeset
167 for twInd in twowheels:
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diff changeset
168 tw = objects[twInd]
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diff changeset
169 tw.nBBoxes = len(tw.bboxes)
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diff changeset
170 twCost = []
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171 for pedInd in pedestrians:
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diff changeset
172 ped = objects[pedInd]
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diff changeset
173 nmatches = 0
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diff changeset
174 for t in tw.bboxes:
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diff changeset
175 if t in ped.bboxes:
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diff changeset
176 #print(tw.num, ped.num, t, box_iou(tw.bboxes[t], ped.bboxes[t]))
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diff changeset
177 if not tw.commonTimeInterval(ped).empty() and box_iou(tw.bboxes[t], ped.bboxes[t]).item() > args.cyclistIou:
1237
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diff changeset
178 nmatches += 1
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diff changeset
179 twCost.append(nmatches/tw.nBBoxes)
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diff changeset
180 costs.append(twCost)
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diff changeset
181
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diff changeset
182 costs = -np.array(costs)
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diff changeset
183
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diff changeset
184 if costs.size > 0:
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diff changeset
185 # before matching, scan for pedestrians with good non-overlapping temporal match with different bikes
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diff changeset
186 for pedInd in range(costs.shape[1]):
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diff changeset
187 nMatchedBikes = (costs[:,pedInd] < -args.cyclistMatchingProportion).sum()
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188 if nMatchedBikes == 0: # peds that have no bike matching: see if they have been classified as bikes sometimes (more than args.bikeProportion)
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diff changeset
189 userTypeStats = Counter(obj.userTypes)
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diff changeset
190 if (moving.userType2Num['cyclist'] in userTypeStats or (moving.userType2Num['motorcyclist'] in userTypeStats and moving.userType2Num['cyclist'] in userTypeStats and userTypeStats[moving.userType2Num['motorcyclist']]<=userTypeStats[moving.userType2Num['cyclist']])) and userTypeStats[moving.userType2Num['motorcyclist']]+userTypeStats[moving.userType2Num['cyclist']] > args.bikeProportion*userTypeStats.total(): # verif if not turning all motorbike into cyclists
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diff changeset
191 obj.setUserType(moving.userType2Num['cyclist'])
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diff changeset
192 # elif nMatchedBikes > 1: # try to merge bikes first
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193 # twIndices = np.nonzero(costs[:,pedInd] < -args.cyclistMatchingProportion)[0]
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diff changeset
194 # # we have to compute temporal overlaps of all 2 wheels among themselves, then remove the ones with the most overlap (sum over column) one by one until there is little left
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diff changeset
195 # nTwoWheels = len(twIndices)
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diff changeset
196 # twTemporalOverlaps = np.zeros((nTwoWheels,nTwoWheels))
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diff changeset
197 # for i in range(nTwoWheels):
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diff changeset
198 # for j in range(i):
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diff changeset
199 # twi = objects[twowheels[twIndices[i]]]
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diff changeset
200 # twj = objects[twowheels[twIndices[j]]]
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diff changeset
201 # twTemporalOverlaps[i,j] = len(set(twi.bboxes).intersection(set(twj.bboxes)))/max(len(twi.bboxes), len(twj.bboxes))
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diff changeset
202 # #twTemporalOverlaps[j,i] = twTemporalOverlaps[i,j]
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diff changeset
203 # tw2merge = list(range(nTwoWheels))
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diff changeset
204 # while len(tw2merge)>0 and (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).max() >= 2:
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diff changeset
205 # i = (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).argmax()
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diff changeset
206 # del tw2merge[i]
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diff changeset
207 # twIndices = [twIndices[i] for i in tw2merge]
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diff changeset
208 # tw1 = objects[twowheels[twIndices[0]]]
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diff changeset
209 # twCost = costs[twIndices[0],:]*tw1.nBBoxes
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diff changeset
210 # nBBoxes = tw1.nBBoxes
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diff changeset
211 # for twInd in twIndices[1:]:
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diff changeset
212 # mergeObjects(tw1, objects[twowheels[twInd]])
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diff changeset
213 # twCost = twCost + costs[twInd,:]*objects[twowheels[twInd]].nBBoxes
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diff changeset
214 # nBBoxes += objects[twowheels[twInd]].nBBoxes
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diff changeset
215 # twIndicesToKeep = list(range(costs.shape[0]))
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diff changeset
216 # for twInd in twIndices[1:]:
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diff changeset
217 # twIndicesToKeep.remove(twInd)
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diff changeset
218 # del objects[twowheels[twInd]]
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diff changeset
219 # twowheels = [twowheels[i] for i in twIndicesToKeep]
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diff changeset
220 # costs = costs[twIndicesToKeep,:]
1237
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diff changeset
221
1247
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diff changeset
222 twIndices, matchingPedIndices = linear_sum_assignment(costs)
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diff changeset
223 for twInd, pedInd in zip(twIndices, matchingPedIndices): # caution indices in the cost matrix
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diff changeset
224 if -costs[twInd, pedInd] >= args.cyclistMatchingProportion:
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diff changeset
225 tw = objects[twowheels[twInd]]
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diff changeset
226 ped = objects[pedestrians[pedInd]]
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diff changeset
227 mergeObjects(tw, ped)
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diff changeset
228 del objects[pedestrians[pedInd]]
1271
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diff changeset
229 # link ped to each assigned bike, remove bike from cost (and ped is temporal match is high)
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diff changeset
230
1247
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diff changeset
231 #TODO Verif overlap piéton vélo : si long hors overlap, changement mode (trouver exemples)
1271
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diff changeset
232 # TODO continue assigning if leftover bikes (if non temporally overlapping with existing bikes assigned to ped)
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diff changeset
233
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diff changeset
234 # interpolate and save image coordinates
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235 for num, obj in objects.items():
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236 for f in obj.getFeatures():
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237 if f.length() != len(f.tmpPositions): # interpolate
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238 f.positions = moving.Trajectory.fromPointDict(f.tmpPositions)
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239 else:
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240 f.positions = moving.Trajectory.fromPointList(list(f.tmpPositions.values()))
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241 if not args.notSavingImageCoordinates:
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242 storage.saveTrajectoriesToSqlite(utils.removeExtension(databaseFilename)+'-bb.sqlite', list(objects.values()), 'object')
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243 # project and smooth
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244 for num, obj in objects.items():
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245 features = obj.getFeatures()
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246 # possible to save bottom pedestrians? not consistent with other users
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247 # if moving.userTypeNames[obj.getUserType()] == 'pedestrian':
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248 # assert len(features) == 2
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249 # t1 = features[0].getPositions()
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250 # t2 = features[1].getPositions()
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251 # t = [[(p1.x+p2.x)/2., max(p1.y, p2.y)] for p1, p2 in zip(t1, t2)]
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252 # else:
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253 t = []
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254 for instant in obj.getTimeInterval():
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255 points = [f.getPositionAtInstant(instant) for f in features if f.existsAtInstant(instant)]
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256 t.append(moving.Point.agg(points, np.mean).aslist())
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257 #t = sum([f.getPositions().asArray() for f in features])/len(features)
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258 #t = (moving.Trajectory.add(t1, t2)*0.5).asArray()
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259 projected = cvutils.imageToWorldProject(np.array(t).T, intrinsicCameraMatrix, distortionCoefficients, homography)
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260 featureNum = features[0].getNum()
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261 feature = moving.MovingObject(featureNum, obj.getTimeInterval(), moving.Trajectory(projected.tolist()))
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262 if smoothingHalfWidth is not None: # smoothing
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263 feature.smoothPositions(smoothingHalfWidth, replace = True)#f.positions = f.getPositions().filterMovingWindow(smoothingHalfWidth)
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264 feature.computeVelocities()
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265 obj.features=[feature]
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266 obj.featureNumbers = [featureNum]
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267 #saving
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268 storage.saveTrajectoriesToSqlite(databaseFilename, list(objects.values()), 'object')
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269
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271
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272 # todo save bbox and mask to study localization / representation
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273 # apply quality checks deviation and acceleration bounds?
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274
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275 # def mergeBBoxes(tw, ped):
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276 # 'merges ped into tw (2nd obj into first obj)'
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277 # timeInstants = set(tw.bboxes).union(set(ped.bboxes))
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278 # for t in timeInstants:
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279 # if t in tw.bboxes and t in ped.bboxes:
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280 # tw.features[0].tmpPositions[t] = moving.Point(min(tw.features[0].tmpPositions[t].x, ped.features[0].tmpPositions[t].x),
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281 # min(tw.features[0].tmpPositions[t].y, ped.features[0].tmpPositions[t].y))
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282 # tw.features[1].tmpPositions[t] = moving.Point(max(tw.features[1].tmpPositions[t].x, ped.features[1].tmpPositions[t].x),
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283 # max(tw.features[1].tmpPositions[t].y, ped.features[1].tmpPositions[t].y))
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284 # elif t in ped.bboxes:
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285 # tw.features[0].tmpPositions[t] = ped.features[0].tmpPositions[t]
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286 # tw.features[1].tmpPositions[t] = ped.features[1].tmpPositions[t]
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287 # tw.timeInterval = moving.TimeInterval(min(tw.getFirstInstant(), ped.getFirstInstant()), max(tw.getLastInstant(), ped.getLastInstant()))