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annotate scripts/dltrack.py @ 1237:31a441efca6c

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ped-bike grouping working, bike merging left todo
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 02 Oct 2023 16:51:43 -0400
parents 100fe098abe9
children b684135d817f
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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 copy import copy
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5 from collections import Counter
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6 import numpy as np
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7 from scipy.optimize import linear_sum_assignment
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8 from ultralytics import YOLO
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9 from torch import cat
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10 from torchvision.ops import box_iou
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11 import cv2
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12
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13 from trafficintelligence import cvutils, moving, storage, utils
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14
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15 parser = argparse.ArgumentParser(description='The program tracks objects following the ultralytics yolo executable.')#, epilog = 'Either the configuration filename or the other parameters (at least video and database filenames) need to be provided.')
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16 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file', required = True)
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17 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
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18 parser.add_argument('-m', dest = 'detectorFilename', help = 'name of the detection model file', required = True)
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19 parser.add_argument('-t', dest = 'trackerFilename', help = 'name of the tracker file', required = True)
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20 parser.add_argument('--display', dest = 'display', help = 'show the results (careful with long videos, risk of running out of memory)', action = 'store_true')
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21 parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to process', type = int, default = 0)
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22 parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to process', type = int, default = float('Inf'))
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23 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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24 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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25 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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26 # mask!!
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27 args = parser.parse_args()
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28
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29 # required functionality?
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30 # # filename of the video to process (can be images, eg image%04d.png)
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31 # video-filename = laurier.avi
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32 # # filename of the database where results are saved
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33 # database-filename = laurier.sqlite
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34 # # filename of the homography matrix
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35 # homography-filename = laurier-homography.txt
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36 # # filename of the camera intrinsic matrix
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37 # intrinsic-camera-filename = intrinsic-camera.txt
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38 # # -0.11759321 0.0148536 0.00030756 -0.00020578 -0.00091816
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39 # distortion-coefficients = -0.11759321
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40 # distortion-coefficients = 0.0148536
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41 # distortion-coefficients = 0.00030756
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42 # distortion-coefficients = -0.00020578
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43 # distortion-coefficients = -0.00091816
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44 # # undistorted image multiplication
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45 # undistorted-size-multiplication = 1.31
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46 # # Interpolation method for remapping image when correcting for distortion: 0 for INTER_NEAREST - a nearest-neighbor interpolation; 1 for INTER_LINEAR - a bilinear interpolation (used by default); 2 for INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood; 3 for INTER_LANCZOS4
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47 # interpolation-method = 1
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48 # # filename of the mask image (where features are detected)
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49 # mask-filename = none
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50 # # undistort the video for feature tracking
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51 # undistort = false
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52 # # load features from database
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53 # load-features = false
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54 # # display trajectories on the video
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55 # display = false
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56 # # original video frame rate (number of frames/s)
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57 # video-fps = 29.97
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58 # # number of digits of precision for all measurements derived from video
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59 # # measurement-precision = 3
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60 # # first frame to process
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61 # frame1 = 0
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62 # # number of frame to process: 0 means processing all frames
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63 # nframes = 0
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64
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65 # TODO add option to refine position with mask for vehicles
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66
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67 # use 2 x bytetrack track buffer to remove objects from existing ones
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68
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69
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70 # check if one can go to specific frame https://docs.ultralytics.com/modes/track/#persisting-tracks-loop
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71
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72 # Load a model
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73 model = YOLO(args.detectorFilename) # seg yolov8x-seg.pt
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74 # seg could be used on cropped image... if can be loaded and kept in memory
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75 # model = YOLO('/home/nicolas/Research/Data/classification-models/yolo_nas_l.pt ') # AttributeError: 'YoloNAS_L' object has no attribute 'get'
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76
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77 # Track with the model
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78 if args.display:
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79 windowName = 'frame'
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80 cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
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81
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82 capture = cv2.VideoCapture(args.videoFilename)
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83 #results = model.track(source=args.videoFilename, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(moving.cocoTypeNames.keys()), stream=True)
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84 objects = []
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85 currentObjects = {}
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86 featureNum = 0
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87
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88 frameNum = args.firstFrameNum
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89 capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
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90 lastFrameNum = args.lastFrameNum
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91
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92 success, frame = capture.read()
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93 results = model.track(frame, tracker=args.trackerFilename, classes=list(moving.cocoTypeNames.keys()), persist=True, verbose=False)
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94 # create object with user type and list of 3 features (bottom ones and middle) + projection
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95 while capture.isOpened() and success and frameNum <= lastFrameNum:
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96 #for frameNum, result in enumerate(results):
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97 result = results[0]
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98 print(frameNum, len(result.boxes), 'objects')
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99 for box in result.boxes:
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100 #print(box.cls, box.id, box.xyxy)
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101 if box.id is not None: # None are objects with low confidence
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102 num = int(box.id.item())
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103 #xyxy = box.xyxy[0].tolist()
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104 if num in currentObjects:
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105 currentObjects[num].timeInterval.last = frameNum
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106 currentObjects[num].bboxes[frameNum] = copy(box.xyxy)
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107 currentObjects[num].userTypes.append(moving.coco2Types[int(box.cls.item())])
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108 currentObjects[num].features[0].tmpPositions[frameNum] = moving.Point(box.xyxy[0,0].item(), box.xyxy[0,1].item())
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109 currentObjects[num].features[1].tmpPositions[frameNum] = moving.Point(box.xyxy[0,2].item(), box.xyxy[0,3].item())
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110 else:
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111 inter = moving.TimeInterval(frameNum,frameNum)
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112 currentObjects[num] = moving.MovingObject(num, inter)
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113 currentObjects[num].bboxes = {frameNum: copy(box.xyxy)}
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114 currentObjects[num].userTypes = [moving.coco2Types[int(box.cls.item())]]
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115 currentObjects[num].features = [moving.MovingObject(featureNum), moving.MovingObject(featureNum+1)]
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116 currentObjects[num].featureNumbers = [featureNum, featureNum+1]
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117 currentObjects[num].features[0].tmpPositions = {frameNum: moving.Point(box.xyxy[0,0].item(), box.xyxy[0,1].item())}
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118 currentObjects[num].features[1].tmpPositions = {frameNum: moving.Point(box.xyxy[0,2].item(), box.xyxy[0,3].item())}
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119 featureNum += 2
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120 if args.display:
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121 cvutils.cvImshow(windowName, result.plot()) # original image in orig_img
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122 key = cv2.waitKey()
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123 if cvutils.quitKey(key):
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124 break
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125 frameNum += 1
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126 success, frame = capture.read()
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127 results = model.track(frame, persist=True)
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128
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129 # classification
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130 for num, obj in currentObjects.items():
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131 #obj.setUserType(utils.mostCommon(obj.userTypes)) # improve? mix with speed?
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132 userTypeStats = Counter(obj.userTypes)
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133 if (4 in userTypeStats or (3 in userTypeStats and 4 in userTypeStats and userTypeStats[3]<=userTypeStats[4])) and userTypeStats[3]+userTypeStats[4] > args.bikeProportion*userTypeStats.total(): # 3 is motorcycle and 4 is cyclist (verif if not turning all motorbike into cyclists)
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134 obj.setUserType(4)
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135 else:
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136 obj.setUserType(userTypeStats.most_common()[0][0])
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137
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138 # merge bikes and people
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139 twowheels = [num for num, obj in currentObjects.items() if obj.getUserType() in (3,4)]
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140 pedestrians = [num for num, obj in currentObjects.items() if obj.getUserType() == 2]
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141
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142 costs = []
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143 for twInd in twowheels:
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144 tw = currentObjects[twInd]
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145 twCost = []
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146 for pedInd in pedestrians:
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147 ped = currentObjects[pedInd]
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148 nmatches = 0
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149 for t in tw.bboxes:
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150 if t in ped.bboxes:
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151 #print(tw.num, ped.num, t, box_iou(tw.bboxes[t], ped.bboxes[t]))
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152 if box_iou(tw.bboxes[t], ped.bboxes[t]).item() > args.cyclistIou:
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153 nmatches += 1
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154 twCost.append(nmatches/len(tw.bboxes))
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155 costs.append(twCost)
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156
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157 costs = -np.array(costs)
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158 # before matching, scan for pedestrians with good non-overlapping temporal match with different bikes
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159 for pedInd in costs.shape[1]:
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160 if sum(costs[:,pedInd] < -args.cyclistMatchingProportion) >1:
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161 twIndices = np.nonzero(costs[:,pedInd] < -args.cyclistMatchingProportion)
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162 # we have to compute temporal overlaps with everyone else, then remove the ones with the most overlap (sum over column) one by one until there is little left
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163 temporalOverlaps = np.zeros((len(twIndices),len(twIndices)))
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164
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165
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166 twIndices, matchingPedIndices = linear_sum_assignment(costs)
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167 for twInd, pedInd in zip(twIndices, matchingPedIndices): # caution indices in the cost matrix
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168 if -costs[twInd, pedInd] >= args.cyclistMatchingProportion:
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169 tw = currentObjects[twowheels[twInd]]
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170 ped = currentObjects[pedestrians[pedInd]]
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171 timeInstants = set(tw.bboxes).union(set(ped.bboxes))
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172 for t in timeInstants:
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173 if t in tw.bboxes and t in ped.bboxes:
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174 tw.features[0].tmpPositions[t] = moving.Point(min(tw.features[0].tmpPositions[t].x, ped.features[0].tmpPositions[t].x),
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175 min(tw.features[0].tmpPositions[t].y, ped.features[0].tmpPositions[t].y))
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176 tw.features[1].tmpPositions[t] = moving.Point(max(tw.features[1].tmpPositions[t].x, ped.features[1].tmpPositions[t].x),
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177 max(tw.features[1].tmpPositions[t].y, ped.features[1].tmpPositions[t].y))
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178 elif t in ped.bboxes:
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179 tw.features[0].tmpPositions[t] = ped.features[0].tmpPositions[t]
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180 tw.features[1].tmpPositions[t] = ped.features[1].tmpPositions[t]
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181 tw.timeInterval = moving.TimeInterval(min(tw.getFirstInstant(), ped.getFirstInstant()), max(tw.getLastInstant(), ped.getLastInstant()))
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182 del currentObjects[pedestrians[pedInd]]
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183 #Verif overlap piéton vélo : si long hors overlap, changement mode (trouver exemples)
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184
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185 # interpolate and generate velocity (?) before saving
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186 for num, obj in currentObjects.items():
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187 obj.features[0].timeInterval = copy(obj.getTimeInterval())
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188 obj.features[1].timeInterval = copy(obj.getTimeInterval())
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189 if obj.length() != len(obj.features[0].tmpPositions): # interpolate
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190 obj.features[0].positions = moving.Trajectory.fromPointDict(obj.features[0].tmpPositions)
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191 obj.features[1].positions = moving.Trajectory.fromPointDict(obj.features[1].tmpPositions)
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192 else:
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193 obj.features[0].positions = moving.Trajectory.fromPointList(list(obj.features[0].tmpPositions.values()))
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194 obj.features[1].positions = moving.Trajectory.fromPointList(list(obj.features[1].tmpPositions.values()))
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195
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196 storage.saveTrajectoriesToSqlite(args.databaseFilename, list(currentObjects.values()), 'object')
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197
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198 # todo save bbox and mask to study localization / representation
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199 # apply quality checks deviation and acceleration bounds?