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annotate scripts/dltrack.py @ 1240:bb14f919d1cb

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cleaned use of centile (np only) and added info in classify-objects
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 05 Feb 2024 14:14:14 -0500
parents b684135d817f
children 4cd8ace3552f
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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 using the ultralytics models and trakcers.')
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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('-o', dest = 'homographyFilename', help = 'filename of the homography matrix', default = 'homography.txt')
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21 parser.add_argument('-k', dest = 'maskFilename', help = 'name of the mask file')
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22 parser.add_argument('--undistort', dest = 'undistort', help = 'undistort the video', action = 'store_true')
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23 parser.add_argument('--intrinsic', dest = 'intrinsicCameraMatrixFilename', help = 'name of the intrinsic camera file')
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24 parser.add_argument('--distortion-coefficients', dest = 'distortionCoefficients', help = 'distortion coefficients', nargs = '*', type = float)
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25 parser.add_argument('--display', dest = 'display', help = 'show the raw detection and tracking results', action = 'store_true')
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26 parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to process', type = int, default = 0)
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27 parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to process', type = int, default = float('Inf'))
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28 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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29 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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30 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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31 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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32 args = parser.parse_args()
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33
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34 # TODO add option to refine position with mask for vehicles
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35
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36 # use 2 x bytetrack track buffer to remove objects from existing ones
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37
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38 # Load a model
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39 model = YOLO(args.detectorFilename) # seg yolov8x-seg.pt
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40 # seg could be used on cropped image... if can be loaded and kept in memory
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41 # model = YOLO('/home/nicolas/Research/Data/classification-models/yolo_nas_l.pt ') # AttributeError: 'YoloNAS_L' object has no attribute 'get'
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42
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43 # Track with the model
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44 if args.display:
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45 windowName = 'frame'
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46 cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
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47
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48 capture = cv2.VideoCapture(args.videoFilename)
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49 objects = {}
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50 featureNum = 1
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51 frameNum = args.firstFrameNum
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52 capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
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53 lastFrameNum = args.lastFrameNum
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54
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55 success, frame = capture.read()
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56 if not success:
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57 print('Input {} could not be read. Exiting'.format(args.videoFilename))
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58 import sys; sys.exit()
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59 results = model.track(frame, tracker=args.trackerFilename, classes=list(moving.cocoTypeNames.keys()), persist=True, verbose=False)
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60 # create object with user type and list of 3 features (bottom ones and middle) + projection
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61 while capture.isOpened() and success and frameNum <= lastFrameNum:
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62 #for frameNum, result in enumerate(results):
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63 result = results[0]
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64 if frameNum %10 == 0:
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65 print(frameNum, len(result.boxes), 'objects')
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66 for box in result.boxes:
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67 #print(box.cls, box.id, box.xyxy)
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68 if box.id is not None: # None are objects with low confidence
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69 num = int(box.id.item())
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70 #xyxy = box.xyxy[0].tolist()
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71 if num in objects:
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72 objects[num].timeInterval.last = frameNum
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73 objects[num].features[0].timeInterval.last = frameNum
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74 objects[num].features[1].timeInterval.last = frameNum
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75 objects[num].bboxes[frameNum] = copy(box.xyxy)
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76 objects[num].userTypes.append(moving.coco2Types[int(box.cls.item())])
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77 objects[num].features[0].tmpPositions[frameNum] = moving.Point(box.xyxy[0,0].item(), box.xyxy[0,1].item())
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78 objects[num].features[1].tmpPositions[frameNum] = moving.Point(box.xyxy[0,2].item(), box.xyxy[0,3].item())
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79 else:
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80 inter = moving.TimeInterval(frameNum, frameNum)
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81 objects[num] = moving.MovingObject(num, inter)
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82 objects[num].bboxes = {frameNum: copy(box.xyxy)}
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83 objects[num].userTypes = [moving.coco2Types[int(box.cls.item())]]
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84 objects[num].features = [moving.MovingObject(featureNum, copy(inter)), moving.MovingObject(featureNum+1, copy(inter))]
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85 objects[num].featureNumbers = [featureNum, featureNum+1]
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86 objects[num].features[0].tmpPositions = {frameNum: moving.Point(box.xyxy[0,0].item(), box.xyxy[0,1].item())}
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87 objects[num].features[1].tmpPositions = {frameNum: moving.Point(box.xyxy[0,2].item(), box.xyxy[0,3].item())}
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88 featureNum += 2
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89 if args.display:
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90 cvutils.cvImshow(windowName, result.plot()) # original image in orig_img
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91 key = cv2.waitKey()
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92 if cvutils.quitKey(key):
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93 break
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94 frameNum += 1
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95 success, frame = capture.read()
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96 results = model.track(frame, persist=True)
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97
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98 # classification
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99 for num, obj in objects.items():
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100 obj.setUserType(utils.mostCommon(obj.userTypes)) # improve? mix with speed?
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101
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102 # add quality control: avoid U-turns
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103
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104 # merge bikes and people
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105 twowheels = [num for num, obj in objects.items() if obj.getUserType() in (3,4)]
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106 pedestrians = [num for num, obj in objects.items() if obj.getUserType() == 2]
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107
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108 def mergeObjects(obj1, obj2):
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109 obj1.features = obj1.features+obj2.features
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110 obj1.featureNumbers = obj1.featureNumbers+obj2.featureNumbers
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111 obj1.timeInterval = moving.TimeInterval(min(obj1.getFirstInstant(), obj2.getFirstInstant()), max(obj1.getLastInstant(), obj2.getLastInstant()))
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112
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113 costs = []
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114 for twInd in twowheels:
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115 tw = objects[twInd]
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116 tw.nBBoxes = len(tw.bboxes)
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117 twCost = []
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118 for pedInd in pedestrians:
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119 ped = objects[pedInd]
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120 nmatches = 0
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121 for t in tw.bboxes:
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122 if t in ped.bboxes:
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123 #print(tw.num, ped.num, t, box_iou(tw.bboxes[t], ped.bboxes[t]))
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124 if not tw.commonTimeInterval(ped).empty() and box_iou(tw.bboxes[t], ped.bboxes[t]).item() > args.cyclistIou:
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125 nmatches += 1
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126 twCost.append(nmatches/tw.nBBoxes)
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127 costs.append(twCost)
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128
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129 costs = -np.array(costs)
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130
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131 # before matching, scan for pedestrians with good non-overlapping temporal match with different bikes
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132 for pedInd in range(costs.shape[1]):
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133 nMatchedBikes = (costs[:,pedInd] < -args.cyclistMatchingProportion).sum()
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134 if nMatchedBikes == 0: # peds that have no bike matching: see if they have been classified as bikes sometimes
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135 userTypeStats = Counter(obj.userTypes)
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136 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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137 obj.setUserType(4)
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138 elif nMatchedBikes > 1: # try to merge bikes first
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139 twIndices = np.nonzero(costs[:,pedInd] < -args.cyclistMatchingProportion)[0]
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140 # 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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141 nTwoWheels = len(twIndices)
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142 twTemporalOverlaps = np.zeros((nTwoWheels,nTwoWheels))
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143 for i in range(nTwoWheels):
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144 for j in range(i):
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145 twi = objects[twowheels[twIndices[i]]]
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146 twj = objects[twowheels[twIndices[j]]]
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147 twTemporalOverlaps[i,j] = len(set(twi.bboxes).intersection(set(twj.bboxes)))/max(len(twi.bboxes), len(twj.bboxes))
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148 #twTemporalOverlaps[j,i] = twTemporalOverlaps[i,j]
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149 tw2merge = list(range(nTwoWheels))
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150 while len(tw2merge)>0 and (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).max() >= 2:
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151 i = (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).argmax()
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152 del tw2merge[i]
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153 twIndices = [twIndices[i] for i in tw2merge]
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154 tw1 = objects[twowheels[twIndices[0]]]
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155 twCost = costs[twIndices[0],:]*tw1.nBBoxes
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156 nBBoxes = tw1.nBBoxes
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157 for twInd in twIndices[1:]:
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158 mergeObjects(tw1, objects[twowheels[twInd]])
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159 twCost = twCost + costs[twInd,:]*objects[twowheels[twInd]].nBBoxes
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160 nBBoxes += objects[twowheels[twInd]].nBBoxes
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161 twIndicesToKeep = list(range(costs.shape[0]))
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162 for twInd in twIndices[1:]:
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163 twIndicesToKeep.remove(twInd)
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164 del objects[twowheels[twInd]]
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165 twowheels = [twowheels[i] for i in twIndicesToKeep]
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166 costs = costs[twIndicesToKeep,:]
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167
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168 twIndices, matchingPedIndices = linear_sum_assignment(costs)
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169 for twInd, pedInd in zip(twIndices, matchingPedIndices): # caution indices in the cost matrix
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170 if -costs[twInd, pedInd] >= args.cyclistMatchingProportion:
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171 tw = objects[twowheels[twInd]]
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172 ped = objects[pedestrians[pedInd]]
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173 mergeObjects(tw, ped)
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174 del objects[pedestrians[pedInd]]
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175 #TODO Verif overlap piéton vélo : si long hors overlap, changement mode (trouver exemples)
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176
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177 # interpolate and generate velocity (?) for the features (bboxes) before saving
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178 for num, obj in objects.items():
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179 #obj.features[1].timeInterval = copy(obj.getTimeInterval())
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180 for f in obj.getFeatures():
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181 if f.length() != len(f.tmpPositions): # interpolate
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182 f.positions = moving.Trajectory.fromPointDict(f.tmpPositions)
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183 #obj.features[1].positions = moving.Trajectory.fromPointDict(obj.features[1].tmpPositions)
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184 else:
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185 f.positions = moving.Trajectory.fromPointList(list(f.tmpPositions.values()))
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186 #obj.features[1].positions = moving.Trajectory.fromPointList(list(obj.features[1].tmpPositions.values()))
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187
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188 storage.saveTrajectoriesToSqlite(args.databaseFilename, list(objects.values()), 'object')
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189
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190 # todo save bbox and mask to study localization / representation
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191 # apply quality checks deviation and acceleration bounds?
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192
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193 # def mergeBBoxes(tw, ped):
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194 # 'merges ped into tw (2nd obj into first obj)'
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195 # timeInstants = set(tw.bboxes).union(set(ped.bboxes))
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196 # for t in timeInstants:
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197 # if t in tw.bboxes and t in ped.bboxes:
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198 # tw.features[0].tmpPositions[t] = moving.Point(min(tw.features[0].tmpPositions[t].x, ped.features[0].tmpPositions[t].x),
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199 # min(tw.features[0].tmpPositions[t].y, ped.features[0].tmpPositions[t].y))
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200 # tw.features[1].tmpPositions[t] = moving.Point(max(tw.features[1].tmpPositions[t].x, ped.features[1].tmpPositions[t].x),
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201 # max(tw.features[1].tmpPositions[t].y, ped.features[1].tmpPositions[t].y))
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202 # elif t in ped.bboxes:
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203 # tw.features[0].tmpPositions[t] = ped.features[0].tmpPositions[t]
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204 # tw.features[1].tmpPositions[t] = ped.features[1].tmpPositions[t]
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205 # tw.timeInterval = moving.TimeInterval(min(tw.getFirstInstant(), ped.getFirstInstant()), max(tw.getLastInstant(), ped.getLastInstant()))