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annotate scripts/dltrack.py @ 1234:dd969637381e

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work on tracker interface
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 14 Sep 2023 16:18:36 -0400
parents d5695e0b59d9
children 855abc69fa99
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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 ultralytics import YOLO
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6 import cv2
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7
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8 from trafficintelligence import cvutils, moving, storage, utils
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9
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10 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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11 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file', required = True)
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12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
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13 parser.add_argument('-m', dest = 'detectorFilename', help = 'name of the detection model file', required = True)
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14 parser.add_argument('-t', dest = 'trackerFilename', help = 'name of the tracker file', required = True)
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15 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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16 parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to process', type = int, default = 0)
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17 parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to process', type = int, default = float('Inf'))
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18 args = parser.parse_args()
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19
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20 # required functionality?
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21 # # filename of the video to process (can be images, eg image%04d.png)
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22 # video-filename = laurier.avi
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23 # # filename of the database where results are saved
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24 # database-filename = laurier.sqlite
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25 # # filename of the homography matrix
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26 # homography-filename = laurier-homography.txt
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27 # # filename of the camera intrinsic matrix
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28 # intrinsic-camera-filename = intrinsic-camera.txt
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29 # # -0.11759321 0.0148536 0.00030756 -0.00020578 -0.00091816
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30 # distortion-coefficients = -0.11759321
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31 # distortion-coefficients = 0.0148536
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32 # distortion-coefficients = 0.00030756
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33 # distortion-coefficients = -0.00020578
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34 # distortion-coefficients = -0.00091816
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35 # # undistorted image multiplication
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36 # undistorted-size-multiplication = 1.31
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37 # # 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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38 # interpolation-method = 1
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39 # # filename of the mask image (where features are detected)
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40 # mask-filename = none
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41 # # undistort the video for feature tracking
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42 # undistort = false
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43 # # load features from database
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44 # load-features = false
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45 # # display trajectories on the video
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46 # display = false
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47 # # original video frame rate (number of frames/s)
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48 # video-fps = 29.97
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49 # # number of digits of precision for all measurements derived from video
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50 # # measurement-precision = 3
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51 # # first frame to process
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52 # frame1 = 0
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53 # # number of frame to process: 0 means processing all frames
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54 # nframes = 0
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55
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56 # TODO add option to refine position with mask for vehicles
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57
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58 # use 2 x bytetrack track buffer to remove objects from existing ones
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59
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60
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61 # check if one can go to specific frame https://docs.ultralytics.com/modes/track/#persisting-tracks-loop
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62
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63 # Load a model
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64 model = YOLO('/home/nicolas/Research/Data/classification-models/yolov8x.pt', ) # seg yolov8x-seg.pt
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65 # seg could be used on cropped image... if can be loaded and kept in memory
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66 # model = YOLO('/home/nicolas/Research/Data/classification-models/yolo_nas_l.pt ') # AttributeError: 'YoloNAS_L' object has no attribute 'get'
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67
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68 # Track with the model
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69 #results = model.track(source=args.videoFilename, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(moving.cocoTypeNames.keys()), show=True) # , save_txt=True
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70 if args.display:
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71 windowName = 'frame'
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72 cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
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73
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74 capture = cv2.VideoCapture(args.videoFilename)
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75 #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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76 objects = []
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77 currentObjects = {}
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78 featureNum = 0
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79
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80 frameNum = args.firstFrameNum
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81 capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
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82 lastFrameNum = args.lastFrameNum
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83
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84 success, frame = capture.read()
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85 results = model.track(frame, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(moving.cocoTypeNames.keys()), persist=True)
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86 # create object with user type and list of 3 features (bottom ones and middle) + projection
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87 while capture.isOpened() and success and frameNum <= lastFrameNum:
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88 #for frameNum, result in enumerate(results):
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89 result = results[0]
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90 print(frameNum, len(result.boxes))
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91 for box in result.boxes:
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92 #print(box.cls, box.id, box.xyxy)
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93 if box.id is not None: # None are objects with low confidence
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94 num = int(box.id)
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95 xyxy = box.xyxy[0].tolist()
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96 if num in currentObjects:
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97 currentObjects[num].timeInterval.last = frameNum
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98 currentObjects[num].userTypes.append(moving.coco2Types[int(box.cls)])
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99 currentObjects[num].features[0].tmpPositions[frameNum] = moving.Point(xyxy[0],xyxy[1])
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100 currentObjects[num].features[1].tmpPositions[frameNum] = moving.Point(xyxy[2],xyxy[3])
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101 #features[0].getPositions().addPositionXY(xyxy[0],xyxy[1])
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102 #features[1].getPositions().addPositionXY(xyxy[2],xyxy[3])
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103 else:
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104 inter = moving.TimeInterval(frameNum,frameNum)
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105 currentObjects[num] = moving.MovingObject(num, inter)
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106 currentObjects[num].userTypes = [moving.coco2Types[int(box.cls)]]
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107 currentObjects[num].features = [moving.MovingObject(featureNum), moving.MovingObject(featureNum+1)]
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108 currentObjects[num].featureNumbers = [featureNum, featureNum+1]
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109 currentObjects[num].features[0].tmpPositions = {frameNum: moving.Point(xyxy[0],xyxy[1])}
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110 currentObjects[num].features[1].tmpPositions = {frameNum: moving.Point(xyxy[2],xyxy[3])}
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111 featureNum += 2
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112 if args.display:
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113 cvutils.cvImshow(windowName, result.plot()) # original image in orig_img
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114 key = cv2.waitKey()
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115 if cvutils.quitKey(key):
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116 break
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117 frameNum += 1
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118 success, frame = capture.read()
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119 results = model.track(frame, persist=True)
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120
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121 # interpolate and generate velocity before saving
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122 for num, obj in currentObjects.items():
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123 obj.setUserType(utils.mostCommon(obj.userTypes))
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124 obj.features[0].timeInterval = copy(obj.getTimeInterval())
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125 obj.features[1].timeInterval = copy(obj.getTimeInterval())
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126 if obj.length() != len(obj.features[0].tmpPositions): # interpolate
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127 obj.features[0].positions = moving.Trajectory.fromPointDict(obj.features[0].tmpPositions)
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128 obj.features[1].positions = moving.Trajectory.fromPointDict(obj.features[1].tmpPositions)
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129 else:
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130 obj.features[0].positions = moving.Trajectory.fromPointList(list(obj.features[0].tmpPositions.values()))
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131 obj.features[1].positions = moving.Trajectory.fromPointList(list(obj.features[1].tmpPositions.values()))
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132
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133 storage.saveTrajectoriesToSqlite(args.databaseFilename, list(currentObjects.values()), 'object')
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134
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135 # todo save bbox and mask to study localization / representation
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136 # apply quality checks deviation and acceleration bounds?