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annotate scripts/dltrack.py @ 1231:6487ef10c0e0

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work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 24 Aug 2023 17:06:16 -0400
parents c582b272108f
children d5695e0b59d9
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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
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5 from trafficintelligence import cvutils, moving, storage
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6 from ultralytics import YOLO
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7 import cv2
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8
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9 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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10 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
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11 # detect model
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12 # tracker model
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13 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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14 args = parser.parse_args()
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15
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16 # required functionality?
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17 # # filename of the video to process (can be images, eg image%04d.png)
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18 # video-filename = laurier.avi
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19 # # filename of the database where results are saved
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20 # database-filename = laurier.sqlite
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21 # # filename of the homography matrix
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22 # homography-filename = laurier-homography.txt
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23 # # filename of the camera intrinsic matrix
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24 # intrinsic-camera-filename = intrinsic-camera.txt
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25 # # -0.11759321 0.0148536 0.00030756 -0.00020578 -0.00091816
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26 # distortion-coefficients = -0.11759321
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27 # distortion-coefficients = 0.0148536
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28 # distortion-coefficients = 0.00030756
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29 # distortion-coefficients = -0.00020578
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30 # distortion-coefficients = -0.00091816
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31 # # undistorted image multiplication
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32 # undistorted-size-multiplication = 1.31
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33 # # 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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34 # interpolation-method = 1
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35 # # filename of the mask image (where features are detected)
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36 # mask-filename = none
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37 # # undistort the video for feature tracking
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38 # undistort = false
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39 # # load features from database
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40 # load-features = false
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41 # # display trajectories on the video
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42 # display = false
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43 # # original video frame rate (number of frames/s)
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44 # video-fps = 29.97
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45 # # number of digits of precision for all measurements derived from video
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46 # # measurement-precision = 3
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47 # # first frame to process
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48 # frame1 = 0
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49 # # number of frame to process: 0 means processing all frames
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50 # nframes = 0
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51
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52 # TODO add option to refine position with mask for vehicles
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53
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54 # use 2 x bytetrack track buffer to remove objects from existing ones
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55
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56 # Load a model
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57 model = YOLO('/home/nicolas/Research/Data/classification-models/yolov8x.pt') # seg yolov8x-seg.pt
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58 # seg could be used on cropped image... if can be loaded and kept in memory
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59 # model = YOLO('/home/nicolas/Research/Data/classification-models/yolo_nas_l.pt ') # AttributeError: 'YoloNAS_L' object has no attribute 'get'
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60
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61 # Track with the model
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62 if args.display:
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63 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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64 else:
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65 windowName = 'frame'
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66 cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
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67
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68 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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69 objects = []
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70 currentObjects = {}
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71 featureNum = 0
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72 # create object with user type and list of 3 features (bottom ones and middle) + projection
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73 for frameNum, result in enumerate(results):
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74 print(frameNum, len(result.boxes))
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75 for box in result.boxes:
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76 num = int(box.id)
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77 xyxy = box.xyxy[0].tolist()
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78 if num in currentObjects:
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79 currentObjects[num].timeInterval.last = frameNum
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80 features = currentObjects[num].features
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81 features[0].getPositions().addPositionXY(xyxy[0],xyxy[1])
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82 features[1].getPositions().addPositionXY(xyxy[2],xyxy[3])
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83 else:
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84 currentObjects[num] = moving.MovingObject(num, moving.TimeInterval(frameNum,frameNum), userType = moving.coco2Types[int(box.cls)])
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85 currentObjects[num].features = [moving.MovingObject(featureNum, moving.TimeInterval(frameNum, frameNum), moving.Trajectory([[xyxy[0]],[xyxy[1]]])),
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86 moving.MovingObject(featureNum+1, moving.TimeInterval(frameNum, frameNum), moving.Trajectory([[xyxy[2]],[xyxy[3]]]))]
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87 currentObjects[num].featureNumbers = [featureNum, featureNum+1]
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88 featureNum += 2
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89 print(box.cls, box.xyxy)
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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
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95 storage.saveTrajectoriesToSqlite('test.sqlite', list(currentObjects.values()), 'object')
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96
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97 # todo save bbox and mask to study localization / representation