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annotate scripts/dltrack.py @ 1219:8a626226793e

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update where optimization uses either nomad-parameter file depending on optimizing 1 or 2 steps
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 19 Jun 2023 17:09:56 -0400
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children 5a207c838323
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1219
8a626226793e update where optimization uses either nomad-parameter file depending on optimizing 1 or 2 steps
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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1 #! /usr/bin/env python3
8a626226793e update where optimization uses either nomad-parameter file depending on optimizing 1 or 2 steps
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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.moving import cocoUserTypeNames
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6 from ultralytics import YOLO
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7
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8 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.')
8a626226793e update where optimization uses either nomad-parameter file depending on optimizing 1 or 2 steps
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9 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
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10 # detect model
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11 # tracker model
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12 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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13 args = parser.parse_args()
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14
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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15 # Load a model
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16 model = YOLO('/home/nicolas/Research/Data/classification-models/yolov8x.pt ') # seg yolov8x-seg.pt
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17 # seg could be used on cropped image... if can be loaded and kept in memory
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18 # model = YOLO('/home/nicolas/Research/Data/classification-models/yolo_nas_l.pt ') # AttributeError: 'YoloNAS_L' object has no attribute 'get'
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19
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20 # Track with the model
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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21 if args.display:
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22 results = model.track(source=args.videoFilename, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(cocoUserTypeNames.keys()), show=True) # , save_txt=True
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23 else:
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24 results = model.track(source=args.videoFilename, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(cocoUserTypeNames.keys()), stream=True)
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25 for result in results:
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26 for box in result.boxes:
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27 print(box.xyxy)