Mercurial Hosting > traffic-intelligence
changeset 1231:6487ef10c0e0
work in progress
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
---|---|
date | Thu, 24 Aug 2023 17:06:16 -0400 |
parents | c582b272108f |
children | 83ca1493d55c |
files | scripts/dltrack.py |
diffstat | 1 files changed, 37 insertions(+), 6 deletions(-) [+] |
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--- a/scripts/dltrack.py Mon Aug 21 15:49:32 2023 -0400 +++ b/scripts/dltrack.py Thu Aug 24 17:06:16 2023 -0400 @@ -2,8 +2,9 @@ # from https://docs.ultralytics.com/modes/track/ import sys, argparse -from trafficintelligence.moving import cocoTypeNames +from trafficintelligence import cvutils, moving, storage from ultralytics import YOLO +import cv2 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.') parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)') @@ -50,6 +51,8 @@ # TODO add option to refine position with mask for vehicles +# use 2 x bytetrack track buffer to remove objects from existing ones + # Load a model model = YOLO('/home/nicolas/Research/Data/classification-models/yolov8x.pt') # seg yolov8x-seg.pt # seg could be used on cropped image... if can be loaded and kept in memory @@ -57,10 +60,38 @@ # Track with the model if args.display: - results = model.track(source=args.videoFilename, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(cocoTypeNames.keys()), show=True) # , save_txt=True + 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 else: - results = model.track(source=args.videoFilename, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(cocoTypeNames.keys()), stream=True) - for result in results: - print(len(result.boxes)) + windowName = 'frame' + cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) + + results = model.track(source=args.videoFilename, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(moving.cocoTypeNames.keys()), stream=True) + objects = [] + currentObjects = {} + featureNum = 0 + # create object with user type and list of 3 features (bottom ones and middle) + projection + for frameNum, result in enumerate(results): + print(frameNum, len(result.boxes)) for box in result.boxes: - print(box.xyxy) + num = int(box.id) + xyxy = box.xyxy[0].tolist() + if num in currentObjects: + currentObjects[num].timeInterval.last = frameNum + features = currentObjects[num].features + features[0].getPositions().addPositionXY(xyxy[0],xyxy[1]) + features[1].getPositions().addPositionXY(xyxy[2],xyxy[3]) + else: + currentObjects[num] = moving.MovingObject(num, moving.TimeInterval(frameNum,frameNum), userType = moving.coco2Types[int(box.cls)]) + currentObjects[num].features = [moving.MovingObject(featureNum, moving.TimeInterval(frameNum, frameNum), moving.Trajectory([[xyxy[0]],[xyxy[1]]])), + moving.MovingObject(featureNum+1, moving.TimeInterval(frameNum, frameNum), moving.Trajectory([[xyxy[2]],[xyxy[3]]]))] + currentObjects[num].featureNumbers = [featureNum, featureNum+1] + featureNum += 2 + print(box.cls, box.xyxy) + cvutils.cvImshow(windowName, result.plot()) # original image in orig_img + key = cv2.waitKey() + if cvutils.quitKey(key): + break + +storage.saveTrajectoriesToSqlite('test.sqlite', list(currentObjects.values()), 'object') + +# todo save bbox and mask to study localization / representation