Mercurial Hosting > traffic-intelligence
changeset 1234:dd969637381e
work on tracker interface
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
---|---|
date | Thu, 14 Sep 2023 16:18:36 -0400 |
parents | d5695e0b59d9 |
children | 855abc69fa99 |
files | scripts/dltrack.py |
diffstat | 1 files changed, 25 insertions(+), 10 deletions(-) [+] |
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--- a/scripts/dltrack.py Fri Sep 08 17:09:12 2023 -0400 +++ b/scripts/dltrack.py Thu Sep 14 16:18:36 2023 -0400 @@ -8,11 +8,13 @@ from trafficintelligence import cvutils, moving, storage, utils 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)') -# detect model -# tracker model +parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file', required = True) +parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True) +parser.add_argument('-m', dest = 'detectorFilename', help = 'name of the detection model file', required = True) +parser.add_argument('-t', dest = 'trackerFilename', help = 'name of the tracker file', required = True) parser.add_argument('--display', dest = 'display', help = 'show the results (careful with long videos, risk of running out of memory)', action = 'store_true') -#parser.add_argument('-f', dest = 'firstFrameNum', help = 'show the results (careful with long videos, risk of running out of memory)', action = 'store_true') +parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to process', type = int, default = 0) +parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to process', type = int, default = float('Inf')) args = parser.parse_args() # required functionality? @@ -59,7 +61,7 @@ # check if one can go to specific frame https://docs.ultralytics.com/modes/track/#persisting-tracks-loop # Load a model -model = YOLO('/home/nicolas/Research/Data/classification-models/yolov8x.pt') # seg yolov8x-seg.pt +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 # model = YOLO('/home/nicolas/Research/Data/classification-models/yolo_nas_l.pt ') # AttributeError: 'YoloNAS_L' object has no attribute 'get' @@ -68,13 +70,23 @@ if args.display: 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) + +capture = cv2.VideoCapture(args.videoFilename) +#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 + +frameNum = args.firstFrameNum +capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum) +lastFrameNum = args.lastFrameNum + +success, frame = capture.read() +results = model.track(frame, tracker="/home/nicolas/Research/Data/classification-models/bytetrack.yaml", classes=list(moving.cocoTypeNames.keys()), persist=True) # create object with user type and list of 3 features (bottom ones and middle) + projection -for frameNum, result in enumerate(results): +while capture.isOpened() and success and frameNum <= lastFrameNum: +#for frameNum, result in enumerate(results): + result = results[0] print(frameNum, len(result.boxes)) for box in result.boxes: #print(box.cls, box.id, box.xyxy) @@ -102,8 +114,11 @@ key = cv2.waitKey() if cvutils.quitKey(key): break + frameNum += 1 + success, frame = capture.read() + results = model.track(frame, persist=True) -# interpolate before saving +# interpolate and generate velocity before saving for num, obj in currentObjects.items(): obj.setUserType(utils.mostCommon(obj.userTypes)) obj.features[0].timeInterval = copy(obj.getTimeInterval()) @@ -115,7 +130,7 @@ obj.features[0].positions = moving.Trajectory.fromPointList(list(obj.features[0].tmpPositions.values())) obj.features[1].positions = moving.Trajectory.fromPointList(list(obj.features[1].tmpPositions.values())) -storage.saveTrajectoriesToSqlite('test.sqlite', list(currentObjects.values()), 'object') +storage.saveTrajectoriesToSqlite(args.databaseFilename, list(currentObjects.values()), 'object') # todo save bbox and mask to study localization / representation # apply quality checks deviation and acceleration bounds?