Mercurial Hosting > traffic-intelligence
view scripts/dltrack.py @ 1234:dd969637381e
work on tracker interface
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 14 Sep 2023 16:18:36 -0400 |
parents | d5695e0b59d9 |
children | 855abc69fa99 |
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#! /usr/bin/env python3 # from https://docs.ultralytics.com/modes/track/ import sys, argparse from copy import copy from ultralytics import YOLO import cv2 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', 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 = '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? # # filename of the video to process (can be images, eg image%04d.png) # video-filename = laurier.avi # # filename of the database where results are saved # database-filename = laurier.sqlite # # filename of the homography matrix # homography-filename = laurier-homography.txt # # filename of the camera intrinsic matrix # intrinsic-camera-filename = intrinsic-camera.txt # # -0.11759321 0.0148536 0.00030756 -0.00020578 -0.00091816 # distortion-coefficients = -0.11759321 # distortion-coefficients = 0.0148536 # distortion-coefficients = 0.00030756 # distortion-coefficients = -0.00020578 # distortion-coefficients = -0.00091816 # # undistorted image multiplication # undistorted-size-multiplication = 1.31 # # 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 # interpolation-method = 1 # # filename of the mask image (where features are detected) # mask-filename = none # # undistort the video for feature tracking # undistort = false # # load features from database # load-features = false # # display trajectories on the video # display = false # # original video frame rate (number of frames/s) # video-fps = 29.97 # # number of digits of precision for all measurements derived from video # # measurement-precision = 3 # # first frame to process # frame1 = 0 # # number of frame to process: 0 means processing all frames # nframes = 0 # TODO add option to refine position with mask for vehicles # use 2 x bytetrack track buffer to remove objects from existing ones # 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 # 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' # Track with the model #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 if args.display: windowName = 'frame' cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) 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 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) if box.id is not None: # None are objects with low confidence num = int(box.id) xyxy = box.xyxy[0].tolist() if num in currentObjects: currentObjects[num].timeInterval.last = frameNum currentObjects[num].userTypes.append(moving.coco2Types[int(box.cls)]) currentObjects[num].features[0].tmpPositions[frameNum] = moving.Point(xyxy[0],xyxy[1]) currentObjects[num].features[1].tmpPositions[frameNum] = moving.Point(xyxy[2],xyxy[3]) #features[0].getPositions().addPositionXY(xyxy[0],xyxy[1]) #features[1].getPositions().addPositionXY(xyxy[2],xyxy[3]) else: inter = moving.TimeInterval(frameNum,frameNum) currentObjects[num] = moving.MovingObject(num, inter) currentObjects[num].userTypes = [moving.coco2Types[int(box.cls)]] currentObjects[num].features = [moving.MovingObject(featureNum), moving.MovingObject(featureNum+1)] currentObjects[num].featureNumbers = [featureNum, featureNum+1] currentObjects[num].features[0].tmpPositions = {frameNum: moving.Point(xyxy[0],xyxy[1])} currentObjects[num].features[1].tmpPositions = {frameNum: moving.Point(xyxy[2],xyxy[3])} featureNum += 2 if args.display: cvutils.cvImshow(windowName, result.plot()) # original image in orig_img key = cv2.waitKey() if cvutils.quitKey(key): break frameNum += 1 success, frame = capture.read() results = model.track(frame, persist=True) # 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()) obj.features[1].timeInterval = copy(obj.getTimeInterval()) if obj.length() != len(obj.features[0].tmpPositions): # interpolate obj.features[0].positions = moving.Trajectory.fromPointDict(obj.features[0].tmpPositions) obj.features[1].positions = moving.Trajectory.fromPointDict(obj.features[1].tmpPositions) else: 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(args.databaseFilename, list(currentObjects.values()), 'object') # todo save bbox and mask to study localization / representation # apply quality checks deviation and acceleration bounds?