from grid.model.perception.tracking.mft import MFTcar = AirGenCar()# We will be capturing an image from the AirGen simulator # and run model inference on it.img = car.getImage("front_center", "rgb").dataqueries = torch.tensor([ [0., 600., 350.], [0., 600., 250.], [10., 600., 500.], [20., 750., 600.], [30., 900., 200.]])model = MFT(queries = queries, save_results=False, use_local = True)for frame in video_frames: model.run(frame)
from grid.model.perception.tracking.mft import MFTcar = AirGenCar()# We will be capturing an image from the AirGen simulator # and run model inference on it.img = car.getImage("front_center", "rgb").dataqueries = torch.tensor([ [0., 600., 350.], [0., 600., 250.], [10., 600., 500.], [20., 750., 600.], [30., 900., 200.]])model = MFT(queries = queries, save_results=False, use_local = True)for frame in video_frames: model.run(frame)
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