from grid.model.perception.segmentation.gopenseed import Openseedcar = AirGenCar()# We will be capturing an image from the AirGen simulator # and run model inference on it.img = car.getImage("front_center","rgb").datamodel = Openseed(use_local =True)result = model.run(rgbimage=img, prompt=<prompt>)print(result.shape)
The Openseed class implements a wrapper for the Openseed model, which segments images based on given queries.
This code is licensed under the Apache 2.0 License.
This code is licensed under the Apache 2.0 License.
from grid.model.perception.segmentation.gopenseed import Openseedcar = AirGenCar()# We will be capturing an image from the AirGen simulator # and run model inference on it.img = car.getImage("front_center","rgb").datamodel = Openseed(use_local =True)result = model.run(rgbimage=img, prompt=<prompt>)print(result.shape)
from grid.model.perception.segmentation.gopenseed import Openseedcar = AirGenCar()# We will be capturing an image from the AirGen simulator # and run model inference on it.img = car.getImage("front_center","rgb").datamodel = Openseed(use_local =True)result = model.run(rgbimage=img, prompt=<prompt>)print(result.shape)