from grid.model.perception.segmentation.gsam import GroundedSAM
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = GroundedSAM(use_local = False)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result.shape)
The GroundedSAM class provides a wrapper for the GroundedSAM model, which segments objects in RGB images based on text prompts.
​
class GroundedSAM()
​
use_local
boolean
default:"True"
If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to False.
This model is currently not available via Cortex.
​
def run()
​
rgbimage
np.ndarray
required
The input RGB image of shape (M,N,3)(M, N, 3).
​
prompt
str
required
The text prompt to use for segmentation.
​
Returns
np.ndarray
The predicted segmentation mask of shape (M,N)(M, N).
from grid.model.perception.segmentation.gsam import GroundedSAM
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = GroundedSAM(use_local = False)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result.shape)
This code is licensed under the Apache 2.0 License.