Grounded SAM

The GroundedSAM class provides a wrapper for the GroundedSAM model, which segments objects in RGB images based on text prompts.

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.

np.ndarray

required

The input RGB image of shape

(M, N, 3)

str

required

The text prompt to use for segmentation.

np.ndarray

The predicted segmentation mask of shape

(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)

License
Source

This code is licensed under the Apache 2.0 License.

⌘I

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)

Get Started

Open GRID

GRID Enterprise

Robot API

Simulation

AI Layer

Deployment

Data Generation Pipelines

FAQ