LSeg

The LSegModel class provides core functionality for this module.

boolean

default:"False"

If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to False.

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.lseg import LSegModel
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 = LSegModel(use_local = False)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result.shape)

License
Source

This code is licensed under the MIT License.

⌘I

from grid.model.perception.segmentation.lseg import LSegModel
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 = LSegModel(use_local = False)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result.shape)

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