from grid.model.perception.segmentation.sapiens_segmentation import SapiensSegmentation
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 = SapiensSegmentation(use_local = False)
result = model.run(rgbimage=img)
print(result.shape)

The SapiensSegmentation class provides a wrapper for the Sapiens body-part segmentation model.

This model is specifically trained for images with humans as the primary subject.
class SapiensSegmentation()
use_local
boolean
default:"False"

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

def run()
rgbimage
np.ndarray
required

The input RGB image of shape (M,N,3)(M, N, 3).

Returns
np.ndarray

The predicted segmentation mask of shape (M,N)(M, N).

This code is licensed under the CC-by-NC 4.0 License.

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