from grid.model.perception.depth.metric3d import Metric3D
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 = Metric3D(use_local = False)
result = model.run(rgbimage=img)
print(result.shape)

The Metric3D class provides a wrapper for the Metric3D model, which estimates depth maps from RGB images using a variety of encoder types.

​
class Metric3D()
​
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 depth map of shape (M,N)(M,N).

from grid.model.perception.depth.metric3d import Metric3D
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 = Metric3D(use_local = False)
result = model.run(rgbimage=img)
print(result.shape)

This code is licensed under the BSD 2-Clause License.

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