from grid.model.perception.depth.zoedepth import ZoeDepth
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 = ZoeDepth(use_local = False)
result = model.run(rgbimage=img)
print(result.shape)
The ZoeDepth class provides a wrapper for the ZoeDepth model, which estimates depth maps from RGB images using the pipeline method from the Hugging Face transformers library.
​
class ZoeDepth()
​
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.zoedepth import ZoeDepth
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 = ZoeDepth(use_local = False)
result = model.run(rgbimage=img)
print(result.shape)
This code is licensed under the MIT License.