from grid.model.perception.depth.depth_anything_v2 import DepthAnything_V2
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 = DepthAnything_V2(use_local = False, mode='relative')
result = model.run(rgbimage=img)
print(result.shape)

The DepthAnything_V2 class implements a wrapper for the DepthAnything_V2 model, which estimates depth maps from RGB images. The model supports ‘metric’ and ‘relative’ modes, which load different pre-trained models based on the specified mode. We use the VIT Large encoder.

​
class DepthAnything_V2()
​
mode
string
default:"metric"

Flag to specify the mode of the model. Can be ‘metric’ or ‘relative’. Defaults to ‘metric’.

​
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.depth_anything_v2 import DepthAnything_V2
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 = DepthAnything_V2(use_local = False, mode='relative')
result = model.run(rgbimage=img)
print(result.shape)

This code is licensed under the Apache 2.0 License.