from grid.model.perception.segmentation.sam2 import SAM2
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
prompts = np.array([[616, 208]])
labels = np.array([1])

model = SAM2(use_local = False)
result = model.run(rgbimage=img, prompts, labels)
print(result.shape)
The SAM2 class implements a wrapper for the Segment Anything 2.1 (SAM2) model, which segments objects in RGB images and videos based on input prompts.
​
class SAM2()
​
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).
​
prompts
np.ndarray
required
Points in the form of coordinates of shape (x,y)(x, y) to segment.
​
input_labels
np.ndarray
required
The labels of the points to segment.
​
multimask_output
boolean
default:"False"
If True, returns a list of masks of shape (M,N)(M,N). Defaults to False. The mutlimask feature is only available for local inference.
​
Returns
np.ndarray
The predicted output.
from grid.model.perception.segmentation.sam2 import SAM2
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
prompts = np.array([[616, 208]])
labels = np.array([1])

model = SAM2(use_local = False)
result = model.run(rgbimage=img, prompts, labels)
print(result.shape)
This code is licensed under the Apache 2.0, and BSD-3 License.