from grid.model.perception.matching.glightglue import LightGlue
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
img1 = img.copy()

model = LightGlue(use_local = False)
matches1, points0_1, points1_1  = model.run(img, img1)
The LightGlue model computes point matches between two images using SuperPoint and LightGlue.
​
class LightGlue()
​
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()
​
image0
np.ndarray
required
Input RGB image 1 of shape (M,N,3)(M,N,3).
​
image1
np.ndarray
required
Input RGB image 2 of shape (M,N,3)(M,N,3).
​
Returns
List
List of point correspondences (matches) between the images.
​
points0
np.ndarray
Feature points detected in image 1 of shape (K,2)(K,2).
​
points1
np.ndarray
Feature points detected in image 2 of shape (K,2)(K,2).
from grid.model.perception.matching.glightglue import LightGlue
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
img1 = img.copy()

model = LightGlue(use_local = False)
matches1, points0_1, points1_1  = model.run(img, img1)
This code is licensed under the Apache 2.0 License.