CLIPSeg

The CLIPSeg implements a wrapper for the CLIPSeg model, which segments images based on a given text prompt.

boolean

default:"False"

If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to False.

np.ndarray

required

The input RGB image of shape

(M, N, 3)

str

required

The text prompt to use for segmentation.

np.ndarray

The predicted segmentation mask of shape

(M, N)

from grid.model.perception.segmentation.clipseg import CLIPSeg
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 = CLIPSeg(use_local = False)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result.shape)

License
Source

This code is licensed under the MIT License.

⌘I

from grid.model.perception.segmentation.clipseg import CLIPSeg
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 = CLIPSeg(use_local = False)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result.shape)

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