OWLv2

The OWLv2 implements a wrapper for the OWLv2 model, which detects objects in RGB images based on a text prompt.

boolean

default:"False"

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

float

default:"0.4"

Confidence threshold for bounding box detection.

np.ndarray

required

The input RGB image of shape

(M,N,3)

str

required

Text prompt for object detection. Multiple prompts can be separated by a ”,”.

List[float], List[float], List[str]

Returns three lists: bounding boxes coordinates, confidence scores, and label strings.

from grid.model.perception.detection.owlv2 import OWLv2
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 = OWLv2(use_local = False)
box, scores, labels = model.run(rgbimage=img, prompt=<prompt>)
print(box, scores, labels)

## if you want to use the model locally, set use_local=True
model = OWLv2(use_local = True)
box, scores, labels = model.run(rgbimage=img, prompt=<prompt>)
print(box, scores, labels)

License
Source

This code is licensed under the Apache 2.0 License.

⌘I

from grid.model.perception.detection.owlv2 import OWLv2
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 = OWLv2(use_local = False)
box, scores, labels = model.run(rgbimage=img, prompt=<prompt>)
print(box, scores, labels)

## if you want to use the model locally, set use_local=True
model = OWLv2(use_local = True)
box, scores, labels = model.run(rgbimage=img, prompt=<prompt>)
print(box, scores, labels)

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