GRID Docs | General Robotics

Segment objects in image using text prompt.

Parameters

ImageInput

required

RGB image as file path, URL, PIL Image, or numpy array.

str

required

Text description of objects to segment.

float | None

Optional confidence threshold (0.0-1.0) for filtering detections.

float | None

Optional text confidence threshold (0.0-1.0).

float | None

Optional Non-Maximum-Suppression threshold (0.0-1.0).

float | None

Optional timeout in seconds for the HTTP request.

Returns

np.ndarray: Binary segmentation mask as numpy array (H, W) with dtype uint8. Foreground pixels are 255, background pixels are 0.

Example

from grid_cortex_client import CortexClient, ModelType
import numpy as np
from PIL import Image
client = CortexClient()
image = np.array(Image.open("cat.jpg"))
mask = client.run(ModelType.GSAM2, image_input=image, prompt="a cat")
print(mask.shape)  # (480, 640)

Combines Grounding DINO with SAM 2.

Example Output

from grid_cortex_client import CortexClient
import numpy as np
from PIL import Image

client = CortexClient()
img = Image.open("scene.jpg")  # 640x480 RGB
mask = client.run(model_id="gsam2", image_input=img, prompt="street")

print(mask.shape, mask.dtype)
# (480, 640) uint8

foreground = np.count_nonzero(mask)
print(f"foreground pixels: {foreground} ({foreground / mask.size * 100:.1f}%)")
# foreground pixels: 138737 (45.2%)

Overview

Cortex Models

gsam2

Parameters

Returns

Example

Example Output

​Parameters

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​Example

​Example Output

Parameters

Returns

Example

Example Output