GRID Docs | General Robotics

Segment objects in image using text prompt.

Input images up to 1280 x 720 (width x height). Larger images are rejected with HTTP 413, not downscaled — resize first.

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%)

⌘I

Overview

Cortex Models

gsam2

Parameters

Returns

Example

Example Output

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Parameters

Returns

Example

Example Output