> ## Documentation Index
> Fetch the complete documentation index at: https://docs.generalrobotics.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# da3metric

> Estimate metric depth from single RGB image

Estimate metric depth from single RGB image.

## Parameters

<ParamField body="image_input" type="ImageInput" required>
  RGB image as file path, URL, PIL Image, or numpy array.
</ParamField>

<ParamField body="timeout" type="float | None">
  Optional timeout in seconds for the HTTP request.
</ParamField>

## Returns

np.ndarray: Depth map as numpy array (H, W) with dtype float32.
Values represent metric depth in meters.

## Example

```python theme={null}
from grid_cortex_client import CortexClient, ModelType
import numpy as np
from PIL import Image
client = CortexClient()
image = np.array(Image.open("cat.jpg"))
depth = client.run(ModelType.DA3METRIC, image_input=image)
print(depth.shape)  # (480, 640)
```

Uses Depth Anything 3.

## Example Output

<img src="https://mintcdn.com/scaledfoundations/lgZgGQWKvUIw0C7n/assets/images/cortex/da3metric-output.jpg?fit=max&auto=format&n=lgZgGQWKvUIw0C7n&q=85&s=ebc65e61106d912ad8921fd39d6188b2" alt="DA3Metric input and depth map output" width="1136" height="552" data-path="assets/images/cortex/da3metric-output.jpg" />

```python theme={null}
from grid_cortex_client import CortexClient
from PIL import Image

client = CortexClient()
image = Image.open("scene.jpg")  # 640x480 RGB
depth = client.run(model_id="da3metric", image_input=image)

print(depth.shape, depth.dtype)
# (378, 504) float32 — note: output resolution differs from input

print(f"min={depth.min():.4f}, max={depth.max():.4f}, mean={depth.mean():.4f}")
# min=1.6177, max=53.1848, mean=12.9778  (meters)
```


## Related topics

- [da3metric](/models/cortex/da3metric.md)