> ## 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.

# ZoeDepth

> Monocular depth estimation from a single RGB image

Estimate depth from a single RGB image using ZoeDepth. Returns metric depth values in meters.

## Parameters

<ParamField body="image_input" type="str | PIL.Image | np.ndarray" 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 of shape `(H, W)` with dtype `float16` (the client converts to `float32` on read). Values represent metric depth in meters.

## Example Output

<img src="https://mintcdn.com/scaledfoundations/lgZgGQWKvUIw0C7n/assets/images/cortex/zoedepth-output.jpg?fit=max&auto=format&n=lgZgGQWKvUIw0C7n&q=85&s=dbd87a8ed7545ba071363256181075e8" alt="ZoeDepth input and depth map output" width="1136" height="552" data-path="assets/images/cortex/zoedepth-output.jpg" />

## Example

```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="zoedepth", image_input=image)

print(depth.shape, depth.dtype)
# (480, 640) float32

print(f"min={depth.min():.4f}, max={depth.max():.4f}, mean={depth.mean():.4f}")
# min=1.5889, max=10.2891, mean=4.6381  (meters)
```