---
library_name: UniDepth
tags:
- monocular-metric-depth-estimation
- pytorch_model_hub_mixin
- model_hub_mixin
repo_url: https://github.com/lpiccinelli-eth/UniDepth
---

This model has been pushed to the Hub using **UniDepth**:
- Repo: https://github.com/lpiccinelli-eth/UniDepth

## Installation

First install the UniDepth package as follows:

```python
!git clone -b add_hf https://github.com/NielsRogge/UniDepth.git
!cd UniDepth
!pip install -r requirements.txt
```

## Usage

Next, one can load the model and perform inference as follows:

```python
from unidepth.models import UniDepthV1HF
import numpy as np
from PIL import Image

model = UniDepthV1HF.from_pretrained("nielsr/unidepth-v1-convnext-large")

# Move to CUDA, if any
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Load the RGB image and the normalization will be taken care of by the model
rgb = torch.from_numpy(np.array(Image.open(image_path))).permute(2, 0, 1) # C, H, W

predictions = model.infer(rgb)

# Metric Depth Estimation
depth = predictions["depth"]

# Point Cloud in Camera Coordinate
xyz = predictions["points"]

# Intrinsics Prediction
intrinsics = predictions["intrinsics"]
```