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---
title: depth-pro
sdk: gradio
emoji: 🚀
colorFrom: yellow
colorTo: yellow
pinned: false
app_file: app.py
short_description: web ui for depth-pro
---
<h1 align="center">Web UI for Depth-Pro</h1>
<p align="center">
<img src="./example.jpg" alt="Web UI for Depth-Pro Depth Estimation" />
</p>
Metric depth estimation determines real-world distances to objects in a scene from images. This repo provides a web UI that allows users to estimate metric depth and visualize depth maps by easily uploading images using the Depth Pro model through a simple Gradio UI interface.
## 🛠️ Getting Started
### 📦 Installation
1. **Create and activate a virtual environment:**
```bash
conda create -n depth-pro -y python=3.9
conda activate depth-pro
```
2. **Clone the depth-pro repository:**
```bash
git clone https://github.com/apple/ml-depth-pro.git
cd ml-depth-pro
pip install -e .
```
3. **Clone the web UI repository:**
```bash
git clone https://github.com/spacewalk01/depth-pro-webui.git
cd depth-pro-webui
pip install -r requirements.txt
```
4. **Download the model:**
Download the model from [depth_pro.pt](https://ml-site.cdn-apple.com/models/depth-pro/depth_pro.pt) and place the file in a newly created `checkpoints` folder.
### 🚀 Running the Application
1. Launch the Gradio interface:
```bash
python main.py
```
2. Open the provided local URL in your web browser to access the interface.
## 🖼️ Usage
1. **Upload an Image:** Use the image uploader to select an image for depth estimation.
2. **Adjust Options:**
- **Auto Rotate:** Enable or disable auto-rotation of the image.
- **Remove Alpha:** Enable or disable the removal of the alpha channel.
3. **View Results:**
- The depth map will be displayed on the interface.
- The focal length in pixels will also be shown.
## 📜 License
This project is licensed under the Apple License. See the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- [Gradio](https://www.gradio.app/) for providing an easy interface for machine learning models.
- [Depth Pro](https://github.com/apple/ml-depth-pro.git) for enabling sharp monocular metric depth estimation in less than a second. |