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---
license: mit
title: 'Structura AI: Depth & Structural Masking'
sdk: docker
emoji: 🚀
colorFrom: indigo
colorTo: blue
pinned: true
app_port: 8000
---

# Structura AI: Depth & Structural Masking Web Application

Structura AI is a web-based image analysis tool that generates a structural mask by combining monocular depth estimation and texture-based feature detection.
The system integrates MiDaS (DPT-Hybrid) for depth prediction with OpenCV-based edge, corner, and texture fusion to produce a detailed, high-frequency structural representation of any input image.

This mask is useful for segmentation workflows, diffusion model conditioning, preprocessing for generative AI, architectural analysis, and general computer vision pipelines.

---

## Features

* MiDaS Depth Estimation: High-quality depth prediction using the DPT-Hybrid model.
* Combined Structural Masking: Fusion of depth gradients, edges (Canny), Laplacian detail, and Harris corner responses.
* FastAPI Backend: Clean API endpoint (`POST /mask/`) returning PNG masks with inference time metadata.
* Web UI: A simple and modern interface built with HTML, CSS, and JavaScript allowing image upload, preview, overlay visualization, and mask download.
* Docker Support: Fully dockerized and ready for deployment on services such as Hugging Face Spaces.

---

## Demo

This section is reserved for demo images and demo videos.
Add examples such as:

* Original input image
* Generated structural mask
* Overlay visualization
* A short screen recording demonstrating the UI

---

## Technology Stack

Backend: FastAPI, Uvicorn
AI / Computer Vision: PyTorch, MiDaS, OpenCV (headless), NumPy
Frontend: HTML, CSS, JavaScript
Deployment: Docker, Hugging Face Spaces

---

## Repository Structure

```
structura-ai/
├── app.py
├── depth_texture_mask.py
├── requirements.txt
├── Dockerfile
├── templates/
│   └── index.html
└── static/
    ├── styles.css
    └── app.js
```

---

## Local Installation and Usage

### 1. Clone the Repository

```
git clone https://github.com/PritamTheCoder/midas-depth-texture-mask-api.git
cd midas-depth-texture-mask-api
```

### 2. Create and Activate a Virtual Environment

```
python -m venv venv

# Windows
venv\Scripts\activate

# macOS / Linux
source venv/bin/activate
```

### 3. Install Dependencies

```
pip install -r requirements.txt
```

### 4. Start the Application

```
uvicorn app:app --reload --host 0.0.0.0 --port 8000
```

### 5. Access the Web UI

Open the browser and navigate to:
[http://127.0.0.1:8000/](http://127.0.0.1:8000/)

The MiDaS model weights will automatically download on the first startup.

---

## Deployment on Hugging Face Spaces (Docker)

The project includes a Dockerfile configured for seamless deployment on Hugging Face Spaces.

### Required File Structure

Ensure the following files are present at the repository root:

```
Dockerfile
app.py
depth_texture_mask.py
requirements.txt
templates/
static/
```

### Deployment Steps

1. Create a new Hugging Face Space.
2. Select "Docker" as the runtime.
3. Upload or commit all repository files.
4. Hugging Face will automatically:

   * Build the Docker image
   * Install dependencies
   * Expose the correct port
   * Start the FastAPI server

The application will then be available as a hosted interactive web demo.

---

## License

This project is licensed under the MIT License.
See the LICENSE file for details.

---

## Acknowledgements

MiDaS by Intel-ISL
FastAPI for backend framework
OpenCV for feature detection and image processing
PyTorch for deep learning inference support
Hugging Face for deployment infrastructure

---