---
license: agpl-3.0
language:
- en
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
  - pytorch
  - YOLO11
  - Ultralytics
  - Stereograph
  - Stereographs
  - Stereoscope
  - Underwood & Underwood
library_name: ultralytics
---

VitageReality Model
===================

# Purpose

This model can be used to seperate the left and right half image from a stereograph card. The have been historically used with a [stereoscope](https://en.wikipedia.org/wiki/Stereoscope).

Keep in mind that there have been several simila systems in the 19th and 20th century, currently this model is anly trained to work with the [Underwood & Underwood](https://en.wikipedia.org/wiki/Underwood_%26_Underwood) sterescope (sometimes also known as Holmes sterescope). Other types aren't handled yet.

# Description and use case

Hve a look at these two blog posts for a introduction and use case:
* [AI segmentation for stereoscopic cards](https://christianmahnke.de/en/post/vintagereality-ai/)
* [Historical stereograms on modern hardware](https://christianmahnke.de/en/post/vintagereality-apple-spatial/)

# Basic usage

The Model can be used with Python

## Required dependencies

```bash
pip install ultralytics
```

## Usage

```python
from ultralytics import YOLO
from PIL import Image

pil_image = Image.open(args.image)
model = YOLO(model_path)
results = model(pil_image, verbose=False, retina_masks=True)

left_img = None
right_img = None

r = results[0]

if not r.boxes or len(r.boxes) != 2:
    raise Exception(f"YOLO detected {len(r.boxes) if r.boxes else 0} regions, expected exactly 2.")
    boxes = r.boxes.xyxy.cpu().numpy()

    # This is needed to get the order right
    indexed_boxes = sorted(enumerate(boxes), key=lambda x: x[1][0])

    left_idx, left_box = indexed_boxes[0]
    right_idx, right_box = indexed_boxes[1]
    masks = r.masks.data.cpu().numpy()
```

Afterwars you can either use `left_box` and `right_box` to extract the half images. You can also use the masks to extract the half image only, without the arch at the top.