# Retuve for Developers

## High Level Code Structure

All of Retuve's functions follow the same model:

- High Level Control Functions in `retuve.funcs`
- AI Plugins through the `modes_func` and `modes_func_kwargs_dict` args of `retuve.funcs.retuve_run` (and the other high-level funcs)
- If the model is Ultrasound-based, it will run `retuve.hip_us.modes.seg.segs_2_landmarks_us` to convert the segmentation to landmarks. Each metric has their own metric specific function (if needed) in `retuve.hip_us.metrics`
- Only Landmark models are currently supported by X-Ray, so this step is not needed.
- The Landmarks are then used to calculate the metrics in `retuve.hip_xray.landmarks.landmarks_2_metrics_xray` and `retuve.hip_us.modes.landmarks.landmarks_2_metrics_us` for X-Ray and Ultrasound respectively. Each metric has their own metric specific function (if needed) in `retuve.hip_xray.metrics` and `retuve.hip_us.metrics`
- For 3D Ultrasound `retuve.hip_us.multiframe.get_3d_metrics_and_visuals` is used to get any 3D Data.
- Information is then drawn using `retuve.hip_us.draw.draw_hips_us` and `retuve.hip_xray.draw.draw_hips_xray`. Each metric has their own metric specific function (if needed) in `retuve.hip_xray.metrics` and `retuve.hip_us.metrics`

**So to highlight, that is `high-level function -> AI Plugin -> Segmentation -> Landmarks -> Metrics -> Draw`**

## Different Intergration Types and Methods

For AI Plugins, please see the examples: https://github.com/radoss-org/retuve/tree/main/examples/

Retuve has 4 basic points of intergration:

### Directly with the Retuve Module

The recommended way to intergrate retuve into your own system is with the high-level functions
in `retuve.funcs`. This is the easiest way to get started with Retuve.

### Via the REST API (Experimental)

Retuve exposes a REST API that can be used to interact with Retuve. This is useful if you want to
use Retuve in a web application or other system that can interact with a REST API.

```bash
retuve --task trak --keyphrase_file ./scripts/common/hip.py
```

### Via the CLI (Experimental)

Retuve also has a CLI that can be used to interact with Retuve. This is useful if you want to use
Retuve in a script or other system that can interact with a CLI.

`single` will run the task on a single file, `batch` will run the task on a directory of files in parallel. (experimental)

```bash
retuve --task {single|batch} --keyphrase_file ./scripts/common/hip.py
```

### Using the UI (Experimental)

Retuves UI was built to be relatively simple and modular. At some point in the future, it will be documented how to make changes to the UI and how to use it in your own system.

## Definition of Hip and Xray Model Landmarks

Hip Ultrasound has 6 Landmarks:

- Left
- Apex
- Right
- Point D
- Point d
- Mid Coverage Point

They are labelled on the following diagram:

<img src="https://github.com/radoss-org/radoss-creative-commons/blob/main/other/172535_0_labels.jpg?raw=true" alt="drawing" width="500"/>

Attribution of the above Ultrasound Images: Case courtesy of Ryan Thibodeau from https://radiopaedia.org 172535 (https://radiopaedia.org/cases/172535)

Hip X-Ray has the following Landmarks:

- pel_l_o: The Outer Pelvis Landmark on the Left
- pel_l_i: The Inner Pelvis Landmark on the Left
- pel_r_o: The Outer Pelvis Landmark on the Right
- pel_r_i: The Inner Pelvis Landmark on the Right
- fem_l: The Femoral Landmark on the Left (Not used in the current model)
- fem_r: The Femoral Landmark on the Right (Not used in the current model)

They are labelled on the following diagram:

<img src="https://github.com/radoss-org/radoss-creative-commons/blob/main/other/224_ddh_115_labels.jpg?raw=true" alt="drawing" width="500"/>

Attribution of the above X-Ray Images: Fraiwan, Mohammad; Al-Kofahi, Noran; Hanatleh, Omar; ibnian, ali (2022), “A dataset of DDH x-ray images”, Mendeley Data, V2, doi: 10.17632/jf3pv98m9g.2


## Retuve Trak

Retuve Trak is the tool that exposes the REST API and UI Interface.

It is a work in progress that will be documented soon.