semvec/pypi-packages huggingface dataset:
pyosirix-example-project 0.0.1.dev24
An example of a Python project that can be used to install into OsiriX
Keywords: Dicom, Medical Imaging, Image Processing

# pyOsiriX Example Project
An example project to demonstrate a method for creating a full project for use in pyOsiriX.

![WelcomeOsiriXgrpc!](https://raw.githubusercontent.com/osirixgrpc/pyosirix_example_project/main/docs/assets/explanation.png)

Tools used here:

 - [PIP](pip.md): Used for packaging your code for your users.
 - [GitHub](github.md): Used for CI/CD and version control.
 - [DVC](dvc): Used for model or data versioning.
 - [pyTest](pytest.md): Used for testing your code automatically.
 - [MkDocs](mkdocs.md): Used for generating documentation.
 - [bump2version](bumpversion.md): Used for semantic version control.
 - [gRPC](grpc): Used to communicate with the server and with osirix.
table-transformer 1.0.6
Table Transformer
Requires Python: >=3.10

# Table Transformer Library

Original repository: https://github.com/microsoft/table-transformer

## Introduction
This is the Table Transformer Model developed by Brandon Smock et al. of Microsoft AI. This repository consists of **Table Structure Recognition (TATR)** for detecting and extracting table infomation into popular formats such as CSV or HTML table, plus text recognition using EasyOCR.

## Installation
```
pip install table-transformer
```

## Usage

The full model usage can be found here:

```
from table_transformer import TableExtractionPipeline

pipe = TableExtractionPipeline(det_device="cpu", str_device="cpu",
                 det_model_path=".\path\to\pubtables1m_detection_detr_r18.pth",
                 str_model_path=".\path\to\TATR-v1.1-Pub-msft.pth")

img = "\path\to\image.jpg"

table_objects, table_cells_coordinates, table_cells_text = pipe(img)

print(table_cells_text[0])  # Should be DataFrame
```


## Evaluation

With structure recognition, the original author has evaluated the v1.0 model on PubTables-1M with great results. With other datasets such as PubTabNet, the score is quite good.

You can check out the score and run the evaluation with your own dataset in [this](https://colab.research.google.com/drive/1-1yRr9djVi5OxITrSf3iZsg__MNE5hN5?usp=sharing) link.

## Version history
- v1.0.6: Added Table Detection, ending up with a full Table Extraction Pipeline, fixed bug.
- v1.0.3: Removed unnecessary code and added new functionalities.
- v1.0.2: Initial version.