semvec/pypi-packages huggingface dataset:
torchcontentarea 0.4.0
A PyTorch tool kit for estimating the content area in endoscopic footage.
License: MIT

# Torch Content Area
A PyTorch tool kit for estimating the circular content area in endoscopic footage. This implementation is released alongside our publication:

<ul><b>Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset</b>,<br>
    Charlie Budd, Luis C. Garcia-Peraza-Herrera, Martin Huber, Sebastien Ourselin, Tom Vercauteren.<br>
    [ <a href="https://arxiv.org/abs/2210.14771">arXiv</a> ]
</ul>

If you make use of this work, please cite the paper.

[![Build Status](https://github.com/charliebudd/torch-content-area/actions/workflows/build.yml/badge.svg)](https://github.com/charliebudd/torch-content-area/actions/workflows/build.yml)

![Example GIF](example.gif?raw=true)

## Installation
To install the latest version, simply run...
```
pip install torchcontentarea
```

## Usage
```python
from torchvision.io import read_image
from torchcontentarea import estimate_area, get_points, fit_area

# Image in NCHW format, byte/uint8 type is expected
image = read_image("my_image.png").unsqueeze(0)

# Either directly estimate area from image...
area = estimate_area(image, strip_count=16)

# ...or get the set of points and then fit the area.
points = get_points(image, strip_count=16)
area = fit_area(points, image.shape[2:4])
```

## Performance
Performance is measured against the CholecECA subset of the [Endoscopic Content Area (ECA) dataset](https://github.com/charliebudd/eca-dataset).
<!-- performance stats start -->

Performance Results (handcrafted cpu)...
- Avg Time (Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz): 2.501ms
- Avg Error (Hausdorff Distance): 3.535
- Miss Rate (Error > 15): 2.1%
- Bad Miss Rate (Error > 25): 1.0%

Performance Results (learned cpu)...
- Avg Time (Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz): 4.662ms
- Avg Error (Hausdorff Distance): 4.388
- Miss Rate (Error > 15): 2.6%
- Bad Miss Rate (Error > 25): 1.4%

Performance Results (handcrafted cuda)...
- Avg Time (NVIDIA GeForce GTX 980 Ti): 0.171ms
- Avg Error (Hausdorff Distance): 4.289
- Miss Rate (Error > 15): 2.4%
- Bad Miss Rate (Error > 25): 1.3%

Performance Results (learned cuda)...
- Avg Time (NVIDIA GeForce GTX 980 Ti): 1.349ms
- Avg Error (Hausdorff Distance): 4.641
- Miss Rate (Error > 15): 2.6%
- Bad Miss Rate (Error > 25): 1.3% 
<!-- performance stats end -->
python-twonms-config 0.1.3
Python package to manage configs based on OmegaConf.
License: MIT
Requires Python: >=3.6.1,<4.0

# Python 2NMS config manager

<p align="center">
<a href="https://pypi.python.org/pypi/python_twonms_config">
    <img src="https://img.shields.io/pypi/v/python_twonms_config.svg"
        alt = "Release Status">
</a>

<a href="https://github.com/mwallraf/python_twonms_config/actions">
    <img src="https://github.com/mwallraf/python_twonms_config/actions/workflows/main.yml/badge.svg?branch=release" alt="CI Status">
</a>

<a href="https://python-twonms-config.readthedocs.io/en/latest/?badge=latest">
    <img src="https://readthedocs.org/projects/python-twonms-config/badge/?version=latest" alt="Documentation Status">
</a>

</p>

Python package to manage application configurations. This is a wrapper around the OmegaConf create function.

This package makes it easy to define parameters for your application. It's possible to define parameters in different ways (in order of precedence):

-   programmatically defined default values
-   environment variables/dotenv files
-   configuration files in YAML format
-   cli arguments

## Documentation

Check out the [Github Docs](https://mwallraf.github.io/python_twonms_config/)

## Features

-   generates an [OmegaConf](https://omegaconf.readthedocs.io) dictionary object
-   supports environment variables
-   supports dotenv
-   reads yaml config files
-   supports cli parameters
-   allows programmatic initialization of parameters

## Credits

This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [zillionare/cookiecutter-pypackage](https://github.com/zillionare/cookiecutter-pypackage) project template.