semvec/pypi-packages huggingface dataset:
icon-registration 1.1.6
A package for image registration regularized by inverse consistency
Requires Python: >=3.4

![Demo figure](notebooks/paper_figures/Intro_NewLabels-2.png)


# ICON: Learning Regular Maps through Inverse Consistency

[<img src="https://github.com/uncbiag/ICON/actions/workflows/test-action.yml/badge.svg">](https://github.com/uncbiag/ICON/actions)
[<img src="https://img.shields.io/pypi/v/icon_registration.svg?color=blue">](https://pypi.org/project/icon-registration)
[<img src="https://readthedocs.org/projects/icon/badge/?version=master">](https://icon.readthedocs.io/en/master/)


This is the official repository for  

**ICON: Learning Regular Maps through Inverse Consistency.**   
Hastings Greer, Roland Kwitt, Francois-Xavier Vialard, Marc Niethammer.  
_ICCV 2021_ https://arxiv.org/abs/2105.04459

**GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency.**  
Lin Tian, Hastings Greer, Francois-Xavier Vialard, Roland Kwitt, Raúl San José Estépar, Marc Niethammer.  
_CVPR 2023_ https://arxiv.org/abs/2206.05897

## Cite this work
```
@InProceedings{Greer_2021_ICCV,
    author    = {Greer, Hastings and Kwitt, Roland and Vialard, Francois-Xavier and Niethammer, Marc},
    title     = {ICON: Learning Regular Maps Through Inverse Consistency},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3396-3405}
}
```

```
@article{Tian_2022_arXiv,
  title={GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency},
  author={Tian, Lin and Greer, Hastings and Vialard, Fran{\c{c}}ois-Xavier and Kwitt, Roland and Est{\'e}par, Ra{\'u}l San Jos{\'e} and Niethammer, Marc},
  journal={arXiv preprint arXiv:2206.05897},
  year={2022}
}
```

## uniGradICON and multiGradICON

If you are interested in general purpose deep learning registration approaches check out [uniGradICON and multiGradICON](https://github.com/uncbiag/uniGradICON). These networks were trained using the GradICON technology but over many different datasets allowing them to generalize to different unimodal and multimodal registration tasks. 

## Video Presentation

[<img src="https://img.youtube.com/vi/7kZsJ3zWDCA/maxresdefault.jpg" width="50%">](https://youtu.be/7kZsJ3zWDCA)


## Running our code

We are available on PyPI!
```bash
pip install icon-registration
```


To run our pretrained model in the cloud on sample images from OAI knees, visit [our knee google colab notebook](https://colab.research.google.com/drive/1svftgw-vYWnLp9lSf3UkrG547atjbIrg?usp=sharing)

To run our pretrained model for lung CT scans on an example from COPDgene, visit [our lung google colab notebook](https://colab.research.google.com/github/uncbiag/ICON/blob/master/notebooks/ICON_lung_demo.ipynb)

----------------

To train from scratch on the synthetic triangles and circles dataset:

```bash
git clone https://github.com/uncbiag/ICON
cd ICON

pip install -e .

python training_scripts/2d_triangles_example.py
```
spidercreator 0.0.1
Automated web scraping spider generation using Browser Use and LLMs.
Keywords: scraping, spider, scrapy, automation, browser, AI, LLM, web-scraping

This is a stub library for the project https://github.com/carlosplanchon/spidercreator.

We need to ensure that the package works correctly before publishing it to PyPI.

By: Carlos A. Planchón.
flask-buzz 4.0.0
Extra bindings on py-buzz specifically for flask apps
Requires Python: >=3.10

[![PyPI Versions](https://img.shields.io/pypi/v/flask-buzz?style=plastic&label=pypi-version)](https://img.shields.io/pypi/v/flask-buzz?style=plastic&label=pypi-version)
[![Build Status](https://github.com/dusktreader/flask-buzz/actions/workflows/push.yaml/badge.svg)](https://github.com/dusktreader/flask-buzz/actions/workflows/push.yaml)
![Documentation Status](https://github.com/dusktreader/flask-buzz/actions/workflows/docs.yml/badge.svg)

# flask-buzz

_py-buzz bindings specifically for flask applications_

This is an extension of the [py-buzz](https://github.com/dusktreader/py-buzz) package.

It adds extra functionality especially for flask. Predominately, it adds the ability to register an error handler with
Flask that will automatically package any handled `FlaskBuzz` exceptions in a nicely formatted JSON response with the
appropriate `status_code` and message. There is also a method to explicitly `jsonify` a FlaskBuzz error with some
control over what is included in the error body.

## Super-quick Start

Requires: Python 3.10 to 3.13

Install through pip:

```bash
pip install flask-buzz
```

Minimal usage example: [examples/basic.py](https://github.com/dusktreader/flask-buzz/tree/master/examples/basic.py)


## Documentation

The complete documentation can be found at the
[flask-buzz home page](https://github.dusktreader.io/flask-buzz)