semvec/pypi-packages huggingface dataset:
django4-keycloak 2.0.0.dev0
Install Django Keycloak.
License: MIT

===============
Django Keycloak
===============

.. image:: https://www.travis-ci.org/Peter-Slump/django-keycloak.svg?branch=master
   :target: https://www.travis-ci.org/Peter-Slump/django-keycloak
   :alt: Build Status
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   :target: http://django-keycloak.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status
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   :target: https://codecov.io/gh/Peter-Slump/django-keycloak
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   :alt: Maintainability

Django app to add Keycloak  support to your project.

`Read documentation <http://django-keycloak.readthedocs.io/en/latest/>`_

http://www.keycloak.org/

An showcase/demo project is added in the `example folder <example/README.md>`_.

Development
===========

Install development environment:

.. code:: bash

  $ make install-python

------------
Writing docs
------------

Documentation is written using Sphinx and maintained in the docs folder.

To make it easy to write docs Docker support is available.

First build the Docker container:

.. code:: bash

    $ docker build . -f DockerfileDocs -t django-keycloak-docs

Run the container

.. code:: bash

    $ docker run -v `pwd`:/src --rm -t -i -p 8050:8050 django-keycloak-docs

Go in the browser to http://localhost:8050 and view the documentation which get
refreshed and updated on every update in the documentation source.

--------------
Create release
--------------

.. code:: bash

    $ git checkout master
    $ git pull
    $ bumpversion release
    $ make deploy-pypi
    $ bumpversion --no-tag patch
    $ git push origin master --tags

Release Notes
=============

**unreleased**

**v0.1.2-dev**

**v0.1.1**

* Added support for remote user. Handling identities without registering a User
  model. (thanks to `bossan <https://github.com/bossan>`_)
* Addes support for permissions using resources and scopes.
  (thanks to `bossan <https://github.com/bossan>`_)
* Added example project.
* Updated documentation.

**v0.1.0**

* Correctly extract email field name on UserModel (thanks to `swist <https://github.com/swist>`_)
* Add support for Oauth2 Token Exchange to exchange tokens with remote clients.
  Handy when using multiple applications with different clients which have to
  communicate with each other.
* Support for session iframe
govsic 1.1.5
A small Python library to interface the Standard Industrial Classification protocol
License: MIT
Requires Python: >=3.6

<div align="center">

<h1><a href="https://www.github.com/ellsphillips/govsic"><img height=30 src="https://latex.codecogs.com/svg.latex?{\textsf{\bfseries\color[RGB]{233,80,14}govsic}}" alt="govsic"></a></h1>

[![CI status](https://github.com/ellsphillips/sic/actions/workflows/config.yml/badge.svg)](https://github.com/ellsphillips/govsic)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/govsic.svg)](https://test.pypi.org/project/govsic/)
[![PyPI version](https://img.shields.io/pypi/v/govsic.svg)](https://test.pypi.org/project/govsic/)
[![License](https://img.shields.io/pypi/l/pyisic.svg)](https://github.com/ellsphillips/govsic/blob/master/LICENSE)
[![Downloads](https://img.shields.io/pypi/dm/govsic?color=33cd57)](https://img.shields.io/pypi/dm/govsic?color=33cd57)

</div>

A lightweight library to parse and interface Standard Industrial Classification instances using the current UK SIC 2007 methodology.

`govsic` supports the UK SIC 2007 framework by unifying the classification of business establishments and other statistical units by their type of economic activity/engagement into a common structure.

## Data

Data used has been transformed using the [Office for National Statistics](https://www.ons.gov.uk/methodology/classificationsandstandards/ukstandardindustrialclassificationofeconomicactivities/uksic2007)' published opensource SIC structure and volume datasets.

## Installation

The `govsic` package is available on PyPI.

```bash
pip install govsic
```

## Usage

`govsic` provides the `SIC` class to represent Standard Industrial Classifications. Get started by initialising a new `SIC` object to interface, compare, and evaluate UK SIC 2007 codes.

See example usage in the `examples/` repo directory

```python
from govsic import SIC

sic = SIC(8110)

print(f"{sic = }")
# sic = [B] 08.11/0

for label, prop in (
    ("Code value", sic.code),
    ("Is valid?", sic.is_valid),
    ("Section", sic.section),
    ("Component", sic.component),
):
    print(f"{label}:\t{prop}")
# Code value:     08110
# Is valid?:      True
# Section:        B
# Component:      CLASS
```
xtreme-vision 1.6.7
A Python Library for Computer Vision tasks like Object Detection, Segmentation, Pose Estimation etc
License: MIT
Requires Python: >=3.5, <4
Keywords: object detection,computer vision,pose estimation,machine learning,deep learning,artificial intelligence,xtreme vision,segmentation,yolo,retinanet,centernet,yolov4,deeplab,tinyyolo

# Xtreme-Vision

[![Build Status](https://camo.githubusercontent.com/6446a7907a4d4f8de024ec85750feb07d7914658/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70617472656f6e2d646f6e6174652d79656c6c6f772e737667)](https://patreon.com/adeelintizar) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE.txt)

![](assets/intro.gif)

`Go to PyPI page`> [Here](https://pypi.org/project/xtreme-vision/)

This is the Official Repository of Xtreme-Vision. Xtreme-Vision is a High Level Python Library which is built with simplicity in mind for Computer Vision Tasks, such as Object-Detection, Human-Pose-Estimation, Segmentation Tasks, it provides the support of a list of state-of-the-art algorithms, You can Start Detecting with Pretrained Weights as well as You can train the Models On Custom Dataset and with Xtreme-Vision you have the Power to detect/segment only the Objects of your interest

Currently, It Provides the Solution for the following Tasks:
   - Object Detection
   - Pose Estimation
   - Object Segmentation
   - Human Part Segmentation


For Detection with pre-trained models it provides:
  - RetinaNet
  - CenterNet
  - YOLOv4
  - TinyYOLOv4
  - Mask-RCNN
  - DeepLabv3+ (Ade20k)
  - CDCL (Cross Domain Complementary Learning)

For Custom Training It Provides:
  - YOLOv4
  - TinyYOLOv4
  - RetinaNet with (resnet50, resnet101, resnet152) 

![](assets/pose.gif)

>If You Like this Project, Sponser it here [![Build Status](https://camo.githubusercontent.com/6446a7907a4d4f8de024ec85750feb07d7914658/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70617472656f6e2d646f6e6174652d79656c6c6f772e737667)](https://patreon.com/adeelintizar)


### Dependencies:
  - tensorflow >= 2.3.0
  - keras
  - opencv-python
  - numpy
  - pillow
  - matplotlib
  - pandas
  - scikit-learn
  - scikit-image
  - imgaug
  - labelme2coco
  - progressbar2
  - scipy
  - h5py
  - configobj


## **`Get Started:`**
```python
!pip install xtreme-vision
```
 >### `For More Tutorials of Xtreme-Vision, Click` [Here](https://github.com/Adeel-Intizar/Xtreme-Vision/tree/master/Tutorials)
# **`YOLOv4` Example** 


### **`Image Object Detection` Using `YOLOv4`** 

```python
from xtreme_vision.Detection import Object_Detection

model = Object_Detection()
model.Use_YOLOv4()
model.Detect_From_Image(input_path='kite.jpg',
                        output_path='./output.jpg')

from PIL import Image
Image.open('output.jpg')
```