semvec/pypi-packages huggingface dataset:
yale-dhlab-keras-preprocessing 1.1.1
Easy data preprocessing and data augmentation for deep learning models
License: MIT


Keras Preprocessing is the data preprocessing
and data augmentation module of the Keras deep learning library.
It provides utilities for working with image data, text data,
and sequence data.

Read the documentation at: https://keras.io/

Keras Preprocessing may be imported directly
from an up-to-date installation of Keras:

```
from keras import preprocessing
```

Keras Preprocessing is compatible with Python 2.7-3.6
and is distributed under the MIT license.
newsscraper 0.1.4
News scraping and reporting.
License: MIT
Requires Python: >=3.6
Keywords: python3,webscraping,selenium,beautifulsoup4

============
newsscraper
============

``newsscraper`` provides a framework for scraping web news with
`Selenium <https://www.seleniumhq.org/>`_ and `Beautiful Soup <https://www.crummy.com/software/BeautifulSoup/>`_. ``newsscraper`` will take care of remembering which news items where already read and creates
results in form of ``json``, ``csv``, or ``html`` files.

A minimal scraper for `newsscraper` that fetches the newest questions from
https://stackoverflow.com/questions can look like this:

.. code-block:: python3

 # content of stackoverflow.py
 import newsscraper
 import sys

 with newsscraper.Scraper(sys.argv) as scraper:
    driver = scraper.get_chrome()
    driver.get('https://stackoverflow.com/questions')
    for question in driver.find_elements_by_xpath('//a[@class="question-hyperlink"]'):
        scraper.add(question.get_attribute('href'), question.text)

Additional configuration can be provided with arguments:

.. code-block:: sh

 python3 stackoverflow.py --headless --verbose report=html --out=$(date '+%Y-%m-%d %H:%M:%S').html

Run ``python3 stackoverflow.py -h`` for a list of all arguments.

********
Features
********

- Remember already added news items
- Create reports in `json`, `csv`, `html`, or a custom format
- Merge multiple `json` reports
- Custom command-line arguments

*******
Roadmap
*******

- Proxy support
- Sort items by date in HTML5 report
- Tags in HTML5 report
- Custom report templates
- RSS reports
- python2 support

************
Installation
************

.. code-block:: sh

 pip3 install newsscraper

If you want to use the selenium drivers you have to download the corresponding `third party drivers <https://www.seleniumhq.org/download/#thirdPartyDrivers>`_
in the ``./assets/`` subdirectory to your script. ``newsscraper`` will also
automatically load all add-ons you place in ``./assets/``.

*******
Changes
*******

- 0.1.0 initial version

*******
License
*******

This project is licensed under the MIT License - see the `LICENSE <https://github.com/wichern/newsscraper/blob/master/LICENSE>`_ for details.
baseadmin 1.0.13
A Pythonic base for building administrator tools for distributed (IoT) applications.
License: MIT

# baseadmin

> A Pythonic base for building administrator tools for distributed (IoT) applications.

[![Latest Version on PyPI](https://img.shields.io/pypi/v/baseadmin.svg)](https://pypi.python.org/pypi/baseadmin/)
[![Supported Implementations](https://img.shields.io/pypi/pyversions/baseadmin.svg)](https://pypi.python.org/pypi/baseadmin/)
[![Build Status](https://secure.travis-ci.org/christophevg/baseadmin.svg?branch=master)](http://travis-ci.org/christophevg/baseadmin)
[![Documentation Status](https://readthedocs.org/projects/baseadmin/badge/?version=latest)](https://baseadmin.readthedocs.io/en/latest/?badge=latest)
[![Coverage Status](https://coveralls.io/repos/github/christophevg/baseadmin/badge.svg?branch=master)](https://coveralls.io/github/christophevg/baseadmin?branch=master)
[![Built with PyPi Template](https://img.shields.io/badge/PyPi_Template-v0.0.6-blue.svg)](https://github.com/christophevg/pypi-template)

## Rationale

Having developed several (IoT) solutions that included an administrative backend, common patterns emerged. With baseAdmin I'm extracting the common parts into a framework that can be used to build context-specific applications. The main goal is to have a working solution out of the box that can be extended with minimal effort.

### Philosophy

As the main goal of baseAdmin is out of the box functionality that is extendable with minimal effort, it is highly driven by conventions. Trying to break away from these conventions will probably result in pain. If you feel that any of the conventions don't fit your needs, you better don't use baseAdmin.

## Documentation

Visit [Read the Docs](https://baseadmin.readthedocs.org) for the full documentation, including overviews and full setup walkthroughs.
pyUntis 0.4
Python parser for Untis substitution tables
License: MIT

# pyUntis 0.4

Initial working prototype for a Python parser for Untis substitution tables

## Todo

- Cleanup and modularize code
- export JSON

## Installation

    pip install pyUntis

## Usage

    import pyuntis

    untis = pyuntis.PyUntis(data)
    print(untis.parse())
fine-grained-segmentation 0.1.9
Mask R-CNN for Fine-Grained segmentation
License: MIT
Requires Python: >=3.4

# fine-grained-segmentation library

Python library for segmenting clothing items in images, implemented in Python 3 and ONNX. A deep learning model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on [Matterport Mask R-CNN](https://github.com/matterport/Mask_RCNN)

The library is also available on the [Python Package Index](https://pypi.org/project/fine-grained-segmentation/)

A demo web app is up at https://fine-grained-segmentation.vinnypalumbo.com

## Requirements

Python 3.5, ONNX runtime, and other common packages listed in `requirements.txt`.

## Installation

1. Clone this repository
2. Run setup to install the library
   ```bash
   python3 setup.py install
   ```
   If it failed to install the dependencies, run
   ```bash
   pip3 install -r requirements.txt
   ```
3. Download pre-trained weights (mrcnn.onnx) from the [releases page](https://github.com/vinny-palumbo/fine_grained_segmentation/releases)

## Usage

Here is how to use the library from the command line:
```bash
fashion-segmentator --image=<path/to/image/file>
```
This will generate a ```result.png``` file in the current directory