semvec/pypi-packages huggingface dataset:
omnicv 1.1.3
A computer vision library for 360-degree cameras
License: MIT
Keywords: Deep Learning,Helper functions

OmniCV Library
==============

A **computer vision library for omnidirectional(360-degree) cameras**. This package is divided into two parts:
* **Basic functions** for inter-conversion of different types of mappings associated with omni directional cameras, virtual reality and 360-degree; videos, like cubemap, spherical projections, perspective projection and equirectangular projection.
* **Software applications** like **360-degree; video viewer**, **fisheye image generator** with variable intrinsic properties, GUI to determine **fisheye camera paraeters**.


Objectives of the OmniCV library
================================

This library has been developed with the following obectives:

* **Quick and easy** to use API to encourage and enhance the research in areas using omni directional cameras.
* To support **real time** applications.
* Provide extensions in **python** as well as **C++** as they are the languages used by researchers.
* Provide **ROS package** to use in robotics research.

Click `here <https://kaustubh-sadekar.github.io/OmniCV-Lib/index.html>`_ to view the documentation page. There are several examples and other details shared on the documentation page.

Installation guide
==================

A custom make file has been written which provides quick and easy options for installing and testing the library.

.. code:: shell

    git clone https://github.com/kaustubh-sadekar/OmniCV-Lib
    cd OmniCV-Lib/omnicv/
    # To build c++ as well as python files
    make build-all
    # To build only python files
    make build-python
    # To build only c++ files
    make build-cpp

Installing OmniCV in a virtual environment using pipenv. Pipfile and Pipfile.lock files have been provided. Copy both the files to the present working directory. Then simply run the following commands to setup OmniCV in a local environment.

.. code:: shell

    pipenv install
    pipenv shell    


Running Tests
=============

.. code:: shell

    cd OmniCV-Lib/omnicv/
    # To test only python extension of the project
    make test-py
    # To test only c++ extension of the project
    make test-cpp
    # To test python as well as c++ extension of the project
    make test-all


Examples are available `here <https://kaustubh-sadekar.github.io/OmniCV-Lib/index.html>`_ on the official documentation page.
export-torrent-cache 0.1
Export/backup the cache directory of a torrent client, restoring original filenames and organized by tracker
License: MIT
Requires Python: >=3.6

Most torrent clients maintain a directory containing all the torrents that are currently loaded in the client.
However they are unorganized and typically have been renamed to the infohash of the torrent. This script processes these torrents
and restores them to their original name and organized by tracker based on the announce URL. It was written with private torrents in mind, which
generally only have one announce URL. If the script encounters a torrent with multiple announce URLs,
it will only consider the first URL for organization.

This has only been tested with qBittorrent, but it should work with any directory containing a flat list of valid torrents. Subdirectories are not traversed.

## Dependencies:
* [torrent_parser](https://github.com/7sDream/torrent_parser)

## Usage:
```
$ export_torrent_cache <source_dir> <destination_dir>
```

## Examples:
```
$ ls ~/.local/share/data/qBittorrent/BT_backup/
[lots of .torrent files with unhelpful names]

$ export_torrent_cache ~/.local/share/data/qBittorrent/BT_backup/ ~/torrents_backup
Processing ~/.local/share/data/qBittorrent/BT_backup/[infohash].torrent 
All torrents processed succesfully

$ tree ~/torrents_backup
~/torrents_backup
├── Tracker URL
│   ├── Original_Filename.torrent

```

## Installation
### From PyPI
```
$ pip install export_torrent_cache
```

### From source repository
```
$ git clone https://github.com/codeclem/export_torrent_cache
$ cd export_torrent_cache
```
From here you can manually install `torrent_parser` and run the `export_torrent_cache.py` script directly, or build and install a package like so:
```
$ python setup.py sdist
$ pip install dist/export_torrent_cache-0.1.tar.gz
```
Which will also fetch and install `torrent_parser` as a dependency for you.
make-to-batch 0.2.3
A Makefile to Batch converter.
License: MIT
Keywords: Makefile,Batch,Shell,Windows,Linux,Converter

# Makefile to Batch

[![License](https://img.shields.io/github/license/espositoandrea/Make-to-Batch)](https://github.com/espositoandrea/Make-to-Batch/blob/master/LICENSE)
[![GitHub Workflow Status](https://img.shields.io/github/workflow/status/espositoandrea/Make-to-Batch/Python%20package)](https://github.com/espositoandrea/Make-to-Batch/actions)
[![GitHub release (latest by date)](https://img.shields.io/github/v/release/espositoandrea/Make-to-Batch?include_prereleases)](https://github.com/espositoandrea/Make-to-Batch/releases/latest)
[![PyPI](https://img.shields.io/pypi/v/make-to-batch)](https://pypi.org/project/make-to-batch/)
[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=espositoandrea_Make-to-Batch&metric=alert_status)](https://sonarcloud.io/dashboard?id=espositoandrea_Make-to-Batch)

This is a simple tool to convert a GNU Makefile into a Windows Batch file.

## Usage

Install the tool by using [PyPI](https://pypi.org/project/make-to-batch/) using
the following command:

```bash
pip install make-to-batch
```

You can also install by downloading the source code:

```bash
cd path/to/source/
python setup.py install
```

The tool provides only a command, `make-to-batch`, that converts a Makefile in
the current directory to a batch file in the same directory. The tool behaviour
can be customized using some options: here is the help of the tool.

```text
$ make-to-batch -h
usage: make-to-batch [-h] [-v] [-i INPUT] [-o OUTPUT]

Convert a Makefile to a Batch (Windows) file.

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit
  -i INPUT, --input INPUT
                        set the makefile to be converted. Defaults is
                        './Makefile'
  -o OUTPUT, --output OUTPUT
                        set he name of the output batch file. Defaults is
                        './make.bat'
```

## Contributing

For more information on how to contribute, go to the file
[CONTRIBUTING.md](CONTRIBUTING.md)
ggenerator 0.3
A tool capable to generate fake data with a given specification defined as a JSON DSL
License: MIT

# GGenerator
[![Travis Build Status](https://travis-ci.com/Datenworks/ggenerator.svg?branch=master)](https://travis-ci.com/Datenworks/ggenerator)

## What is it? 

**GGenerator** is a command line interface that generates randomic data for tests purposes. It aims to be a easy client for programmers, data scientists, data analysts and data engineers to create a huge amount of data with a variety of complex data using JSON specification and a few commands on terminal.

## Requirements

You need Python 3.7 or later and [pip](https://github.com/pypa/pip) to install and run **ggenerator** as a command line tool.

## How to install

A easy way to install **ggenerator** cli is to use [pip](https://github.com/pypa/pip):

```bash
pip install ggenerator
```

After that you just have to learn how to use it.

## How to use it

If you do have a specification file, you just follow the next steps:

```bash
ggenerator generate --spec /file/path/example.json
```

If you dont, [click here](https://github.com/datenworks/ggenerator/wiki) and learn how to create a specification.


### MAC OS X Users
If you use MAC OS X, you need to setup two environment variables, for GGENERATOR be able to get your default language system
```bash
export LC_ALL=en_US.UTF-8
export LANG=en_US.UTF-8 
```
## How to build it

You want to use the develop version or something like that?

Here you will have instruction of how to build this command line interface and have it ready for use.

The first thing you have to do is clone this repository:

```bash
git clone git@github.com:Datenworks/ggenerator.git
```

Now, you will need to create a virtualenv and install all the requirements. If you have [pipenv](https://github.com/pypa/pipenv) installed:

```bash
pipenv install --dev
```

If you dont have pipenv, you can learn how to install it by going to pipenv repository: https://github.com/pypa/pipenv

After that you can make your updates and fixes, so when you're ready execute:

```
pipenv run python setup.py build
pipenv run python setup.py install
```

Done, now you have built a new ggenerator client on your OS.

## License

[MIT](LICENSE)

## Contribute

We dont have it organized yet, but as soon as possible you will be able to contribute.