semvec/pypi-packages huggingface dataset:
marigold 0.1.0
Marigold is a tiny server used to examine and compare the contents of hierarchical structured files.
License: MIT
Requires Python: ~= 3.7
Keywords: simulation productivity hierarchy

# Marigold

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/marigold)](https://pypi.org/project/marigold/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Commits](https://img.shields.io/github/last-commit/tilmann-bartsch/marigold/master)](https://github.com/tilmann-bartsch/marigold/commits/master)

Marigold is a tiny server used to examine and compare the contents of hierarchical structured files.

<p align="center">
  <img width="65%" src="https://github.com/tilmann-bartsch/marigold/raw/master/example/marigold/african/1.jpg">
</p>

## Installation

Pull docker image from [hub.docker.com](hub.docker.com) and run it for the files located in
the folder `content`.
```shell
docker pull tbartsch/marigold
docker run \
    -p 8080:8080 \
    --mount type=bind,source="$(pwd)"/content,target=/app/content \
    tbartsch/marigold:latest
```

Run development server in repository
```shell
python app/run.py --directory example/marigold
```

Build and run docker image from repository
```shell
docker image build -t marigold:latest . || exit
export PORT=8080
export DIRECTORY=example/
docker run -it \
    -p 8080:8080 \
    --mount type=bind,source="$(pwd)"/example/marigold,target=/app/content \
    marigold:latest
```

## Icons

This project uses icons of [remixicon](https://github.com/Remix-Design/remixicon) project.

## Changelog
This library adheres to a [semantic versioning](https://semver.org/) scheme.
See [CHANGELOG.md](https://github.com/tilmann-bartsch/rportion/blob/master/CHANGELOG.md) for the list of changes.

## Contributions
Contributions are very welcome! Feel free to report bugs or suggest new features using GitHub issues and/or pull requests.

## License
Distributed under [MIT License](https://github.com/tilmann-bartsch/rportion/blob/master/LICENSE).
plugable 1.2.2
A framework for writing extensible Python components
License: MIT
Requires Python: >=3.8,<4.0

# Plugable: A Python Plugin Framework

## Badges:
 - **Build:** [![Build Status](https://travis-ci.com/jacobneiltaylor/plugable.svg?branch=master)](https://travis-ci.com/jacobneiltaylor/plugable)
 - **Coverage:** [![Coverage Status](https://coveralls.io/repos/github/jacobneiltaylor/plugable/badge.svg?branch=master)](https://coveralls.io/github/jacobneiltaylor/plugable?branch=master)


This package exposes a framework for writing extensible applications.

Specifically, it allows consumers to mark an abstract class as "Plugable", using standard inheritance semantics and ABC decorators:

```python3
from abc import abstractmethod
from plugable import Plugable

class AbstractInterface(Plugable):
    @abstractmethod
    def example(self):
        pass
```

Following this, you can create subclasses for differing implementations, and register them with the `AbstractInterface` registry, like so:

```python3
class ExampleOne(AbstractInterface, register="one"): # Integrated registration
    def example(self):
        print("You ran ExampleOne!")

class ExampleTwo(AbstractInterface):
    def example(self):
        print("You ran ExampleTwo!")

AbstractInterface.registry.register("two", ExampleTwo) # Explicit registration
```

Finally, you can access and consume the registered implementations, like so:

```python3
AbstractInterface.get("one").example()  # You ran ExampleOne!
AbstractInterface.get("two").example()  # You ran ExampleTwo!
```
pytwovision-skycorvus96 1.0
A package to implement a stereo vision system trained with deep learning
License: MIT
Requires Python: >=3.6

# A package to do stereo vision using object detection

Pytwovision will allow you to determine the
homogeneous coordinates of a detected object with a neural network, in conjunction with stereo algorithms that allow you to find the disparity maps, to finally apply segmentation within each detection window and it is these segmented pixels that are used to find the real position of the object in 3D space.
These segmented pixels are used to find the actual position of the object in 3D space.

## **Installation**
Open a console and write this: 

`pip install pytwovision`

**Note**: If you already have the opencv package installed, you have to uninstall it with pip uninstall opencv-python and install opencv-contrib-python version 4.6.0.66.

## **Application areas**
- Robotica
- Computer Vision
- Web apps with Object detection
- Surveillance

If you want to learn how to use it I recommend see inside tutorials folder because it have some tools and notebooks that can help you to understand how it's works.

Or you can see our documentation website here: https://corvus96.github.io/PyTwoVision/html/index.html