semvec/pypi-packages huggingface dataset:
pernaf 0.0.14
An implementation of the Normalized Advantage Function Reinforcement Learning Algorithm with Prioritized Experience Replay
Requires Python: >=3.6

# PER-NAF
An implementation of the Normalized Advantage Function Reinforcement Learning Algorithm with Prioritized Experience Replay

## Summary
* The original paper of this code is: https://arxiv.org/abs/1509.02971
* The code is mainly based on: https://github.com/carpedm20/NAF-tensorflow/
* Additionally I added the prioritized experience replay: https://arxiv.org/abs/1511.05952
* Using the OpenAI baseline implementation: https://github.com/openai/baselines/blob/master/baselines/deepq/replay_buffer.py

Thanks openAI and Kim!


## Some Advices from experience in RL

* Normalize the state and action space as well as the reward is a good practice
* Visualise as much as possible to get an intuition about the method as possible bugs
* If it does not make sense it is a bug with very high probability



> Coding makes happy
applyai-vision 0.6
A small example package
Requires Python: >=3.6

# applyai Vision

Applyai Vision plugins are a suite of python modules designed to solve common image processing and object recognition tasks. They are built upon the OpenCV and TensorFlow libraries. They share a common API. Each plugin solves one minor image processing task. When integrated and organised by a supervising application they can be cascaded and combined to create large scale image processing systems. 

- signed - the applyai vision system team
pytest-django-dotenv 0.1.2
Pytest plugin used to setup environment variables with django-dotenv

## pytest-django-dotenv
Pytest plugin used to specify dotenv for your tests. This project uses
django-dotenv to load your environment variables from your dotenv file.

### Installation
```
pip install pytest-django-dotenv
```

### Setup
Base setup uses `.env` file which is placed equally with virtual environment
directory. If you want to change that behavior you have to add `env_path` to your
`pytest.ini` file which allow you to change path to dotenv file starting from
parent of virtualenv directory.

Example:
If we have virtualenv i `a` directory and dotenv file is placed in parent of `a`
directory then you should add this to your `pytest.ini` file.

```
[pytest]
env_path = 
    ../.env
```
deepbay 0.8.3
Tensorflow/Keras Plug-N-Play Deep Learning Models Compilation
Requires Python: >=3.6
Keywords: Keras,Model,Plug And Play,Tensorflow

[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](https://www.python.org/)
[![Development Status](https://badgen.net/github/status/ElPapi42/DeepBay/development)](https://github.com/ElPapi42/DeepBay/actions?query=workflow%3A%22Test+Package%22)
[![Release](https://badgen.net/github/release/ElPapi42/DeepBay)](https://github.com/ElPapi42/DeepBay/releases)
[![Last Commit](https://badgen.net/github/last-commit/ElPapi42/DeepBay)](https://github.com/ElPapi42/DeepBay/graphs/commit-activity)


# DeepBay

This project was created with the objective of compile Machine Learning Architectures created using Tensorflow or Keras. The architectures must be provided as a ready-to-use Plug-and-Play module that can be easily integrated into any existing project or architecture design

## Installation

You can use pip for install this from PyPi, just use:

```
pip install deepbay
```

https://pypi.org/project/deepbay
sprite-detection-tool 3.2.7
An useful detect sprite of image package
Requires Python: >=3.6

# **SPRITE-DETECTION PROJECT**
Sprite-detection is a tool using python library to detect sprite sheet in a image

This tool will help you spend less time when you want to detect sprite to making a game or animetion, ...

## **How to using this tool**
First you must **Download** our tool and import to your device

**Before start** using you should download all depencencies of this tool

Second pass an image path or Image object to **SpriteSheet** Object of our. That's it easy right^^.

### EXAMPLE: 
```
from sprite_detection_tool.spriteutil import SpriteSheet

a = SpriteSheet(path/Image object)
```

We have 2 method of SpriteSheet object in this tool you can using:

- find_sprites() method will return a list [sprites, label_map]

### EXAMPLE: 
```
from sprite_detection_tool.spriteutil import SpriteSheet

a = SpriteSheet(path/Image object)
sprites, label_map = a.find_sprites()
```

- create_sprite_labels_image() method **return an Image object**

### EXAMPLE: 
```
from sprite_detection_tool.spriteutil import SpriteSheet

a = SpriteSheet(path/Image object)
sprites, label_map = a.find_sprites()
a.create_sprite_labels_image().show()
```

## **How you can help**

- Use our community test server and write up issues
- If you are a developer, install the software for development
SOMGraySclae 1.0.1
Using mainifest to improve a grayscale method based on Color-to-gray conversion using ISOMAP [Cui et al. 2010]
Requires Python: >=3.6


图像灰度化  Image Grayscale
====

本文的图像灰度化实现主要基于：

1. Data-driven Color Manifolds [Nguyen et al. 2015].
2. Color-to-gray conversion using ISOMAP [Cui et al. 2010].
3. 一种快速映射Isomap算法 [圣少友 et al. 2009].

使用方法:
===

##### 获取灰度图
    gray, gray_reverse = som.get_gray_scal(os.path.dirname(__file__) + '/datasets/apple/apple_0.png')
    plt.imsave(os.path.dirname(__file__) + '/gray.png', gray, cmap='gray',vmin = 0, vmax = 1)
    plt.imsave(os.path.dirname(__file__) + '/gray_reverse.png', gray, cmap='gray', vmin=0, vmax=1)
##### 获取一维流形：
    som = SOMGraySclae()
    manifest_1d = som.get_1d_manifold(os.path.dirname(__file__) + '/datasets/apple/apple_0.png')
    plt.imsave(os.path.dirname(__file__) + '/manifest_1d.png', manifest_1d, cmap='gray', vmin=0, vmax=1)

##### 获取二维流形：
    som = SOMGraySclae()
    manifest_2d = som.get_2d_manifold(os.path.dirname(__file__) + '/datasets/apple/apple_0.png')
    plt.imsave(os.path.dirname(__file__) + '/manifest_2d.png', manifest_2d, cmap='gray', vmin=0, vmax=1)

##### 获取debug plt：
    som = SOMGraySclae()
    som.color_to_gray_debug(os.path.dirname(__file__) + '/datasets/apple/apple_0.png',
                       gray_name='gray', gray_reverse_name='gray_reverse', debug=True)    

灰度化结果
===

#### 输入图像：

![](https://raw.githubusercontent.com/freedomofme/Grayscale/master//som_cm/datasets/apple/apple_0.png)

#### 获取灰度图，输出图像：

*
![](https://raw.githubusercontent.com/freedomofme/Grayscale/master/som_cm/results/gray.png)
*
![](https://raw.githubusercontent.com/freedomofme/Grayscale/master/som_cm/results/gray_reverse.png)

#### for debug:
![](https://raw.githubusercontent.com/freedomofme/Grayscale/master/som_cm/results/gray_debug.png)

#### 所有结果：
![](https://raw.githubusercontent.com/freedomofme/Grayscale/master/som_cm/results/result_all.png)


感谢
===

项目中采用Nguyen等人的论文实现，基于以下开源项目：
[https://github.com/tody411/SOM-ColorManifolds](https://github.com/tody411/SOM-ColorManifolds)


## License

The MIT License 2017 (c) tody