semvec/pypi-packages huggingface dataset:
lioprog 0.2.6.1
Server Client authorization and encrypted communication module
License: MIT
Keywords: socket,server,client,encryption,communication,authorize,multithread

|image0| |image1|   |image2| |image3|    |image4|   |image5|

**Lioprog** project
===================

Server Clients encrypted communication module.
----------------------------------------------

*Lioprog project is a python module based on python socket and provides
authorization, encrypted communication, dynamic protocol values, and
encrypted file transfer between 'server' and 'clients' in an easy way*

Features
--------

    -  send and receive encrypted msg
    -  send and receive encrypted list
    -  send and receive encrypted files
    -  multi thread clients
    -  auto clients authorization
    -  dynamic random protocol values
    -  send and receive files in one line of code
    -  send and receive encrypted msg,list in one line of code
    -  parameters loaded from 'conf' file
    -  client module works on linux and windows as well

How it works?
-------------

    1.  Server wait for connection..
    2.  Connection accepted, start authorization process
    3.  Server generate a new 2048 RSA pairs, and send PUBLIC key to
        client
    4.  Client encrypt his TOKEN and send it to server
    5.  Server compare token hash and send validation signal to client
    6.  Client generate a new 2048 RSA pairs, encrypt and send his
        PUBLIC key to server
    7.  Client encrypt and send his 'files encryption' KEY to server
    8.  Server generate a new random PROTOCOL values, encrypt and send
        to client
    9.  Server register client\`s definitions and privilleges
    10. Client update the new received protocol values
    11. Ready for further instruction (✨here comes your code✨)
    

`Examples and
Ducomentation <https://github.com/lioprog/lioprog>`__

MIT License

.. |image0| image:: https://img.shields.io/github/languages/top/lioprog/lioprog
.. |image1| image:: https://img.shields.io/pypi/pyversions/lioprog
.. |image2| image:: https://img.shields.io/github/v/release/lioprog/lioprog?color=g
.. |image3| image:: https://img.shields.io/github/release-date/lioprog/lioprog
.. |image4| image:: https://img.shields.io/github/issues-raw/lioprog/lioprog?color=red
.. |image5| image:: https://img.shields.io/github/license/lioprog/lioprog
objectdaddy 0.0.5
A python yolov3-tiny-based object recognizer.
License: MIT
Keywords: rtsp stream python

# Object Daddy
A simple, python-based object recognizer, basically an implementation of yolo-v3-tiny. This package is used for personal projects; I don't maintain it, so use at your own risk, and feel free to fork.


## Usage
Basic usage is as follows. This project is built to work with rtsparty (also on pypi).

You can get and recognize one frame using the following:
```python
from rtsparty import Stream
from objectdaddy import Daddy


stream = Stream()
daddy = Daddy()

frame = stream.get_frame()
results, frame = daddy.process_frame(frame)
for detection in results:
    detection.identify()
```

For continuous recognition, use the following code.
```python
from rtsparty import Stream
from objectdaddy import Daddy


stream = Stream()
daddy = Daddy()


try:
    while True:
        frame = stream.get_frame()
        if stream.is_frame_empty(frame):
            continue
        daddy.process_frame(frame)
        for detection in daddy.get_current_detections():
            if not detection.has_been_processed_downstream:
                detection.identify()
                detection.has_been_processed_downstream = True
except KeyboardInterrupt:
    pass
```

The code makes the attempt to recognize objects across multiple frames, and keep a list of objects in memory accessed with the get_current_detections() function.


## Notes
- This package supports only CPU-based inference.
rtsparty 0.0.2
A simple package for getting live frames from rtsp streams.
License: MIT
Keywords: rtsp stream python

# RTSParty
A RTSP module that makes it easy to grab live frames from a RTSP stream. This package basically just wraps cv2 and lets you grab the latest frame from the camera, instead of relying on the buffer.


## Usage
```python
from rtsparty import Stream


stream = Stream('rtsp://username:password@10.0.0.1/endpoint/', live=True)

# Get a live frame from the camera
live_frame_from_camera = stream.get_frame()

# Force a reconnection
stream.reconnect()

# Show a live view of the stream
stream.view()
```

To grab the latest frame from the camera no matter what, construct the stream with live=True (or leave it out, it's the default):

```python
stream = Stream(live=True)
stream = Stream()
```

To use the buffer, which may not provide the most live images from the camera, set live to False.

```python
stream = Stream(live=False)
```

By leaving out the first argument in the Stream constructor, it will by default use the default video capture device on your machine (your webcam most likely).
fadetext 0.0.1
This module allows to use Pastebin API very easily.
License: MIT
Keywords: pastebin

> # PASTEBIN API
```
# Module made by @venaxyt on Github
import fadetext

mytext = """
TEXT
TEXT
TEXT
"""
textfade.purplefade(mytext)
>>> Output : Faded purple text
```

> ### **Download** : ``pip install fadetext``
textfade 0.0.1
This module allows to use Pastebin API very easily.
License: MIT
Keywords: pastebin

> # PASTEBIN API
```
# Module made by @venaxyt on Github
import textfade

mytext = """
TEXT
TEXT
TEXT
"""
textfade.fade(mytext)
>>> Output : Faded text
```

> ### **Download** : ``pip install textfade``
aihubcli 1.0.1
A simple cli utility to generate ML project structure for quickly starting ML projects
License: MIT
Requires Python: >=2.7

### Project Structure for MLflow integrated ML Projects

This cli tool generates the following directory structure for quickstart ML projects

installaton:  

    pip install aihubcli

example use:  
    aihubcli create myProject
    

    myProject/
    │
    ├── input/
    │   ├── raw/                <-- Raw data here
    │   ├── interim/            <-- Any intermediate data, to pause and continue experiments
    │   └── processed/          <-- Processed data ready for ML pipeline
    │
    ├── output/
    │   ├── models/             <-- Model pickle or model weights stored here
    │   ├── artifacts/          <-- Serialized artifacts like LabelEncoder, Vectorizer etc
    │   ├── figures /            <-- All plots and visualizations goes here
    │   └── results/            <-- If the results needs to be stored for review, save here
    │
    ├── notebooks/              <-- All notebooks and experiments resides here
    │   ├── eda_plots.ipynb     <-- ┌───────────────────────────────────────────┐
    │   ├── ml_rnn.ipynb        <-- │ free to name notebooks any way you prefer │
    │   └── ml_seq2seq.ipynb    <-- └───────────────────────────────────────────┘
    │
    ├── src/                    <-- Final program, with training and prediction pipeline
    │   ├── __init__.py         <-- Makes src a Python module                    
    │   ├── preprocess.py       <-- code related to preprocessing the data and storing it in input/processed/
    │   ├── model.py            <-- model definition here, can be used in train or prediction
    │   ├── train.py            <-- all code related to training model goes here
    │   ├── hyperopt.py         <-- hyperparameter optimizations related code
    │   ├── package.py          <-- packaging the trained model with preprocessing logic for MLflow
    │   ├── predict.py          <-- prediction logic, usually loads the model from Mlflow registry and predict
    │   └── server.py           <-- any API interface like Flask etc. Create as needed
    │
    README.md                   <-- Description and instruction about the project
    MLProject                   <-- MLflow project file. If you want to use this directory as MLflow project
    requirements.txt            <-- python dependencies
    config.yml                  <-- configuration key value pairs in yaml format
comlib-python 0.1.1
My short description for my project.
License: MIT
Requires Python: >=3.6.0


# comlib-python

#### 介绍
自用常用库

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目
5.  Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)