semvec/pypi-packages huggingface dataset:
grisly 0.0.4
your_description_here
Requires Python: >=3.7

# grisly:
[![PyPI version](https://badge.fury.io/py/grisly.svg)](https://badge.fury.io/py/grisly)
![PyPI - Downloads](https://img.shields.io/pypi/dm/grisly)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![Tests](https://github.com/CangyuanLi/grisly/actions/workflows/tests.yaml/badge.svg)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

## What is it?

**grisly** is a set of Polars functions that help you deal with unclean, grisly data.

# Usage:

## Dependencies

## Installing

The easiest way is to install **grisly** is from PyPI using pip:

```sh
pip install grisly
```

## Running

First, import the library.

```python
import grisly
```

# TODO:
control-scnu 0.8.9
The code package for HuaGuang AI Education
Requires Python: >=3.7, <3.11

English | [简体中文](README_cn.md)
- [0.control_scnu](#0control_scnu)
- [1.Install control](#1install-control)
- [2.Each corresponding file description](#2each-corresponding-file-description)
  - [template](#template)
  - [_init_.py](#initpy)
  - [file_operation.py](#file_operationpy)
  - [gpio.py](#gpiopy)
  - [jiami.py](#jiamipy)
  - [machine_learning.py](#machine_learningpy)
  - [maths.py](#mathspy)
  - [requirements.txt](#requirementstxt)
  - [shijue (shijue0,shijue1,shijue2)](#shijue-shijue0shijue1shijue2)
  - [unique.py](#uniquepy)
  - [yuyin.py](#yuyinpy)
# 0.control_scnu
The library was developed for Huaguang AI Education Innovation Team [Case Department]    
Applicable to artificial intelligence education

# 1.Install control
```python
pip3 install control-scnu
```

# 2.Each corresponding file description
## template
Where the various model files are saved

## _init_.py
The init file  
The version information of the software, programming block and library is indicated in this file

## file_operation.py
File manipulation related libraries

## gpio.py
Car hardware related library 
The car forward routine (This item needs to run on the car):
```
from control import gpio
import time

m=gpio.Mecanum_wheel()
m.uart_init()
m.car_go(200)
time.sleep(2)
m.car_stop()
```

## jiami.py
A library for encrypted files

## machine_learning.py
Library for machine learning    
Iris machine learning routine:
```
from control import machine_learning as ml

datasets=ml.DatasetsNew(ml.data_name["鸢尾花"])
model= ml.ModelNew(ml.model_name['神经网络'])
model.train(datasets.x_train, datasets.y_train,dataName=datasets.data_name)
model.test(datasets.x_test,datasets.y_test)
print(model.test_score,flush=True)
model.predict(datasets.x_test)
print(model.pred,flush=True)
model.save(name='myFirstModel')
model1=ml.ModelNew('myFirstModel.proto')
model1.test(datasets.x_test,datasets.y_test)
print(model.pred,flush=True)
```

## maths.py
Library related to basic mathematics

## requirements.txt
Library dependent TXT file

## shijue (shijue0,shijue1,shijue2)
Visual related libraries   
The camera obtains the image and binarizes the display routine:
```
from control import shijue1

a=shijue1.Img()
a.camera(0)
a.name_windows('img')
while  True:
    a.get_img()
    a.BGR2GRAY()
    a.GRAY2BIN()
    a.show_image('img')
    a.delay(1)
```

## unique.py
Put something special in it

## yuyin.py
Speech correlation library    
Routines for speech recognition and retelling:
```
from control import yuyin

s=yuyin.Yuyin(online=True) 
s.my_record(3,"speech")   
print(s.stt("speech"),flush=True)  
s.play_txt(s.stt("speech"))
LPMatcher 0.0.1
points and lines matcher based hungarian match
Requires Python: >=3.6

# Points and lines matcher

## This module is baesd on  hungarian match with cost  matrix

## Example:

```python
 xmin, ymin, xmax, ymax = [97, 348, 533, 620], [131, 133, 180, 50], [197, 451, 581, 660], [186, 209, 281, 120]
 test_points = list(zip(xmin, ymin, xmax, ymax))
 test_lines = [((638, 2), (638, 47)),((455, 159), (638, 159)), ((638, 124), (638, 159)), ((639, 159), (713, 159)), ((3, 159), (93, 159)),
                  ((201, 159), (344, 159)), ((557, 161), (557, 176))]
 tm = topology_match(img_path='./img/test.png', lines=test_lines, points=test_points)
 print("the number of lines: " ,tm.get_line_num)
 print("the number of points: ", tm.get_point_num)
 match_map = tm.hungarian_match_with_cost(distance_threshold=60, show=False)
 print(match_map)
```
cujirax 0.8.1
Cucumber result to Jira Xray Test repository

# cujirax
`Cu`cumber `Ji`ra and `X`ray

## Setup environment variables
```sh
export JIRA_DOMAIN="urdomain.atlassian.net"
export JIRA_EMAIL="youremail@yourcompany.com"
export JIRA_SECRET="xxxxx",

export XRAY_CLIENT_ID="xxxxx"
export XRAY_CLIENT_SECRET="xxxxx"
```

## Maintaining cucumber result json file
Make sure it is alway updated from 
`https://raw.githubusercontent.com/cucumber/cucumber-json-schema/main/schema.json`

Generating model from 
`datamodel-codegen --input schema.json --output cujirax/cucumber.py`