sk-cv 0.2.5
Image processing kit.
License: MIT
Requires Python: >=3.6.0


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**Table of Contents**  *generated with [DocToc](https://github.com/thlorenz/doctoc)*

- [📦 setup.py (for humans)](#-setuppy-for-humans)
  - [Installation](#installation)
  - [To Do](#to-do)
  - [Requirements](#requirements)
  - [Modules included](#modules-included)
    - [image_io](#image_io)
      - [read_image_opencv](#read_image_opencv)
      - [read_image_image](#read_image_image)
      - [image_save](#image_save)
      - [display_image](#display_image)
    - [image_processing](#image_processing)
      - [image_type_trans_opencv_image](#image_type_trans_opencv_image)
      - [image_type_transform](#image_type_transform)
      - [rotate_angle](#rotate_angle)
      - [image_lightup](#image_lightup)
      - [image_erosion](#image_erosion)
      - [image_dilate](#image_dilate)
      - [edge_detection](#edge_detection)
      - [hsv_extract](#hsv_extract)
      - [remove_outliers](#remove_outliers)
      - [hough_lines](#hough_lines)
      - [filtering_function](#filtering_function)
      - [contour_detection](#contour_detection)
      - [draw_contour](#draw_contour)
      - [max_area_contours](#max_area_contours)
      - [filter_contours_area](#filter_contours_area)
      - [filter_contours_ratio](#filter_contours_ratio)
      - [compute_ssim](#compute_ssim)
  - [More Resources](#more-resources)
  - [License](#license)

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📦 setup.py (for humans)
=======================

This repo exists to provide [an example setup.py] file, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices for `setup.py`, as well as some
commented–out nice–to–haves.

For example, this `setup.py` provides a `$ python setup.py upload`
command, which creates a *universal wheel* (and *sdist*) and uploads
your package to [PyPi] using [Twine], without the need for an annoying
`setup.cfg` file. It also creates/uploads a new git tag, automatically.

In short, `setup.py` files can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)

[Check out the example!][an example setup.py]

Installation
-----

```bash
cd your_project

# Download the setup.py file:
#  download with wget
wget https://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py -O setup.py

#  download with curl
curl -O https://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py
```

To Do
-----

-   Tests via `$ setup.py test` (if it's concise).

Pull requests are encouraged!


Requirements
--------------
- opencv_python_headless==4.4.0.46
- numpy==1.19.3
- scipy==1.5.2
- matplotlib==3.3.2
- Pillow==8.2.0


Modules included
-------------------
### image_io

######            &emsp;read_image_opencv&emsp;使用opencv读取图片(支持中英文路径)

###### &emsp;read_image_image&emsp;使用PIL中的Image读取图片

###### &emsp;image_save&emsp;保存图片(支持中英文路径)

###### &emsp;display_image&emsp;显示图片

### image_processing

######          &emsp;image_type_trans_opencv_image&emsp;opencv和Image打开的图片格式转换

######         &emsp;image_type_transform&emsp;常用图片格式转换

######         &emsp;rotate_angle&emsp; 旋转图片

######         &emsp;image_lightup&emsp; 增加亮度

######     &emsp;image_erosion&emsp; 腐蚀

######     &emsp;image_dilate&emsp; 膨胀

######     &emsp;edge_detection&emsp;边缘检测

######     &emsp;hsv_extract&emsp;颜色提取

######     &emsp;remove_outliers&emsp;过滤连通区域

######     &emsp;hough_lines&emsp;霍夫变换

######     &emsp;filtering_function&emsp;均值滤波、高斯滤波、中值滤波

######     &emsp;contour_detection&emsp;检测轮廓

######     &emsp;draw_contour&emsp;在图像上画出全部轮廓

######     &emsp;max_area_contours&emsp;寻找最大轮廓，并画出该轮廓最小外接矩形

######     &emsp;filter_contours_area&emsp;过滤面积较小的轮廓

######     &emsp;filter_contours_ratio&emsp;根据轮廓的最小外接矩形长宽比过滤轮廓

######     &emsp;compute_ssim&emsp;ssim算法计算两张图的相似度
​      
​      

More Resources
--------------

-   [What is setup.py?] on Stack Overflow
-   [Official Python Packaging User Guide](https://packaging.python.org)
-   [The Hitchhiker's Guide to Packaging]
-   [Cookiecutter template for a Python package]

License
-------

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.

[an example setup.py]: https://github.com/navdeep-G/setup.py/blob/master/setup.py
[PyPi]: https://docs.python.org/3/distutils/packageindex.html
[Twine]: https://pypi.python.org/pypi/twine
[image]: https://farm1.staticflickr.com/628/33173824932_58add34581_k_d.jpg
[What is setup.py?]: https://stackoverflow.com/questions/1471994/what-is-setup-py
[The Hitchhiker's Guide to Packaging]: https://the-hitchhikers-guide-to-packaging.readthedocs.io/en/latest/creation.html
[Cookiecutter template for a Python package]: https://github.com/audreyr/cookiecutter-pypackage