semvec/pypi-packages huggingface dataset:
dpayocr 1.0.2
识别支付宝微信收款码信息
Requires Python: >=3.7

# DPayOcr

[![PyPI version](https://badge.fury.io/py/dpayocr.svg)](https://badge.fury.io/py/dpayocr)

## 介绍

`dpayocr` 可以识别支付宝微信收款码信息。例如支付类型，支付金额，支付链接。

## 安装

```bash
$ pip install dpayocr
```

## 使用

### 命令行使用

```
dpayocr 66.JPG
```

输出

```
PayQrCode(pay_type=1, price=66.0, url='https://qr.alipay.com/xxx')
```

### 接口

```python
>>> from dpayocr import parse_pay_qr_code
>>> fp = '66.JPG' # 可以传入文件名或者二进制IO
>>> parse_pay_qr_code(fp)
PayQrCode(pay_type=1, price=66.0, url='https://qr.alipay.com/xxx')
```

## 问题

### 在虚拟环境找不到 Tesseract 命令

```
# If you don't have tesseract executable in your PATH, include the following:
pytesseract.pytesseract.tesseract_cmd = r'<full_path_to_your_tesseract_executable>'
# Example tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract'
```

## 参考链接

- [tesseract](https://github.com/tesseract-ocr/tesseract)
- [pytesseract](https://pypi.org/project/pytesseract/)
- [pyzbar](https://pypi.org/project/pyzbar/)
- [A comprehensive guide to OCR with Tesseract, OpenCV and Python](https://nanonets.com/blog/ocr-with-tesseract/)
rref 0.3.1
Package to help transform 2-D matrix into reduced row-echelon form.
Requires Python: >=3.6.*
Keywords: rref,matrix,row reduced echelon form,echelon,Gaussian elimination,linear algebra

# Reduced Row-Echelon Form (RREF) library

This is a simple library for transforming a 2-D matrix to reduced row-echelon form (RREF)<sup>[1]</sup>.

Definition<sup>[2]</sup>:

>In linear algebra, a matrix is in echelon form if it has the shape resulting from a Gaussian elimination.

>A matrix being in row echelon form means that Gaussian elimination has operated on the rows, and column echelon form means that Gaussian elimination has operated on the columns. In other words, a matrix is in column echelon form if its transpose is in row echelon form. Therefore, only row echelon forms are considered in the remainder of this article. The similar properties of column echelon form are easily deduced by transposing all the matrices.

A matrix is in reduced row-echelon form if it satisfies the following:
1. In each row, the left-most nonzero entry is 1 and the column that contains this 1 has all other entries equal to 0. This 1 is called a leading 1.
2. The leading 1 in the second row or beyond is to the right of the leading 1 in the row just above.
3. Any row containing only 0's is at the bottom.

Below is a screenshot showing RREF matrices<sup>[3]</sup>:

![RREF examples](/static/rref1.png)

---
## Example Usage
``` python
import rref

### Create an uninstanced matrix helper (MatrixMadness)
mm = rref.main.MatrixMadness()

### Create a sample matrix of 20 x 20 with random integers 
### in the range of -5 to 20.
matrix = mm.creatrix(20, [-5, 20])

### Create an RREF instance with your matrix.
r = rref.RREF(matrix)

### Run the processor
# Note: The output matrix will be in the r.mm.matrix variable.
r.run()

### Print the matrix to check results.
result = r.mm.matrix
print([i for i in result])

```


[1]: https://people.math.carleton.ca/~kcheung/math/notes/MATH1107/wk04/04_reduced_row-echelon_form.html
[2]: https://en.wikipedia.org/wiki/Row_echelon_form
[3]: https://stattrek.com/statistics/dictionary.aspx?definition=reduced_row_echelon_form
apk-user-cert 1.0.3
Set network_security_config.xml and android:networkSecurityConfig

apk-user-cert
============================================================
.. code:: sh

  $ pip install apk-user-cert

###############
USAGE
###############
.. code:: sh

  $ apk-user-cert sample.apk
    Decompiling ...
    networkSecurityConfig attrib not founded
    network_security_config.xml not founded
    Rebuilding ...
    ./sample/dist/sample.apk