semvec/pypi-packages huggingface dataset:
QcureUi 0.0.3
Used for pyqt5 beautification

﻿## 快速美化PyQt--QcureUi

---

### QcureUi
- 快速美化PyQt应用
- 项目地址(欢迎star):[https://github.com/Amiee-well/cureUi](https://github.com/Amiee-well/cureUi)

### 使用方法
- pip install QcureUi

- 调用QcureUi.cure.Windows()

- 共有五个参数填写：

  1.第一个参数为QWidget面板类（必填）

  2.第二个参数为托盘名字（必填）

  3.第三个参数为托盘图标，必须为jpg格式（必填）

  3.第四个参数为选择美化颜色面板（选填，默认为default）

  现已有参数为：blue，blueDeep，blueGreen，pink四种

  该参数为True时将随机从已有颜色库返回其一

  4.第五个参数为窗口名字（选填，默认为QCureWindow）

  5.第六个参数为窗口图标（选填，默认为空）

### 注意事项
- 注意:托盘图标默认名称为icon.jpg,改变该图标可在运行目录下放置该图片
- 使用示例:
    from QcureUi import cure
    app = QApplication(sys.argv)
    win = cure.Windows(Examples(), 'tray name', 'tray.jpg',True, 'program name', 'myicon.ico')
    sys.exit(app.exec_())
sphfile 1.0.3
Numpy-based NIST SPH audio-file reader

sphfile
=======

.. image:: https://img.shields.io/pypi/v/sphfile.svg
    :target: https://pypi.python.org/pypi/sphfile
    :alt: Latest PyPI version

Numpy-based NIST SPH audio-file reader. This is for use 
with NIST SPH audio-files, the most likely use being 
extracting the TEDLIUM_release2 audio into formats that
standard tools can easily process.

Note that this library doesn't require any external tools
such as `vox` or `gstreamer`. It just loads the data into a
numpy array and then lets you dump it back out to wave 
files.

Note that the library does not support files with
`embedded-shorten-*` encodings, only the base ulaw
encoding. You will need to convert such files with::

    sph2pipe file.sph file-raw.sph

to allow them to be loaded.

Usage
-----

.. code:: python

    from sphfile import SPHFile
    sph =SPHFile( 
        'TEDLIUM_release2/test/sph/JamesCameron_2010.sph' 
    )
    # Note that the following loads the whole file into ram
    print( sph.format )
    # write out a wav file with content from 111.29 to 123.57 seconds
    sph.write_wav( 'test.wav', 111.29, 123.57 )

Requirements
------------

* numpy

License
-------

    MIT License (c) 2017 Mike C. Fletcher

Authors
-------

`sphfile` was written by `Mike C. Fletcher <mcfletch@vrplumber.com>`_.


History
-------

* 1.0.3 -- Allow for other header keys during header format parsing
* 1.0.2 -- Use signed integers for 2 and 4-byte sample_n_bytes
* 1.0.1 -- Fix to allow for files that have non-sample-multiple bytes in the data section
* 1.0.0 -- Initial release
scene-text 0.2.3
Finding text in photos
Requires Python: ==3.6.*
Keywords: scene text detection recognition

# Scene Text Detection and Recognition

This project packages scene text algorithms for easy usage. Scenes as in photos are much harder for text detection and recognition than doing the same for scanned documents. The latter is mostly referred to as OCR and it is a well solved problem. Note that due to the usage of Deep Learning algorithms, text detection and recognition are rather slow, in particular on CPU-only machines and for images with a large number of words. Accuracies have reached an impressive level but are still below human performance. Currently, the following algorithms are available.

[EAST](https://arxiv.org/abs/1704.03155) is used for detection, where the implementation is mostly copied from [Jan Zdenek](https://github.com/kurapan/EAST ).

[MORAN](https://arxiv.org/abs/1901.03003) is used for recognition, where the implementation is mostly copied from [Canjie Luo](https://github.com/Canjie-Luo/MORAN_v2).


Installation:
```
pip install scene_text
```

Basic usage from Python:
```
import cv2

# The first import after installation can take a little longer as it downloads
# pre-trained models and compiles some native code.
from scene_text import AllWordsRecognizer

# initialize models
pipeline = AllWordsRecognizer()

# read an image
img = cv2.imread('path/to/my/image/file')[:, :, ::-1]

# detect and recognize all words in the image
words, boxes = pipeline.get_all_words(img)
```

Basic usage from command line:
```
scene_text my/input/image/folder my/output/folder
```

If you have trouble with the complex dependencies try Docker:
```
docker build -t scene_text .

docker run --rm -i -t -v ${PWD}:/scene_text scene_text

# ...
```
truckman 0.0.1
Save and load Docker named volumes.
Requires Python: >=3.5
Keywords: docker volume save load backup transfer

## Truckman

Simpler backing-up of named Docker volumes to tar archives and unpacking of them
into new named Docker volumes.

### Save a volume

`truckman --save volume_name > archive.tar`

### Load a volume

`truckman --load archive.tar --name volume_name`