semvec/pypi-packages huggingface dataset:
YOLODetectionAugment 0.0.1
A data augmentation package for converting segmentation data to detection data.
Requires Python: >=3.6

<div align="center">
 

 [![PyPI](https://img.shields.io/pypi/v/YOLODetectionAugment.svg)](https://pypi.org/project/YOLODetectionAugment/) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) 
 <br/>
  [中文](https://github.com/Huuuuugh/YOLODetectionAugment/blob/main/README_CN.md) ｜  English <br/>
 
</div>



## Overview

This project is a collection of tools for processing datasets used in the YOLO object detection model. It covers functions such as dataset augmentation, format conversion, and division, aiming to help users quickly and effectively prepare high-quality datasets suitable for training the YOLO model.

## Functional Features

1. **Data Augmentation**: Perform diverse data augmentation operations on images and their corresponding annotation files (in XML format), such as rotation, scaling, cropping, adding noise, etc. This expands the scale of the dataset and improves the generalization ability of the model.
2. **Format Conversion**: Achieve the mutual conversion between XML format annotation files and TXT format annotation files required by YOLO, meeting the needs for annotation formats in different scenarios.
3. **Dataset Division**: Automatically divide the original dataset into a training set, a validation set, and a test set according to the proportion specified by the user, ensuring the rational use of the dataset and facilitating the training and evaluation of the model.

## Installation Guide

1. **Environment Requirements**: Python version 3.6 and above.

   ```shell
   pip install YOLODetectionAugment
   ```

## Quick Start

```python
from YOLODetectionAugment.AugmentHelper import process_yolo_dataset
img_path = 'dataset/img' # Your image path
label_path = 'dataset/label' # Your label path
split_list = [0.9, 0.1] # Proportion of the training set and the test set
dics={"battery":0,"bottle":1,"brick":2,"can":3,'carrot':4,'glass':5,'medicine':6,'mooli':7,'package':8,'pebble':9,'potato':10} # Replace these mapping pairs with your own ones

process_yolo_dataset(img_path, label_path, split_list, dics)
```

## Augmentation Effects

![167Q1ERM0_MM1](images/README/167Q1ERM0_MM1.png)![167Q1ERM4_MM3](images/README/167Q1ERM4_MM3.png)

![167Q1ERM0_MM3](images/README/167Q1ERM0_MM3.png)
kivy-garden.simpletablelayout 0.1
Simple Table Layout – HTML table-like layout that supports rowspan and colspan, built into Kivy Garden.
Keywords: Kivy kivy-garden

![screenshot](screenshot.png)

Simple Table Layout
===================

Simple Table like Layout that understands rowspan and colspan properties of children (like HTML tables).

Usage (kv):

Example 1:

    SimpleTableLayout:
        rows: 2
        cols: 2

        Button:
            text: "C1"
            colspan: 2
        Button:
            text: "C2"
        Button:
            text: "C3"

creates:
    
    ###########
    
    #    C1   #
    
    ###########
    
    # C2 # C3 #
    
    ###########

Example 2:
    
    SimpleTableLayout:
        rows: 2
        cols: 2

        Button:
            text: "C1"
            rowspan: 2
        Button:
            text: "C2"
        Button:
            text: "C3"

creates:
    
    ###########
    
    #    # C2 #
    
    # C1 ######
    
    #    # C3 #
    
    ###########

Lastly, SimpleTableLayout.cell(row, col) returns widget at that position in the grid.

License
=======
MIT license.

Credits
=======
Author: Jeyson Molina <jeyson.mco@gmail.com>
bronkhorst-propar 1.2.0
Communicate to Bronkhorst Instruments using the propar protocol over USB, RS232, or RS485.

# Bronkhorst Propar

The Bronkhorst Propar module provides an implementation of a propar master for communication with Bronkhorst (Mass) Flow Meters and Controllers (such as the EL-Flow, ES-Flow, (mini) CORI-FLOW, IQ+FLOW, and others), Pressure Meters and Controllers (EL-PRESS), and others using the default RS232/RS485 interface.

Using the Bronkhorst Propar module it is possible to directly communicate with a single instrument, or to multiple instruments when they are connected to a FLOW-BUS network. The Bronkhorst Propar module communicates directly with the instruments using Python, and does not require tools such as FlowDDE to be installed. Therefore the module is platform independent and has been tested on both Windows and Linux (the module depends on pyserial for serial communication and should work on all platforms that support it).

![Gas Flow](https://github.com/bronkhorst-developer/bronkhorst-propar/raw/master/docs/img/gas-flow.png "Gas Flow") ![Liquid Flow](https://github.com/bronkhorst-developer/bronkhorst-propar/raw/master/docs/img/liquid-flow-uc.png "Liquid Flow") ![Pressure](https://github.com/bronkhorst-developer/bronkhorst-propar/raw/master/docs/img/pressure.png "Pressure")

## Installation

```pip install bronkhorst-propar```

## Documentation

[bronkhorst-propar on Read the Docs](https://bronkhorst-propar.readthedocs.io/en/latest/introduction.html)

## Contact

If you have any questions about this module, our other products or services you can contact us by:

- email: info@bronkhorst.com
- website: www.bronkhorst.com
- phone: +31 573 45 88 00
- mail: Nijverheidsstraat 1A NL-7261 AK Ruurlo (The Netherlands)