semvec/pypi-packages huggingface dataset:
centernet-ilvo 0.0.1.post1
The ilvo plant centre model package
Requires Python: >=3.9

# CenterNet ILVO

## Introduction

This python package can be used to run the CenterNet model for plant centre detection.
The plant CenterNet model is used for autonomous intrarow weeding as described in the paper of Willekens et al., 2025.

```
Willekens, A., Callens, B., Pieters, J., Wyffels, F., & Cool, S. (2025). 
Cauliflower centre detection and 3-dimensional tracking for robotic intrarow weeding. 
Precision Agriculture. Springer.
 ```

The regression output was removed from the CenterNet architecture only to output a heatmap prediction (Vierbergen et al., 2023; Duan et al., 2019).
This architecture is shown in the image below.

![CenterNet architecture](./centernet_architecture.svg "CenterNet architecture")

An example for using this model is provided in [Hugging Face - CenterNet ILVO](https://huggingface.co/axelwillekens/centernet_ilvo).

## Installation

1. Install [pytorch (torch and torchvision)](https://pytorch.org/get-started/locally/)
2. Install dependencies and the package
```bash
pip install numpy opencv-python scikit-image
pip install cams-ilvo-utils
pip install centernet-ilvo
``` 

## Licence

This project is under the ``ILVO LICENCE``.

```
Copyright (c) 2024 Flanders Research Institute for Agriculture, Fisheries and Food (ILVO)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

When the Software, including modifications and extensions, is used for:
- commercial or non-commercial machinery: the ILVO logo has to be clearly
   visible on the machine or on any promotion material which may be used in any
   agricultural fair or conference, in a way it is clear that ILVO contributed
   to the development of the software for the machine.
- a scientific or vulgarising publication: a reference to ILVO must be made as
   well as to the website of the living lab Agrifood Technology of ILVO:
   https://www.agrifoodtechnology.be

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
tools-zy 0.1.8.1
Some tools for AI model training and inference.
Requires Python: >=3.10

# tools_zy[![Version][version-badge]][version-link] ![MIT License][license-badge]


此工具包旨在给AI工作者提供一把趁手的工具，尽量简化非核心的工作。\
其中包含深度学习模型过程中可能会用到的一些工具，比如数据整理、格式转换、划分数据集等。

因此也要注意，其中某些函数可能并没有提供过多自由度，如果需要，请自行修改。

### 安装

```bash
$ pip install tools-zy
```

### 使用

含有的功能示例如下：
```python
import tools_zy as tz

# 复制、移动文件夹中以.bmp结尾的文件。（还支持指定文件名，支持递归操作）
tz.copy_file("/home/org_folder", "/home/new_folder", format=".bmp")
tz.move_file("/home/org_folder", "/home/new_folder", format=".bmp")

# 获取（复制、移动）文件夹中以.bmp结尾的一些随机文件。
tz.copy_some_random_files("/home/org_folder", "/home/new_folder", 1000, format='.bmp')
tz.move_some_random_files("/home/org_folder", "/home/new_folder", 1000, format='.bmp')

# 划分分类数据集
img_folder = r"/home/classify/rawData"  # 包含n个名称为数字序列的文件夹
out_folder = r"/home/classify/splitData"  # 
tz.split_classifid_images(img_folder, out_folder, (0.8, 0.2, 0), format=".bmp")
# 划分lablmes数据集 (划分图片和json文件到train, val, test文件夹)
labelme_folder = r"D:\Zhiyuan\pics_get\keypoints\20241118"
data_folder = tz.split_labelmes(labelme_folder, ratio=(0.85, 0.1, 0.05), format=".bmp")


# 将labelme的json格式转化为coco格式，要求图片和json文件名相同且在同一文件夹下
labelme_json_folder = r'D:\Zhiyuan\pics_get\keypoints\keypoints\20241030' 
coco_json_path = r'D:\Zhiyuan\pics_get\keypoints\keypoints\20241030.json'
categories_json_file = r"D:\Zhiyuan\code\tz\categories_person.json"
tz.labelmes2coco(labelme_json_folder, coco_json_path, categories_json_file, bbox=[1, 1, 511, 1023])
coco_json_path = r'D:\Zhiyuan\pics_get\keypoints\keypoints\20241121.json'
labelme_json_folder = r'D:\Zhiyuan\pics_get\keypoints\keypoints\20241121'
tz.coco2labelmes(coco_json_path, labelme_json_folder)



```
#### labelme标注文件连续操作：划分labelme数据集->转化为coco格式
```python
labelme_folder = r"D:\Zhiyuan\pics_get\keypoints\20241118"
splited_labelmes_folder = tz.split_labelmes(labelme_folder, ratio=(0.85, 0.1, 0.05), format='.bmp')
categories_json_file = r"D:\Zhiyuan\code\tz\categories_person.json"
tz.splited_labelmes2cocos(splited_labelmes_folder, categories_json_file=categories_json_file)
```


### License

[MIT](https://github.com/wzy-777/tools_zy/blob/main/LICENSE)


[version-badge]:   https://img.shields.io/badge/version-0.1-brightgreen.svg
[version-link]:    https://pypi.org/project/tools-zy/
[license-badge]:   https://img.shields.io/github/license/pythonml/douyin_image.svg
uquery 0.1.1
Abstract away the complexity of multi-data source access with a unified interface
Requires Python: >=3.9

# UQuery

uQuery is a tool designed for data analysis that simplifies reading data from multiple sources.


## Usage

1. write a config file `.uquery.toml` and place it in your home directory or project working directory
```toml
[uquery.endpoint.my_data_src]
db_type = 'doris'
host = '<your db host>'
port = '<your db port>'
user = '<your db username>'
password = '<your db password>'
```

We currently support the following database types:
* oracle
* postgres
* doris


2. read data

```python
import pandas as pd
import uquery

data: pd.DataFrame = uquery.read_sql('select * from table', endpoint='my_data_src')
print(data)
```


## Installation

```bash
pip install uquery
```