semvec/pypi-packages huggingface dataset:
ezconfig-client 0.7
ezconfig client

# EZConfig 客户端程序

## 使用方法

```text
pip install ezconfig-client
```

传入自定义参数，获取最新配置

```python
from ezconfig_client import loader
res = loader.get_latest_config_by_params("DEV", "app_id", "secret", "https://xxxx.xxx.xxx")
print(res)
```

从环境变量中获取参数，从而获取配置的方法

```python
from ezconfig_client import loader
res = loader.get_latest_config()
print(res)
```

使用前需要设置环境变量

| 环境变量 | 说明 |
| --- | --- |
| EZCONFIG_ENV | 环境变量 |
| EZCONFIG_APPID | 应用ID |
| EZCONFIG_SECRET | 应用密钥 |
| EZCONFIG_HOST | EZConfig服务地址 |


环境依赖：

```text
requests>=2.22.0
retrying>=1.3.4
```

**测试参考代码**

EZCONFIG_ENV=DEV EZCONFIG_APPID=xxx.xx.xxx EZCONFIG_SECRET=xxxxxxx EZCONFIG_HOST=https://xxxx.xx python test/test.py

## 版本说明

| 版本  | 变更                                                        |
|-----|-----------------------------------------------------------|
| 0.4 | 降低requests版本，修改bug                                        |
| 0.5 | 增加访问错误重试机制，请求超时30秒，失败重试3次                                 |
| 0.6 | 新增get_latest_config_by_params方法，传自定义参数获取最新配置，而不是走环境变量获取参数 |
| 0.7 | 去掉库里面的打印信息                                                |
optilogs 0.1.4
A simple logging library to send logs to a server
Requires Python: >=3.6

# Optilogs

Optilogs is a simple logging library that sends logs to a server via HTTP requests.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install optilogs.

```bash
pip install optilogs
```

Example

```bash
from optilogs.logger import Optilogs

# Create an instance of Optilog with the IP and port of your log server
optilogs = Optilogs(server_ip="0.0.0.0", server_port=5050)

# Send a log
optilogs.log("Ceci est un log de test")
```
wheresmycar 0.0.2.5
Detect vehicle license plates
Requires Python: >=3.9
Keywords: machine learning, object detection, educational

# wheresmycar

First attempt at utilizing YOLOv8 model for vehicle number plate detection.

> [!NOTE]
> This project is for education and skills presentation purposes.


# Motivation

Gain practical knowledge with machine learning technologies in real-world example.

The main goal was to gain hands-on experience in machine learning project utilizing PyTorch library and several technologies to improve software engineering skills.

# About the project

Small Python package providing a class for object detection, utilizing YOLOv8 model which was trained to detect number plates on vehicles.

# Documentation

<a id="plate_detector"></a>

# plate\_detector

plate_detector module

<a id="plate_detector.PlateDetector"></a>

## PlateDetector Objects

```python
class PlateDetector()
```

Class for Vehicle Number Plate Detection based on pretrained YOLOv8 model.

<a id="plate_detector.PlateDetector.load_model"></a>

#### load\_model

```python
def load_model(device: str) -> YOLO
```

Function to load pretrained model.
params:
- device <str>: device on which the model should run
returns:
- <ultralytics.YOLO>: pretrained YOLOv8 model

<a id="plate_detector.PlateDetector.model"></a>

#### model

```python
@property
def model() -> YOLO
```

Access pretrained model
returns:
- <ultralytics.YOLO>: pretrained YOLOv8 model

<a id="plate_detector.PlateDetector.get_device"></a>

#### get\_device

```python
def get_device(enable_cuda: bool) -> str
```

Gets target device for inference.
params:
- enable_cude <bool>: if True, will return cuda if available
returns:
- <str> either cuda or cpu

<a id="plate_detector.PlateDetector.detect"></a>

#### detect

```python
def detect(target_path: str, conf: float = 0.5, **kwargs) -> Results
```

Get predictions on given input.
params:
- target_path <str>: path to directory with images or image's file path
- conf <float>: minimum confidence threshold for detection
returns:
- <ultralytics.engine.results.Results>: inference results