semvec/pypi-packages huggingface dataset:
hanzi-pinyin 0.2.8
Query the pinyin of Chinese characters.
Requires Python: >=3.6

# 汉字拼音查询包 (Hanzi-Pinyin)

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<!-- [English](README_en.md) -->

## 描述
`hanzi_pinyin` 是一个用于查询汉字拼音（罗马化）的包。它提供了获取简体和详细拼音表示的工具。

## 安装
你可以使用 pip 来安装这个包：

```bash
pip install hanzi_pinyin
```

## 使用方法

### 导入包
你可以按以下方式从包中导入函数：

```python
from hanzi_pinyin import pinyin, detailed_pinyin, alls
```

### 函数

#### `pinyin(char: str) -> List[str] | None`
返回给定汉字的拼音表示列表，不带声调。

**示例:**
```python
result = pinyin('汉')  # ['han']
print(result)
```

#### `detailed_pinyin(char: str) -> List[str] | None`
返回给定汉字的详细拼音表示列表，包括声调。

**示例:**
```python
result = detailed_pinyin('汉')  # ['hàn']
print(result)
```

#### `alls() -> Dict[str, List[str]]`
返回整个汉字到含声调拼音映射的字典。

**示例:**
```python
dict_ = alls()
print(dict_['汉'])  # ['hàn']
```

## 数据来源

本包中的数据来源为：[汉语国学](https://www.hanyuguoxue.com/zidian/)

爬取脚本可见 [fetcher.ts](https://github.com/neooier/hanzi-pinyin/blob/main/cmd/fetcher.ts)

## 许可证
本包采用 MIT 许可证。

## 贡献
欢迎贡献！请提交问题或拉取请求。

## 作者
- Neooier
autotrain-vision 0.0.3
Automate the manual labelling and training for supervised models, YOLOv8
Requires Python: >=3.8

# AutoTrain
**Auto training pipeline for object detection models**

This pipeline trains object detection model(YOLOv8) using real time inference data. It is for automating the supervised learning, specifically cutting out the manual labelling task and training the model for it to remember the object as per the label we want.

## Requirements
* Python >= 3.8
* GPU (optional but prefferable)

## Package Installation
```
pip install autotrain-vision
```
Pip package : [autotrain-vision](https://pypi.org/project/autotrain-vision/)

For more details, refer to the [GitHub repository](https://github.com/orangewood-co/Auto-train).

### Common Installation Errors
**Error:** Rebuilt the library with Windows, GTK+ 2.x or Carbon support.

Try

```
sudo apt install libgtk2.0-dev pkg-config
pip install opencv-contrib-python
```

## To use auto train:
```
from autotrain_vision import AutoTrain
at = AutoTrain(combined_folder="/path/to/local/folder")
at.run()
```

### Arguments:
- `data_folder` (str, required): Path to local folder to store the new data
- `prev_data_folder` (str): Path to local previous folder containing images and labels folder
- `new_weights` (boolean): True if to not use any previous data
- `abs_yaml_file` (str): Absolute path to the YAML file for given prev_data_folder
- `draw_bb` (boolean): True to draw bounding boxes on previous image dataset
- `image_threshold` (int): Number of images to capture for creating new dataset
- `number_aug` (int): Number of times to apply augmentations
- `epochs` (int): Number of epochs for training
- `map_threshold` (float): value<=1 ; Threshold to compare mAP50 score
- `inference` (boolean): True to perform the inference on live feed
- `inference_threshold` (float): value<=1 ; Threshold for inference confidence score
- `camera_range` (int): Range of camera indexes to look for

### Output:
- `weights.pt` : Weights file for trained model.

-----

### Capabilities
* Creates new annotated data 
* Combines previous annotated data with newly captured
* Annotates the data with visible bounding boxes, given images and corresponding v8 txt label files
* Generates new weights (pt) file for given dataset
* Generates analysis graphs and metrices for validation

### To-dos
* Add multiple objects annotation in single frame
* RnD on Florence capabilities for giving in text prompt to ZSD model
* RnD on discarding faulty annotations from procured dataset