semvec/pypi-packages huggingface dataset:
mx-process-manager 1.0.3
This is a tool that provides a GUI to manage processes and process trees.
Requires Python: >=3.12

# 进程管理器

这是一个使用Python开发的现代化进程管理器，提供了直观的图形界面来管理系统进程。

![Example](https://raw.githubusercontent.com/darklee/process/refs/heads/master/assets/test.png)

## 功能特点

- 现代化的深色主题界面
- 树形结构显示进程关系
- 实时进程信息显示
- 进程搜索功能
- 自动刷新进程列表
- 进程终止功能（支持优雅终止和强制终止）

## 安装依赖

```bash
pipx install mx-process-manager
```

## 运行程序

```bash
mx-process-manager
```

## 使用说明

1. 界面顶部有搜索框，可以实时搜索进程
2. 进程列表以树形结构显示，可以展开/折叠查看子进程
3. 右键点击进程可以选择终止操作
4. 界面底部可以调整自动刷新间隔（默认5秒）
5. 进程信息包括：
   - PID（进程ID）
   - CPU时间
   - 启动时间
   - 父进程ID
   - 内存占用
   - 进程命令

## 注意事项

- 需要管理员/root权限才能终止某些系统进程
- 终止进程时会先尝试优雅终止（SIGTERM），10秒后如果进程仍在运行则强制终止（SIGKILL）
vid-prepper 0.1.2
Package for preparing videos for deep learning models
Requires Python: >=3.10

# vid-prepper

Usage examples available at: https://github.com/jpetcon/vid-prepper/tree/main/examples

Package for preparing videos for deep learning models. Built on the giant shoulders of FFMPEG, NVIDIA DALI, OpenCV, Kornia, PySceneDetect, Torchvision and PyTorch.

This package attempts to bring some common video pre-processing methods together in an efficient way for both CPU and GPU.

## Installation

### Prerequisites

Before installing vid-prepper, you need to install the following external dependencies:

#### FFmpeg (Required)
FFmpeg is required for video processing operations.

**Ubuntu/Debian:**
```bash
sudo apt update
sudo apt install ffmpeg
```

**macOS (with Homebrew):**
```bash
brew install ffmpeg
```

**Windows:**
Download from [https://ffmpeg.org/download.html](https://ffmpeg.org/download.html) and add to PATH.

#### CUDA/NVDEC (Optional but Recommended)
For GPU acceleration, install CUDA toolkit and ensure your system supports NVDEC:

**Ubuntu/Debian:**
```bash
# Install CUDA toolkit
sudo apt install nvidia-cuda-toolkit
```

**macOS:**
CUDA is not supported on macOS. The package will automatically fall back to CPU processing.

**Windows:**
Download CUDA toolkit from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads).

### Package Installation

```bash
pip install vid-prepper
```

## Usage

```python
from vid_prepper import metadata, standardize

# Example usage
metadata_extractor = metadata.Metadata("video.mp4")
video_info = metadata_extractor.run()

standardizer = standardize.VideoStandardizer(
        size="224x224",
        fps=25,
        codec="h264",
        color="rgb",
        use_gpu=False  # Set to True if you have CUDA
    )

standardizer.standardize_video(video_input="video.mp4", output_path="video_standardized.mp4")
```

## Features

- Video metadata extraction and validation
- Video standardization and preprocessing
- Object detection and scene analysis
- Video augmentation for deep learning
- Efficient tensor loading with GPU acceleration
len-sentence 0.1.1
Length of sentence utilities for counting the number of words/characters in a sentence
Requires Python: >=3.6

# len_sentence

A Python library for counting words and characters in sentences across different languages and writing systems.

## Overview

`len_sentence` provides intelligent sentence length counting that adapts to different languages and writing systems. It uses ISO15924 script codes to determine the appropriate counting method for each language, handling everything from space-separated languages like English to character-based languages like Chinese and Japanese.

## Features

- **Multi-language support**: Handles various writing systems including Latin, Chinese (Traditional/Simplified), Japanese, Thai, Khmer, Myanmar, Tibetan, and more
- **ISO15924 compliance**: Uses standard 4-letter script codes for language identification
- **Special character handling**: Properly processes numbers, punctuation, and language-specific separators
- **Intelligent counting**: Automatically switches between word counting and character counting based on the script type

## Installation

```bash
pip install len_sentence
```

## Supported Language Scripts
The goal of the script is able to count the words from all the langauge of the world.

## API Reference

### `count_sentence(sentence, lang_code)`

Count the number of words or characters in a sentence based on the language script.

**Parameters:**
- `sentence` (str): The sentence to count
- `lang_code` (str): 4-letter ISO15924 script code, limited support for 2 letters ISO639-1 code

**Returns:**
- `int`: Number of words or characters in the sentence

**Raises:**
- `ValueError`: If the language code is not valid

## Examples

```python
from len_sentence import count_sentence

# Different scripts, different counting methods
count_sentence("Hello world!", "Latn")
count_sentence("你好世界", "Hans")
count_sentence("こんにちは", "Jpan")
count_sentence("བཀྲ་ཤིས་བདེ་ལེགས", "Tibt")
count_sentence("مرحبا بالعالم", "Arab")
count_sentence("Привет мир", "Cyrl")
```

## Development

### Setup
```bash
git clone <repository-url>
cd len-sentence
pip install -e .
```

### Building and Publishing
```bash
python setup.py sdist bdist_wheel
twine upload dist/*
```

## Contributing
Contributions are welcome! Please feel free to submit issues and pull requests.

## Author

**JackyHe398**  
Email: hekinghung@gmail.com

---

*Note: This library uses ISO15924 script codes. For a complete list of supported scripts, refer to the [Unicode Script Codes](https://unicode.org/iso15924/iso15924-codes.html) specification.*