semvec/pypi-packages huggingface dataset:
linpxdens 0.0.1
linpxdens (Linear pixel density) is a Python package for analyzing geometric line structures and spacing patterns in images. It provides tools for line fitting, center estimation, outlier filtering, and periodic pattern analysis based on image data. From the periodic structure, the linear pixel density is calculated.
License: MIT
Requires Python: <4.0.0,>=3.12.9

# linpxdens

linpxdens (Linear pixel density) is a Python package for analyzing geometric line structures and spacing patterns in images. It provides tools for line fitting, center estimation, outlier filtering, and periodic pattern analysis based on image data. From the periodic structure, the linear pixel density is calculated.

## Installation

```bash
$ pip install linpxdens
```

## Usage

- TODO

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`linpxdens` was created by Clemens Trunner. It is licensed under the terms of the MIT license.

## Credits

`linpxdens` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
securetrading 1.0.24
Python API to connect to Secure Trading
License: MIT
Keywords: securetrading api python trustpayments

Secure Trading Python API
=========================

Installation
^^^^^^^^^^^^

You don't need the source code to install this package, the recommended way is to use PIP by running the following command:

* pip install --upgrade securetrading

Alternatively you can download the project and install it locally.

Supported
^^^^^^^^^

We recommend using at least Python 2.7.9  as any version prior to this may give a SNI warning when the library is instantiated. For further information please look at http://urllib3.readthedocs.org/en/latest/security.html#installing-urllib3-with-sni-support-and-certificates and http://docs.python-requests.org/en/master/community/faq/ regarding SNI. Additionally version "2.9" of the "requests" library is required, this is to ensure that the latest certificates have been installed.

Documentation
^^^^^^^^^^^^^

Please see https://help.trustpayments.com/hc/en-us/sections/360005821218-Webservices-API for the most up-to-date documentation.

Bugs
^^^^

Please report any bugs to devapi@securetrading.com
gucken-image-reader 1.1.3
MCP server for image reader using OpenAI vision API (gpt-4o-mini)
License: MIT
Requires Python: >=3.12

[English](#english) | [한국어](#한국어)

---

## English

**gucken-image-reader** is an MCP (Model Context Protocol) server that uses OpenAI's Vision API to generate descriptive text for images. It provides a single `describe_image` tool via stdio, enabling any MCP-compatible client (such as Claude or custom applications) to send an image file path and receive a detailed textual description in response.

### Installation

**Prerequisite: UV**

If you don't have UV installed, follow these steps:

* **macOS / Linux**

  ```bash
  curl -LsSf https://astral.sh/uv/install.sh | sh
  ```

* **Windows (PowerShell)**

  ```powershell
  powershell -ExecutionPolicy ByPass -Command "irm https://astral.sh/uv/install.ps1 | iex"
  ```

### Configuration

In your working directory, create a `.env` file or export the environment variable directly:

```dotenv
OPENAI_API_KEY=your_openai_api_key_here
```

### Integration with an MCP host

Add the following entry to your `claude_desktop_config.json` (or other MCP-compatible host configuration):

```json
{
  "mcpServers": {
    "image-reader": {
      "command": "uvx",
      "args": ["gucken-image-reader"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here"
      }
    }
  }
}
```

* **command**: `uvx`
* **args**: `gucken-image-reader`
* **env**: Ensure that `OPENAI_API_KEY` is set.

### Usage

1. Complete the installation and configuration steps above.

2. Start or restart your MCP host (e.g., the Claude desktop app); it will launch the `gucken-image-reader` server over stdio.

3. Send an MCP request (JSON-RPC) calling the `describe_image` method with a local image file 

4. Receive a JSON response containing the image description generated by the model.

### License

MIT

---

## 한국어

**gucken-image-reader**은 OpenAI Vision API를 활용해 이미지에 대한 설명을 생성하는 MCP(Model Context Protocol) 서버입니다. `describe_image` 툴을 stdio 방식으로 사용해 이미지를 텍스트로 설명해줍니다

### 설치

**UV 설치 (아직 설치하지 않으셨다면)**

* **macOS / Linux**

  ```bash
  curl -LsSf https://astral.sh/uv/install.sh | sh
  ```
* **Windows (PowerShell)**

  ```powershell
  powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
  ```

### 설정

다음 내용을 담은 `.env` 파일을 작업 디렉터리에 생성하거나, 환경 변수를 직접 설정하세요:

```dotenv
OPENAI_API_KEY=your_openai_api_key_here
```

### MCP 호스트 통합

`claude_desktop_config.json`(또는 다른 MCP 호환 호스트 설정)에 다음 항목을 추가하세요:

```json
{
  "mcpServers": {
    "image-reader": {
      "command": "uvx",
      "args": [
        "gucken-image-reader"
      ],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here"
      }
    }
  }
}
```

* **command**: `uvx`
* **args**: 콘솔 스크립트 이름 `gucken-image-reader`
* **env**: `OPENAI_API_KEY` 설정 필수

### 사용 방법

1. 위 설치·설정 과정을 완료합니다.
2. MCP 호스트(예: Claude 앱)를 실행하거나 재시작하면 서버가 stdio로 구동됩니다.
3. `describe_image` 툴을 호출하며 로컬 이미지 파일 경로를 전송합니다:
4. 모델이 생성한 이미지 묘사 텍스트를 응답으로 받습니다.

### 라이선스

MIT