semvec/pypi-packages huggingface dataset:
dvr-ftp-tools 0.1.0
Ferramentas para listar e baixar arquivos de DVR via FTP
Requires Python: >=3.6

pip install -e .



pip install build
python -m build

from dvr_ftp.client import get_ftp_files_filtered, download_ftp_files

# Use your credentials
creds = {"host": "192.168.15.60", "user": "dvr", "password": "1"}

# Get file paths from DVR (Depth: Date -> Channel -> Files)
files = get_ftp_files_filtered(**creds, extensions=('.mp4', '.dav'), depth_level=3)

# Download files for processing
download_ftp_files(**creds, path_list=files, target_folder="./raw_footage")
markdantic 0.1.0
Generate Markdown-formatted text based on a Pydantic model.
Requires Python: >=3.11

# Markdantic

## 概要

Pydantic の型情報を元に mkdocs 等で使用する Markdown 形式のテキストデータを出力する。
このとき、**mkdocstrings では表現しきれない型情報を、ロスレスに Markdown 化する**ことを目的とする。

## デザインポリシー

本ツールは「見た目よりも型情報の正確さ」を最優先とする。

- 型は完全修飾クラス (FQCN) でレンダリングする
- 組み込み型 (int, str, list, None など) は読みやすさを考慮して短縮形でレンダリングする
- 出力の整形や短縮は行わず、ロスレスな情報提供を優先する
- Markdown レンダラーとの互換性を優先する
- テーブルの破損を防ぐため、半角のパイプ記号を全角に変換する

## 非対応・割り切り

- Markdown の装飾最適化（見栄え調整）は行わない
- 出力結果の整形・短縮は利用者側で後処理することを前提とする
- HTML や PDF の直接生成は対象外とする

## Enum の展開

- 通常の Enum
- 数値ベースの Enum (IntEnum)

"メンバー値=メンバー名" を展開して description の末尾に出力する。

## 使用例

mkdocs / mkdocstrings 等での利用を想定した例：

### コード

`enums.py`

```python
from enum import Enum

class Area(Enum):
    TOKYO = 1
    OSAKA = 2
```

`models.py`

```python
from pathlib import Path
from pydantic import BaseModel, Field
from enums import Area

class SampleModel(BaseModel):
    """テストのモデル"""
    num:   int | None = Field(None, description="Number of Files")
    name:  str        = Field("", description="Name")
    files: list[Path] = Field(default_factory=list, description="list of Path")
    view:  bool       = Field(False, description="View flag")
    area:  Area       = Field(..., description="Area")
```

`main.py`

```python
from markdantic import generator
from models import SampleModel

print(generator(SampleModel))
```

### 出力

以下は `main.py` を実行して生成されるMarkdownの例：

```txt
## `SampleModel`

**Base:** `BaseModel`

テストのモデル

**Parameters:**

|Name|Type|Description|Default|
|---|---|---|---|
|`num`|`int｜None`|Number of Files|`None`|
|`name`|`str`|Name|`''`|
|`files`|`list[pathlib.Path]`|list of Path|`[]`|
|`view`|`bool`|View flag|`False`|
|`area`|`enums.Area`|Area (members: 1=`TOKYO`, 2=`OSAKA`)|_Required_|
```

テーブル部分のレンダリング結果：

|Name|Type|Description|Default|
|---|---|---|---|
|`num`|`int｜None`|Number of Files|`None`|
|`name`|`str`|Name|`''`|
|`files`|`list[pathlib.Path]`|list of Path|`[]`|
|`view`|`bool`|View flag|`False`|
|`area`|`enums.Area`|Area (members: 1=`TOKYO`, 2=`OSAKA`)|_Required_|

## インストール方法

```bash
pip install markdantic
```

uv を使用する場合：

```bash
uv add markdantic
```

※ PyPI で公開予定。
moviesigdb 0.1.0
A tool to generate and analyze color barcodes from video files.
Requires Python: >=3.8

# moviesigdb: The Movie Color DNA Analyzer

![Python Version](https://img.shields.io/badge/python-3.8%2B-blue)
![License](https://img.shields.io/badge/license-MIT-green)
![Status](https://img.shields.io/badge/status-active-success)

**moviesigdb** is a powerful Python toolkit designed to extract, visualize, and archive the color palette of video files. By condensing thousands of frames into a single "Movie Barcode" or projecting them into 3D space, you can analyze the cinematography, mood, and lighting evolution of any film or TV episode.

> **What is a Movie Barcode?** > It is a time-compressed visualization where every vertical slice of the image represents the average color of a specific moment in the video.

---

## 📸 Visualization Capabilities

**moviesigdb** goes beyond simple stripes. It offers three distinct ways to view your data:

1.  **The Barcode:** A linear timeline of the video's color.
2.  **3D RGB Cube:** Plots every frame as a point in 3D space (Red, Green, Blue axes) to show color clusters and separation.
3.  **HSV Polar Wheel:** Maps the mood.
    * **Angle** = Hue (Color)
    * **Radius** = Saturation (Intensity)
    * *Great for seeing if a movie is "Teal and Orange" or "Neon Noir".*

---

## 🚀 Features

* **Smart Extraction:** Uses `tqdm` to show a real-time progress bar while processing large video files.
* **Adjustable Granularity:** Extract 1 frame per second, 10 frames per second, or 1 frame per minute using the `processing_fps` parameter.
* **Metadata Archiving:** Uniquely embeds JSON data (Show Name, Season, Episode, FPS) directly into the output PNG file headers.
* **Scientific Plotting:** Leverages `matplotlib` for high-resolution, publication-quality figures.

---

## 📦 Installation

### Option 1: From Source (Recommended)
Cloning the repo allows you to modify the source code easily.

```bash
git clone [https://github.com/shreyanbruh/moviesigdb.git](https://github.com/shreyanbruh/moviesigdb.git)
cd moviesigdb
pip install -e .
```



```python 
import moviesigdb as msdb

# 1. Load your video
video_path = "path/to/your_video.mp4"
cap = msdb.get_video_capture(video_path)

# 2. Extract frames (e.g., 1 frame per second)
frames = msdb.extract_frames_fast(cap, processing_fps=1.0)

# 3. Calculate average colors
avg_colors = msdb.get_average_colors(frames)

# 4. Display the results
msdb.display_movie_barcode(avg_colors, title="My Movie Analysis")
msdb.plot_hsv_polar(avg_colors)
```
dxsync 0.2.0
CLI tool for syncing encrypted .env files across teams using dotenvx
Requires Python: >=3.11
Keywords: dotenv, dotenvx, encryption, env, share

# dxsync

CLI tool for syncing encrypted `.env` files across teams using dotenvx.

## Features

- **Simple CLI** - `dxsync init`, `dxsync encrypt`, `dxsync decrypt`
- **Per-environment encryption** - Separate keys for dev, stg, prd
- **Non-destructive** - Never overwrites your local plaintext `.env` files
- **Configurable** - Customize paths via `dxsync.json`

## Prerequisites

- Python 3.11+
- [dotenvx](https://dotenvx.com/docs/install) - Install via your preferred method:

```bash
# npm
npm install -g @dotenvx/dotenvx

# brew
brew install dotenvx/brew/dotenvx

# curl
curl -sfS https://dotenvx.sh | sh
```

## Installation

```bash
pip install dxsync
```

## Quick Start

```bash
# 1. Initialize in your project
cd your-project
dxsync init

# 2. Create your plaintext env file
echo "API_KEY=secret123" > .env/dev

# 3. Encrypt it
dxsync encrypt dev
# → Creates enc/.env.dev.enc and .env/keys/dev.keys

# 4. Share .env/keys/dev.keys with your team securely

# 5. Commit the encrypted file
git add enc/.env.dev.enc
git commit -m "Add encrypted dev env"
```

## Directory Structure

After `dxsync init`:

```
your-project/
├── dxsync.json             # Configuration
├── enc/                    # Encrypted files (commit these)
│   └── .env.dev.enc
├── .env/                   # Local files (gitignored)
│   ├── dev                 # Plaintext source
│   ├── keys/               # Key files
│   │   └── dev.keys
│   └── latest/             # Decrypted output
│       └── .env.dev
└── .gitignore              # Updated by dxsync init
```

## Usage

### Encrypt

Add or update keys in your plaintext file, then encrypt:

```bash
# Edit .env/dev with your values
dxsync encrypt dev
```

- First run: auto-generates key file and `.enc`
- Subsequent runs: adds new keys to existing `.enc`

### Decrypt

Get the key file from your team, then:

```bash
dxsync decrypt dev
# → Output: .env/latest/.env.dev
```

### Change existing values

1. Remove the key from `enc/.env.dev.enc`
2. Update value in `.env/dev`
3. Run `dxsync encrypt dev`

## Configuration

Edit `dxsync.json` to customize:

```json
{
  "envs": ["dev", "stg", "prd"],
  "env_dir": ".env",
  "enc_dir": "enc",
  "work_dir": "tmp/dxsync"
}
```

Or use `pyproject.toml`:

```toml
[tool.dxsync]
envs = ["dev", "stg", "prd"]
env_dir = ".env"
enc_dir = "enc"
```

## Design Principles

- **Append-only** - Existing keys in `.enc` are never auto-overwritten
- **Non-destructive** - Decryption outputs to `latest/`, not your working `.env`
- **Explicit reset** - Delete `.enc` manually to regenerate

## License

MIT

## References

- [dotenvx Documentation](https://dotenvx.com/docs)
anime-seg 0.1.5
Anime Character Segmentation with DINOv2
Requires Python: >=3.8

# AnimeSeg

<p>
    <a href="https://pepy.tech/project/anime_seg"><img alt="GitHub release" src="https://static.pepy.tech/badge/anime_seg"></a>
    <a href="https://github.com/suzukimain/AnimeSeg/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/suzukimain/AnimeSeg.svg"></a>
    <img src="https://visitor-badge.laobi.icu/badge?page_id=suzukimain.AnimeSeg" alt="Visitor Badge">
</p>


Anime Character Segmentation using DINOv2 + U-Net++ with LoRA fine-tuning.

## sample image

<p align="center">
    <img src="https://raw.githubusercontent.com/suzukimain/AnimeSeg/refs/heads/main/images/sample.png" alt="sample image" width="50%">
</p>


## Installation

```bash
pip install anime_seg
```

## Usage

```python
from anime_seg import AnimeSegPipeline

# Initialize pipeline (auto-downloads latest model from Hugging Face)
pipe = AnimeSegPipeline()

# Run segmentation
mask = pipe("path/to/image.jpg")

# Save result
mask.save("output.png")
```

## Advanced Usage

```python
# Specify custom repo or filename
pipe = AnimeSegPipeline(
    repo_id="suzukimain/AnimeSeg",
    filename="models/anime_seg_dinov2_large_v1.safetensors",
    device="cuda"  # or "cpu"
)

# Use PIL Image
from PIL import Image
img = Image.open("image.jpg")
mask = pipe(img)
```

## Model Files

Models should follow the naming convention:
```
models/anime_seg_{architecture}_{size}_v{version}.safetensors
```

Example:
- `models/anime_seg_dinov2_large_v1.safetensors`
- `models/anime_seg_dinov2_base_v2.safetensors`

## Segmentation Classes

- Background
- Skin
- Face
- Hair (main)
- Hair (thin)
- Eyes (left/right)
- Eyebrows (left/right)
- Nose
- Mouth
- Clothes
- Unknown