semvec/pypi-packages huggingface dataset:
vsmuxtools 0.4.2
The extension to muxtools with vapoursynth and encoding stuff
Requires Python: >=3.11

# vs-muxtools

The extension to muxtools with vapoursynth and encoding stuff

## How do I use this?

You might wanna check out the [guide/wiki](https://muxtools.vodes.pw/).

## Installation

Git is always the most updated one obviously but I can't guarantee that everything is in a working state.

You can also grab the latest stable ish versions from pip.

[![PyPI version](https://badge.fury.io/py/vsmuxtools.svg)](https://badge.fury.io/py/vsmuxtools)
ezMM 0.5.0
Lightweight Python kit for easy multimodal data processing.
Requires Python: >=3.11

# ezMM: Mini-Suite for Easy Multimodal Data Processing
This lightweight Python package aims to streamline and simplify the processing of multimodal data. The core philosophy of ezMM is to treat any data (whether strings, images, audios, tables, etc.) as a **multimodal sequence**.

## Usage
Core is the `MultimodalSequence` class. Here is an example:
```python
from ezmm import MultimodalSequence, Image

img1 = Image("in/roses.jpg")
img2 = Image("in/garden.jpg")

seq = MultimodalSequence("The image", img1, "shows two beautiful roses while",
                         img2, "shows a nice garden with many flowers.")
```

`seq` comprehensively aggregates the different modalities into one handy object. It also offers some useful features:

### `MultimodalSequence` is stringifyable
```python
print(seq)
```
will return
```
The image <image:1> shows two beautiful roses while <image:2> shows a nice garden with many flowers.
```
That is, non-string items in the `MultimodalSequence` get replaced by their unique reference when turned into strings. 

### `MultimodalSequence` understands references
Conversely, you can do
```python
seq2 = MultimodalSequence("The image <image:1> shows two beautiful roses while <image:2> shows a nice garden with many flowers.")
```
which obeys `seq == seq2`. That is, `MultimodalSequence` resolves references within the input string and loads the corresponding items under the hood.

### Access `MultimodalSequence` like a list
You can apply list comprehension to `seq`. For example,
`seq[1] == img`.

### Easy modality checks
You can check for specific modalities like images quickly, e.g., with `seq.has_images()`.

## Feature Overview
- ✅ Image support
- ✅ Video support
- ✅ Saving and organizing media in a database along with their origin URL
- ✅ Rendering `MultimodalSequence` in a web UI
- ⏳ Duplication management: Identify and re-use duplicates
muxtools 0.4.1
A library for various muxing and automation tools for anything and everything fansubbing related
Requires Python: >=3.10

# muxtools

A library for various muxing and automation tools for anything and everything fansubbing related

## How do I use this?

You might wanna check out the [guide/wiki](https://muxtools.vodes.pw/).

## Installation

Git is always the most updated one obviously but I can't guarantee that everything is in a working state.

You can also grab the latest stable ish versions from pip.

[![PyPI version](https://badge.fury.io/py/muxtools.svg)](https://badge.fury.io/py/muxtools)
pygameextra 2.0.86
Pygame. Made easier.
Keywords: python

Pygame Extra is an extension for pygame, 
you can easily make complex games and or apps with much less code then you would otherwise, 
Pygame Extra is a engine for creating complex interactions without the need for making everything yourself 

The online documentation can be found at: https://pygame-extra.readthedocs.io/en/latest/

github: https://github.com/JustRedTTG/PGE
gpxt 0.0.1
GPX Tools - A collection of tools to work with GPX files.
Requires Python: >=3.12

# GPX Tools

[![CI](https://github.com/neri14/gpxt/actions/workflows/ci.yml/badge.svg?branch=master)](https://github.com/neri14/gpxt/actions/workflows/ci.yml)
[![Coverage Status](https://codecov.io/gh/neri14/gpxt/branch/master/graph/badge.svg)](https://codecov.io/gh/neri14/gpxt)
[![PyPI - Version](https://img.shields.io/pypi/v/gpxt)](https://pypi.org/project/gpxt/)

[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/)

**GPX Tools** - A collection of tools to work with GPX files, a younger cousin of [FITT](https://github.com/neri14/fitt).


## Usage

TODO
bdgs 2.1.0
Static gestures recognition tool
Requires Python: >=3.11

# BDGS library development setup

## Prerequisites

- Python 3.11
- PIP

## Development

### Installation

1. Create/activate virtual environment
2. Install required packages with `pip install -r requirements.txt`

### Create new algorithm

To create a new algorithm, use algorithm creation script: `./scripts/generate_algorithm.py`

### Validate algorithms

To validate algorithms use scripts located in `./validation` directory. Json files with validation results are located
in `./validation/results` directory

## Usage

### Import library

To use library in external project, use `pip install bdgs`.

### Sample use cases

To predict gesture on selected image, run the code below. You can select desired algorithm by using values
on `ALGORITHM` enum. Some algorithms require their own payload (e.g. hand coordinates or background image
without hand). You can import specific payload from `bdgs.algorithms.<alg>.<alg>_payload`.

```python
import cv2
from bdgs import classify
from bdgs.data.algorithm import ALGORITHM
from bdgs.models.image_payload import ImagePayload

image = cv2.imread("resources/image.jpg")
result = classify(algorithm=ALGORITHM.EID_SCHWENKER, payload=ImagePayload(image=image))

print(result)
```

To show image processed by the selected algorithm, run:

```python
import cv2
from bdgs import process_image
from bdgs.data.algorithm import ALGORITHM
from bdgs.models.image_payload import ImagePayload

image = cv2.imread("resources/image.jpg")
processed_image = process_image(algorithm=ALGORITHM.EID_SCHWENKER, payload=ImagePayload(image=image))

cv2.imshow("Image", processed_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
```

To learn your own algorithm's model (e.g. on other image base than ours), run:

```python
import cv2
from bdgs import learn
from bdgs.data.algorithm import ALGORITHM
from bdgs.data.gesture import GESTURE
from bdgs.models.learning_data import LearningData

image = cv2.imread("resources/image.jpg")
acc, loss = learn(algorithm=ALGORITHM.EID_SCHWENKER, target_model_path="models",
                  learning_data=[LearningData(image_path="resources/image.jpg", label=GESTURE.FIVE)] * 10)

print(acc, loss)
```