semvec/pypi-packages huggingface dataset:
pesummary 1.6.1
Python package to produce summary pages for Parameter estimation codes
License: MIT
Requires Python: >=3.10

# PESummary

## Release status

[![PyPI version](https://badge.fury.io/py/pesummary.svg)](http://badge.fury.io/py/pesummary)
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[![License](https://img.shields.io/pypi/l/pesummary.svg)](https://choosealicense.com/licenses/mit/)
[![Python versions](https://img.shields.io/pypi/pyversions/pesummary.svg)](https://img.shields.io/pypi/pyversions/pesummary.svg)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4771187.svg)](https://doi.org/10.5281/zenodo.4771187)

## Development status

[![Pipeline Status](https://git.ligo.org/lscsoft/pesummary/badges/master/pipeline.svg)](https://git.ligo.org/lscsoft/pesummary/commits/master)
[![Coverage report](https://docs.ligo.org/lscsoft/pesummary/coverage_badge.svg)](https://docs.ligo.org/lscsoft/pesummary/htmlcov/index.html)

This package helps the user to generate summary webpages to visualise the output from any sample generating code.

 * [Installation instructions](https://docs.ligo.org/lscsoft/pesummary/installation.html)
 * [Documentation](https://docs.ligo.org/lscsoft/pesummary)
 * [Issue Tracker](https://git.ligo.org/lscsoft/pesummary/issues)
framestoryx 0.1.2
An extended version of FrameStory, for creating video descriptions.
License: MIT
Requires Python: >=3.11

[![PyPI version](https://badge.fury.io/py/framestoryx.svg)](https://badge.fury.io/py/framestoryx)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/framestoryx)](https://pepy.tech/project/framestoryx)

# framestory

`framestoryx` is a successor to [FrameStory](https://github.com/chigwell/frame_story/) for better compatibility with modern Python tooling. Leveraging state-of-the-art machine learning models, it can provide detailed descriptions of video content, making it a powerful tool for content analysis, accessibility, and summarization.

## Installation

To install `framestoryx`, you can use pip:

```bash
pip install framestoryx
```

## Usage

Using `framestoryx` is straightforward. Below are examples demonstrating how to extract and describe significant frames from videos with various parameters.

### Describing Video by URL

```python
from framestoryx.video_describer import VideoDescriber

video_url = "https://example.com/video.mp4"
describer = VideoDescriber(show_progress=True)
descriptions = describer.get_video_descriptions(video_url=video_url)
print(descriptions)
```

### Describing Video from Local Path

```python
video_path = "/path/to/your/video.mp4"
describer = VideoDescriber(show_progress=True, max_tokens=50)
descriptions = describer.get_video_descriptions(video_path=video_path)
print(descriptions)
```

### Customizing Extraction Threshold

The `extract_significant_frames` method allows you to customize the threshold for what constitutes a "significant" change between frames.

```python
video_url = "https://example.com/video.mp4"
describer = VideoDescriber(threshold=25000)
descriptions = describer.get_video_descriptions(video_url=video_url)
print(descriptions)
```

These examples demonstrate the versatility of `frame_story` in processing videos from different sources and with various levels of detail in descriptions.

## Features

- Extraction of significant frames from videos for detailed analysis.
- Generation of descriptive text for each significant frame using state-of-the-art image captioning models.
- Support for videos from URLs or local file paths.
- Customizable settings for progress display, description length, and frame extraction threshold.
- Easy to integrate into Python projects for content analysis, summarization, and accessibility applications.

## Contributing

We welcome contributions, be it issues or feature requests.

Feel free to check the [issues page](https://codeberg.org/fossiaorg/framestoryx/issues) and adhere to our [contributing guidelines](https://fossia.org/resources/contributing) before submitting a pull request.

## License

This project is licensed under the [MIT License](https://choosealicense.com/licenses/mit/).