semvec/pypi-packages huggingface dataset:
EyesOnIt 2.0.4
Advanced Large Vision Model computer vision supporting usage through no-code UI, SDK and REST interfaces.
License: MIT
Requires Python: >=3.8

# EYESONIT Python SDK #

EyesOnIt makes advanced computer vision accessible to anyone without data science or computer programming skills. Through the EyesOnIt configuration user interface, you can use English text to describe the objects and events that you want to detect. You can monitor your video streams and start receiving alerts by text message in just a few minutes. If you have basic programming skills, you can use our SDK or REST API to quickly enable endless scenarios like these:

- Motion-based detection: trigger computer vision after motion is detected
- Event recording: capture and store your own data about detections
- Image categorization: categorize a collection of images according to the contents

Traditional computer vision involves a complex process to train and tune a computer vision model. This process typically requires weeks or months of time from experts with an advanced and specialized skillset. The cost for this process is often $50,000 to $100,000 USD or more. EyesOnIt is different. With EyesOnIt, you describe what you want to detect with English text. EyesOnIt compares your text to your image or video and tells you if it detected what you described.

See this guide to learn how to download and run the EyesOnIt Docker container:
https://www.eyesonit.us/userguide

### Installation ###

The python SDK can be installed with PIP

```
pip install EyesOnIt
```

### Usage ###

```
from EyesOnIt import EyesOnItSDK, EOIPrompt, EOIRegion


prompt_one = EOIPrompt(text='person', background_prompt=False, threshold=90)
prompt_two = EOIPrompt(text='trees', background_prompt=True)
prompts = [prompt_one, prompt_two]

region_one = EOIRegion(top_left_x=4, top_left_y=9, width=300, height=300)
regions = [region_one]


app = EyesOnItSDK('http://127.0.0.1:8000')

result = app.process_image(prompts=prompts, regions=regions, image="my_image.png")
print(result.success)
print(result.message)

```

### Questions? ###

email us at support@eyesonit.us
scikit-tda 1.1.1
Topological Data Analysis for humans
License: MIT
Requires Python: >3.3
Keywords: topology data analysis, algebraic topology, unsupervised learning, persistent homology, persistence images, persistence diagrams, uniform manifold approximation and projection, sheaf theory, mapper, data visualization

[![DOI](https://zenodo.org/badge/129452930.svg)](https://zenodo.org/badge/latestdoi/129452930)
![PyPI - Version](https://img.shields.io/pypi/v/scikit-tda)
![PyPI - Downloads](https://img.shields.io/pypi/dm/scikit-tda)

Scikit-TDA is a home for Topological Data Analysis Python libraries intended for non-topologists.

This project aims to provide a curated library of TDA Python tools that are widely usable and easily approachable. It is structured so that each package can stand alone or be used as part of the `scikit-tda` bundle.

# Documentation

For complete documentation please checkout [docs.scikit-tda.org](https://docs.scikit-tda.org/en/latest/).

# Contact

If you would like to contribute, please reach out to us on
[github](https://github.com/scikit-tda) by starting a [discussion
topic](https://github.com/orgs/scikit-tda/discussions), [creating an
issue](https://github.com/scikit-tda/scikit-tda/issues), or reaching out on
[twitter](https://twitter.com/scikit_tda).

# Setup

To install all these libraries

```
    pip install scikit-tda
```

# Citations

If you would like to cite Scikit-TDA, please use the following citation/bibtex

> Saul, Nathaniel and Tralie, Chris. (2019). Scikit-TDA: Topological Data Analysis for Python. Zenodo. http://doi.org/10.5281/zenodo.2533369

```
@misc{scikittda2019,
  author       = {Nathaniel Saul, Chris Tralie},
  title        = {Scikit-TDA: Topological Data Analysis for Python},
  year         = 2019,
  doi          = {10.5281/zenodo.2533369},
  url          = {https://doi.org/10.5281/zenodo.2533369}
}
```

# License

This package is licensed with the MIT license.

# Contributing

Contributions are more than welcome! There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from code to notebooks to examples and documentation are all equally valuable so please don't feel you can't contribute. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.