semvec/pypi-packages huggingface dataset:
iris-recognition-system 0.0.1
Python Application for an Iris Recognition System using OpenCV. The project aims to create a database system and iris analyzer for fast fast and accurate iris recognition. See for more, https://github.com/elymsyr/iris-recognition.
License: MIT

# Iris Recognition System

This project is an iris recognition system ([*See original project.*](https://github.com/andreibercu/iris-recognition)) to analyze and recognize iris images. It includes functionality for feature extraction, comparison of iris features, and performance improvements for various scenarios, including suboptimal camera conditions. The project has been updated to run on Python 3.9.x and includes functionality to save iris data to a database. There are also plans to develop a dynamic system with a GUI or mobile app for real-time use.

## Table of Contents

- [Development](#development)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

## Development
- [ ] **Improved Iris Analysis**: Improving analyze speed and extraction of features from iris images and creating a dictionary for recognition.
- [ ] **Performance Improvements**: Enhancements for recognition performance and speed, especially under suboptimal camera conditions.
- [x] **Database Integration**: Save and retrieve iris data using a database.
  - [ ] Optimization for fast query.
- [ ] **Future Developments**: Plans to create a GUI or mobile app for dynamic and in real-time use.

## Installation

For latest release:
```
pip install iris_recognition_system
```
or

#### 1. Clone the repository:
```
git clone https://github.com/elymsyr/iris-recognition.git
cd iris-recognition
```

#### 1. Create a virtual environment:
```
conda create -n venv_name python=3.9
```

#### 1. Install the required dependencies:
```
pip install -r requirements.txt
```

## Usage
See [iris_recognition.py](iris_recognition.py).

## Contributing
Contributions are welcome! Please submit a pull request with your changes or improvements. Ensure that you follow the project's coding standards and provide relevant documentation. See the [CONTRIBUTING](CONTRIBUTING.md) file for details.

## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE.md) file for details.

## Contact
For questions or further information, please contact [orhun868@gmail.com].
pytest-pydantic-schema-sync 0.2.1
Pytest plugin to synchronise Pydantic model schemas with JSONSchema files
License: MIT
Requires Python: >=3.10

# pytest-pydantic-schema-sync

`pytest-pydantic-schema-sync` is a pytest plugin that automates the process of saving JSON schemas for Pydantic
models during test execution. It builds upon the functionality provided by the `pydantic-schema-sync` package.

## Features

- Automatically syncs JSON schemas for Pydantic models during pytest execution
- Configurable schema storage location (package root or repository root)
- Easy setup using pytest markers and Enum-based configuration

## Installation

You can install `pytest-pydantic-schema-sync` using pip:

```
pip install pytest-pydantic-schema-sync
```

## Usage

Define an Enum class with your Pydantic model paths, marked with the `pydantic_schema_sync` marker:

```python
from pytest import mark
from enum import Enum

@mark.pydantic_schema_sync
class ModelSchemas(Enum):
    user = "myapp.models.User"
    product = "myapp.models.Product"
```

Running your pytest suite will generate tests for each of these, which fail if the sync is unsuccessful.
The plugin will automatically generate and sync JSON schemas for the specified models using `pydantic-schema-sync`.

## Configuration

You can configure the plugin behavior in your `pytest.ini` or `pyproject.toml` file:

```ini
[tool.pytest.ini_options]
pydantic_schema_sync = {schema_location = "package_root", schema_dir = "schemas", repo_flatten = false, mjs_kwargs = {}}
```

These are the defaults, but it demonstrates use of JSON to configure the plugin.

- `schema_location`: Where to store schema files. Options are "package_root" (default) or "repo_root".
  - `repo_flatten`: If "repo_root" is chosen, then if this option is set to True (default: False)
    all JSON files will be written to a single directory rather than in subfolders per-package.
- `schema_dir`: Name of the directory to store schema files under whichever schema location root. Default is "schemas".
- `mjs_kwargs`: Keyword arguments to pass to the Pydantic models' `model_json_schema()` method.
  Default is `{}` (i.e. do not override any kwargs).
  - A common kwarg you may wish to set is `by_alias` (default: True)

## How It Works

The plugin uses the `pydantic-schema-sync` package to generate JSON schemas for your Pydantic models.
It creates a separate test item for each model specified in your Enum, ensuring that schemas are synced
even if no other tests are run for those models.

## Example

Check out the [simple-plugin-demo](https://github.com/lmmx/pydantic-schema-sync/tree/master/packages/simple-plugin-demo)
for a working example of how to use this plugin.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
iris-recognition 0.0.1
Python Application for an Iris Recognition System using OpenCV. The project aims to create a database system and iris analyzer for fast fast and accurate iris recognition. See for more, https://github.com/elymsyr/iris-recognition.
License: MIT

# Iris Recognition System

This project is an iris recognition system ([*See original project.*](https://github.com/andreibercu/iris-recognition)) to analyze and recognize iris images. It includes functionality for feature extraction, comparison of iris features, and performance improvements for various scenarios, including suboptimal camera conditions. The project has been updated to run on Python 3.9.x and includes functionality to save iris data to a database. There are also plans to develop a dynamic system with a GUI or mobile app for real-time use.

## Table of Contents

- [Development](#development)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

## Development
- [ ] **Improved Iris Analysis**: Improving analyze speed and extraction of features from iris images and creating a dictionary for recognition.
- [ ] **Performance Improvements**: Enhancements for recognition performance and speed, especially under suboptimal camera conditions.
- [x] **Database Integration**: Save and retrieve iris data using a database.
  - [ ] Optimization for fast query.
- [ ] **Future Developments**: Plans to create a GUI or mobile app for dynamic and in real-time use.

## Installation

For latest release:
```
pip install iris_recognition_system
```
or

#### 1. Clone the repository:
```
git clone https://github.com/elymsyr/iris-recognition.git
cd iris-recognition
```

#### 1. Create a virtual environment:
```
conda create -n venv_name python=3.9
```

#### 1. Install the required dependencies:
```
pip install -r requirements.txt
```

## Usage
See [iris_recognition.py](iris_recognition.py).

## Contributing
Contributions are welcome! Please submit a pull request with your changes or improvements. Ensure that you follow the project's coding standards and provide relevant documentation. See the [CONTRIBUTING](CONTRIBUTING.md) file for details.

## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE.md) file for details.

## Contact
For questions or further information, please contact [orhun868@gmail.com].