semvec/pypi-packages huggingface dataset:
kymati-radar 0.1.0
A Python library for radar-related tasks.
Requires Python: >=3.6

# Radar Library

**Radar Library** is a Python package developed by **Kymati** to support the CLI tools for processing and interfacing with IMX8 systems. This library includes modules for handling various radar-related functionalities such as packet processing, GPIO management, EEPROM access, and configuration handling.

---

## Features

- **CLI Integration**: Seamlessly integrates with CLI tools for IMX8 processing.
- **EEPROM Utilities**: Provides functions for reading and managing product information stored in EEPROM.
- **GPIO Control**: Offers utilities to manage GPIO commands effectively.
- **Packet Processing**: Includes classes and functions for efficient radar packet handling.
- **Configuration Management**: Simplifies configuration handling for radar systems.

---

## Installation

Install the library locally from the source:

```bash
pip install .
```

Or add it to your project requirements:

```bash
pip install kymati_radar
```

---

## Usage

### Example: Accessing Modules

#### Importing and Using `packet`

```python
# TODO: ADD EXAMPLE HERE
```

#### Reading EEPROM Information

```python
# TODO: ADD EXAMPLE HERE
```

#### GPIO Management

```python
# TODO: ADD EXAMPLE HERE
```

#### Configuration Handling

```python
# TODO: ADD EXAMPLE HERE
```

#### CLI Tools Integration

```python
# TODO: ADD EXAMPLE HERE
```

---

## Modules Overview

### `cli`

Provides command-line interaction utilities for IMX8 systems.

### `eeprom`

Includes functions to read and manage EEPROM product information.

### `gpio`

Enables GPIO management, including sending commands and toggling pins.

### `packet`

Contains utilities for processing radar packets efficiently.

### `radar_config`

Handles radar system configuration files and parsing.

---

## Dependencies

The library requires the following external dependencies (they will be installed by default using the pip install command):

- `smbus2`: For I2C communication.
- `pyserial`: Serial communication.
- `numpy`: Numerical computations.
- `toml`: Parsing configuration files.

Install them via:

```bash
pip install smbus2 pyserial numpy toml
```

---

## Contributing

Contributions are welcome! If you find a bug or have a feature request, please reach out to the Kymati team.

1. Fork the repository.
2. Create a feature branch (`git checkout -b feature-branch-name`).
3. Commit your changes (`git commit -m 'Add feature'`).
4. Push to the branch (`git push origin feature-branch-name`).
5. Open a Pull Request.

---

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

---

## Contact

For further inquiries, please contact **Kymati** at [support@kymati.com](mailto\:support@kymati.com).
seatools-starter-server-gunicorn 1.0.0
Seatools gunicorn server starter.
Requires Python: >=3.9
Keywords: seatools, starter

# seatools gunicorn 服务器启动器

该框架在`gunicorn`层完成`seatools.ioc`的加载, 使用该项目后无需在每个进程额外执行`seatools.ioc.run`函数, 仅在启动时传递一个`ioc`启动的函数即可

## 使用指南
1. 安装, `poetry add seatools-starter-server-gunicorn`
2. 这里以`fastapi`为例, 假设`xxx.boot`模块存在`start`的自定义启动`ioc`函数

```python
from seatools.ioc import run


def start():
    run(scan_package_names='xxx', config_dir='./config')

```
命令行启动`gunicorn xxx.boot:start xxx.fastapi.app:app`, 其他参数与官方`gunicorn`一致, 在`gunicorn`基础上增加了一个`ioc_app`的参数, 需要指明`ioc`应用启动的函数
若配置`application.yml`或`application-[*].yml`中存在`seatools.server.gunicorn.app`配置, 则可不传递`gunicorn`的`app`仅传递`ioc_app`即可, 示例`gunicorn xxx.boot:start`
若同时使用了`seatools-starter-web-*`的`web`启动包, 则`gunicorn`的`app`直接可省略
4. 支持配置`config/application.yml`
```yaml
seatools:
  server:
    gunicorn:
      # 配置该参数后启动参数可忽略app参数, 配置与官方gunicorn.app.wsgiapp.run一致, 若安装 seatools-starter-web-* 可省略app配置 
      app: xxx.fastapi.app:app
      bind: ':8000'
      workers: 2
      worker_class: gevent
      ...
    
```
ichello 0.0.5
A sample Python app
Requires Python: >=3.9
Keywords: Sample App

# ichello

This is a 'hello world' Python wrapped C++ app to show a sample packaging approach. It is based on the [Reference Nanobind Example](https://github.com/wjakob/nanobind_example) but will diverge over time.

It uses:

* `nanobind` to do the wrapping, defined in `src/binding.cpp`, 
* `CMake` to build the module shared library, defined in `CMakeLists.txt`
* `scikit-build-core` to interface the Python project definition in `pyproject.toml` and CMake.
* `cibuildwheel` to build wheels for various platforms using the CI.

To locally install and use the package you can do:

``` shell
pip install -e .
```

from the project directory.

Then you can do:

``` python
import ichello
ichello.get_sum([1.0, 2.0, 3.0])
>> 6.0
```

in a shell. The `get_sum` function is an instantiation of the template defined in `include.hpp` for the `double` type.
im2geojson 0.2.1
GeoJSON from image metadata
Requires Python: >=3.8
Keywords: GeoJSON, Image, GPS metadata

im2geojson
==========


[![Unittests](https://github.com/MJBishop/im2geojson/actions/workflows/test.yml/badge.svg)](https://github.com/MJBishop/im2geojson/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/MJBishop/im2geojson/graph/badge.svg?token=9C03IBN0Z3)](https://codecov.io/gh/MJBishop/im2geojson)



im2geojson is a python package for parsing GeoJSON from image metadata.

<br>

Installation
------------
The recommended way to install im2geojson is via pip:

    pip install im2geojson

<br>

Usage
-----

Simply set the `input_directory` to the path to your image folders:

    python -m im2geojson <path-to-image-folders>


* Image folders are parsed to GeoJSON FeatureCollections
  
* Images are parsed to GeoJSON Features.
  
* GeoJSON files are saved to `output_directory` in a folder named `geojson`
  
* The default is `./assets/geojson`

<br>


Options
-------

### Save Images

`--save_images`  or  `-s`  will save images stripped of metadata:

    python -m im2geojson <path-to-image-folders> -s

* Images are saved to `output_directory` in a folder named `images`

* The default is `./assets/images/`
  
<br>

### Save Thumbnails

`--save_thumbanails`  or  `-t`  will save image thumbnails:

    python -m im2geojson <path-to-image-folders> -t

* Thumbnails are saved to `output_directory` in a folder named `images`

* The default is `./assets/images/`
  
<br>

### Output Directory

`-o` or `--output_directory` will set the `output_directory`:

    python -m im2geojson <path-to-image-folders> -o <output_directory>

<br>

For example, to set the `output_directory` to `./output`:

    python -m im2geojson <path-to-image-folders> -o ./output

<br>


API Documentation
-----------------
Take a look at the [API Documentation](https://mjbishop.github.io/im2geojson/im2geojson.html) if you would like to use im2geojson in your own code.

<br>
cnn-methods 1.2.0
Deep learning project for object classification, detection and classification using YOLO and SAM
Requires Python: >=3.8

# CNN & YOLO+SAM Segmentation

<img width="100%" src="https://kajabi-storefronts-production.kajabi-cdn.com/kajabi-storefronts-production/file-uploads/blogs/22606/images/f8d6362-3e5e-c73-a7a4-e54525b5431a_banner-yolov8.png" alt="YOLO Vision banner"></a>
<br><br>
<img width="100%" src="https://github.com/rorro6787/rorro6787/blob/main/Images/sam.jpg" alt="SAM"></a>

This project explores the application of advanced computer vision techniques for fruit classification and segmentation. A custom Convolutional Neural Network (CNN) was designed and implemented to classify different types of fruits, focusing on achieving high accuracy with an efficient architecture. The CNN was trained on a labeled dataset of fruit images, utilizing techniques such as data augmentation and optimization strategies to enhance performance and robustness.  

Additionally, the project integrates a YOLO (You Only Look Once) model for real-time object detection and leverages the combination of YOLO and SAM (Segment Anything Model) for instance segmentation. This enables precise identification and segmentation of individual fruit instances, including segmentation with bounding boxes for more detailed analysis. The combination of custom and pre-trained models demonstrates versatility and effectiveness across multiple computer vision tasks.

## Table of Contents
- [Requirements](#requirements)
- [Installation and Usage](#installation-and-usage)
- [Contributors](#contributors)
- [Contributing](#contributing)

## Requirements
- Python 3.X.X
- Linux / MacOS

## Installation and Usage
For a detailed walkthrough of the steps to install and use the Python package and Jupyter notebooks, refer to the official library's documentation here: [Official Documentation](https://rorro6787.github.io/CNN-YOLO-SAM-segmentation/)

## Contributors
- [![GitHub](https://img.shields.io/badge/GitHub-100000?style=flat&logo=github&logoColor=white)](https://github.com/rorro6787) [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=flat&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/emilio-rodrigo-carreira-villalta-2a62aa250/) **Emilio Rodrigo Carreira Villalta**

## Contributing
Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (`git checkout -b feature-branch`)
3. Commit your changes (`git commit -m 'Add new feature'`)
4. Push to the branch (`git push origin feature-branch`)
5. Create a new Pull Request