semvec/pypi-packages huggingface dataset:
labelme2coco 0.2.6
Convert labelme annotations into coco format in one step
Requires Python: >=3.7

<div align="center">
<h1>
  labelme2coco
</h1>

<a href="https://pepy.tech/project/labelme2coco"><img src="https://pepy.tech/badge/labelme2coco" alt="downloads"></a>
<a href="https://badge.fury.io/py/labelme2coco"><img src="https://badge.fury.io/py/labelme2coco.svg" alt="pypi version"></a>
<a href="https://github.com/fcakyon/labelme2coco/actions/workflows/ci.yml"><img src="https://github.com/fcakyon/labelme2coco/workflows/CI/badge.svg" alt="ci"></a>
<a href="https://twitter.com/fcakyon"><img src="https://img.shields.io/badge/twitter-fcakyon_-blue?logo=twitter&style=flat" alt="fcakyon twitter">

<h4>
  A lightweight package for converting your <a href="https://github.com/wkentaro/labelme">labelme</a> annotations into COCO object detection format.
</h4>

<h4>
    <img width="700" alt="teaser" src="https://user-images.githubusercontent.com/34196005/148746639-9a7b9c08-2156-42ca-abae-a4e6aad095dd.gif">
</h4>
</div>

## Convert LabelMe annotations to COCO format in one step
[labelme](https://github.com/wkentaro/labelme) is a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats.
However, widely used frameworks/models such as Yolact/Solo, Detectron, MMDetection etc. requires COCO formatted annotations.

You can use this package to convert labelme annotations to COCO format.

## Getting started
### Installation
```
pip install -U labelme2coco
```

### Basic Usage

```python
labelme2coco path/to/labelme/dir
```

```python
labelme2coco path/to/labelme/dir --train_split_rate 0.85
```

```python
labelme2coco path/to/labelme/dir --category_id_start 1
```

### Advanced Usage

```python
# import package
import labelme2coco

# set directory that contains labelme annotations and image files
labelme_folder = "tests/data/labelme_annot"

# set export dir
export_dir = "tests/data/"

# set train split rate
train_split_rate = 0.85

# set category ID start value
category_id_start = 1

# convert labelme annotations to coco
labelme2coco.convert(labelme_folder, export_dir, train_split_rate, category_id_start=category_id_start)
```

```python
# import functions
from labelme2coco import get_coco_from_labelme_folder, save_json

# set labelme training data directory
labelme_train_folder = "tests/data/labelme_annot"

# set labelme validation data directory
labelme_val_folder = "tests/data/labelme_annot"

# set path for coco json to be saved
export_dir = "tests/data/"

# set category ID start value
category_id_start = 1

# create train coco object
train_coco = get_coco_from_labelme_folder(labelme_train_folder, category_id_start=category_id_start)

# export train coco json
save_json(train_coco.json, export_dir+"train.json")

# create val coco object
val_coco = get_coco_from_labelme_folder(labelme_val_folder, coco_category_list=train_coco.json_categories, category_id_start=category_id_start)

# export val coco json
save_json(val_coco.json, export_dir+"val.json")
```
airflow-provider-sapiq 0.1.5
SAP IQ provider for Apache Airflow
Requires Python: >=3.8

# SAP IQ Provider for Apache Airflow

[![PyPI - Version](https://img.shields.io/pypi/v/airflow-provider-sapiq.svg)](https://pypi.org/project/airflow-provider-sapiq)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/airflow-provider-sapiq.svg)](https://pypi.org/project/airflow-provider-sapiq)

-----

**Table of Contents**

- [Installation](#installation)
- [Configuration](#configuration)
- [License](#license)

## Installation

```console
pip install airflow-provider-sapiq
```

## Configuration

In the Airflow user interface, configure a connection with the `Conn Type` set to SAP IQ.
Configure the following fields:

- `Conn Id`: How you wish to reference this connection.
    The default value is `sapiq_default`.
- `userid`: Login for the SAP IQ server.
- `password`: Password for the SAP IQ server.,
- `port`: Port for the SAP IQ server, typically 2638.
- `host`: Host of the SAP IQ server.

## Modules

### SAP IQ Operator

The `SapIqOperator` exequtes SQL query on SAP IQ server.

Import into your DAG using:

```Python
from sapiq_provider.operators.SapIqOperator import SapIqOperator
```

## License

`airflow-provider-sapiq` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.