semvec/pypi-packages huggingface dataset:
EasyFacesPy 0.0.2
This library will help you with face recognition in photos.
Keywords: python,face,opencv

# EasyFacePy
This library will help you with face recognition in photos.

### Examples
```py
from EasyFacePy import FaceDetect

face = FaceDetect("face.png")
face.show()
```

```py
from EasyFacePy import FaceDetect

face = FaceDetect("face.png")
face.save("face.png")
```
aws-sqs-batch-sender 1.0.0b1
AWS SQS Batch Sender for boto3
Requires Python: >=3.6

# AWS SQS Batch Sender for boto3

![Written in Python](https://img.shields.io/badge/python-3.6,%203.7,%203.8,%203.9-blue.svg) [![PyPI](https://img.shields.io/pypi/v/aws-sqs-batch-sender)](https://pypi.org/project/aws-sqs-batch-sender/) [![Build](https://github.com/cbuschka/aws-sqs-batch-sender-python/workflows/build/badge.svg)](https://github.com/cbuschka/aws-sqs-batch-sender-python) [![License](https://img.shields.io/github/license/cbuschka/aws-sqs-batch-sender-python.svg)](https://github.com/cbuschka/aws-sqs-batch-sender-python/blob/main/license.txt)

This is the AWS SQS batch sender offered to boto3 as [pull request #2590](https://github.com/boto/boto3/pull/2590). As long as the code has not been merged the batch sender can be added as a dependency through this library.

## Development Prerequisites

* GNU make
* docker-compose
* python >= 3.6

## License

Copyright (c) 2021 by [Cornelius Buschka](https://github.com/cbuschka).

[MIT](./license.txt)
biapol-taurus 0.1.1
Bio-image analysis tools for the taurus cluster at TU Dresden
Requires Python: >=3.6

# BiA-PoL taurus tools
Collection of tools to work on Bio-image Analysis tools on the [taurus cluster of TU Dresden](https://tu-dresden.de/zih/hochleistungsrechnen/hpc?set_language=en).

Before you can start working on the taurus cluster, you need to file a [project request](https://tu-dresden.de/zih/hochleistungsrechnen/hpc?set_language=en#section-1).

## ProjectFileTransfer
If you own a project space on taurus and want to transfer files to it, you can ask [HPC support](https://tu-dresden.de/zih/hochleistungsrechnen/support) to enable a connection to your fileserver.
Afterwards, you can use [TUD ZIH datamover](https://doc.zih.tu-dresden.de/data_transfer/datamover/) to transfer files from the fileserver to the cluster. If you want to do this conveniently from python,
see the [ProjectFileTransfer demo]().

## Contributing
Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

## License
Distributed under the terms of the BSD-3 license, "biapol-taurus" is free and open source software.

## Support
If you need support with the tools listed here, please [open an issue](https://github.com/BiAPoL/biapol-taurus/issues).
TayTules-pkg-taytek 0.1.0
A set of tools made by me!
Requires Python: >=3.6

# TayTules

No documentation yet :/

python3 -m pip install --upgrade twine
python3 -m twine upload --repository testpypi dist/VERSION/*

pip install -i https://test.pypi.org/simple/ TayTules-pkg-taytek==VERSION
pip install TayTules-pkg-taytek==0.0.6
NaverService 0.0.1
innovata-NaverService
Requires Python: >=3.8


# Naver Services


##### ============================================================
## Overview
##### ============================================================


##### ============================================================
## IDE :: Platform Layer
##### ============================================================

mkdir project_name
mkdir package_name
git init
python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

##### ============================================================
## IDE :: Application Layer
##### ============================================================

### Terminal

python ide.py

### Jupyter

import os
import sys
sys.path.append(f"{os.environ['HOME']}/pjts/PROJECT_NAME")
import ide
ide.main(ide='jupyter')
ide.setup_logpath(modulepath='jupyter.MODULE_NAME')
EasyFacePy 0.0.1
This library will help you with face recognition in photos.
Keywords: python,face,opencv

# EasyFacePy
This library will help you with face recognition in photos.

### Examples
```py
from EasyFacePy import FaceDetect

face = FaceDetect("face.png")
face.show()
```

```py
from EasyFacePy import FaceDetect

face = FaceDetect("face.png")
face.save("face.png")
```
PyAutoMakerHuman 1.2.1
얼굴, 포즈, 손의 랜드마크를 쉽게 가져오기위한 모듈
Requires Python: >=3.6

# PyAutoMakerHuman
사람과 관련된 자동화 모듈


# 주의
이 프로젝트는 아직 개발중입니다
내보내는 API는 언제든지 바뀔 수 있습니다



# 설치
pip install PyAutoMakerHuman

또는

git clone https://github.com/boa9448/PyAutoMakerHuman.git <br>
cd PyAutoMakerHuman <br>
python setup.py install <br>


# 사용

데이터셋 폴더 구조

    dataset
    ├─face
    └─hand
        ├─paper
        ├─rock
        └─scissors



분류기 학습

    import os
    import PyAutoMakerHuman as pamh

    #데이터셋 경로 지정
    dataset_path = os.path.join("dataset", "hand")

    #손 검출 임계치 0.7
    hand = pamh.HandUtil(min_detection_confidence=0.7)

    #데이터셋의 경로를 넘겨주면 사전형 값을 리턴함
    data = hand.extract_dataset(dataset_path)
    train_data, name = data["data"], data["name"]

    trainer = pamh.SvmUtil()
    #svm 학습
    trainer.train_svm(train_data, name)

    #학습된 svm 모델을 myModel폴더에 저장
    trainer.save_svm("myModel")
    


이미지에서 손모양 분류

    import cv2
    import imutils
    import os
    import PyAutoMakerHuman as pamh
    import random

    hand = pamh.HandUtil(min_detection_confidence=0.7)
    trainer = pamh.SvmUtil()

    #학습된 svm모델을 불러옴
    #myModel폴더에 있는 모델과 라벨을 불러옴
    trainer.load_svm("myModel")

    #이미지를 읽어옴
    img_path = "test.jpg"
    img = cv2.imread(img_path)

    #이미지 리사이즈
    img = imutils.resize(img, width=600)
    #이미지에서 박스와 랜드마크를 추출함
    data_list = hand.extract(img)

    color_dict = {}
    #이미지에서 탐지를 성공했다면...
    if data_list:
        for idx, data in enumerate(data_list):
            landmark_1d = data[1]
            name, proba = trainer.predict([landmark_1d])

            box = data[0]
            color = (0, 0, 0)

            if name in color_dict:
                color = color_dict[name]
            else:
                color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
                color_dict[name] = color


            cv2.putText(img, f"{name} : {proba:.2f}", (box[0], box[1] - 25)
                        , cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2, cv2.LINE_AA)
            cv2.rectangle(img, (box[0], box[1]), (box[0] + box[2], box[1] + box[3])
                            , color, 2)


    cv2.imshow("view", img)
    cv2.waitKey()
    cv2.destroyAllWindows()


# 추후 개발
간단하게 모델을 학습 시킬 수 있는 GUI도구


# GUI도구 미리 둘러보기
패키지 설치 후 
python -m PyAutoMakerHuman
yahoo-search-api 1.0.0
Yahoo!広告 検索広告 API リファレンス / Yahoo! JAPAN Ads Search Ads API Reference
Keywords: OpenAPI,OpenAPI-Generator,Yahoo!広告 検索広告 API リファレンス / Yahoo! JAPAN Ads Search Ads API Reference

&lt;div lang&#x3D;\&quot;ja\&quot;&gt;Yahoo!広告 検索広告 APIのWebサービスについて説明します。&lt;/div&gt; &lt;div lang&#x3D;\&quot;en\&quot;&gt;Search Ads API Web Services supported in Yahoo! JAPAN Ads API.&lt;/div&gt; &lt;div&gt;&lt;a target&#x3D;\&quot;_blank\&quot; href&#x3D;\&quot;https://github.com/yahoojp-marketing/ads-search-api-documents/blob/master/design/v6/Route.yaml\&quot;&gt;OpenAPI Specification&lt;/a&gt;&lt;/div&gt; &lt;div lang&#x3D;\&quot;ja\&quot;&gt;&lt;a target&#x3D;\&quot;_blank\&quot; href&#x3D;\&quot;https://github.com/yahoojp-marketing/ads-search-api-documents/blob/master/bestpractice/ja\&quot;&gt;Best Practice&lt;/a&gt;&lt;/div&gt; &lt;div lang&#x3D;\&quot;en\&quot;&gt;&lt;a target&#x3D;\&quot;_blank\&quot; href&#x3D;\&quot;https://github.com/yahoojp-marketing/ads-search-api-documents/blob/master/bestpractice/en\&quot;&gt;Best Practice&lt;/a&gt;&lt;/div&gt;   # noqa: E501
tracardi-rabbitmq-publisher 0.6.0
The purpose of this plugin is publish payload to rabbitmq.
Requires Python: >=3.8
Keywords: tracardi,plugin

# RabbitMQ publisher

The purpose of this plugin is to publish payload to RabbitMQ.

It reads payload and sends it to defined RabbitMQ. RabbitMq must be defined as source in Tracardi.

# Configuration

This node requires configuration.

*Example configuration*

```json
{
  "source": {
    "name": "RabbitMQ",
    "id": "79c315aa-2780-4742-bc70-6444bf8ea444"
  },
  "queue": {
    "name": "test-test",
    "routing_key": "test",
    "queue_type": "direct",
    "compression": null,
    "auto_declare": true,
    "serializer": "json"
  }
}
```

# Input payload

This node reads input payload and sends it to the queue.

# Output

This node has no output.
tracardi-event-counter 0.6.0
This plugin reads how many events of defined type were triggered within defined time.
Requires Python: >=3.8
Keywords: tracardi,plugin

# Event counter

This plugin reads how many events of defined type were triggered within defined time.

## Configuration

*Example*
```
{
 "event_type": "page-view",
 "time_span": "-15m"
}
```

* `event_type` - value should be a string containing type of event that user wants to count.
* `time_span` - should be a string containing time span which user wants to search in, for example using ```"-1h30min"``` as ```"time_span"``` on 1st March 2022, 1:00 (a.m.) results in searching through all events that happened between 28th February 2022, 23:30 and 1st March 2022, 1:00 (a.m.). Presence of ```"-"``` sign in timeSpan does not matter, since it is stripped anyway. For more details, visit [pytimeparse library documentation](https://pypi.org/project/pytimeparse/).


## Output

Plugin outputs number of found events.

Example:
```
{
    "events" : 39
}
```
yahoo-display-api 1.0.0
Yahoo!広告 ディスプレイ広告 API リファレンス / Yahoo! JAPAN Ads Display Ads API Reference
Keywords: OpenAPI,OpenAPI-Generator,Yahoo!広告 ディスプレイ広告 API リファレンス / Yahoo! JAPAN Ads Display Ads API Reference

&lt;div lang&#x3D;\&quot;ja\&quot;&gt;Yahoo!広告 ディスプレイ広告 APIのWebサービスについて説明します。&lt;/div&gt; &lt;div lang&#x3D;\&quot;en\&quot;&gt;Display Ads API Web Services supported in Yahoo! JAPAN Ads API.&lt;/div&gt; &lt;div&gt;&lt;a target&#x3D;\&quot;_blank\&quot; href&#x3D;\&quot;https://github.com/yahoojp-marketing/ads-display-api-documents/blob/master/design/v6/Route.yaml\&quot;&gt;OpenAPI Specification&lt;/a&gt;&lt;/div&gt; &lt;div lang&#x3D;\&quot;ja\&quot;&gt;&lt;a target&#x3D;\&quot;_blank\&quot; href&#x3D;\&quot;https://github.com/yahoojp-marketing/ads-display-api-documents/blob/master/bestpractice/ja\&quot;&gt;Best Practice&lt;/a&gt;&lt;/div&gt; &lt;div lang&#x3D;\&quot;en\&quot;&gt;&lt;a target&#x3D;\&quot;_blank\&quot; href&#x3D;\&quot;https://github.com/yahoojp-marketing/ads-display-api-documents/blob/master/bestpractice/en\&quot;&gt;Best Practice&lt;/a&gt;&lt;/div&gt;   # noqa: E501