semvec/pypi-packages huggingface dataset:
pycrafter 0.0.7
Text extraction from images using ONNX runtime and CRAFT net
Requires Python: >=3.8
Keywords: CRAFT,OCR,neural net,onnx,text detection

# Crafter
CRAFT text detection with ONNX Runtime

Based on the [craft-text-detector](https://github.com/innodatalabs/craft-text-detector). See also the source of the fork [here](https://github.com/fcakyon/craft-text-detector).

## Installation
```bash
$ pip install crafter
```

## Usage
```python
from crafter import Crafter

crafter = Crafter()

prediction = crafter('crafter/test/resources/idcard2.jpg')
for p1, p2, p3, p4 in prediction['boxes']:
    print(p1, p2, p3, p4)
```

## Developing
```bash
$ pip install .
$ pip install onnx git@github.com:innodatalabs/craft-text-detector.git pytest
```

To download Pytorch weights and convert to ONNX, run this (once):
```bash
$ python convert/craftnet.py
$ python convert/refinenet.py
```
This will (re-)create the ONNX files in `crafter/resources`.

## Testing
```bash
$ PYTHONPATH+. pytest
```

## Building
```bash
$ make
```
celery-context 0.0.3.23092222
实现celery在flask/django下的上下文一致性的简单扩展
Requires Python: >=3.4

celery_context
==============

Documentation
-------------

The documentation is hosted at https://github.com/BingerYang/celery_context


Installation
------------

adding the trace_id, task_name and task_id at celery log format

.. code:: shell

     pip install celery_context

Usage
-----

example:

::


    # -*- coding: utf-8 -*-
    from flask import Flask
    from celery_context import Celery
    from flask import request
    from .. import celery_settings  # 存放celery 配置的文件
    """celery_settings
    worker_log_format = '[%(trace_id)s%(task_name)s%(task_id)s%(process)s %(thread)s %(asctime)s %(pathname)s:%(lineno)s] %(levelname)s: %(message)s'
    worker_task_log_format = worker_log_format
    """

    config = dict(redis={"host": "*****", "port": 31320, "password": "lab@2019"})
    redis_url = "redis://:{password}@{host}:{port}".format(**config["redis"])
    app = Flask("example.run")
    # app.config['CELERY_BROKER_URL'] = "{}/1".format(redis_url)
    # app.config['CELERY_RESULT_BACKEND'] = "{}/2".format(redis_url)
    celery = Celery(app=app, broker=redis_url, backend=redis_url)
    celery.config_from_object(celery_settings)
    celery.add_flask_content(app)


    @celery.task(bind=True)
    def add(self, a, b):
        print("a+ n:", a + b, self.request.content)
        return a + b


    @app.route("/")
    def index():
        ret = add.delay(1, 3)
        print(ret.get())
        return ret.id


    if __name__ == "__main__":
        app.run()
pyidoit 0.0.2
An Simple python i-doit Client.
Requires Python: >=3.7

# pyidoit
A Simple and Easy-to-use python client for  [i-doit](https://www.i-doit.com/)'s JSON-RPC API

## Installation

```console
$ pip install pyidoit
```

## Example

* Create a file python file(`main.py`) with:

```Python
from pyidoit import IDoitClient

PYIDOIT_HOST = "http://localhost/idoit-22/src/jsonrpc.php"
PYIDOIT_API_KEY = "XXXXX-XXXXX-XXX"

def main():
    client = IDoitClient(
        host=PYIDOIT_HOST,
        apikey=PYIDOIT_API_KEY,
        username="",
        password="",
    )

    data = client.cmdb_objects_read()
    print(data)

if __name__ == "__main__":
    main()
```

Then run the file

```console
$ python main.py
```


## CONTRIBUTION GUIDE LINES