semvec/pypi-packages huggingface dataset:
mxcube-video-streamer 1.2.0
FastAPI Based video streamer
License: MIT
Requires Python: >=3.7,<3.11
Keywords: mxcube,fast-api-streamer,video_streamer,mxcubeweb

# video-streamer
Video streamer to be used with MXCuBE. The streamer currently suports streaming from Tango (Lima) devices but can be extened to used with other camera solutons aswell. The output streams are either MJPEG or MPEG1.

![Screenshot from 2023-03-03 14-36-02](https://user-images.githubusercontent.com/4331447/222733892-c7d3af26-26ca-4a3c-b9f4-ab56fc91e390.png)

### Installation

```
git clone https://github.com/mxcube/video-streamer.git
cd video-streamer

# optional 
conda env create -f conda-environment.yml

# For development
pip install -e .

# For usage 
pip install .
```

### Usage
```
usage: video-streamer [-h] [-c CONFIG_FILE_PATH] [-tu TANGO_URI] [-hs HOST] [-p PORT] [-q QUALITY] [-s SIZE] [-of OUTPUT_FORMAT] [-id HASH] [-d]

mxcube-web Backend server command line utility.

options:
  -h, --help            show this help message and exit
  -c CONFIG_FILE_PATH, --config CONFIG_FILE_PATH
                        Configuration file path
  -tu TANGO_URI, --tango-uri TANGO_URI
                        Tango device URI
  -hs HOST, --host HOST
                        Host name to listen on for incomming client connections defualt (0.0.0.0)
  -p PORT, --port PORT  Port
  -q QUALITY, --quality QUALITY
                        Compresion rate/quality
  -s SIZE, --size SIZE  size
  -of OUTPUT_FORMAT, --output-format OUTPUT_FORMAT
                        output format, MPEG1 or MJPEG1
  -id HASH, --id HASH   Sream id
  -d, --debug           Debug true or false
```

There is the possibility to use a configuration file instead of command line arguments. All  command line arguments except debug are ignored if a config file is used. The configuration  file also makes it possible to configure several sources while the command line only allows  configuration of a single source.

### Example command line (for testing):
```
video-streamer -d -of MPEG1 -tu test
```

#### Example configuration file (config.json):
The configuration file format is JSON. A test image is used when the input_uri is set to "test". The example below creates one MPEG1 stream and one MJPEG stream from the test image. There is a defualt test/demo UI to see the video stream on http://localhost:[port]/ui. In example below case:
  
 MPEG1: http://localhost:8000/ui
 
 MJPEG: http://localhost:8001/ui


```
video-streamer -c config.json

config.json:
{
    "sources": {
        "0.0.0.0:8000": {
            "input_uri": "test",
            "quality": 4,
            "format": "MPEG1"
        },
        "0.0.0.0:8000": {
            "input_uri": "test",
            "quality": 4,
            "format": "MJPEG"
        }
    }
}
```
python-wrapper-for-contentful-rich-text-to-markdown-converter 0.1
A Python wrapper for converting Contentful rich text to Markdown
License: MIT

# Python Wrapper For Contentful Rich Text To Markdown Converter

This module converts Contentful Rich Text objects to Markdown. It is a python wrapper for a node module [contentful-rich-text-to-markdown](https://www.npmjs.com/package/contentful-rich-text-to-markdown-converter)
H2oMojoPredict 0.1.3
In H2oMojoPreidct, you can perform predictions on MOJO files without the need for H2O initialization or dependency on an H2O cluster. You are free to use Python to make predictions using MOJO files.
License: MIT

# H2oMojoPredict 


### In H2oMojoPreidct, you can perform predictions on MOJO files without the need for H2O initialization or dependency on an H2O cluster. You are free to use Python to make predictions using MOJO files. 


## Running 
Follow these steps to set up and run :

### Prerequisites
- Python 3.7

### Installation

`pip install H2oMojoPredict`


Use demo :

    from H2oMojoPredict import H2oMojoPredicto
    # 初始化
    predictor = H2oMojoPredictor("XGBoost_model_python_1696751279647_2.zip", "binomial")
    # 输入数据格式
    data = pd.DataFrame({
            "os_act_first_diff": [55.0, 100.0],
            "life_cycle_string": ['4','3'],
        })
    # 启动java服务
    java_service = predictor.start_java_service()
    result = predictor.predict_with_java_service(java_service, data)
    print(result)`
    # 预测完毕，关闭java服务
    java_service.terminate()
    java_service.wait()
    if java_service.returncode == 0:
        print("Java service exited successfully.")
    else:
        print("Java service exited with an error code:", java_service.returncode)