semvec/pypi-packages huggingface dataset:
mr-hub-gui 0.0.2
A GUI to add packages to MR-Hub
Requires Python: >=3.6

# mr-hub-gui

A Python GUI to add packages to MR-Hub.

This tool generates the required JSON description for MR-Hub, and then helps in creating the forked Github repository.

## Requirements

This tool is written in Python. Version 3.6 or more are required.

In addition to Python, you will also need:
- A Github account.
- Git installed on your system, and in the path.
- Git properly configured (with global user.name and user.email variables set).

## Installation

You can install this program directly from pip:

`pip install mr-hub-gui`

and subsequently you can directly launch `mr-hub-gui`

Alternatively, you can clone the github repository and launch the `mr-hub-gui` script directly, or install it from the
local clone with `pip install -e .` .

The following python libraries are required: `PyGithub`, `PySide2`, `requests`.

## Usage

Launch the `mr-hub-gui` program. Fill in all the required fields.
You can load and save your settings as JSON (which you can also then use to manually create the repository and the pull
request, if you wish).

Once you are ready, you can select Prepare MR-Hub submission from the File menu. This will fork the main MR-Hub repo
inside Github, create a local copy, and modify it. Finally, the pull request page will be open in your browser. 
Review it and approve it.

See the [MR hub github page](https://github.com/ISMRM/mrhub) for more information.

## Compatibility

This program was tested with Linux and Windows.
LuaStakky 0.1.2
Full stack lua web framework
Requires Python: >=3.6

# LuaStakky
LuaStakky - full stack lua web framework

## What's under the hood?
+ Docker (docker-compose)
+ Openresty (Nginx with lua) with build-in modules:
    * lua-cmsgpack
    * etlua (lua-based template language)
    * inspect
    * lua-resty-http
    * lua-resty-cookie
+ Tarantool (DB) with 2 build modes:
    * Trivial app (as classic 1 main lua file+ lua modules)
    * Advanced app (custom build system with microservice architecture)
+ fengari (js lua implementation) *WIP*

## Installation

Install docker on your system before.

    pip3 install LuaStakky

## Some usefull examples

* [Example-Management](https://github.com/LuaStakky/Example-Management) and [GUI for it](https://github.com/LuaStakky/Example-Management-DesktopGUI).
  This example show advanced build system for tarantool.

## Official templates

* [LuaStakkyStaticSimpleAppTemplate](https://github.com/LuaStakky/LuaStakkyStaticSimpleAppTemplate)
* [LuaStakkyStaticTemplate](https://github.com/LuaStakky/LuaStakkyStaticTemplate)

## License
[MIT](https://choosealicense.com/licenses/mit/)
hubisModel-pkg-yjko 0.0.1
A small example package
Requires Python: >=3.5

# tensorflow-object-contour-detection

This is a tensorflow implimentation of Object Contour Detection with a Fully Convolutional Encoder-Decoder Network (https://arxiv.org/pdf/1603.04530.pdf) . 

**REQUIREMENTS :**

```
pip install requirements.txt
```
**Label Preparation :**

To prepare the labels for contour detection from PASCAL Dataset , run create_lables.py and edit the file to add the path of the labels and new labels to be generated . Use this path for labels during training. 

**TRAINING :**

```
python train.py \
    --max_to_keep=50 \
    --Epochs=100 \
    --momentum=0.9 \
    --learning_rate=.0000001 \
    --train_crop_size=480 \
    --clip_by_value=1.0 \
    --train_text = ${path to text file} \
    --log_dir = ${path to where logs will be saved} \
    --tf_initial_checkpoint=${PATH_TO_CHECKPOINT} \
    --label_dir = ${path to label directory} \
    --image_dir = ${path to image directory}
```
**EVALUATION :**
```
python eval.py \
    --checkpoint=${path to checkpoint to be evaluated} \
    --save_preds=${path to folder where predictions will be saved} \
    --image_dir = ${path to image directory} \
    --eval_crop_size=480 \
    --eval_text =  ${path to eval text file}

```
**Results :**

<img src="./000999.jpg" alt="Image_1"/>

<img src="./000999.png" alt="prediction_1"/>

<img src="./000129.jpg" alt="Image_1"/>

<img src="./000129.png" alt="prediction_1"/>
example-pkg-yjko 0.0.1
A small example package
Requires Python: >=3.5

# tensorflow-object-contour-detection

This is a tensorflow implimentation of Object Contour Detection with a Fully Convolutional Encoder-Decoder Network (https://arxiv.org/pdf/1603.04530.pdf) . 

**REQUIREMENTS :**

```
pip install requirements.txt
```
**Label Preparation :**

To prepare the labels for contour detection from PASCAL Dataset , run create_lables.py and edit the file to add the path of the labels and new labels to be generated . Use this path for labels during training. 

**TRAINING :**

```
python train.py \
    --max_to_keep=50 \
    --Epochs=100 \
    --momentum=0.9 \
    --learning_rate=.0000001 \
    --train_crop_size=480 \
    --clip_by_value=1.0 \
    --train_text = ${path to text file} \
    --log_dir = ${path to where logs will be saved} \
    --tf_initial_checkpoint=${PATH_TO_CHECKPOINT} \
    --label_dir = ${path to label directory} \
    --image_dir = ${path to image directory}
```
**EVALUATION :**
```
python eval.py \
    --checkpoint=${path to checkpoint to be evaluated} \
    --save_preds=${path to folder where predictions will be saved} \
    --image_dir = ${path to image directory} \
    --eval_crop_size=480 \
    --eval_text =  ${path to eval text file}

```
**Results :**

<img src="./000999.jpg" alt="Image_1"/>

<img src="./000999.png" alt="prediction_1"/>

<img src="./000129.jpg" alt="Image_1"/>

<img src="./000129.png" alt="prediction_1"/>
fontgen 0.0.4
Python package for generating fonts
Requires Python: >=3.6

# fontgen  
  
Python package for generating fonts.   
  
## Installation  
  
This is one time process.  
  
Fontgen requires Python 3.8 or later.  
  
### First you need to create a virtual environment for python  
  
On macOS and Linux:  
  
`python3 -m venv venv`  
  
On Windows:  
  
`py -m venv venv`  
  
### Activating a virtual environment  
  
On macOS and Linux:  
  
`source venv/bin/activate`  
  
On Windows:  
  
`.\venv\Scripts\activate`  
  
### Downloading  and Installing fontgen  
  
Run following command
  
`pip install fontgen`
  
## Usage  
  
Make sure the virtual environment is activated.  
  
Refer to [Activating a virtual environment](https://github.com/itfoundry/fontgen#activating-a-virtual-environment)  
  
Set a configuration before running a command for the first time. Refer to democonfig.ini for config format.

Load a configuration if you have any custom configuration.

`fontgen -lc myconfig.ini`

If you don't want to use config file. Tyle this command to set empty config.

`fontgen -lc reset`

Compile a font:  
  
`fontgen -f "Font Family Name" -o var`  
  
For italic version you will have to type "Font Family Name Italic"  
  
For detailed usage, type:  
   
 `fontgen -h`
 
 ## Directory structure
 
You can keep designspace file or glyphs file under masters directory. If it is glyphs file, you can convert it to
designspace by running following command. Make sure to create info.txt file in the directory.

`fontgen -f "Font Family Name" -gd`

    
    / Base Directory (Name of the Directory = Font family name)
        / masters
            /Family Name.designspace (or)
            /Family Name.glyphs
        /info.txt
filetreee 0.0.1
Scrape File System and output different formats
Requires Python: >=3.6

# FileTree

## Sample Output

### GithubMarkdownContentFormatter

```
- OSCP
- [Notes](./Notes)
  - Tools
    - [Python](./Notes/Tools/Python.md)
    - [nmap](./Notes/Tools/nmap.md)
    - [Netcat](./Notes/Tools/Netcat.md)
    - [Metasploit](./Notes/Tools/Metasploit.md)
  - [common](./Notes/common.md)
  - [FileTransfer](./Notes/FileTransfer.md)
  - [Service](./Notes/Service.md)
  - [Bash](./Notes/Bash.md)
```

- OSCP
  - [Notes](./Notes)
    - Tools
      - [Python](./Notes/Tools/Python.md)
      - [nmap](./Notes/Tools/nmap.md)
      - [Netcat](./Notes/Tools/Netcat.md)
      - [Metasploit](./Notes/Tools/Metasploit.md)
    - [common](./Notes/common.md)
    - [FileTransfer](./Notes/FileTransfer.md)
    - [Service](./Notes/Service.md)
    - [Bash](./Notes/Bash.md)

### TreeCommandFormatter

```
OSCP
└── Notes
    ├── Tools
    │   ├── Python.md
    │   ├── nmap.md
    │   ├── Netcat.md
    │   └── Metasploit.md
    ├── common.md
    ├── FileTransfer.md
    ├── README.md
    ├── Service.md
    └── Bash.md
```

## Sample Code

```python
path_ = pathlib2.Path('/OSCP')
scraper = Scraper(path_, scrape_now=False, keep_empty_dir=False)
scraper.add_filter(filter_=MarkdownFilter())
scraper.run()

# GNU Tree Format
formatter = TreeCommandFormatter(scraper.get_tree())
stringio = formatter.generate()
print(stringio.getvalue())

# GitHub Content Format
formatter = GithubMarkdownContentFormatter(scraper.get_tree())
stringio = formatter.generate()
print(stringio.getvalue())
formatter.to_stream(sys.stdout)
```
filetree-huakunshen 0.0.1
Scrape File System and output different formats
Requires Python: >=3.6

# FileTree

## Sample Output

### GithubMarkdownContentFormatter

```
- OSCP
- [Notes](./Notes)
  - Tools
    - [Python](./Notes/Tools/Python.md)
    - [nmap](./Notes/Tools/nmap.md)
    - [Netcat](./Notes/Tools/Netcat.md)
    - [Metasploit](./Notes/Tools/Metasploit.md)
  - [common](./Notes/common.md)
  - [FileTransfer](./Notes/FileTransfer.md)
  - [Service](./Notes/Service.md)
  - [Bash](./Notes/Bash.md)
```

- OSCP
  - [Notes](./Notes)
    - Tools
      - [Python](./Notes/Tools/Python.md)
      - [nmap](./Notes/Tools/nmap.md)
      - [Netcat](./Notes/Tools/Netcat.md)
      - [Metasploit](./Notes/Tools/Metasploit.md)
    - [common](./Notes/common.md)
    - [FileTransfer](./Notes/FileTransfer.md)
    - [Service](./Notes/Service.md)
    - [Bash](./Notes/Bash.md)

### TreeCommandFormatter

```
OSCP
└── Notes
    ├── Tools
    │   ├── Python.md
    │   ├── nmap.md
    │   ├── Netcat.md
    │   └── Metasploit.md
    ├── common.md
    ├── FileTransfer.md
    ├── README.md
    ├── Service.md
    └── Bash.md
```

## Sample Code

```python
path_ = pathlib2.Path('/Users/huakunshen/Local/Dev/OSCP')
scraper = Scraper(path_, scrape_now=False, keep_empty_dir=False)
scraper.add_filter(filter_=MarkdownFilter())
scraper.run()

# GNU Tree Format
formatter = TreeCommandFormatter(scraper.get_tree())
stringio = formatter.generate()
print(stringio.getvalue())

# GitHub Content Format
formatter = GithubMarkdownContentFormatter(scraper.get_tree())
stringio = formatter.generate()
print(stringio.getvalue())
formatter.to_stream(sys.stdout)
```