semvec/pypi-packages huggingface dataset:
tfdbnet 0.0.3
A TensorFlow 2 wrapper of DBNet

# TFDBNet
A TensorFlow 2 reimplementation of [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947) available as a Python package and using [TedEval](https://github.com/clovaai/TedEval) as evaluation metrics. 

## Data Preparation
![](./demo/preparation.png)

Store images in `imgs` folder and groundtruths in `gts` folder. Then, prepare text files for training and validate data in the following format with '\t' as a separator:
- Example for ICDAR 2015 `train.txt`:
```
./datasets/train/train_imgs/img_1.jpg	./datasets/train/train_gts/gt_img_1.txt
./datasets/train/train_imgs/img_2.jpg	./datasets/train/train_gts/gt_img_2.txt
```
- Example for ICDAR 2015 `validate.txt`:
```
./datasets/validate/validate_imgs/img_1.jpg	./datasets/validate/validate_gts/gt_img_1.txt
./datasets/validate/validate_imgs/img_2.jpg	./datasets/validate/validate_gts/gt_img_2.txt
```
You can customize the script in [dir2paths.sh](dir2paths.sh) to generate the above `train.txt` and `validate.txt` for your own dataset. And the groundtruths can be `.txt` files, with the following format:
```
x1, y1, x2, y2, x3, y3, x4, y4, annotation
```
Below is the content of `./datasets/train/train_gts/gt_img_1.txt`:
```
377,117,463,117,465,130,378,130,Genaxis Theatre
493,115,519,115,519,131,493,131,[06]
374,155,409,155,409,170,374,170,###
492,151,551,151,551,170,492,170,62-03
376,198,422,198,422,212,376,212,Carpark
494,190,539,189,539,205,494,206,###
374,1,494,0,492,85,372,86,###
```

## Quick Start
```
pip install tfdbnet
```
After above installation, see the [demo](demo/demo.ipynb) on ICDAR 2015 dataset to know how to use
![](/demo/demo.png)

## Reference
- https://github.com/MhLiao/DB
- https://github.com/zonasw/DBNet
- https://github.com/WenmuZhou/DBNet.pytorch
- https://github.com/xuannianz/DifferentiableBinarization
- https://github.com/clovaai/TedEval
resolv3r 1.0.3
A simple python package aimed at simplifying the vendor resolution process for network devices
Requires Python: >=3.8,<4.0

# Overview
Resolv3r is a simple python package aimed at resolving the vendor associated with a given ip

**Note: this package only resolves devices as either cisco or aruba!**

To install using pip, simply run:
```commandline
pip3 install resolv3r
```

# Dependencies
Tested in 3.10 (Use in older versions of python at your own risk)

Look in pyproject.toml for more details into the project dependencies:
 - asyncssh
 - netmiko

# How to use?
In your python project, simply write:
```commandline
from resolv3r import Resolver
```
Now, to resolve a given ip to a vendor, you first need to create a Resolver object:
```commandline
resolver = Resolver("192.168.0.1", "username", "password")
```
Now, begin the resolution process using:
```commandline
device_vendor = resolver.detect_vendor()
```
This should return the correct vendor of the ip in question: either "cisco_ios" or "hp_procurve"

# How does this work?
First, resolv3r uses asyncssh to connect to the device in question and determine device type using a single command ("sh version")

If that fails, resolv3r moves onto using netmiko's autodetection feature to resolve the vendor.

Finally, if that doesn't work, resolv3r raises a LookupError, indicating that the vendor resolution process was not successful.
CompositeRecommenders 0.0.1
Composite recommenders based on SparseMatrixRecommender and LatentSemanticAnalyzer.
Requires Python: >=3.7
Keywords: sparse,matrix,recommender,sparse matrix recommender,linear algebra,linear,algebra,recommendations,SMR,LSA,composite,composite design pattern

# Composite recommenders

***Based on "SparseMatrixRecommender" and "LatentSemanticAnalyzer"***

## Introduction

This package has functions and classes for the creation and utilization 
of composite recommenders.

-------

## Usage examples

- `LSAEndowedSMR` : ["LSAEndowedSMR-recommend-by-profile-and-text.py"](./examples/LSAEndowedSMR-recommend-by-profile-and-text.py)

-------

## References

[AAp1] Anton Antonov,
[SparseMatrixRecommender Python package](https://pypi.org/project/SparseMatrixRecommender/),
(2021),
[PyPI.org/antononcube](https://pypi.org/user/antononcube/).

[AAp2] Anton Antonov,
[LatentSemanticAnalyzer Python package](https://pypi.org/project/LatentSemanticAnalyzer/),
(2021),
[PyPI.org/antononcube](https://pypi.org/user/antononcube/).