semvec/pypi-packages huggingface dataset:
pyedgeloop 0.0.2
An efficient and fast library to detect loops, edges and outer boundary in binary images
Requires Python: >=3.6

## pyedgeloop
PyEdgeLoop is a fast and simple python package to detect loops, outer boundary and edges in binary images.<br />
This library was initially created to help detect loops and boundaries/edges in word images. It plays an important role in segmentation of cursive handwriting 
segmentation. But it can be used for many other purposes also.

## Installation
pip install pyedgeloop

## Usage
import pyedgeloop as detect <br />
import cv2 <br />
Im = cv2.imread(your_filename)<br />
Loops, Edges, Boundary = detect.DetectLoopsEdges(Im) <br />

## Examples

<b>Here are some examples where we are finding loops and boundary of a handwritten word image-</b> <br /><br />
<b>Input</b> <br /><br />

1 ![alt input1](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image1/word1.png)

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;

2 ![alt input2](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image2/word2.png)<br />

<br />

<b> Output </b>  <br /><br /><br />
Loops

1 ![alt output1](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image1/Loops.png)<br /><br />

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;

2 ![alt output1](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image2/Loops.png)<br /><br />

Outer Boundary

1 ![alt output2](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image1/Boundary.png)<br /><br />

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;

2 ![alt output2](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image2/Boundary.png)<br /><br />

Edges

1 ![alt output3](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image1/Edges.png)<br /><br />

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;

2 ![alt output3](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image2/Edges.png)<br /><br />


<b>Here is another example where we need to detect only edges-</b> <br /><br />

<b>Input</b> <br /><br />

![alt input3](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image3/leaf.jpeg)<br />

<b>Output Edges</b> <br /><br />

![alt output31](https://raw.githubusercontent.com/AdityaDutt/Loop-and-Edges-Detector/master/Images/Image3/Edges_leaf.png)<br /><br />


## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## Author
Aditya Dutt - https://github.com/AdityaDutt 

## License
[MIT](https://choosealicense.com/licenses/mit/)
xrayimg 0.0.1
A library for convert Rigaku img file to csv or png file
Requires Python: >=3.7, <4

# xrayimg

Rigaku 拡張子がimgのラウエ写真のcsv, pngへの変換

## install

``` bash
pip install xrayimg
```

## How to use

```python
from xrayimg import XimgConverter
converter = XimgConverter(path_to_img_file)
converter.to_csv()
converter.to_png()
```
InterviewTranscriber 0.0.2
Python based interface for interacting with Google Cloud's Speech-to-Text API and Cloud Storage API.
Requires Python: >=3.6
Keywords: sociolinguistics,linguistics,automatic analysis,transcription,textgrids

# TranscribeInterviews
Python interface for interacting with the Google Speech-to-Text API and Google Cloud Storage API for the purposes of automatic audio transcription. Usage requires a google cloud account with a credential's file issued from it with permissions for both APIs.

## Installation
```bash
pip install InterviewTranscriber
```

## Example End-to-End Workflow
```python
# 1) Upload audio file(s) to Google Coud Coldline Storage bucket.
from Transcriber.Google.Storage import Storage
from Transcriber.Google.Transcriber import TranscriberBeta

storage_connection = Storage(project_id='<project_id>',
                             credential_path='<path_to_credential_file')
storage_connection.upload_file(bucket_name='<cloud_bucket_name>',
                               source_file_path='<path_to_source_file>')

# 2) Get URI list of files in bucket
file_list = storage_connection.get_uris(bucket_name='<cloud_bucket_name>')

# 3) Send API call to transcribe audio
transcriber = TranscriberBeta(output_directory='<path_to_output_directory>',
                              credential_path='<path_to_credential_file>',
                              contexts_path='<path_to_contexts_file.txt>',
                              speaker_count=2,
                              diarization=True)

transcriber.get_transcriptions(uris=file_list) 

# 4) Export as a Praat TextGrid
transcriber.export_textgrids()                    
```