semvec/pypi-packages huggingface dataset:
Imagetotextegs 0.1
Image to text convertion package

This is image to text convertion format package.
Example:
--------

import Imagetotextegs
text_data = Imagetotextegs.image_to_text("image_url.jpg")
print("Text data:",text_data)

changelog
==========

Image_OCR(0.1)(22/06/2023)
-----------------------------
   - First release of image to text convertion EGS
cnwigee 1.0.2
Your description of the project

# CNWI-GEE
Canadian National Wetland Inventory Google Earth Engie Random Forest Classifications. Provides high
level access for doing standardized random forest wetland classifications.

# Installation and Setup
This package is built to be used with the annaconda project. For best user experience use some 
recent version of conda. These walk though will be using a miniconda3
## Dependencies
- Geopandas
- google earth engine

```sh
conda create -n cnwi-gee python=3.10 -c conda-forge earthengine-api geopandas pandas
```

```sh
# Step 2): activate new conda env and authenticate earth engine api
$ conda activate cnwi-gee
# authenticate earth engine api
(cnwi-gee) $ earthengine authenticate
```

# Example Pipeline
```python
from dataclasses import dataclass

import ee

from cnwi import inputs, rf, td, funcs, sfilters
from cnwi import prebuilt



def main():
    # load dataset
    dataset = ee.FeatureCollection("users/ryangilberthamilton/BC/williston/fpca/willistonA_no_floodplain")
    williston = prebuilt.WillistonA()
    
    # create a training object
    training = td.TrainingData(
        collection=dataset,
        label='cDesc'
    )
    
    # create s1 inputs
    s1s: List[ee.Image] = inputs.s1_inputs(williston.s1)
    
    # create s2 inputs
    s2s: List[ee.Image] = inputs.s2_inputs(williston.s2)
    
    # create elevation inputs
    elevation = inputs.nasa_dem()
    filter = sfilters.gaussian_filter(3)
    smoothed = filter(elevation)
    slope = ee.Terrain.slope(smoothed)
    
    # Create the inputs stack
    stack = ee.Image.cat(*s1s, *s2s, smoothed, slope)
    
    # sample the stack
    training.sample(
        stack=stack
    )
    
    # create the rf model
    model = rf.RandomForestModel()
    # train the model
    trained = model.train(
        training_data=training.samples,
        predictors=stack.bandNames(),
        classProperty=training.value
    )
    
    # classify the image
    classified_img = stack.classify(trained).uint8()
    
    # export image and samples to cloud
    return sys.exit(0)
    
    
if __name__ == '__main__':
    main()
```
catesegy 0.0.1
Python client for extracting data from CATE archives and writing as segy data
Requires Python: >=3.10

-----------
DESCRIPTION
-----------

Python client for extracting data from CATE archives and writing as segy data

------------
INSTALLATION
------------

    pip install catesegy


-----
USAGE
-----

See also `catenp.catesegy.Example`


    from catesegy import Authenticate,WriteFile

    # Authenticate to the server
    tk = Authenticate(serverAddress,serverPort,cateUserName,catePassword)
   
    # Get the data and save as SEGY
    WriteFile(serverAddress,serverPort,cateUserName,
        tstart,tstop,
        cstart,cstop,
        "test.sgy"
        )
aspect-ratio-too 0.1.1
Aspect ratio calculations.
Requires Python: >=3.8

# Aspect Ratio Too


## Overview
Aspect ratio calculations.

## Installation

    pip install aspect-ratio-too



## Development

    make env
    make reqs
    pip install -e .

## Testing

    pytest


## Usage
Know the aspect ratio you want but don't know the corresponding width/height? Try this:

    from aspect_ratio_too import AspectRatioCalculator

    arc = AspectRatioCalculator(16, 9)

    # Get (width/height) tuple
    arc.width_to_dimensions(1920)  # (1920, 1080)
    arc.height_to_dimensions(1080)  # (1920, 1080)

    arc.width_to_height(1920)  # 1080
    arc.height_to_width(1080)  # 1920

Or calculate the aspect ratio given know pixel dimensions:

    from aspect_ratio_too import aspect_ratio, aspect_ratio_str

    aspect_ratio(1920, 1080)  # 1.7777777777777777

    aspect_ratio_str(1920, 1080)  # 16:9


## Issues

If you experience any issues, please create an [issue](https://bitbucket.org/xstudios/aspect-ratio-too/issues) on Bitbucket.

# History
All notable changes to this project will be documented in this file. This project adheres to [Semantic Versioning](http://semver.org/).

## 0.1.1 (2023-06-22)
* Update packaging method to `pyproject.toml`.

## 0.0.1 (2022-11-07)
* First release