semvec/pypi-packages huggingface dataset:
exemplar-based-inpainting 0.2.0
Python implementation of the exemplar-based inpainting method of Criminisi et al.
Requires Python: >=3.7

# Exemplar-based Inpainting

Python implementation of the exemplar-based inpainting method of Criminisi et al.:

> `Criminisi A, Pérez P, Toyama K. Region filling and object removal by exemplar-based image inpainting[J]. IEEE Transactions on image processing, 2004, 13(9): 1200-1212.`

## Installation

This project requires Python >= 3.7. To install it using `pip`:

```
cd <path_to>/exemplar_based_inpainting
pip install .
```

## Compile the docs

This project uses `mkdocs`. Therefore, compiling the documentation is as simple as running the following command from the same directory containing the `mkdocs.yml` file:

```
mkdocs build
```

And serving the documentation to read it locally:

```
mkdocs serve
```

## Usage

After installation, you should have the `exemplar_based_inpainting` command line tool available.

The only required parameter is the input image to inpaint. If you want to manually set the inpainting mask, and you do not need to store the results, you can just call it as follows:

```
exemplar_based_inpainting <image_path>
```

Please check the documentation for more information on the different parameters of this tool. We also provide some examples to test the tool in the `data` folder of this project.

## Acknowledgements

This project has been developed by Coronis Computing S.L. within the EMODnet Bathymetry (High Resolution Seabed Mapping) project.

* EMODnet: http://www.emodnet.eu/
* EMODnet (bathymetry): http://www.emodnet-bathymetry.eu/
* Coronis: http://www.coronis.es
stock-super 0.0.1
简单实现获取股票数据
Requires Python: >=3.10

# Stock Analysis Tool

这是一个用于分析股票数据的工具，能够根据股票名称、起始时间和结束时间，返回包含股票名称、代码、日期、开盘价、收盘价、最高价、最低价、成交量、成交额、涨跌幅、涨跌额、换手率的 DataFrame。

## 安装

你可以通过 pip 安装这个工具：

```bash
pip install stock_super
herberta 0.1.0
A Python package for converting texts to embeddings using pretrained models.
Requires Python: >=3.10

<!--
 * @Date: 2024-12-04 16:54:16
 * @LastEditors: yangyehan 1958944515@qq.com
 * @LastEditTime: 2024-12-04 17:10:31
 * @FilePath: /herberta/README.md
 * @Description: 
-->
# Text Embedding Package

A Python package for converting texts into embeddings using pretrained transformer models.

## Installation

```bash
pip install herberta

```python
from herberta.embedding import TextToEmbedding

# Initialize the embedding model
embedder = TextToEmbedding("path/to/your/model")

# Single text input
embedding = embedder.get_embeddings("This is a sample text.")

# Multiple text input
texts = ["This is a sample text.", "Another example."]
embeddings = embedder.get_embeddings(texts)
```
easyocr-unstructured-text 1.0.0
Parse unstructured text from PDFs
Requires Python: >=3.8


# EasyOCR Unstructured

EasyOCR Unstructured is a powerful library for Optical Character Recognition (OCR) that can extract text from PDFS, then group the text based on proximity.

It is intended for PDF files that have text that doesn't follow the left to right top to bottom standard of document writing.


## Getting Started

pip install easyocr-unstructured

```
import easyocr_unstructured

# Initialize the EasyOCR Unstructured object
easyocr = EasyocrUnstructured()

# Invoke the OCR process on your PDF file
result = easyocr.invoke('/path/to/your_pdf_file.pdf')

#result will be a list of lists containing strings
from pprint import pprint as pp
pp(result)
```

## Example Output

The output will look something like this:

```python
[
    ["This is the piece of text. Nothing near it"],
    ["This is the second piece of text.", "This is the third piece of text that was close to the second"],
    ["This is the fourth piece of text. Nothing near it"],
    ...
]
```

### Prerequisites

- Python 3.12 +

### Installing

pip install easyocr-unstructured

## Usage

```
import easyocr_unstructured

easyocr = EasyocrUnstructured()
result = easyocr.invoke('/path/to/your_pdf_file.pdf')
```

## Running the tests

No tests yet

## Built With

- Wing Pro
- Python 3.12
- numpy
- easyocr
- pdf2image
- hashlib

## Contributing

Please do, any sensible and safe change will be added!

## Authors

Kevin Fink

## License

MIT

## Acknowledgments
cityblocks 0.0.2
Python workflow to generate LCZ tiles for rendering in 3D in QGIS

# Cityblocks

Python utility to:

- Download global LCZ map from [Demuzere et al.](https://zenodo.org/records/7670653).
- Extract an area of interest
- Generate a new dataset where each pixel is replaced by a 2D tile corresponding to the LCZ type

The generated file can be displayed in QGIS to create stunning visualizations in 3D.

## How to use

```sh
# Install the tool
pip install git+https://github.com/Urban-M4/cityblocks.git

# Download global LCZ data. 
cityblocks download

# Extract area of interest
cityblocks extract "4.724808,52.273620,5.182114,52.458729"

# Convert LCZ data to 2d tiles
cityblocks convert
```

Data is downloaded in folder where the command is executed.


## QGIS styling
Then, import the dataset in QGIS and visualize it. We created a QGIS styling file [cityblocks_LCZ.qml](qgis_files/cityblocks_LCZ.qml), which you can load as follows:

1. Load layer in [QGIS](https://qgis.org/) (we used 3.34.8 LTR)
![Load layer in QGIS](images/1.png)

2. Go to properties:
![Go to properties](images/2.png)

3. Load Style from [cityblocks_LCZ.qml](\qgis_files\cityblocks_LCZ.qml) 
![Load style](images/3.png) 

4. You can modify the style as you like 
![modify the style](images/4.png)

5. Create a 3D map view
![Create a 3D map view](images/5.png)

6. Browse through you Local Climate Zones Cityblocks!
![Go to properties](images/6.png)

Note that you can add styled 2D layers in your QGIS environment to show them in your 3D render.


## Example output

The file generated by the cityblocks command line utility looks like this when imported in QGIS.

![2d version of LCZ tiles](images/Amsterdam_2D.png)

By extruding the surface and applying a bit of styling, we can generate images like below:

![3d version of LCZ tiles](images/Amsterdam_3D.png)

The tiles can also be combined with other map layers:

![3d tiles with background image](images/Amsterdam_3D_bg.png)
easyorc-unstructured 1.0.0
Parse unstructured text from PDFs
Requires Python: >=3.8


# EasyOCR Unstructured

EasyOCR Unstructured is a powerful library for Optical Character Recognition (OCR) that can extract text from PDFS, then group the text based on proximity.

It is intended for PDF files that have text that doesn't follow the left to right top to bottom standard of document writing.


## Getting Started

pip install easyocr-unstructured

```
import easyocr_unstructured

# Initialize the EasyOCR Unstructured object
easyocr = EasyocrUnstructured()

# Invoke the OCR process on your PDF file
result = easyocr.invoke('/path/to/your_pdf_file.pdf')

#result will be a list of lists containing strings
from pprint import pprint as pp
pp(result)
```

## Example Output

The output will look something like this:

```python
[
    ["This is the piece of text. Nothing near it"],
    ["This is the second piece of text.", "This is the third piece of text that was close to the second"],
    ["This is the fourth piece of text. Nothing near it"],
    ...
]
```

### Prerequisites

- Python 3.12 +

### Installing

pip install easyocr-unstructured

## Usage

```
import easyocr_unstructured

easyocr = EasyocrUnstructured()
result = easyocr.invoke('/path/to/your_pdf_file.pdf')
```

## Running the tests

No tests yet

## Built With

- Wing Pro
- Python 3.12
- numpy
- easyocr
- pdf2image
- hashlib

## Contributing

Please do, any sensible and safe change will be added!

## Authors

Kevin Fink

## License

MIT

## Acknowledgments