semvec/pypi-packages huggingface dataset:
image-helpers-system 0.1.2
A simple image processing utility library
Requires Python: >=3.6

```markdown
# Image Helpers

A simple image processing utility library.

## Features

- Resize images
- Convert image formats
- Apply blur filters
- Apply sharpen filters

## Installation

You can install the package using pip:

```sh
pip install image_helpers_system
```

## Usage

Here are some examples of how to use the library:

### Importing the Library

```python
from image_helpers_system.image_ops import resize_image, convert_image_format, apply_blur_filter, apply_sharp_filter
```

### Resizing an Image

Resize an image to the specified dimensions:

```python
resize_image('input.jpg', 'output.jpg', (800, 600))
```

### Converting Image Format

Convert an image from one format to another:

```python
convert_image_format('input.jpg', 'output.png', 'PNG')
```

### Applying Blur Filter

Apply a Gaussian blur filter to an image:

```python
apply_blur_filter('input.jpg', 'output.jpg', radius=5)
```

### Applying Sharpen Filter

Apply a sharpen filter to an image:

```python
apply_sharp_filter('input.jpg', 'output.jpg')
```

## Functions

### `resize_image(input_path, output_path, size)`

Resize the image located at `input_path` to the specified `size` and save it to `output_path`.

- `input_path`: Path to the input image.
- `output_path`: Path to save the resized image.
- `size`: Tuple specifying the new size `(width, height)`.

### `convert_image_format(input_path, output_path, format)`

Convert the image located at `input_path` to the specified `format` and save it to `output_path`.

- `input_path`: Path to the input image.
- `output_path`: Path to save the converted image.
- `format`: The format to convert the image to (e.g., 'PNG', 'JPEG').

### `apply_blur_filter(input_path, output_path, radius=2)`

Apply a Gaussian blur filter to the image located at `input_path` and save it to `output_path`.

- `input_path`: Path to the input image.
- `output_path`: Path to save the blurred image.
- `radius`: Radius of the blur (default is 2).

### `apply_sharp_filter(input_path, output_path)`

Apply a sharpen filter to the image located at `input_path` and save it to `output_path`.

- `input_path`: Path to the input image.
- `output_path`: Path to save the sharpened image.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
SIST02-converter 0.1.6
Conversion of input URL to SIST02 style.

## パッケージについて

ようこそ、**SIST02-converter** へ！

**SIST02-converter** は、参考文献として使用したWebサイトをSIST02スタイルに変換する作業を簡略化することを目的とした Python ライブラリです。初心者から経験豊富な開発者まで、**SIST02-converter** は直感的なツールとユーティリティを提供し、Python プロジェクトのワークフローを支援します。

## 主な特徴

- 入力されたURLからSIST02スタイルに変換したテキストを出力します

## SIST02について
SIST02は、日本国内で定められている科学技術情報流通技術基準の文献情報の記述スタイルである<br>
```
[基本形]

著者名. "Webページのタイトル". Webサイトの名称. 更新日付. 入手先, (参照年月日).
```

## インストール

pip を使用して **SIST02-converter** をインストールできます

```bash
pip install SIST02-converter
```

## 使用法

ここでは、**SIST02-converter** の使用方法の簡単な例を示します

```python
from SIST02_converter import Convert_URL

url = "https://example.com"
text = Convert-URL(url)
print(text)
```

## ライセンス

**SIST02_converter** は MIT ライセンス の下でライセンスされています。これは、個人および商用のプロジェクトの両方で自由に使用できることを意味します。
data-cleaning-assistant 0.1.0
A comprehensive data cleaning assistant for pandas DataFrames.
Requires Python: >=3.6

# Data Cleaning Assistant

Data Cleaning Assistant is a comprehensive tool for cleaning and preprocessing pandas DataFrames.

## Features

- Fill missing values
- Remove duplicates
- Detect and handle outliers
- Convert data types
- Standardize features
- Generate cleaning reports
- Visualize missing values and outliers

## Installation

```sh
pip install data_cleaning_assistant
DataHandlerForCSV 0.9.1
data cleaner
Requires Python: <3.12,>=3.6

# Data Handler Package

## Description
The Data Handler Package is designed to simplify data handling tasks, particularly focused on CSV files. It provides various classes and methods to streamline common data operations.

## Installation
You can install the package using pip:
```bash
pip install DataHandlerForCSV
```

## Usage

--Missing Value Handler

```python
from program.missing_value_handler import HandleMissingValue as hmv

df = pd.DataFrame({"numbs" : [1, 2, None, 4]})
copy1 = df.copy()
df = hmv.fill_as_mean(copy1, "numbs")

copy2 = df.copy()
df = hmv.fill_as_median(copy2, "numbs")

copy3 = df.copy()
df = hmv.fill_as_constant(copy3, "numbs")

copy4 = df.copy()
df = hmv.drop(copy4, "numbs")
```
tgemoji 1.0.1
Collection of text formatting utils for Telegram related libraries
Requires Python: >=3.6

# This compact library is designed to streamline text formatting and enhance emoji integration for Telegram-related applications.

## converttools.aiogram2pyrogram

**Description:** Replaces aiogram premium emoji tags with pyrogram ones

**Params:**
- **text** (str): Unformatted text

**Returns:**
- **text** (str): Formatted text

## converttools.pyrogram2aiogram

**Description:** Replaces pyrogram premium emoji tags with aiogram ones

**Params:**
- **text** (str): Unformatted text

**Returns:**
- **text** (str): Formatted text
opencv-calibrate 0.1.1
A simple OpenCV checkerboard camera calibration python package
Requires Python: >=3.7

# OpenCV Camera Calibration

![Checkerboard calibration images](assets/smaller_checkerboards.gif "Calibration")

A simple tool for checkerboard camera calibration if you are tired of re-writing from the [OpenCV docs](https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html) on camera calibration.

# Install

```bash
pip install opencv-calibrate 
```

[pypi](https://pypi.org/project/opencv-calibrate/0.1.0/)

## Prerequisites

This package relies on ffmpeg, please ensure it is installed. See [python ffmpeg](https://github.com/kkroening/ffmpeg-python) for more.

# Usage

```python

import opencv_calibrate

# image
image_directory_path = "example_images/"

# image params
checkerboard = (10, 7)
ret, camera_parameters = opencv_calibrate.image(image_directory_path, checkerboard)

# or with video

# video
video_path = "/path/to/video"

# video params
ret, camera_parameters = opencv_calibrate.video(video_path)
```

Then we can acess the parameters in the camera_parameters object:

```python
# camera intrinsic matrix
camera_matrix = camera_parameters.camera_matrix

# distortion matrix
distortion_matrix = camera_parameters.distortion_matrix

# rotation vectors
rotation_vecs = camera_parameters.rotation_vecs

# translation vectors
translation_vecs = camera_parameters.translation_vecs

# projection error
projection_error = camera_parameters.projection_error

# save parameters
camera_parameters.save("/path/to/dir/")

# load
from opencv_calibrate import CameraParameters
# from npz
camera_parameters.load_from_npz("/path/to/params.npz")
# from yaml
camera_parameters.load_from_yaml("/path/to/params.yaml")
```

or from the command line (this is also callable by `python -m opencv_calibrate.calibrate`):

```bash
calibrate [-h] [--video VIDEO] [--image_dir IMAGE_DIR] [--output_dir OUTPUT_DIR] [--checkerboard CHECKERBOARD]

Camera calibration of videos and images containing checkerboard run with DEBUG>1 for more outputs

options:
  -h, --help            show this help message and exit
  --video VIDEO         Path to the video file
  --image IMAGE_DIR
                        Path to the directory containing images
  --output_dir OUTPUT_DIR
                        Path to the output directory
  --checkerboard CHECKERBOARD
                        Number of rows, columns and square size (mm) in comma seperated format e.g. "9, 6, 4"
```

This will output a YAML file with the parameters:

```yaml
camera_matrix:
- [f_x, 0.0, c_x]
- [0.0, f_y, c_y]
- [0.0, 0.0, 1.0]
distortion_matrix:
- [k1, k2, p1, p2 k3]
projection_error: float
```

and a .npz numpy file with addition of the extrinsic matrix.

Run with DEBUG environment variable for more outputs, e.g.

```bash
DEBUG=1 calibrate --image /path/to/images
```
preprocessinglib-tonga-gumustakim 0.5
A comprehensive data preprocessing library for Python
Requires Python: >=3.6

PreprocessingLib
PreprocessingLib is a Python library designed to facilitate data preprocessing steps. It provides various classes and functions to automate the process of cleaning, transforming, and engineering features in datasets.

Features
1. Missing Value Handling
Detect missing values in a dataset.
Fill missing values using mean, median, or a constant value.
Remove rows or columns with missing values.
2. Feature Engineering
Create new features based on existing ones.
3. Date and Time Handling
Extract features like year, month, day, and day of the week from datetime columns.
4. Data Type Conversion
Convert columns to numeric or categorical data types.
5. Categorical Encoding
Perform one-hot encoding or label encoding on categorical variables.
6. Outlier Handling
Detect outliers in numerical data.
Handle outliers by removing or replacing them.
7. Data Scaling
Standardize or normalize numerical data.
8. Text Cleaning
Clean text data by removing punctuation, stop words, and lemmatizing words.
Installation
You can install PreprocessingLib using pip:

pip install preprocessinglib
Usage
Here's how you can use PreprocessingLib in your Python projects:

from mypreprocessinglib import FeatureEngineer, MissingValueHandler, DateTimeHandler, DataTypeConverter, CategoricalEncoder, OutlierHandler, Scaler, TextCleaner
import pandas as pd

# Load sample dataset
data = pd.read_csv("sample_dataset.csv")

# Example usage of preprocessing functions
missing_handler = MissingValueHandler()
filled_data = missing_handler.fill_missing_values(data)

data_with_new_features = FeatureEngineer.create_new_features(data, column1='Column1', column2='Column2')

date_with_features = DateTimeHandler.extract_date_features(data, column='DateColumn')

numeric_data = DataTypeConverter.convert_to_numeric(data, columns=['Column1', 'Column2'])

encoded_data = CategoricalEncoder.one_hot_encode(data, columns=['CategoricalColumn'])

outliers_removed_data = OutlierHandler.handle_outliers(data, method='drop')

scaled_data = Scaler.standardize_data(data)

cleaned_text = TextCleaner.clean_text("example text")
Testing
You can run the unit tests to ensure the proper functioning of the library:

python -m unittest test_data_preprocessing.py
Contributing
Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License
This project is licensed under the MIT License - see the LICENSE file for details.
AISO-processor 0.1.0
A Python library for automatically preprocessing datasets.
Requires Python: >=3.6

# AISO Processor

AISO Processor is a Python library for automatically preprocessing datasets. It provides functionalities such as removing outliers, handling missing values, and unifying data formats.

## Features

- Remove outliers from your data
- Handle missing values with specified thresholds
- Unify data formats for consistent analysis

## Installation

You can install AISO Processor from PyPI:

```sh
pip install AISO-processor


## Usage

### Example Usage

```python
import pandas as pd
from AISO_processor import AISOProcessor

# Load data from a CSV file
file_path = 'sample_data.csv'
processor = AISOProcessor(file_path=file_path)

# Process the data
processed_df = processor.process(outliers=False, missing=True, format=True, missing_threshold=0.2, fill_value=50000)

print(processed_df)
```

### Sample Data

Save the following data into a file named `sample_data.csv`:

```csv
name,age,income,city
Alice,25,50000,New York
Bob,32,60000,Los Angeles
Charlie,22,,Chicago
David,35,55000,New York
Edward,29,70000,San Francisco
```

## License

This project is licensed under the MIT License.
```