semvec/pypi-packages huggingface dataset:
bergcastro-image-processing 0.1.3
Pacote de processamento de imagens
Requires Python: >=3.9

# bergcastro-image-processing

Um pacote Python para processamento de imagens, incluindo funcionalidades para comparar imagens, transferir histogramas e redimensionar imagens.

## Descrição

O pacote `bergcastro-image-processing` oferece ferramentas para:

- Comparar imagens usando similaridade estrutural.
- Transferir histogramas entre imagens.
- Redimensionar imagens com diferentes proporções.

## Instalação

Use o gerenciador de pacotes [pip](https://pip.pypa.io/en/stable/) para instalar `bergcastro-image-processing`.

```bash
pip install bergcastro-image-processing
```

## Uso
### Comparando imagens

import numpy as np
from skimage import io
from bergcastro_image_processing.similarity import find_difference

### Carregue as imagens
image1 = io.imread('imagem1.png')
image2 = io.imread('imagem2.png')

### Encontre a diferença entre as imagens
difference_image = find_difference(image1, image2)

### Salve a imagem da diferença
io.imsave('diferenca.png', difference_image)

## Histograma

from skimage import io
from bergcastro_image_processing.histogram import transfer_histogram

## Carregue as imagens
image1 = io.imread('imagem1.png')
image2 = io.imread('imagem2.png')

## Transfira o histograma da imagem2 para a imagem1
matched_image = transfer_histogram(image1, image2)

## Salve a imagem resultante
io.imsave('histograma_transferido.png', matched_image)


## Autor

- Berg Castro - [https://github.com/BergCastro](https://github.com/BergCastro)

## Licença

Este projeto está licenciado sob a [Licença MIT](https://choosealicense.com/licenses/mit/) - veja o arquivo [LICENSE](LICENSE) para detalhes.
metrixify 0.5.0
A Python library for automatic model evaluation
Requires Python: >=3.7

metrixify 🚀
A lightweight library for automatic model evaluation

📌 Overview
metrixify is an easy-to-use Python library that automatically evaluates machine learning models. It supports both classification and regression models and returns evaluation metrics in a dictionary format.

📥 Installation
You can install metrixify directly from PyPI:

## pip install metrixify 

🛠 Usage
1️⃣ Import the Library

""" from metrixify import evaluate_model """


2️⃣ Classification Model Evaluation

""" from metrixify import evaluate_model

actual_vals = [0, 1, 1, 0, 1]
predicted_vals = [0, 1, 0, 0, 1]

metrics = evaluate_model("classification", actual_vals, predicted_vals)
print(metrics) """


✅ Output:

"""{
    'accuracy': 0.8,
    'precision': 0.75,
    'recall': 0.8,
    'f1_score': 0.76,
    'roc_auc': 'Error: Need probability estimates',
    'log_loss': 'Error: Need probability estimates'
}"""

3️⃣ Regression Model Evaluation
"""
actual_vals = [3.2, 4.5, 6.1, 7.8, 9.0]
predicted_vals = [3.0, 4.8, 5.9, 7.5, 9.2]

metrics = evaluate_model("regression", actual_vals, predicted_vals)
print(metrics)
""""

✅ Output:
"""
{
    'mae': 0.18,
    'mse': 0.036,
    'rmse': 0.19,
    'r2_score': 0.97,
    'adjusted_r2': 0.95,
    'mape': 2.5
}
"""


🛠 Contributing
Contributions are welcome! If you find bugs or want to improve metrixify, feel free to fork the repository and submit a pull request.

📜 License
This project is licensed under the MIT License.

🌟 Support
If you like this project, please give it a ⭐ on GitHub!