# LBBNorm Project

## Coming Soon...

Welcome to **LBBNorm Project** - your go-to solution from the Laboratory of Systems Biology and Bioinformatics (LBB).

## How to use
Here is an implementation guide in Markdown format for using the `LBBNorm` library for image normalization in Python, focusing on the `Reinhard` method. This guide also mentions other available methods within the library.

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# Image Normalization Using LBBNorm in Python

This guide provides instructions on how to use the `LBBNorm` library for image normalization in Python, specifically utilizing the `Reinhard` normalization method. The `LBBNorm` library includes several normalization methods, including `Reinhard`, `Macenko`, `Vahadane`, `AdaptiveColorDeconvolution`, and `ModifiedReinhard`. This guide will focus on the `Reinhard` method.

## Prerequisites

Ensure you have Python installed on your system and the necessary libraries, including `LBBNorm` and `PIL` for image processing. If you haven't installed these libraries yet, you can do so using pip:

```bash
pip install LBBNorm Pillow Numpy
```

## Using the Reinhard Normalization Method

The following steps will guide you through the process of normalizing an image using the `Reinhard` method from the `LBBNorm` library.

### Step 1: Import Required Libraries

First, import the necessary libraries in your Python script.

```python
from LBBNorm import Reinhard
from PIL import Image
import numpy as np
```

### Step 2: Initialize the Normalizer

Create an instance of the `Reinhard` normalizer.

```python
normalizer = Reinhard()
```

### Step 3: Fit the Normalizer to the Target Image

The `fit` method adjusts the normalizer based on a target image, which is the reference for normalization. Replace `target` with your target image array.

```python
# Assuming 'target' is a NumPy array representing the target image
normalizer.fit(target)
```

### Step 4: Normalize a Sample Image

Use the `transform` method to normalize a sample image, replacing `sample` with your sample image array.

```python
# Assuming 'sample' is a NumPy array representing the sample image to normalize
normalized_image = normalizer.transform(sample)
```

### Step 5: Save the Normalized Image

Finally, save the normalized image using the `PIL` library.

```python
Image.fromarray(normalized_image).save('/content/normalized.png')
```

## Other Normalization Methods

The `LBBNorm` library also offers other normalization methods, which can be used similarly by replacing `Reinhard` with any of the following:

- `Macenko`
- `Vahadane`
- `AdaptiveColorDeconvolution`
- `ModifiedReinhard`

For each method, you will initialize the normalizer accordingly, for example:

```python
from LBBNorm import Macenko
normalizer = Macenko()
```

And then follow the same steps to fit the normalizer to your target image, transform your sample image, and save the normalized result.

## Conclusion

This guide has introduced how to perform image normalization using the `Reinhard` method from the `LBBNorm` library in Python. By following the steps outlined, you can easily normalize images for your projects. Remember to explore other normalization methods available in the library to find the one that best suits your needs.

### Sneak Peek

We're working behind the scenes to craft an exceptional product from LBB that addresses complex bioinformatics challenges. Stay tuned for updates and teasers on what we're creating!

### Features to Anticipate

- **Efficient Data Normalization**: Tailored algorithms for high-throughput data processing.
- **User-Friendly Interface**: Designed with the end-user in mind, ensuring a seamless experience.
- **Advanced Analytical Tools**: Cutting-edge tools for insightful data analysis.
- ... and many more!

### Get Notified!

Want to be the first to know when we go live? Drop us your email at [amasoudin@ut.ac.ir](mailto:amasoudin@ut.ac.ir), and we'll make sure you're in the loop.

### Contribute

Eager to contribute or have ideas? We'd love to hear from you! Here's how you can help:
- **Star this repo**: Starring helps to get more visibility and shows your support.
- **Share your ideas**: Open an issue with your suggestions and feature requests.
- **Spread the word**: Tell your friends and colleagues about us.

### Stay Connected

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### Contact Us

Have questions? Reach out to us at [amasoudin@ut.ac.ir](mailto:amasoudin@ut.ac.ir), or drop us a message on our social platforms.

### License

This project is in the process of being licensed - details will be shared soon.

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We can't wait to show you what we're building at the Laboratory of Systems Biology and Bioinformatics (LBB). Stay tuned!

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