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LBBNorm 1.6.0

LBBNorm Project
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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.

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:
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.
from LBBNorm import Reinhard
from PIL import Image
import numpy as np

Step 2: Initialize the Normalizer
Create an instance of the Reinhard normalizer.
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.
# 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.
# 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.
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:
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!

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Want to be the first to know when we go live? Drop us your email at amasoudin@ut.ac.ir, and we'll make sure you're in the loop.
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Contact Us
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License
This project is in the process of being licensed - details will be shared soon.

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