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Project description
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!
Get Notified!
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.
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
Follow us for the latest buzz and updates. Don't miss out on any announcements!
Contact Us
Have questions? Reach out to us at 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.
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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