Sequential_Registration

This repository contains Python scripts for performing image registration using OpenCV. The toolkit includes methods for both homography-based and affine transformation-based registration, supporting both rigid and non-rigid transformations. These scripts are designed to handle images in TIFF format, commonly used in scientific imaging.

Features

1. Normalization: Custom normalization for 16-bit images to enhance feature detection.
2. Robust Feature Matching: Uses AKAZE keypoints for reliable matching.
3. Homography Registration: Applies transformations based on homography matrix computation.
4. Rigid Registration: Utilizes affine transformations for registration.
5. Preservation of Metadata: Original images are used for the final transformation to preserve metadata.

Prerequisites

Before you start, ensure you have the following installed:

1. Python 3.6 or higher
2. OpenCV
3. NumPy
4. PIL

You can install the necessary Python packages using pip:

pip install numpy opencv-python pillow

Structure

The repository contains two main scripts:

1. global_Merged.py: Script for registering images based on homography.
2. rigid_registration.py: Script for registering images using affine transformations.

Usage

To use these scripts, follow these steps:

1. Clone the repository:

[git clone https://github.com/your-username/image-registration-toolkit.git](https://github.com/NabaviLab/Sequential_Registration.git)

2. Place your TIFF images in an input directory.
3. Run the desired registration script. For example, to perform homography-based registration, use:

python global_Merged.py --input ./input --output ./output

To perform rigid registration, use:

python affine_Merged.py --input ./input --output ./output

Parameters

1. --input: Directory containing the input TIFF images.
2. --output: Directory where the registered images will be saved.