Coral Settlement Tile: image analysis

Brief description

This Python code defines a batch-processing workflow to process raw images and analyse coral larval settlement on tiles. The workflow can be split into two main stages:

1. ImageProcess: The images need to be prepared for further analysis. The suggested pipeline for image processing includes several steps to ensure a consistent dataset of images:
* Cropping: Image cropping ensures that each tile defines the region of interest, removing any background.
* Resizing: All images are resized to the exact dimensions.
* Histogram Equalisation: If a reference image is provided, the histograms of the images are equalised to match the reference, ensuring uniform lighting and contrast.
* Metadata Preservation: The original image metadata and any additional information provided by the user are retained in the processed images, ensuring the dataset maintains the same information as the original images.

2. Chimera: coral settlement analysis. Once the images are processed, visible recruits can be identified using annotation software (external), like CVAT. Annotated images can then be analysed to study the evolution of the corals, for example, determining the survival rates of chimeras.

The user does not need to follow all the steps sequentially. If the images are already prepared, such as being the same size and adequately cropped around the object of interest around the object of interest (e.g. tiles), they can skip the preparation steps and proceed directly to the recruitment analysis.