greedy-matching-evaluation

name	greedy-matching-evaluation
description	Evaluating clustering performance using greedy matching of predicted centroids against ground truth points.

Matching Algorithm

For each image:

1. Centroid Extraction: Calculate the arithmetic mean of all points assigned to each cluster ID (excluding noise points labeled -1).
2. Greedy Matching:
   • Calculate a distance matrix between all cluster centroids and expert points.
   • Iteratively pick the pair with the smallest Euclidean distance (if < 100 pixels).
   • Remove these points from the pool and repeat until no pairs are within 100 pixels.
3. Metrics:
   • True Positives (TP): Number of matches found.
   • False Positives (FP): Number of unmatched clusters.
   • False Negatives (FN): Number of unmatched expert points.
   • F1 Score: $2 * TP / (2 * TP + FP + FN)$.
   • Delta: Mean standard Euclidean distance of matched pairs.

Ensure you handle images with zero citizen science points or zero expert points correctly by returning F1=0.0 and delta=NaN.