greedy-point-matching-evaluation

name	greedy-point-matching-evaluation
description	Matching predicted cluster centroids to ground truth points using a greedy distance-based approach to calculate F1 score and precision/recall.

To evaluate clustering against expert "ground truth" points:

1. Centroid Calculation: For each cluster found by DBSCAN, calculate the centroid (mean of $x$ and $y$ coordinates).
2. Distance Matrix: Calculate standard Euclidean distance between all predicted centroids and all expert points for a specific image.
3. Greedy Matching:
   • Find the pair (centroid, expert point) with the smallest Euclidean distance.
   • If distance $\le$ threshold (e.g., 100 pixels), count as a True Positive (TP) and remove both points from further matching for this image.
   • Repeat until no more pairs can be matched under the threshold.
4. Metrics:
   • TP: Number of matched pairs.
   • FP (False Positives): Number of unmatched predicted centroids.
   • FN (False Negatives): Number of unmatched expert points.
   • F1 Score: $2 \cdot TP / (2 \cdot TP + FP + FN)$. If $TP+FP+FN = 0$, F1 is typically 1.0 (though in this task, if no points exist/match, the requirement specifies 0.0).
   • Delta: Average Euclidean distance of all matched TP pairs.