Implemented f1 score calculation

Signed-off-by: Jim Martens <github@2martens.de>

src/twomartens/masterthesis/evaluate.py

@@ -20,6 +20,7 @@ Functionality to evaluate results of networks.
 
 Functions:
     get_number_gt_per_class(...): calculates the number of ground truth boxes per class
+    get_f1_score(...): computes the F1 score for every class
     match_predictions(...): matches predictions to ground truth boxes
 """
 from typing import Sequence, Union, Tuple, List
@@ -199,9 +200,6 @@ def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, int, int,
                 # false positives.
                 false_pos[i] = 1
             else:
-                # If this is not a ground truth that is supposed to be evaluation-neutral
-                # (i.e. should be skipped for the evaluation) or if we don't even have the
-                # concept of neutral boxes.
                 if image_id not in gt_matched:
                     # True positive:
                     # If the matched ground truth box for this prediction hasn't been matched to a
@@ -268,6 +266,32 @@ def get_precision_recall(number_gt_per_class: np.ndarray,
     return cumulative_precisions, cumulative_recalls
 
 
+def get_f1_score(cumulative_precisions: List[np.ndarray],
+                 cumulative_recalls: List[np.ndarray],
+                 nr_classes: int) -> List[np.ndarray]:
+    """
+    Computes the F1 score for every class.
+    
+    Args:
+        cumulative_precisions: cumulative precisions for each class
+        cumulative_recalls: cumulative recalls for each class
+        nr_classes: number of classes
+
+    Returns:
+        cumulative F1 score per class
+    """
+    cumulative_f1_scores = [[]]
+    
+    # iterate over all classes
+    for class_id in range(1, nr_classes + 1):
+        cumulative_precision = cumulative_precisions[class_id]
+        cumulative_recall = cumulative_recalls[class_id]
+        f1_score = 2 * ((cumulative_precision * cumulative_recall) / (cumulative_precision + cumulative_recall))
+        cumulative_f1_scores.append(f1_score)
+    
+    return cumulative_f1_scores
+
+
 def get_mean_average_precisions(cumulative_precisions: List[np.ndarray],
                                 cumulative_recalls: List[np.ndarray],
                                 nr_classes: int) -> List[float]:

src/twomartens/masterthesis/main.py

@@ -247,6 +247,7 @@ def _ssd_test(args: argparse.Namespace) -> None:
                                                                 cum_true_positives,
                                                                 cum_false_positives,
                                                                 ssd.N_CLASSES)
+    f1_scores = evaluate.get_f1_score(cum_precisions, cum_recalls, ssd.N_CLASSES)
     average_precisions = evaluate.get_mean_average_precisions(cum_precisions, cum_recalls, ssd.N_CLASSES)
     mean_average_precision = evaluate.get_mean_average_precision(average_precisions)
     
@@ -257,7 +258,8 @@ def _ssd_test(args: argparse.Namespace) -> None:
         "cumulative_false_positives": cum_false_positives,
         "cumulative_precisions": cum_precisions,
         "cumulative_recalls": cum_recalls,
-        "average precisions": average_precisions,
+        "f1_scores": f1_scores,
+        "mean_average_precisions": average_precisions,
         "mean_average_precision": mean_average_precision
     }