Question 13: Consider the following code snippet, which generates pre…

1# Predicted bounding boxes and confidence scores
2predictions = [
3    {'bbox': [40, 40, 90, 90], 'score': 0.85},
4    {'bbox': [20, 20, 60, 60], 'score': 0.80},
5    {'bbox': [150, 150, 200, 200], 'score': 0.65},
6    {'bbox': [250, 250, 300, 300], 'score': 0.50}
7]
8
9# Ground truth bounding boxes
10ground_truths = [
11    {'bbox': [40, 40, 90, 90]},
12    {'bbox': [20, 20, 60, 60]},
13    {'bbox': [150, 150, 200, 200]}
14]
15
16# Function to calculate IoU (Intersection over Union)
17def calculate_iou(box1, box2):
18    x1 = max(box1[0], box2[0])
19    y1 = max(box1[1], box2[1])
20    x2 = min(box1[2], box2[2])
21    y2 = min(box1[3], box2[3])
22    intersection = max(0, x2 - x1) * max(0, y2 - y1)
23    area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
24    area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
25    union = area1 + area2 - intersection
26    return intersection / union if union > 0 else 0
27
28# Match predictions with ground truths (IoU > 0.5 considered correct)
29threshold = 0.5
30true_positives = 0
31false_positives = 0
32false_negatives = len(ground_truths)
33
34for pred in predictions:
35    matched = False
36    for gt in ground_truths:
37        iou = calculate_iou(pred['bbox'], gt['bbox'])
38        if iou >= threshold:
39            true_positives += 1
40            false_negatives -= 1
41            matched = True
42            break
43    if not matched:
44        false_positives += 1
45
46precision = true_positives / (true_positives + false_positives)
47recall = true_positives / (true_positives + false_negatives)
48
49print(f"Precision: {precision:.2f}")
50print(f"Recall: {recall:.2f}")