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import numpy as np
def calculate_iou(box1, box2):
"""Calculate Intersection over Union (IoU) between two bounding boxes."""
x1 = max(box1[0], box2[0])
y1 = max(box1[1], box2[1])
x2 = min(box1[2], box2[2])
y2 = min(box1[3], box2[3])
intersection = max(0, x2 - x1) * max(0, y2 - y1)
box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])
union = box1_area + box2_area - intersection
return intersection / union if union > 0 else 0
def calculate_ap(predictions, targets, iou_threshold=0.5):
"""Calculate Average Precision (AP) for a single class."""
all_pred_boxes = []
all_pred_scores = []
all_target_boxes = []
for pred, target in zip(predictions, targets):
all_pred_boxes.extend(pred['boxes'].tolist())
all_pred_scores.extend(pred['scores'].tolist())
all_target_boxes.extend(target['boxes'].tolist())
sorted_indices = sorted(range(len(all_pred_scores)), key=lambda k: all_pred_scores[k], reverse=True)
all_pred_boxes = [all_pred_boxes[i] for i in sorted_indices]
all_pred_scores = [all_pred_scores[i] for i in sorted_indices]
tp = np.zeros(len(all_pred_boxes))
fp = np.zeros(len(all_pred_boxes))
for i, pred_box in enumerate(all_pred_boxes):
best_iou = 0
best_target_idx = -1
for j, target_box in enumerate(all_target_boxes):
iou = calculate_iou(pred_box, target_box)
if iou > best_iou:
best_iou = iou
best_target_idx = j
if best_iou >= iou_threshold:
tp[i] = 1
all_target_boxes.pop(best_target_idx)
else:
fp[i] = 1
tp_cumsum = np.cumsum(tp)
fp_cumsum = np.cumsum(fp)
recalls = tp_cumsum / len(all_target_boxes) if len(all_target_boxes) > 0 else np.zeros_like(tp_cumsum)
precisions = tp_cumsum / (tp_cumsum + fp_cumsum)
precisions = np.concatenate(([1], precisions))
recalls = np.concatenate(([0], recalls))
return np.trapz(precisions, recalls)
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