1. 设定IoU和Conf阈值

2. 保存推理结果的txt文件

3. 计算TP, FP, FN

import osclasses = {0: "class 1",1: "class 2"}def iou(box1, box2):box1_x1 = box1[0] - box1[2] / 2box1_y1 = box1[1] - box1[3] / 2box1_x2 = box1[0] + box1[2] / 2box1_y2 = box1[1] + box1[3] / 2box2_x1 = box2[0] - box2[2] / 2box2_y1 = box2[1] - box2[3] / 2box2_x2 = box2[0] + box2[2] / 2box2_y2 = box2[1] + box2[3] / 2inter_x1 = max(box1_x1, box2_x1)inter_y1 = max(box1_y1, box2_y1)inter_x2 = min(box1_x2, box2_x2)inter_y2 = min(box1_y2, box2_y2)inter_area = max(inter_x2 - inter_x1, 0) * max(inter_y2 - inter_y1, 0)box1_area = (box1_x2 - box1_x1) * (box1_y2 - box1_y1)box2_area = (box2_x2 - box2_x1) * (box2_y2 - box2_y1)union_area = box1_area + box2_area - inter_areaiou = inter_area / union_area if union_area > 0 else 0return ioudef read_boxes(file_path):boxes = []confidences = []with open(file_path, 'r') as f:for line in f:parts = line.strip().split()if len(parts) == 6:category = int(parts[0])box = [float(part) for part in parts[1:5]]confidence = float(parts[5])boxes.append((category, box))confidences.append((category, confidence))elif len(parts) == 5:category = int(parts[0])box = [float(part) for part in parts[1:5]]boxes.append((category, box))return boxes, confidencesdef evaluate_folder(annotation_dir, result_dir):stats = {}for annot_file in os.listdir(annotation_dir):annot_path = os.path.join(annotation_dir, annot_file)result_path = os.path.join(result_dir, annot_file)annot_boxes, _ = read_boxes(annot_path)  # 标注不需要置信度result_boxes, result_confidences = read_boxes(result_path) if os.path.exists(result_path) else ([], [])for category, _ in annot_boxes:if category not in stats:stats[category] = {'annotated': 0, 'predicted': 0, 'tp': 0, 'fp': 0, 'fn': 0, 'confidences': []}stats[category]['annotated'] += 1for category, result_box in result_boxes:if category not in stats:stats[category] = {'annotated': 0, 'predicted': 0, 'tp': 0, 'fp': 0, 'fn': 0, 'confidences': []}stats[category]['predicted'] += 1ious = [iou(result_box, box) for cat, box in annot_boxes if cat == category]if max(ious, default=0) >= 0.5:stats[category]['tp'] += 1else:stats[category]['fp'] += 1for category, confidence in result_confidences:stats[category]['confidences'].append(confidence)for category, annot_box in annot_boxes:ious = [iou(annot_box, box) for cat, box in result_boxes if cat == category]if max(ious, default=0) < 0.5:stats[category]['fn'] += 1for category, data in stats.items():confidences = data['confidences']data['min_conf'] = min(confidences, default=0)data['max_conf'] = max(confidences, default=0)data['ave_conf'] = sum(confidences) / len(confidences) if confidences else 0data['precision'] = data['tp'] / (data['tp'] + data['fp']) if data['tp'] + data['fp'] > 0 else 0data['recall'] = data['tp'] / (data['tp'] + data['fn']) if data['tp'] + data['fn'] > 0 else 0del data['confidences']  # 为了清晰，删除置信度列表return stats# path
annotation_dir = 'labels/test'
result_dir = 'runs/detect/exp/labels'# calculate TP, FP, FN
stats = evaluate_folder(annotation_dir, result_dir)
print("index | class | Instance | Detection | TP | FP | FN | Precision | Recall | Min Conf | Max Conf | Ave Conf")
for category, data in sorted(stats.items()):print(f"{category} | {classes[category]} | {data['annotated']} | {data['predicted']} | {data['tp']} | {data['fp']} | {data['fn']} | {data['precision']:.2f} | {data['recall']:.2f} | {data['min_conf']:.2f} | {data['max_conf']:.2f} | {data['ave_conf']:.2f}")