Python中的object_detection.eval_util：用于评估目标检测算法鲁棒性的实用工具

object_detection.eval_util是一个用于评估目标检测算法鲁棒性的实用工具模块，它提供了一些函数和类，可以帮助我们计算模型的评估指标，如平均精确度（Average Precision）和均匀平均精确度（Mean Average Precision）。

下面是object_detection.eval_util模块中一些常用函数和类的使用例子：

1. compute_precision_recall函数：用于计算真阳性（True Positive）、假阳性（False Positive）和假阴性（False Negative）之间的精确度（Precision）和召回率（Recall）。

import object_detection.eval_util as eval_util

groundtruth_boxes = [[0.0, 0.0, 0.5, 0.5]]
groundtruth_classes = [1]
detection_boxes = [[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 1.0]]
detection_scores = [0.8, 0.6]
detection_classes = [1, 2]
num_gt_boxes = 1

precision, recall = eval_util.compute_precision_recall(
    groundtruth_boxes, groundtruth_classes, detection_boxes, detection_scores, detection_classes, num_gt_boxes)

print('Precision:', precision)
print('Recall:', recall)

2. compute_average_precision_per_class函数：用于计算每个类别的平均精确度。

import object_detection.eval_util as eval_util

groundtruth_boxes = [[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 1.0]]
groundtruth_classes = [1, 1]
detection_boxes = [[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 1.0]]
detection_scores = [0.8, 0.6]
detection_classes = [1, 2]

metrics_per_class, _ = eval_util.compute_average_precision_per_class(
    groundtruth_boxes, groundtruth_classes, detection_boxes, detection_scores, detection_classes)

for class_id, metrics in metrics_per_class.items():
    print('Class:', class_id)
    print('Average Precision:', metrics['average_precision'])

3. aggregate_stats类：用于计算模型的均匀平均精确度。

import object_detection.eval_util as eval_util

stats = eval_util.AggregateStats()

# 添加一个样本
groundtruth_boxes = [[0.0, 0.0, 0.5, 0.5]]
groundtruth_classes = [1]
detection_boxes = [[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 1.0]]
detection_scores = [0.8, 0.6]
detection_classes = [1, 2]
num_gt_boxes = 1

stats.update(groundtruth_boxes, groundtruth_classes, detection_boxes, detection_scores, detection_classes, num_gt_boxes)

# 添加另一个样本
groundtruth_boxes = [[0.5, 0.5, 1.0, 1.0]]
groundtruth_classes = [2]
detection_boxes = [[0.0, 0.0, 0.5, 0.5]]
detection_scores = [0.7]
detection_classes = [2]
num_gt_boxes = 1

stats.update(groundtruth_boxes, groundtruth_classes, detection_boxes, detection_scores, detection_classes, num_gt_boxes)

mean_ap, _, _, _ = eval_util.per_image_eval(num_groundtruth_classes=2, eval_dict=stats.aggregate())

print('Mean Average Precision:', mean_ap)

这只是object_detection.eval_util模块的一小部分，还有其他功能和类可用于评估目标检测算法的鲁棒性。详细的函数和类的使用方法可以参考官方文档。