Python 边界框从头开始的平均精度（AP）

我试图评估人脸检测算法的平均精度。我已经创建了一个包含所有预测边界框的CSV文件。我有另一个类似格式的CSV，包含所有地面真相边界框。我在网上找到了很多简单的代码来计算边界框重叠和计算AP，但它们不适用于给定地面真相的可变数量的预测，即，如果一幅图像有8个边界框预测，而地面真相是10，那么如何计算平均精度

我尝试模仿其中一种算法，并以以下格式创建CSV文件：

# Filename-1.jpg
[ x1, y1, x2, y2 ]
[ x1, y1, x2, y2 ]
[ x1, y1, x2, y2 ]
[ x1, y1, x2, y2 ]
# Filename-2.jpg
[ x1, y1, x2, y2 ]
[ x1, y1, x2, y2 ]
[ x1, y1, x2, y2 ]
# Filename-3.jpg
[ x1, y1, x2, y2 ]
[ x1, y1, x2, y2 ]
...
...
...

我还分享了我迄今为止的尝试：

def get_max_iou(pred_boxes, gt_box):
    """
    calculate the iou multiple pred_boxes and 1 gt_box (the same one)
    pred_boxes: multiple predict  boxes coordinate
    gt_box: ground truth bounding  box coordinate
    return: the max overlaps about pred_boxes and gt_box
    """
    # 1. calculate the inters coordinate
    if pred_boxes.shape[0] > 0:
        ixmin = np.maximum(pred_boxes[:, 0], gt_box[:, 0])
        ixmax = np.minimum(pred_boxes[:, 2], gt_box[:, 2])
        iymin = np.maximum(pred_boxes[:, 1], gt_box[:, 1])
        iymax = np.minimum(pred_boxes[:, 3], gt_box[:, 3])

        iw = np.maximum(ixmax - ixmin + 1., 0.)
        ih = np.maximum(iymax - iymin + 1., 0.)

    # 2.calculate the area of inters
        inters = iw * ih

    # 3.calculate the area of union
        uni = ((pred_boxes[:, 2] - pred_boxes[:, 0] + 1.) * (pred_boxes[:, 3] - pred_boxes[:, 1] + 1.) +
               (gt_box[2] - gt_box[0] + 1.) * (gt_box[3] - gt_box[1] + 1.) -
               inters)

    # 4.calculate the overlaps and find the max overlap ,the max overlaps index for pred_box
        iou = inters / uni
        iou_max = np.max(iou)
        nmax = np.argmax(iou)
        return iou, iou_max, nmax