I am trying to evaluate average precision for a face detection algorithm. I have created a CSV file with all the predicted bounding boxes. I have another CSV in a similar format with all the ground-truth bounding boxes. I found a lot of simple codes online to calculate bounding box overlap and calculating AP but they don’t work for a variable number of predictions given ground-truth i.e. if an image has 8 bounding box predictions whereas ground truths are 10 then how do I calculate the average precision?
I tried to mimic one of the algorithms and create a CSV file in the format below:
# 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 ]
...
...
...

I am also sharing what I tried so far:
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