Table of contents
for cls_id in all_classes:
    det_cls = dets[cls_id]
    gdt_cls = gdts[cls_id]
    det_cls = sorted(det_cls, key=lambda det: det.score)
    for sample_id in all_samples:
        det_cls_sample = det_cls[sample_id]
        gdt_cls_sample = gdt_cls[sample_id]
        for det in det_cls_sample:
            for gdt in gdt_cls_sample:
                if gdt.assigned:  # default False
                if compute_IoU(det, gdt) >= iou_threshold:
                    det.tpfp = True     # default False
                    gdt.assigned = True
    tpfp = [det.tpfp for det in det_cls]
    cum_tpfp = np.cumsum(tpfp) 
    precision_cls = cum_tpfp / np.arange(1, len(det_cls) + 1)
    # area style 
    precision[cls_id] = sum(precision_cls * tpfp) / len(gdt_cls)
    ## 11-potions style
    # recall_cls =  cum_tpfp / len(gdt_cls)
    # for thr in np.arange(0, 1 + 1e-3, 0.1):
    #     precs = precision_cls[recall_cls >= thr]
    #     prec = precs.max() if precs.size > 0 else 0
    #     precision_cls += prec
    # precision[cls_id] = precision_cls / 11
mAP = mean([precision[cls_id] for cls_id in all_classes])
  • det.tpfp tells if this detection is true positive or false positive, which is set as False by default (false positive).
  • gdt.assigned tells if this ground truth has already been assigned to one detection, which is set as False by default (not assigned).
  • There are two styles for averaging the precisions, named the area and the 11-points. area is adopted in most of the current work.
  • average precision (AP) represents averaing precisions of different number of top detections. Can be area style of 11-potions style;
  • mean average precision (mAP) refers to the averaing AP of different classes in most cases; but sometimes refers to the averaging AP of different IoU thresholds, in which case the AP in it is already the mAP in the previous case.