example/rcnn/rcnn/dataset/pascal_voc_eval.py [122:175]:
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        sorted_inds = np.argsort(-confidence)
        sorted_scores = np.sort(-confidence)
        bbox = bbox[sorted_inds, :]
        image_ids = [image_ids[x] for x in sorted_inds]

    # go down detections and mark true positives and false positives
    nd = len(image_ids)
    tp = np.zeros(nd)
    fp = np.zeros(nd)
    for d in range(nd):
        r = class_recs[image_ids[d]]
        bb = bbox[d, :].astype(float)
        ovmax = -np.inf
        bbgt = r['bbox'].astype(float)

        if bbgt.size > 0:
            # compute overlaps
            # intersection
            ixmin = np.maximum(bbgt[:, 0], bb[0])
            iymin = np.maximum(bbgt[:, 1], bb[1])
            ixmax = np.minimum(bbgt[:, 2], bb[2])
            iymax = np.minimum(bbgt[:, 3], bb[3])
            iw = np.maximum(ixmax - ixmin + 1., 0.)
            ih = np.maximum(iymax - iymin + 1., 0.)
            inters = iw * ih

            # union
            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                   (bbgt[:, 2] - bbgt[:, 0] + 1.) *
                   (bbgt[:, 3] - bbgt[:, 1] + 1.) - inters)

            overlaps = inters / uni
            ovmax = np.max(overlaps)
            jmax = np.argmax(overlaps)

        if ovmax > ovthresh:
            if not r['difficult'][jmax]:
                if not r['det'][jmax]:
                    tp[d] = 1.
                    r['det'][jmax] = 1
                else:
                    fp[d] = 1.
        else:
            fp[d] = 1.

    # compute precision recall
    fp = np.cumsum(fp)
    tp = np.cumsum(tp)
    rec = tp / float(npos)
    # avoid division by zero in case first detection matches a difficult ground ruth
    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
    ap = voc_ap(rec, prec, use_07_metric)

    return rec, prec, ap
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example/ssd/evaluate/eval_voc.py [126:179]:
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    sorted_inds = np.argsort(-confidence)
    sorted_scores = np.sort(-confidence)
    bbox = bbox[sorted_inds, :]
    image_ids = [image_ids[x] for x in sorted_inds]

    # go down detections and mark true positives and false positives
    nd = len(image_ids)
    tp = np.zeros(nd)
    fp = np.zeros(nd)
    for d in range(nd):
        r = class_recs[image_ids[d]]
        bb = bbox[d, :].astype(float)
        ovmax = -np.inf
        bbgt = r['bbox'].astype(float)

        if bbgt.size > 0:
            # compute overlaps
            # intersection
            ixmin = np.maximum(bbgt[:, 0], bb[0])
            iymin = np.maximum(bbgt[:, 1], bb[1])
            ixmax = np.minimum(bbgt[:, 2], bb[2])
            iymax = np.minimum(bbgt[:, 3], bb[3])
            iw = np.maximum(ixmax - ixmin + 1., 0.)
            ih = np.maximum(iymax - iymin + 1., 0.)
            inters = iw * ih

            # union
            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                   (bbgt[:, 2] - bbgt[:, 0] + 1.) *
                   (bbgt[:, 3] - bbgt[:, 1] + 1.) - inters)

            overlaps = inters / uni
            ovmax = np.max(overlaps)
            jmax = np.argmax(overlaps)

        if ovmax > ovthresh:
            if not r['difficult'][jmax]:
                if not r['det'][jmax]:
                    tp[d] = 1.
                    r['det'][jmax] = 1
                else:
                    fp[d] = 1.
        else:
            fp[d] = 1.

    # compute precision recall
    fp = np.cumsum(fp)
    tp = np.cumsum(tp)
    rec = tp / float(npos)
    # avoid division by zero in case first detection matches a difficult ground ruth
    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
    ap = voc_ap(rec, prec, use_07_metric)

    return rec, prec, ap
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