def motp_mota(obj, hyp, threshold=0.5):
    """Calculate MOTP/MOTA

    obj: list
        Ground truth frame detections.
        detections: numpy int array Cx5 [[id, xmin, ymin, xmax, ymax]]

    hyp: list
        Hypothetical frame detections.
        detections: numpy int array Cx5 [[id, xmin, ymin, xmax, ymax]]

    threshold: IOU threshold
    """

    dist_sum = 0  # a sum of IOU distances between matched objects and hypotheses
    match_count = 0
    missed_count = 0
    false_positive = 0
    mismatch_error = 0

    matches = {}  # matches between object IDs and hypothesis IDs

    # For every frame
    for frame_obj, frame_hyp in zip(obj, hyp):
        # Step 1: Convert frame detections to dict with IDs as keys
        frame_obj = {el[0]: el[1:] for el in frame_obj}
        frame_hyp = {el[0]: el[1:] for el in frame_hyp}

        # Step 2: Iterate over all previous matches
        # If object is still visible, hypothesis still exists
        # and IOU distance > threshold - we've got a match
        # Update the sum of IoU distances and match count
        # Delete matched detections from frame detections
        for m_id in matches:
            if m_id in frame_obj.keys() and m_id in frame_hyp.keys():
                cur_iou = iou_score(frame_obj[m_id], frame_hyp[m_id])
                if cur_iou > threshold:
                    dist_sum += cur_iou
                    match_count += 1
                    del frame_obj[m_id]
                    del frame_hyp[m_id]

        # Step 3: Calculate pairwise detection IOU between remaining frame detections
        # Save IDs with IOU > threshold
        iou_list = []
        for gt_id in frame_obj:
            for h_id in frame_hyp:
                cur_iou = iou_score(frame_obj[gt_id], frame_hyp[h_id])
                if cur_iou > threshold:
                    iou_list.append([gt_id, h_id, cur_iou])

        # Step 4: Iterate over sorted pairwise IOU
        # Update the sum of IoU distances and match count
        # Delete matched detections from frame detections
        del_gt = set()
        del_h = set()

        for val in iou_list:
            if val[0] not in del_gt and val[1] not in del_h:
                dist_sum += val[2]
                match_count += 1
                del_gt.add(val[0])
                del_h.add(val[1])
                if val[0] in matches.keys() and matches[val[0]] != val[1]:
                    mismatch_error += 1

                matches[val[0]] = val[1]

        # Step 5: If matched IDs contradict previous matched IDs - increase mismatch error
        pass

        # Step 6: Update matches with current matched IDs
        pass

        # Step 7: Errors
        # All remaining hypotheses are considered false positives
        # All remaining objects are considered misses
        for i in frame_obj:
            if i not in del_gt:
                missed_count += 1
        for i in frame_hyp:
            if i not in del_h:
                false_positive += 1

    # Step 8: Calculate MOTP and MOTA
    MOTP = dist_sum / match_count
    MOTA = 1 - (missed_count + mismatch_error + false_positive) / (match_count + missed_count)

    return MOTP, MOTA