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  • NMS_非极大值抑制的作用

    参考链接 :NMS(非极大值抑制)

    NMS: non maximum suppression

    翻译为“非极大值抑制”,为什么不翻译成最大值抑制呢?maximum可以翻译为“最大值”,也可以翻译成“极大值”,所以翻译成极大值或者最大值一定要看这个值的含义。

    极大值和最大值的区别就是,极大值是局部最大值。

    NMS的作用:去掉detection任务重复的候选框,只留下预测概率值最大的候选框最为最终预测的结果(非极大值抑制)。

    实现代码如下,来自链接

     1 # 非极大值抑制
     2 def nms(bboxes, scores, score_thresh, nms_thresh, pre_nms_topk, i=0, c=0):
     3     """
     4     nms
     5     """
     6     inds = np.argsort(scores)
     7     inds = inds[::-1]
     8     keep_inds = []
     9     while(len(inds) > 0):
    10         cur_ind = inds[0]
    11         cur_score = scores[cur_ind]
    12         # if score of the box is less than score_thresh, just drop it
    13         if cur_score < score_thresh:
    14             break
    15 
    16         keep = True
    17         for ind in keep_inds:
    18             current_box = bboxes[cur_ind]
    19             remain_box = bboxes[ind]
    20             iou = box_iou_xyxy(current_box, remain_box)
    21             if iou > nms_thresh:
    22                 keep = False
    23                 break
    24         if i == 0 and c == 4 and cur_ind == 951:
    25             print('suppressed, ', keep, i, c, cur_ind, ind, iou)
    26         if keep:
    27             keep_inds.append(cur_ind)
    28         inds = inds[1:]
    29 
    30     return np.array(keep_inds)
    31 
    32 # 多分类非极大值抑制
    33 def multiclass_nms(bboxes, scores, score_thresh=0.01, nms_thresh=0.45, pre_nms_topk=1000, pos_nms_topk=100):
    34     """
    35     This is for multiclass_nms
    36     """
    37     batch_size = bboxes.shape[0]
    38     class_num = scores.shape[1]
    39     rets = []
    40     for i in range(batch_size):
    41         bboxes_i = bboxes[i]
    42         scores_i = scores[i]
    43         ret = []
    44         for c in range(class_num):
    45             scores_i_c = scores_i[c]
    46             keep_inds = nms(bboxes_i, scores_i_c, score_thresh, nms_thresh, pre_nms_topk, i=i, c=c)
    47             if len(keep_inds) < 1:
    48                 continue
    49             keep_bboxes = bboxes_i[keep_inds]
    50             keep_scores = scores_i_c[keep_inds]
    51             keep_results = np.zeros([keep_scores.shape[0], 6])
    52             keep_results[:, 0] = c
    53             keep_results[:, 1] = keep_scores[:]
    54             keep_results[:, 2:6] = keep_bboxes[:, :]
    55             ret.append(keep_results)
    56         if len(ret) < 1:
    57             rets.append(ret)
    58             continue
    59         ret_i = np.concatenate(ret, axis=0)
    60         scores_i = ret_i[:, 1]
    61         if len(scores_i) > pos_nms_topk:
    62             inds = np.argsort(scores_i)[::-1]
    63             inds = inds[:pos_nms_topk]
    64             ret_i = ret_i[inds]
    65 
    66         rets.append(ret_i)
    67 
    68     return rets
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  • 原文地址:https://www.cnblogs.com/lyj0123/p/14467477.html
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