NMS算法(非极大值抑制)是目标检测算法中经典的后处理步骤，其本质是搜索局部最大值，抑制非极大值元素。主要利用目标检测框以及对应的置信度分数，设置一定的阈值来删除重叠较大的边界框。 其算法流程如下：
根据置信度得分进行排序选择置信度最高的目标检测框添加到输出列表中，将其从检测框列表中删除计算该检测框与剩余候选检测框的IOU删除IOU大于阈值的检测框重复上述4步，直至检测框列表为空其算法实现如下
import numpy as np def nms(dets, thresh): # x1, y1, x2, y2, score x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4] areas = (x2 - x1 + 1) * (y2 - y1 + 1) # 各个方框的面积 order = scores.argsort()[::-1] # 按置信度排序后的index， 作为候选集 keep = [] # 保存筛选出来的方框的index while order.size > 0: i = order[0] # 当前置信度最大的方框 keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(0.0, (xx2 - xx1 + 1)) h = np.maximum(0.0, (yy2 - yy1 + 1)) inter = w * h # 当前置信度最大的框和其他所有框的相交面积 overlap = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(overlap <= thresh)[0] # 交并比小于thresh的仍然保留在候选集里， 大的过滤掉 order = order[inds + 1] # inds + 1对应原来order中overlap小于thresh的项 return keep if __name__ == '__main__': detections = [ [10, 20, 100, 100, 0.9], [20, 10, 110, 100, 0.88], [20, 20, 110, 110, 0.86], [40, 50, 200, 200, 0.95], [45, 52, 198, 202, 0.87] ] detections = np.array(detections) keeps = nms(detections, 0.5) print(detections[keeps])