code: def nms(dets, thresh):
      
      x1 = dets[:, 0]
      y1 = dets[:, 1]
      x2 = dets[:, 2]
      y2 = dets[:, 3]
      scores = dets[:, 4]
  
      areas = (x2 - x1 + 1) * (y2 - y1 + 1)
      order = scores.argsort()[::-1]
  
      keep = []
      while order.size > 0:
          i = order[0]
          keep.append(i)
          xx1 = np.maximum(x1[i], x1[order[1:]])
          yy1 = np.maximum(y1[i], y1[order[1:]])
          xx2 = np.minimum(x2[i], x2[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
          ovr = inter / (areas[i] + areas[order[1:]] - inter)
  
          inds = np.where(ovr <= thresh)[0]
          order = order[inds + 1]
  
      return keep
docs: greedily select boxes with high confidence and overlap with current maximum <= thresh
      rule out overlap >= thresh
      :param dets: [[x1, y1, x2, y2 score]]
      :param thresh: retain overlap < thresh
      :return: indexes to keep