iou (N): IoU values. """overlap_left_top = torch.max(boxes0[..., :2], boxes1[..., :2]) overlap_right_bottom = torch.min(boxes0[...,2:], boxes1[...,2:]) overlap_area = area_of(overlap_left_top, overlap_right_bottom) area0 = area_of(boxes0[..., :2], boxes0[....
yy1=y1[order[1:]].clamp(min=y1[i].item()) xx2=x2[order[1:]].clamp(max=x2[i].item()) yy2=y2[order[1:]].clamp(max=y2[i].item()) w=(xx2-xx1).clamp(min=0) h=(yy2-yy1).clamp(min=0) inter=w*h overlap=inter/(areas[i]+areas[order[1:]]-inter) # DIOU计算 xx...
即第二到最后一个)框的IOU,以下都是以向量形式表示和计算xx1 = np.maximum(x1[i], x1[order[1:]])#计算xmin的max,即overlap的xminyy1 = np.maximum(y1[i], y1[order[1:]])#计算ymin的max,即overlap的yminxx2 = np.minimum(x2[i], ...
overlap_left_top=torch.max(boxes0[..., :2],boxes1[..., :2]) overlap_right_bottom=torch.min(boxes0[...,2:],boxes1[...,2:]) overlap_area=area_of(overlap_left_top,overlap_right_bottom) area0=area_of(boxes0[..., :2],boxes0[...,2:]) area1=area_of(boxes1[..., :2]...
(keep)))# 使用非极大值抑制获取不重复的矩形框pick=non_max_suppression_fast(np.array(keep),overlapThresh=0.4)print("%dbounding boxes after nms"%(len(pick)))# loop over the picked bounding boxes and draw themfor(startX,startY,endX,endY)inpick:cv2.rectangle(vis_2,(startX,startY),(endX...
一分钟速学 | NMS, IOU 与 SoftMax 非极大抑制 NMS的英文是Non-maximum suppression的缩写。 简单的说,就是模型给出了多个重叠在一起的候选框,我们只需要保留一个就可以了。其他的重叠的候选框就删掉了,效果可见下图: 交并比 IoU的英文全称Intersection over Union,就是两个候选框区域的交集面积比上并集的面积,...
def stand_nms_non_max_suppression(boxes, probs=None, overlapThresh=0.3, list_res=None): # if there are no boxes, return an empty list if len(boxes) == 0: return [] # if the bounding boxes are integers, convert them to floats -- this ...
xx1 = np.maximum(x1[i], x1[order[1:]]) # calculate the points of overlap yy1 = np...
两个重要的参数是score阈值与overlap阈值,任何低于score阈值的BB将会被拒绝,当两个BB的IOU大于给定的overlap阈值时候,两个检测框将会被聚类分割为同一个对象检测框。Overlap阈值需要平衡精度与抑制效果: 提升: 使用soft-NMS,在soft-NMS中score被乘以负向IOU,图示如下: ...
不同于在NMS中采用单一阈值,对与最大得分检测结果M超过阈值的结果进行抑制,其主要考虑Soft-NMS,对所有目标的检测得分以相应overlap with M的连续函数进行衰减。其伪代码如下: 2.3、ConvNMS 其主要考虑IoU阈值设定得高一些,则可能抑制得不够充分,而...