softNMS
!
import numpy as np
boxes = np.array([[100, 100, 210, 210, 0.72],
[250, 250, 420, 420, 0.8],
[220, 220, 320, 330, 0.92],
[100, 100, 210, 210, 0.72],
[230, 240, 325, 330, 0.81],
[220, 230, 315, 340, 0.9]]) # (x1,y1,x2,y2,score)
def nms(boxes, threshold):
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
scores = boxes[:, 4]
areas = (x2-x1+1)*(y2-y1+1)
keep = []
idxs = scores.argsort()[::-1] # 从大到下排序,argsort函数返回从小到大的索引,[::-1]反序变成从大到小
while(idxs.size > 0):
i = idxs[0]
keep.append(i)
x11 = np.maximum(x1[i], x1[idxs[1:]])
y11 = np.maximum(y1[i], y1[idxs[1:]])
x22 = np.minimum(x2[i], y2[idxs[1:]])
y22 = np.minimum(y2[i], y2[idxs[1:]])
w = np.maximum(0, x22-x11+1)
h = np.maximum(0, y22-y11+1)
overlaps = w*h
ious = overlaps/(areas[i]+areas[idxs[1:]]-overlaps)
idxs2 = np.where(ious < threshold)[0] # np.where函数
idxs = idxs[idxs2+1] # 注意这个+1
return keep
def soft_nms(boxes, sigma=0.5, threshold1=0.7, threshold2=0.1, method=1):
'''
paper:Improving Object Detection With One Line of Code
'''
N = boxes.shape[0]
pos = 0
maxscore = 0
maxpos = 0
for i in range(N):
maxscore = boxes[i, 4]
maxpos = i
tx1 = boxes[i, 0]
ty1 = boxes[i, 1]
tx2 = boxes[i, 2]
ty2 = boxes[i, 3]
ts = boxes[i, 4]
pos = i + 1
# 得到评分最高的box
while pos < N:
if maxscore < boxes[pos, 4]:
maxscore = boxes[pos, 4]
maxpos = pos
pos = pos + 1
# 交换第i个box和评分最高的box,将评分最高的box放到第i个位置
boxes[i, 0] = boxes[maxpos, 0]
boxes[i, 1] = boxes[maxpos, 1]
boxes[i, 2] = boxes[maxpos, 2]
boxes[i, 3] = boxes[maxpos, 3]
boxes[i, 4] = boxes[maxpos, 4]
boxes[maxpos, 0] = tx1
boxes[maxpos, 1] = ty1
boxes[maxpos, 2] = tx2
boxes[maxpos, 3] = ty2
boxes[maxpos, 4] = ts
tx1 = boxes[i, 0]
ty1 = boxes[i, 1]
tx2 = boxes[i, 2]
ty2 = boxes[i, 3]
ts = boxes[i, 4]
pos = i + 1
#softNMS迭代
while pos < N:
x1 = boxes[pos, 0]
y1 = boxes[pos, 1]
x2 = boxes[pos, 2]
y2 = boxes[pos, 3]
s = boxes[pos, 4]
area = (x2 - x1 + 1) * (y2 - y1 + 1)
iw = (min(tx2, x2) - max(tx1, x1) + 1)
if iw > 0:
ih = (min(ty2, y2) - max(ty1, y1) + 1)
if ih > 0:
uinon = float((tx2 - tx1 + 1) *
(ty2 - ty1 + 1) + area - iw * ih)
iou = iw * ih / uinon # 计算iou
if method == 1: # 线性更新分数
if iou > threshold1:
weight = 1 - iou
else:
weight = 1
elif method == 2: # 高斯权重
weight = np.exp(-(iou * iou)/sigma)
else: # 传统 NMS
if iou > threshold1:
weight = 0
else:
weight = 1
boxes[pos, 4] = weight*boxes[pos, 4]#根据和最高分数box的iou来更新分数
#如果box分数太低，舍弃(把他放到最后，同时N-1)
if boxes[pos, 4] < threshold2:
boxes[pos, 0] = boxes[N-1, 0]
boxes[pos, 1] = boxes[N-1, 1]
boxes[pos, 2] = boxes[N-1, 2]
boxes[pos, 3] = boxes[N-1, 3]
boxes[pos, 4] = boxes[N-1, 4]
N = N - 1#注意这里N改变
pos = pos - 1
pos = pos + 1
keep = [i for i in range(N)]
return keep
keep1 = nms(boxes, 0.8)
keep2 = soft_nms(boxes)
print("end")
```
