-
Notifications
You must be signed in to change notification settings - Fork 0
/
evaluate.py
81 lines (69 loc) · 3.04 KB
/
evaluate.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
import scipy.io #实现python对mat数据的读写的库
import torch
import numpy as np
import time
#######################################################################
# Evaluate
def evaluate(qf,ql,qc,gf,gl,gc): #这里的起名规则应该是:q代表query,g代表gallery;f是feature,l是label
query = qf #c是cam
score = np.dot(gf,query) #将两个feature进行点乘 为什么?
# predict index
index = np.argsort(score) #from small to large
index = index[::-1]
#index = index[0:2000]
# good index
query_index = np.argwhere(gl==ql) #找到他们标签相同的
camera_index = np.argwhere(gc==qc) #找到他们摄像机相同的
#np.setdiff1d(x,y)函数的作用是返回在x中但不在y中的元素的集合
good_index = np.setdiff1d(query_index, camera_index, assume_unique=True)#找到所有在qi中而不再ci中
junk_index1 = np.argwhere(gl==-1) #的索引
junk_index2 = np.intersect1d(query_index, camera_index) #公共元素,与setdiff1d类似
junk_index = np.append(junk_index2, junk_index1) #.flatten())
CMC_tmp = compute_mAP(index, good_index, junk_index)
return CMC_tmp
def compute_mAP(index, good_index, junk_index):
ap = 0
cmc = torch.IntTensor(len(index)).zero_()
if good_index.size==0: # if empty
cmc[0] = -1
return ap,cmc
# remove junk_index
mask = np.in1d(index, junk_index, invert=True) #invert为true时表示寻找不同的值,为False时表示寻找相
#同的值
index = index[mask]
# find good_index index
ngood = len(good_index)
mask = np.in1d(index, good_index)
rows_good = np.argwhere(mask==True)
rows_good = rows_good.flatten() #总之就是确定rows就是一个向量,flatten就是让嵌套消失
cmc[rows_good[0]:] = 1
for i in range(ngood):
d_recall = 1.0/ngood
precision = (i+1)*1.0/(rows_good[i]+1)
if rows_good[i]!=0:
old_precision = i*1.0/rows_good[i]
else:
old_precision=1.0
ap = ap + d_recall*(old_precision + precision)/2
return ap, cmc
######################################################################
result = scipy.io.loadmat('pytorch_result.mat') #读取提取出来的特征
query_feature = result['query_f']
query_cam = result['query_cam'][0]
query_label = result['query_label'][0]
gallery_feature = result['gallery_f']
gallery_cam = result['gallery_cam'][0]
gallery_label = result['gallery_label'][0]
CMC = torch.IntTensor(len(gallery_label)).zero_()
ap = 0.0
#print(query_label)
for i in range(len(query_label)):
ap_tmp, CMC_tmp = evaluate(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam)
if CMC_tmp[0]==-1:
continue
CMC = CMC + CMC_tmp
ap += ap_tmp
print(i, CMC_tmp[0])
CMC = CMC.float()
CMC = CMC/len(query_label) #average CMC
print('Rank@1:%f Rank@5:%f Rank@10:%f mAP:%f'%(CMC[0],CMC[4],CMC[9],ap/len(query_label)))