-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathCM.py
113 lines (98 loc) · 5.64 KB
/
CM.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
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
from simplecv import dp_train as train
import torch
from simplecv.util.logger import eval_progress, speed
import time
from module import CLSJE
from simplecv.util import metric
from simplecv.util import registry
from torch.utils.data.dataloader import DataLoader
from simplecv import registry
from simplecv.core.config import AttrDict
from scipy.io import loadmat
import data.dataloader
import time
import matplotlib.pyplot as plt
import os
from sklearn.metrics import confusion_matrix
confusion_matrix =[[0.9718, 0.0266, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0016,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0161, 0.9622, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0217, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.9971, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0029],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.9854, 0.0000e+00, 0.0000e+00,
0.0008, 0.0089, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0049, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00,0.0016, 0.0000e+00, 0.0079, 0.0000e+00, 0.0000e+00,
0.9746, 0.0000e+00, 0.0079, 0.0000e+00, 0.008, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0032, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.9968, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0024, 0.0000e+00, 0.0032, 0.0024, 0.0000e+00,
0.0073, 0.0000e+00, 0.9847, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0033, 0.0000e+00, 0.0000e+00,0.9967, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
1.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 1.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 1.0000e+00]]
# Healthy Grass
# Stressed Grass
# Synthetic Grass
# Tree
# Soil
# Water
# Residential
# Commercial
# Road
# Highway
# Railway
# Parking Lot 1
# Parking Lot 2
# Tennis Court
# Running Track
# C1 = confusion_matrix(mask.view(-1), y_pred.view(-1), self._model.module.config.num_classes)
import seaborn as sns #导入包
# xtick=['water','Hippo grass ','Floodplain grasses1','Floodplain grasses2','Reeds1','Riparian','Fierscar2','Island interior','Acacia woodlands','Acacia shrublands','Acacia grasslands','Short mopane','Mixed mopane','Exposed soils' ]
# ytick=['water','Hippo grass ','Floodplain grasses1','Floodplain grasses2','Reeds1','Riparian','Fierscar2','Island interior','Acacia woodlands','Acacia shrublands','Acacia grasslands','Short mopane','Mixed mopane','Exposed soils' ]
xtick=["Healthy Grass", "Stressed Grass","Synthetic Grass","Tree","Soil","Water","Residential","Commercial","Road","Highway","Railway","Parking Lot 1","Parking Lot 2","Tennis Court","Running Track" ]
ytick=["Healthy Grass", "Stressed Grass","Synthetic Grass","Tree","Soil","Water","Residential","Commercial","Road","Highway","Railway","Parking Lot 1","Parking Lot 2","Tennis Court","Running Track" ]
sns.heatmap(confusion_matrix,fmt='g',cmap='Blues', annot=True,cbar=True,xticklabels=xtick, yticklabels=ytick) #画热力图,annot=True 代表 在图上显示 对应的值， fmt 属性 代表输出值的格式，cbar=False, 不显示 热力棒
# plt.xlabel('x_label',fontsize=7, color='k') #x轴label的文本和字体大小
# plt.ylabel('y_label',fontsize=7, color='k') #y轴label的文本和字体大小
plt.xticks(fontsize=10,rotation=45) #x轴刻度的字体大小（文本包含在pd_data中了）
plt.yticks(fontsize=10) #y轴刻度的字体大小（文本包含在pd_data中了
plt.show()
# plt.matshow(confusion_matrix, cmap=plt.cm.Blues) # 根据最下面的图按自己需求更改颜色
# for i in range(len(confusion_matrix)):
# for j in range(len(confusion_matrix)):
# plt.annotate(confusion_matrix[j, i], xy=(i, j), horizontalalignment='center', verticalalignment='center')
# plt.ylabel('True label')
# plt.xlabel('Predicted label')
# path=r"./001.jpg"
dst1 = os.path.join("confusion_matrix",str(1)+ '.png')
plt.savefig(dst1)
# t=t+1