-
Notifications
You must be signed in to change notification settings - Fork 0
/
eval.py
174 lines (142 loc) · 5.49 KB
/
eval.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
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import os
import numpy as np
import math
from PIL import Image
import SimpleITK as sitk
from medpy.metric.binary import hd95
import time
start = time.clock()
def DSC(pred, target):
smooth = 1e-5
m1 = pred.flatten()#打平
m2 = target.flatten()
intersection = (m1 * m2).sum()
dsc = (2. * intersection ) / (m1.sum() + m2.sum() + smooth)
return dsc
def IOU(pred, target):
smooth = 1e-5
m1 = pred.flatten()#打平
m2 = target.flatten()
intersection = (m1 * m2).sum()
dsc = intersection / (m1.sum() + m2.sum() - intersection)
return dsc
def compute_label_dice(gt, pred):
# 需要计算的标签类别，不包括背景和图像中不存在的区域
'''
cls_lst = [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 61, 62,
63, 64, 65, 66, 67, 68, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 101, 102, 121, 122, 161, 162,
163, 164, 165, 166, 180, 181]'''
cls_lst = [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]
#cls_lst = [255]
dice_lst = []
for cls in cls_lst:
#print('cls: ', cls)
dice = DSC(pred==cls, gt==cls)
dice_lst.append(dice)
#print(dice)
return np.mean(dice_lst), dice_lst
def compute_label_IOU(gt, pred):
cls_lst = [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]
#cls_lst = [255]
IOU_lst = []
for cls in cls_lst:
#print('cls: ', cls)
dice = IOU(pred==cls, gt==cls)
IOU_lst.append(dice)
return np.mean(IOU_lst)
def HD95(pred, target):
# print("pred.shape: ", pred.shape, "target.shape: ", target.shape)
return hd95(pred,target)
def compute_label_hd95(gt, pred):
# 需要计算的标签类别，不包括背景和图像中不存在的区域
'''
cls_lst = [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 61, 62,
63, 64, 65, 66, 67, 68, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 101, 102, 121, 122, 161, 162,
163, 164, 165, 166, 180, 181]'''
cls_lst = [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]
#cls_lst = [255]
hd95_lst = []
for cls in cls_lst:
hd95 = HD95(pred == cls, gt == cls)
#print('cls: ', cls ,'hd95: ', hd95 )
hd95_lst.append(hd95)
return np.mean(hd95_lst)
def Get_Ja(displacement):
D_y = (displacement[:,1:,:-1,:-1,:] - displacement[:,:-1,:-1,:-1,:])
D_x = (displacement[:,:-1,1:,:-1,:] - displacement[:,:-1,:-1,:-1,:])
D_z = (displacement[:,:-1,:-1,1:,:] - displacement[:,:-1,:-1,:-1,:])
D1 = (D_x[...,0]+1)*( (D_y[...,1]+1)*(D_z[...,2]+1) - D_z[...,1]*D_y[...,2])
D2 = (D_x[...,1])*(D_y[...,0]*(D_z[...,2]+1) - D_y[...,2]*D_z[...,0])
D3 = (D_x[...,2])*(D_y[...,0]*D_z[...,1] - (D_y[...,1]+1)*D_z[...,0])
return D1-D2+D3
def NJ_num(img_data):
Jacb = Get_Ja(img_data)
image = Jacb[0, :, :, :]
NJ_num = 0
NJ_num = np.float32(NJ_num)
for i in image:
for j in i:
for k in j:
if k < 0:
NJ_num = NJ_num + 1
return NJ_num
def psnr(img1, img2):
mse = np.mean((img1 / 1. - img2 / 1.) ** 2)
if mse < 1.0e-10:
return 100 * 1.0
psnr = 10 * np.log10(1**2 / mse)
return psnr
def time_difference(starttime, endtime):
seconds = ((endtime - starttime).seconds)
milliseconds = ((endtime - starttime).microseconds)
difference = (seconds + milliseconds / 1000000)
return np.float32(difference)
def mse(img1, img2):
n = len(img1)
mse = sum(np.square(img1 - img2)) / n
return mse
# def mse(img1, img2):
# mse = np.mean((img1 / 1. - img2 / 1.) ** 2)
# return mse
def Dice(img1, img2):
T = (img1.flatten() > 0)
P = (img2.flatten() > 0)
return 2 * np.sum(T * P) / (np.sum(T) + np.sum(P))
#def Dice(img1, img2):
#smooth = 1.
#zq = img1.size(0)
#m1 = img1.view(zq, -1) # Flatten
#m2 = img2.view(zq, -1) # Flatten
#intersection = (m1 * m2).sum()
#return (2. * intersection + smooth) / (m1.sum() + m2.sum() + smooth)
def ssim(y_true, y_pred):
'''u_true = np.mean(y_true)
u_pred = np.mean(y_pred)
var_true = np.var(y_true)
var_pred = np.var(y_pred)
std_true = np.sqrt(var_true)
std_pred = np.sqrt(var_pred)
c1 = np.square(0.01 * 7)
c2 = np.square(0.03 * 7)
ssim = (2 * u_true * u_pred + c1) * (2 * std_pred * std_true + c2)
denom = (u_true ** 2 + u_pred ** 2 + c1) * (var_pred + var_true + c2)'''
#return ssim / denom
a1 = len(y_true.shape)
b1 = len(y_pred.shape)
mu1 = y_true.mean()
mu2 = y_pred.mean()
sigma1 = np.sqrt(((y_true - mu1) ** 2).mean())
sigma2 = np.sqrt(((y_pred - mu2) ** 2).mean())
sigma12 = ((y_true - mu1) * (y_pred - mu2)).mean()
k1, k2, L = 0.01, 0.03, 1
C1 = (k1 * L) ** 2
C2 = (k2 * L) ** 2
C3 = C2 / 2
l12 = (2 * mu1 * mu2 + C1) / (mu1 ** 2 + mu2 ** 2 + C1)
c12 = (2 * sigma1 * sigma2 + C2) / (sigma1 ** 2 + sigma2 ** 2 + C2)
s12 = (sigma12 + C3) / (sigma1 * sigma2 + C3)
ssim = l12 * c12 * s12
return ssim