-
Notifications
You must be signed in to change notification settings - Fork 0
/
eval.py
163 lines (142 loc) · 5.59 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
import main, utils, models
from utils import io, plt
import numpy as np
import scipy
import os
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics import f1_score
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
n = 88
imgs, masks = [], []
print('Loading Data')
# for i in range(1,n+1):
# scan = main.get_scan(n=i)
# x = np.zeros((2,240,240))
# x[0,:,:] = np.expand_dims(scan['flair'], axis=0)
# x[1:,:,:] = np.expand_dims(scan['t2'], axis=0)
# masks.append(scan['seg'])
# imgs.append(x)
for i in range(1, n+1):
npy = np.load('./npy/'+str(i)+'.npy')
x = np.zeros((2,240,240))
x[0,:,:] = npy[0]
x[1,:,:] = npy[1]
masks.append(npy[2])
imgs.append(x)
imgs = np.array(imgs)
masks = np.array(masks)
print('Loading Model')
model = models.UNET()
model.load_weights('weights-full-best.h5')
print('Predicting')
predictions = main.predictor(model=model, batch_size=8, scan=imgs)
# predictions = model.predict(imgs, batch_size=8, verbose=1)
y_scores = predictions.reshape(predictions.shape[0]*predictions.shape[1]*predictions.shape[2]*predictions.shape[3], 1)
print(y_scores.shape)
y_true = masks.reshape(masks.shape[0]*masks.shape[1]*masks.shape[2]*masks.shape[3], 1)
y_scores = np.where(y_scores>0.5, 1, 0)
y_true = np.where(y_true>0.5, 1, 0)
import os
os.mkdir('./output')
output_folder = 'output/'
#Area under the ROC curve
fpr, tpr, thresholds = roc_curve((y_true), y_scores)
AUC_ROC = roc_auc_score(y_true, y_scores)
print ("\nArea under the ROC curve: " +str(AUC_ROC))
roc_curve =plt.figure()
plt.plot(fpr,tpr,'-',label='Area Under the Curve (AUC = %0.4f)' % AUC_ROC)
plt.title('ROC curve')
plt.xlabel("FPR (False Positive Rate)")
plt.ylabel("TPR (True Positive Rate)")
plt.legend(loc="lower right")
plt.savefig(output_folder+"ROC.png")
#Precision-recall curve
precision, recall, thresholds = precision_recall_curve(y_true, y_scores)
precision = np.fliplr([precision])[0]
recall = np.fliplr([recall])[0]
AUC_prec_rec = np.trapz(precision,recall)
print ("\nArea under Precision-Recall curve: " +str(AUC_prec_rec))
prec_rec_curve = plt.figure()
plt.plot(recall,precision,'-',label='Area Under the Curve (AUC = %0.4f)' % AUC_prec_rec)
plt.title('Precision - Recall curve')
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.legend(loc="lower right")
plt.savefig(output_folder+"Precision_recall.png")
#Confusion matrix
threshold_confusion = 0.5
print ("\nConfusion matrix: Custom threshold (for positive) of " +str(threshold_confusion))
y_pred = np.empty((y_scores.shape[0]))
for i in range(y_scores.shape[0]):
if y_scores[i]>=threshold_confusion:
y_pred[i]=1
else:
y_pred[i]=0
confusion = confusion_matrix(y_true, y_pred)
print (confusion)
accuracy = 0
if float(np.sum(confusion))!=0:
accuracy = float(confusion[0,0]+confusion[1,1])/float(np.sum(confusion))
print ("Global Accuracy: " +str(accuracy))
specificity = 0
if float(confusion[0,0]+confusion[0,1])!=0:
specificity = float(confusion[0,0])/float(confusion[0,0]+confusion[0,1])
print ("Specificity: " +str(specificity))
sensitivity = 0
if float(confusion[1,1]+confusion[1,0])!=0:
sensitivity = float(confusion[1,1])/float(confusion[1,1]+confusion[1,0])
print ("Sensitivity: " +str(sensitivity))
precision = 0
if float(confusion[1,1]+confusion[0,1])!=0:
precision = float(confusion[1,1])/float(confusion[1,1]+confusion[0,1])
print ("Precision: " +str(precision))
#Jaccard similarity index
jaccard_index = jaccard_similarity_score(y_true, y_pred, normalize=True)
print ("\nJaccard similarity score: " +str(jaccard_index))
#F1 score
F1_score = f1_score(y_true, y_pred, labels=None, average='binary', sample_weight=None)
print ("\nF1 score (F-measure): " +str(F1_score))
#Save the results
file_perf = open(output_folder+'performances.txt', 'w')
file_perf.write("Area under the ROC curve: "+str(AUC_ROC)
+ "\nArea under Precision-Recall curve: " +str(AUC_prec_rec)
+ "\nJaccard similarity score: " +str(jaccard_index)
+ "\nF1 score (F-measure): " +str(F1_score)
+"\n\nConfusion matrix:"
+str(confusion)
+"\nACCURACY: " +str(accuracy)
+"\nSENSITIVITY: " +str(sensitivity)
+"\nSPECIFICITY: " +str(specificity)
+"\nPRECISION: " +str(precision)
)
file_perf.close()
# Save 10 results with error rate lower than threshold
threshold = 300
predictions = np.where(predictions>0.5, 1, 0)
masks = np.where(masks>0.5, 1, 0)
good_prediction = np.zeros([predictions.shape[0],1], np.uint8)
id_m = 0
for idx in range(predictions.shape[0]):
esti_sample = predictions[idx]
true_sample = masks[idx]
esti_sample = esti_sample.reshape(esti_sample.shape[0]*esti_sample.shape[1]*esti_sample.shape[2], 1)
true_sample = true_sample.reshape(true_sample.shape[0]*true_sample.shape[1]*true_sample.shape[2], 1)
er = 0
for idy in range(true_sample.shape[0]):
if esti_sample[idy] != true_sample[idy]:
er = er +1
if er <threshold:
good_prediction[id_m] = idx
id_m += 1
fig,ax = plt.subplots(10,3,figsize=[15,15])
for idx in range(10):
ax[idx, 0].imshow(np.uint8(imgs[good_prediction[idx,0]]))
ax[idx, 1].imshow(np.squeeze(masks[good_prediction[idx,0]]), cmap='gray')
ax[idx, 2].imshow(np.squeeze(predictions[good_prediction[idx,0]]), cmap='gray')
plt.savefig(output_folder+'sample_results.png')