-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgui_final.py
165 lines (149 loc) · 5.63 KB
/
gui_final.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
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'final.ui'
#
# Created by: PyQt5 UI code generator 5.15.2
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtGui, QtWidgets
#import imutils
from tensorflow import keras
#from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Model
import os
#from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
#from sklearn.model_selection import train_test_split
#from keras.utils import np_utils
#from sklearn.model_selection import train_test_split
from tensorflow.keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt
import cv2
from keras.utils import np_utils
# Data load
E="D:\\code\\PetImages\\Dog"
F="D:\\code\\PetImages\\Cat"
IMAGE_SIZE=200
images=[]
labels=[]
BC = 0
BCW = 0
for i in os.listdir(E):
try:
img1 = cv2.imread(E+"/"+i)
#print("S1")
img1 = cv2.resize(img1, (200, 200))
#print("S2")
images.append(img1)
labels.append(0)
BC+=1
if BC ==20:
break
except:
print("data load error")
for i in os.listdir(F):
try:
img2 = cv2.imread(F+"/"+i)
img2 = cv2.resize(img2, (200, 200))
images.append(img2)
labels.append(1)
BCW+=1
if BCW ==20:
break
except:
print("data load error")
test_final1=np.array(images, dtype=np.float32)
test_final1=test_final1/255.0
# load model
model1 = load_model("D:\\code\\CNN_weight\\dog_cat_havemask.h5")
model2 = load_model("D:\\code\\CNN_weight\\dog_cat_notmask.h5")
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(606, 501)
self.verticalLayout_2 = QtWidgets.QVBoxLayout(Dialog)
self.verticalLayout_2.setObjectName("verticalLayout_2")
self.groupBox = QtWidgets.QGroupBox(Dialog)
self.groupBox.setObjectName("groupBox")
self.verticalLayout = QtWidgets.QVBoxLayout(self.groupBox)
self.verticalLayout.setObjectName("verticalLayout")
self.pushButton = QtWidgets.QPushButton(self.groupBox)
self.pushButton.setObjectName("pushButton")
self.verticalLayout.addWidget(self.pushButton)
self.pushButton_2 = QtWidgets.QPushButton(self.groupBox)
self.pushButton_2.setObjectName("pushButton_2")
self.verticalLayout.addWidget(self.pushButton_2)
self.pushButton_3 = QtWidgets.QPushButton(self.groupBox)
self.pushButton_3.setObjectName("pushButton_3")
self.verticalLayout.addWidget(self.pushButton_3)
self.pushButton_4 = QtWidgets.QPushButton(self.groupBox)
self.pushButton_4.setObjectName("pushButton_4")
self.verticalLayout.addWidget(self.pushButton_4)
self.verticalLayout_2.addWidget(self.groupBox)
self.retranslateUi(Dialog)
QtCore.QMetaObject.connectSlotsByName(Dialog)
def retranslateUi(self, Dialog):
_translate = QtCore.QCoreApplication.translate
Dialog.setWindowTitle(_translate("Dialog", "Dialog"))
self.groupBox.setTitle(_translate("Dialog", "GroupBox"))
self.pushButton.setText(_translate("Dialog", "1. Show Model Structure"))
self.pushButton_2.setText(_translate("Dialog", "2. Show TensorBoard"))
self.pushButton_3.setText(_translate("Dialog", "3. Test"))
self.pushButton_4.setText(_translate("Dialog", "4. Data Augmantatiom"))
def a(model1):
print(model1.summary(),"Resnet50")
def b():
tmp = os.popen('com.sh').readlines()
tmp
def plot_image(image,labels,prediction,idx,num=10):
fig = plt.gcf()
fig.set_size_inches(12, 14)
if num>10:
num=10
for i in range(0, num):
ax = plt.subplot(2,5, 1+i)
ax.imshow(image[idx], cmap='binary')
title = "label=" +str(labels[idx])
if len(prediction)>0:
title+=",perdict="+str(prediction[idx])
ax.set_title(title,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.show()
def c(model1,model2,test_final1,labels):
predict_y = model2.predict(test_final1)
p_y=predict_y.argmax(axis=1)
plot_image(test_final1,labels,p_y,idx=15)
def d (model1,model2,test_final1,labels):
labels_OneHot = np_utils.to_categorical(labels)
scores1 = model1.evaluate(test_final1,labels_OneHot)
print(scores1)
scores2 = model2.evaluate(test_final1,labels_OneHot)
print(scores2)
y = [scores1[1],scores2[1]]
x = ["before Random-Erasing","after Random-Erasing "]
plt.bar(np.arange(len(x)),y)
plt.ylabel('Accuracy')
plt.xticks(np.arange(len(x)), x)
plt.show()
if __name__ == "__main__":
import sys
app = QtWidgets.QApplication(sys.argv)
Dialog = QtWidgets.QDialog()
ui = Ui_Dialog()
ui.setupUi(Dialog)
# 1.mode : model.summary
ui.pushButton.clicked.connect(lambda:a(model1))
# 2.mode : show tensorboard
ui.pushButton_2.clicked.connect(lambda:b())
# 3.mode : test
import cv2
img1=cv2.imread("D:\\code\\PetImages\\Dog\\0.jpg")
img1 = cv2.resize(img1,(200,200))
img1.reshape
np.array(img1, dtype=np.float32)
ui.pushButton_3.clicked.connect(lambda:c(model1,model2,test_final1,labels))
# 4. mode : plot prediction
ui.pushButton_4.clicked.connect(lambda:d(model1,model2,test_final1,labels))
Dialog.show()
sys.exit(app.exec_())