-
Notifications
You must be signed in to change notification settings - Fork 0
/
image-classification-with-cnn-model.py
185 lines (114 loc) · 3.62 KB
/
image-classification-with-cnn-model.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
175
176
177
178
179
180
181
182
183
184
#!/usr/bin/env python
# coding: utf-8
# # Description
# ### This dataset contains 6,899 images from 8 distinct classes compiled from various sources (see Acknowledgements). The classes include airplane, car, cat, dog, flower, fruit, motorbike and person.
# # import lib
# In[2]:
from IPython.display import Image, display
# preprocessing and processing
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.utils import to_categorical
# ploting
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from tensorflow.keras.utils import plot_model
# split data
from sklearn.model_selection import train_test_split
# CNN
from keras import models, layers
# val
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
# In[38]:
import os
labels = os.listdir('..\Computer Vision\imagedata\data\\natural_images')
print(labels)
# # show some images
# In[43]:
num = []
for label in labels:
path = "..\Computer Vision\imagedata\data\\natural_images\{}\\".format(label)
folder_data = os.listdir(path)
k = 0
print('\n',f'===== {label.upper()} =====')
for image_path in folder_data:
if k < 5:
display(Image(path+image_path))
k = k+1
num.append(k)
print(f'count : {k} images , label : {label} class')
# # vis count image for all classes
# In[44]:
fig = go.Figure(data=[go.Bar(
x=labels, y=num,
text=num,
textposition='auto',
)])
fig.update_layout(title_text='NUMBER OF IMAGES CONTAINED IN EACH CLASS')
fig.show()
# # show shaps and stadardizing input data
# In[46]:
x_data =[]
y_data = []
import cv2
for label in labels:
path = "..\Computer Vision\imagedata\data\\natural_images\{}\\".format(label)
folder_data = os.listdir(path)
for image_path in folder_data:
image = cv2.imread(path+image_path)
image_resized = cv2.resize(image, (32,32))
x_data.append(np.array(image_resized))
y_data.append(label)
pass
pass
x_data = np.array(x_data)
y_data = np.array(y_data)
print('the shape of X is: ', x_data.shape, 'and that of Y is: ', y_data.shape)
x_data = x_data.astype('float32')/255
# # converting y data into categorical data
# In[47]:
y_encoded = LabelEncoder().fit_transform(y_data)
y_categorical = to_categorical(y_encoded)
# # shuffle data
# In[48]:
r = np.arange(x_data.shape[0])
np.random.seed(42)
np.random.shuffle(r)
X = x_data[r]
Y = y_categorical[r]
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.33)
# # create a CNN model
# In[49]:
model = models.Sequential()
model.add(layers.Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]))
model.add(layers.MaxPool2D(pool_size=(2, 2)))
model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(layers.MaxPool2D(pool_size=(2, 2)))
model.add(layers.Dropout(rate=0.25))
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dropout(rate=0.5))
model.add(layers.Dense(8, activation='softmax'))
# # compile the CNN model
# In[50]:
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# # training model
# In[51]:
history = model.fit(X_train, Y_train, epochs=50, validation_split=0.2)
# # Plot CNN model
# In[52]:
plot_model(model)
# # Accuracy Score
# In[53]:
Y_pred = np.argmax(model.predict(X_test), axis=1)
Y_test = np.argmax(Y_test, axis = 1)
accuracy_score(Y_pred,Y_test)
# In[54]:
print(classification_report(Y_test, Y_pred))
# In[55]:
model.save('cnnmodel')