forked from avinashkranjan/Amazing-Python-Scripts
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathface_mask_detection_using_cnn.py
136 lines (133 loc) · 5.08 KB
/
face_mask_detection_using_cnn.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
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras import layers
import tensorflow as tf
from keras.preprocessing.image import img_to_array
from tensorflow.keras import backend
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
from sklearn.metrics import classification_report
import sklearn.metrics as metrics
import itertools
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
def data_set(dir_data):
data=[]
target=[]
data_map = {
'with_mask':1,
'without_mask':0
}
skipped=0
root=dir_data+'_annotations.csv'
df1 = pd.read_csv(root)
df1.dataframeName = '_annotations.csv'
nRow, nCol = df1.shape
for i in range(len(df1)):
without_mask='without_mask'
k=dir_data+df1['filename'][i]
image=cv2.imread(k)
xmin=int(df1['xmin'][i])
ymin=int(df1['ymin'][i])
xmax=int(df1['xmax'][i])
ymax=int(df1['ymax'][i])
#image=image[ymin:ymax, xmin:xmax]
try:
# resizing to (70 x 70)
image = cv2.resize(image,(70,70))
except Exception as E:
skipped += 1
print(E)
continue
if(df1['class'][i]=='mask'):
without_mask='with_mask'
image=img_to_array(image)
data.append(image)
target.append(data_map[without_mask])
data = np.array(data, dtype="float") / 255.0
target = tf.keras.utils.to_categorical(np.array(target), num_classes=2)
return data, target
training_data,training_target=data_set('./Face-Mask-Detection/kaggle/input/face-mask-detection/train/')
testing_data,testing_target=data_set('./Face-Mask-Detection/kaggle/input/face-mask-detection/test/')
valid_data,valid_target=data_set('./Face-Mask-Detection/kaggle/input/face-mask-detection/valid/')
plt.figure(0, figsize=(100,100))
for i in range(1,10):
plt.subplot(10,5,i)
plt.imshow(training_data[i])
img_shape=training_data[0].shape
depth, height, width=3, img_shape[0], img_shape[1]
img_shape=(height, width, depth)
chanDim=-1
if backend.image_data_format() == "channels_first": #Returns a string, either 'channels_first' or 'channels_last'
img_shape = (depth, height, width)
chanDim = 1
model=Sequential()
model.add(layers.Conv2D(32,(3,3),input_shape=img_shape))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Conv2D(64,(3,3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Conv2D(128,(3,3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Conv2D(256,(3,3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(64,activation='relu'))
model.add(layers.Dropout(0.4))
model.add(layers.Dense(2,activation='softmax'))
adam =tf.keras.optimizers.Adam(0.001)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
model.summary()
aug = ImageDataGenerator(rotation_range=25, width_shift_range=0.1,
height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode="nearest")
history = model.fit(aug.flow(training_data, training_target, batch_size=10),
epochs=70,
validation_data=(valid_data, valid_target),
verbose=2,
shuffle=True)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.ylabel(['accuracy'])
plt.xlabel(['epoch'])
plt.legend(['accuracy', 'val_accuracy'])
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.ylabel(['loss'])
plt.xlabel(['epoch'])
plt.legend(['loss', 'val_loss'])
loss, accuracy = model.evaluate(testing_data,testing_target)
print('accuracy= ',loss," loss= ",loss)
yhat = model.predict(testing_data)
test_pred=np.argmax(yhat,axis=1)
testing_target=np.argmax(testing_target,axis=1)
report = classification_report(testing_target, test_pred)
print(report)
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.RdYlGn):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
confusion = metrics.confusion_matrix(testing_target, test_pred)
plt.figure()
plot_confusion_matrix(confusion, classes=['without_mask','with_mask'], title='Confusion matrix')