/
ndhdsxzbc.py
105 lines (81 loc) · 3.3 KB
/
ndhdsxzbc.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
import numpy as np
import keras
import keras.backend as k
from keras.layers import Conv2D, MaxPooling2D, SpatialDropout2D, Flatten, Dropout, Dense
from keras.models import Sequential, load_model
from keras.optimizers import adam
from keras.preprocessing import image
import cv2
import datetime
# UNCOMMENT THE FOLLOWING CODE TO TRAIN THE CNN FROM SCRATCH
# BUILDING MODEL TO CLASSIFY BETWEEN MASK AND NO MASK
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPooling2D())
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D())
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
training_set = train_datagen.flow_from_directory(
'train',
target_size=(150, 150),
batch_size=16,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'test',
target_size=(150, 150),
batch_size=16,
class_mode='binary')
model_saved = model.fit_generator(
training_set,
epochs=10,
validation_data=test_set,
)
model.save('mymodel.h5', model_saved)
# To test for individual images
mymodel = load_model('mymodel.h5')
# test_image=image.load_img('C:/Users/Karan/Desktop/ML Datasets/Face Mask Detection/Dataset/test/without_mask/30.jpg',target_size=(150,150,3))
test_image = image.load_img(r'C:/Users/karan/Desktop/FaceMaskDetector/test/with_mask/1-with-mask.jpg',
target_size=(150, 150, 3))
test_image
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
mymodel.predict(test_image)[0][0]
# IMPLEMENTING LIVE DETECTION OF FACE MASK
mymodel = load_model('mymodel.h5')
cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
while cap.isOpened():
_, img = cap.read()
face = face_cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=4)
for (x, y, w, h) in face:
face_img = img[y:y + h, x:x + w]
cv2.imwrite('temp.jpg', face_img)
test_image = image.load_img('temp.jpg', target_size=(150, 150, 3))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
pred = mymodel.predict(test_image)[0][0]
if pred == 1:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)
cv2.putText(img, 'NO MASK', ((x + w) // 2, y + h + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
else:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3)
cv2.putText(img, 'MASK', ((x + w) // 2, y + h + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
datet = str(datetime.datetime.now())
cv2.putText(img, datet, (400, 450), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
cv2.imshow('img', img)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()