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models.py
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models.py
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from keras.models import load_model
# load model
model = load_model('face_model.h5')
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
def load_image(filename):
# load the image
img = load_img(filename, target_size=(224, 224))
# convert to array
img = img_to_array(img)
# reshape into a single sample with 3 channels
img = img.reshape(1, 224, 224, 3)
# center pixel data
img = img.astype('float32')
img = img - [123.68, 116.779, 103.939]
return img
# load an image and predict the class
def run_example():
# load the image
img = load_image('New.jpg')
# predict the class
result = model.predict(img)
print(result)
if (result[0] > 0.5):
print('Apoorve attendance marked')
else:
print('Maaz attendance marked')