Detection of face mask in real time thought for safety against COVID-19.
Build the Convolutional Network consists on two pairs of Conv and MaxPool layers to extract features from Dataset.
Followed by a Flatten and Dropout layers to convert the data in 1D and ensure overfitting.
Two Dense layer for classification.
model = Sequential([
Conv2D(100, (3, 3), activation = 'relu', input_shape = (150, 150, 3)),
MaxPooling2D(2, 2),
Conv2D(100, (3, 3), activation = 'relu'),
MaxPooling2D(2, 2),
Flatten(),
Dropout(0.5),
Dense(50, activation = 'relu'),
Dense(2, activation = 'softmax')
])
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['acc'])
Training the model and save it after each epoch.
checkpoint = ModelCheckpoint('model-{epoch:03d}.h5', monitor='val_loss', verbose = 0, save_best_only = True, mode = 'auto')
history = model.fit_generator(train_generator,
epochs = 30,
validation_data = validation_generator,
callbacks = [checkpoint])Load the xml file
classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
Resize the image to speed up detection
mini = cv2.resize(im, (im.shape[1] // size, im.shape[0] // size))
Detect faces
faces = classifier.detectMultiScale(mini)
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