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facemaskdetection.py
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facemaskdetection.py
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import keras
import cv2
import numpy as np
import pyglet
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')
camera = cv2.VideoCapture(0)
sound = pyglet.media.load("please_use_your_mask.mp3", streaming=False)
model = keras.models.load_model('facemodel.h5')
model.compile(loss=keras.losses.BinaryCrossentropy(),
optimizer='sgd',
metrics=[keras.metrics.BinaryCrossentropy()])
while True:
_, frame = camera.read()
frame = cv2.flip(frame,1)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face = face_cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=3,
minSize = (30, 30)
)
for(x, y, w, h) in face:
stroke = 2
width = x + w
height = y + h
img = frame[y:height,x:width]
img = cv2.resize(img, (75, 75))
img = np.reshape(img, [1, 75, 75, 3])
if model.predict(img)[0][0]>0.5:
cv2.rectangle(frame, (x,y), (width, height), (0, 0, 255), stroke)
frame = cv2.putText(frame, 'Not wearing mask', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
sound.play()
#playsound('please_use_your_mask.mp3')
else:
cv2.rectangle(frame, (x,y), (width, height), (0, 255, 0), stroke)
frame = cv2.putText(frame, 'Wearing mask', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
cv2.imshow('Face Mask Detection Aplication', frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
camera.release()
cv2.destroyAllWindows()