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Video prediction.py
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Video prediction.py
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from tensorflow import keras
import cv2
import numpy as np
from playsound import playsound
import time
from threading import Thread, active_count
def warn():
playsound('Sound Effects/uyari.mp3')
time.sleep(4)
model = keras.models.load_model("BestSavedModel")
IMG_SIZE = 274
face_cascade = cv2.CascadeClassifier('Cascades/haarcascade_frontalface_default.xml')
# face_cascade = cv2.CascadeClassifier('Cascades/test_cascade.xml')
# eye_cascade = cv2.CascadeClassifier('Cascades/haarcascade_eye.xml')
# LBP_cascade = cv2.CascadeClassifier('Cascades/lbpcascade_profileface.xml')
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)
print(active_count())
i = 0
while True:
ret, frame = cap.read()
faces = face_cascade.detectMultiScale(frame, 1.3, 5)
detectedFrame = frame
masked = 0
unmasked = 0
for face in faces:
x, y, w, h = [v for v in face]
face_crop = frame[y:y + h, x:x + w]
test_array = cv2.resize(face_crop, (IMG_SIZE, IMG_SIZE))
test_array = np.array(test_array).reshape(-1, IMG_SIZE, IMG_SIZE, 3)
img_class = model.predict(test_array)
if (img_class > 0.5).astype("int32")[0][0] == 0:
detectedFrame = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
masked += 1
else:
detectedFrame = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
unmasked += 1
if active_count() <= 1:
Thread(target=warn).start()
# print(active_count())
cv2.putText(frame, "Masked: " + str(masked) + " Unmasked: " + str(unmasked),
(10, 470),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(255, 255, 255),
2)
cv2.imshow('frame', detectedFrame)
k = cv2.waitKey(30) & 0xff
if k == 27: # press 'ESC' to quit
break
cap.release()
cv2.destroyAllWindows()