def work():
print('in work')
mixer.init()
sound = mixer.Sound('alarm.wav')
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
score = 0
thicc = 2
while (True):
print('in treu')
ret, frame = cap.read()
# import pdb
# pdb.set_trace()
if frame is not None:
height, width = frame.shape[:2]
label = classify_face(frame)
if (label == 'with_mask'):
print("No Beep")
else:
sound.play()
print("Beep")
cv2.putText(frame, str(label), (100, height - 20), font, 1,
(255, 255, 255), 1, cv2.LINE_AA)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
return 'Closed'
import numpy as np
from pygame import mixer
import time
from label_detect import classify_face
mixer.init()
sound = mixer.Sound('alarm.wav')
#face = cv2.CascadeClassifier('/media/preeth/Data/prajna_files/Drowsiness_detection/haar_cascade_files/haarcascade_frontalface_alt.xml')
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
score=0
thicc=2
#faces = face.detectMultiScale(gray,minNeighbors=5,scaleFactor=1.1,minSize=(25,25))
while(True):
ret, frame = cap.read()
height,width = frame.shape[:2]
label = classify_face(frame)
if(label == 'with_mask'):
print("No Beep")
else:
sound.play()
print("Beep")
cv2.putText(frame,str(label),(100,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def detect_face(frameCount):
# grab global references to the video stream, output frame, and
# lock variables
global vs, outputFrame, lock
# initialize the motion detector and the total number of frames
# read thus far
md = Recog(accumWeight=0.1)
total = 0
# loop over frames from the video stream
frame_counter = 0
face_frame_counter = 0
previous_state = 0
previous_id = 'unknown'
counter_id = 0
flag = False
mainFlag = False
flagTemp = 0
finalFlag = True
while True:
ret, frame = vs.read()
height,width = frame.shape[:2]
label = classify_face(frame)
if(label == 'with_mask'):
print("Mask detected")
else:
print("No mask detected")
cv2.putText(frame,str(label),(100,height-20), font, 1,(255,255,255),1,cv2.LINE_AA)
try:
x = th.is_alive()
if x == False and flagTemp == 0:
flagTemp = 1
frame_counter = 0
face_frame_counter = 0
previous_state = 0
previous_id = 'unknown'
counter_id = 0
flag = False
mainFlag = False
except:
pass
if finalFlag:
# read the next frame from the video stream, resize it,
# convert the frame to grayscale, and blur it
frame_counter+=1
msg = ""
#frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# grab the current timestamp and draw it on the frame
timestamp = datetime.datetime.now()
cv2.putText(frame, timestamp.strftime(
"%A %d %B %Y %I:%M:%S%p"), (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
# if the total number of frames has reached a sufficient
# number to construct a reasonable background model, then
# continue to process the frame
if total > frameCount:
# detect motion in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(int(minW), int(minH)),
)
#print(faces)
if len(faces)>0:
# print("face detected")
if previous_state == 1:
face_frame_counter+=1
previous_state = 1
else:
previous_state = 0
face_frame_counter = 0
if face_frame_counter>70:
#start seq.
print('face recognized')
for(x,y,w,h) in faces:
cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 2)
id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
if counter_id>10:
print(previous_id)
tempstr = 'Hello '+names[id]
finalFlag = False
mainFlag = True
flagTemp = 0
a = 0
th = threading.Thread(target = flow, args= (names[id],))
th.start()
#print(th.is_alive())
flag = True
if(confidence<100):
id = names[id]
if (id==previous_id):
counter_id+=1
previous_id = id
else:
id = 'unknown'
previous_id = id
counter_id = 0
#face_frame_counter = 0
total += 1
# acquire the lock, set the output frame, and release the
# lock
with lock:
outputFrame = frame.copy()
