class DetectFace:
def __init__(self, vid, smile=False, eye=False):
self.active = True
self.smile = smile
self.eye = eye
self.vid = vid
self.faceCascade = CascadeFile("haarcascade_frontalface_default.xml").getClassifier()
if self.eye:
pass # self.eyeCascade = CascadeFile("haarcascade_eye.xml").getClassifier()
if self.smile:
self.smileCascade = CascadeFile("haarcascade_smile.xml").getClassifier()
self.activate()
def activate(self):
while self.active:
ret, frame = self.vid.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.faceCascade.detectMultiScale(
gray,
scaleFactor=1.4,
minNeighbors=5,
minSize=(15, 15)
)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if self.smile:
roi_gray = gray[y:y + h, x:x + w]
roi_color = frame[y:y + h, x:x + w]
smiles = self.smileCascade.detectMultiScale(
roi_gray,
scaleFactor=1.05,
minNeighbors=5,
minSize=(15, 15)
)
for (ex, ey, ew, eh) in smiles[:1]:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (255, 0, 255), 2)
if self.eye:
pass # Implement eye detection
# Display the resulting frame
cv2.imshow('Facial Detection', frame)
k = cv2.waitKey(1)
if k == 27 or k == 113: # Esc key to stop
self.active = False
cv2.destroyAllWindows()
if k == 115: # Toggle smile
self.smile = not self.smile
continue
elif k == -1: # normally -1 returned,so don't print it
continue
else:
continue
def __init__(self, vid, smile=False, eye=False):
self.active = True
self.smile = smile
self.eye = eye
self.vid = vid
self.faceCascade = CascadeFile("haarcascade_frontalface_default.xml").getClassifier()
if self.eye:
pass # self.eyeCascade = CascadeFile("haarcascade_eye.xml").getClassifier()
if self.smile:
self.smileCascade = CascadeFile("haarcascade_smile.xml").getClassifier()
self.activate()