I have a demo.py and videostream.py files that need to use the VideoCapture function from cv2 at the same time. The first file is used to determine if the person is real or is it just an image in fornt of the camera. The second one is used handle the video stream in the website.
My issue is how can I use both without it showing the camera index error.
The demo.py file:
def liveness():
FULL_POINTS = list(range(0, 68))
FACE_POINTS = list(range(17, 68))
JAWLINE_POINTS = list(range(0, 17))
RIGHT_EYEBROW_POINTS = list(range(17, 22))
LEFT_EYEBROW_POINTS = list(range(22, 27))
NOSE_POINTS = list(range(27, 36))
RIGHT_EYE_POINTS = list(range(36, 42))
LEFT_EYE_POINTS = list(range(42, 48))
MOUTH_OUTLINE_POINTS = list(range(48, 61))
MOUTH_INNER_POINTS = list(range(61, 68))
EYE_AR_THRESH = 0.30
EYE_AR_CONSEC_FRAMES = 2
# initializing the parameters
COUNTER_LEFT = 0
TOTAL_LEFT = 0
COUNTER_RIGHT = 0
TOTAL_RIGHT = 0
video_capture = cv2.VideoCapture(0)
print(video_capture.isOpened())
# looping over frames
while True:
# checkpoint 1
ret, frame = video_capture.read()
if frame is None:
video_capture.release()
video_capture = cv2.VideoCapture(0)
continue
if ret:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 0)
frame = imutils.resize(frame, width=600)
for rect in rects:
x = rect.left()
y = rect.top()
x1 = rect.right()
y1 = rect.bottom()
landmarks = np.matrix([[p.x, p.y] for p in predictor(frame, rect).parts()])
left_eye = landmarks[LEFT_EYE_POINTS]
right_eye = landmarks[RIGHT_EYE_POINTS]
left_eye_hull = cv2.convexHull(left_eye)
right_eye_hull = cv2.convexHull(right_eye)
ear_left = eye_aspect_ratio(left_eye)
ear_right = eye_aspect_ratio(right_eye)
# calculating blink wheneer the ear value drops down below the threshold
if ear_left < EYE_AR_THRESH:
COUNTER_LEFT += 1
else:
if COUNTER_LEFT >= EYE_AR_CONSEC_FRAMES:
TOTAL_LEFT += 1
print("Left eye winked")
COUNTER_LEFT = 0
if ear_right < EYE_AR_THRESH:
COUNTER_RIGHT += 1
else:
if COUNTER_RIGHT >= EYE_AR_CONSEC_FRAMES:
TOTAL_RIGHT += 1
print("Right eye winked")
COUNTER_RIGHT = 0
x = TOTAL_LEFT + TOTAL_RIGHT
(h, w) = frame.shape[:2]
temp = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,
(300, 300), (104.0, 177.0, 123.0))
net.setInput(temp)
detections = net.forward()
for i in range(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
# staisfying the union need of veryfying through ROI and blink detection.
if confidence > 0.5 and x > 2:
# detect a bounding box
# take dimensions
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
# get the dimensions
(startX, startY, endX, endY) = box.astype("int")
startX = max(0, startX)
startY = max(0, startY)
endX = min(w, endX)
endY = min(h, endY)
# extract the face ROI and then preproces it in the exact
# same manner as our training data
face = frame[startY:endY, startX:endX]
face = cv2.resize(face, (32, 32))
face = face.astype("float") / 255.0
face = img_to_array(face)
face = np.expand_dims(face, axis=0)
# pass the model to determine the liveness
preds = model.predict(face)[0]
j = np.argmax(preds) label = le.classes_[j]
return label
The videostream.py file:
class Stream(Thread):
def __init__(self):
self.flag = [False]
self.capture_flag = [False]
self.clients = []
Thread.__init__(self, name=Stream.__name__)
def run(self):
self.camera = cv2.VideoCapture(0)
prev_input = 1
while True:
# Read camera and get frame
rval, frame = self.camera.read()
if frame is None:
self.camera.release()
self.camera = cv2.VideoCapture(0)
continue
# Send camera stream to web
if self.flag[0]:
rvel, jpeg = cv2.imencode('.jpg', frame)
encode_string = base64.b64encode(jpeg)
for client in self.clients:
client.write_message(encode_string)
# A visitor pushes button
but = GPIO.input(17)
if(not prev_input and but):
# It affects dlib speed. If frame size is small, dlib would be faster than normal size frame
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
visit.visit(small_frame)
for client in self.clients:
client.write_message("log")
prev_input = but
time.sleep(0.05)
# Click makephotos on web browser
if self.capture_flag[0] == True:
enough_image = collect.make_photo(frame)
if enough_image == "success":
print("Success to register")
else:
print("Fail to register")
for client in self.clients:
client.write_message(enough_image)
self.capture_flag[0] = False
self.camera.release()
Sorry if the code is too much.
