def start_file(file_path):
# Read the file
data = read_file(file_path)
global global_desired_dict
global_desired_dict = data
start()
a = Args()
a.init()
args = a.get_args()
if args.verbose:
print(vars(args))
if args.test:
# Just do printing and do nothing else
gui = ForegroundGUI()
while True:
print("Window: {} | Brightness: {}".format(
gui.get_foreground_window(), brightness.get_brightness()))
time.sleep(1)
if args.start:
# Check if one or file
if args.one:
start_one(args.window_name, args.brightness_true)
elif args.file:
start_file(args.path)
fy=0.5,
interpolation=cv2.INTER_NEAREST)
# Gray scale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
return frame, gray
if __name__ == '__main__':
webcam = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier(
'./haar/haarcascade_frontalface_alt2.xml')
eye_cascade = cv2.CascadeClassifier('./haar/haarcascade_eye.xml')
original_size = None
maximum_size = 0
screen = Brightness()
original_bright = screen.get_brightness()
current_bright = None
while True:
# Read from webcam
got_frame, original_frame = webcam.read()
if got_frame:
frame, gray = reduce_gray_image(original_frame)
# Detect faces
faces = face_cascade.detectMultiScale(gray, SCALE_FACTOR,
MIN_NEIGHBOR)
bright = current_bright
for (fx, fy, fw, fh) in faces:
if DEBUG:
cv2.rectangle(frame, (fx, fy), (fx + fw, fy + fh), BLUE, 2)
area = float(fw * fh)