def __init__(self,
xdo_delay=0,
display=None,
security_token=None,
**kwargs):
"""
:param int xdo_delay: Default pause between keystrokes.
:param str display: reserved for future use.
:param security_token: Static token that must be the same between
client and server.
:param kwargs:
"""
super(XdoPlatformRpcs, self).__init__(**kwargs)
self.display = Xlib.display.Display(display)
self.libxdo = xdo.Xdo(display)
self.xdotool_delay = xdo_delay
# compute and cache {atom_name: atom_value} dict once to save us from
# having to repeatedly query X for this data.
self.x_atoms = {
name: self.display.intern_atom(name)
for name in _X_PROPERTIES
}
class LibxdoKeyboard(BaseX11Keyboard):
"""Static class for typing keys with python-libxdo."""
_log = logging.getLogger("keyboard")
display = Xlib.display.Display()
libxdo = xdo.Xdo(os.environ.get('DISPLAY', ''))
@classmethod
def send_keyboard_events(cls, events):
"""
Send a sequence of keyboard events.
Positional arguments:
events -- a sequence of tuples of the form
(keycode, down, timeout), where
keycode (str): key symbol.
down (boolean): True means the key will be pressed down,
False means the key will be released.
timeout (int): number of seconds to sleep after
the keyboard event.
"""
cls._log.debug("Keyboard.send_keyboard_events %r", events)
# TODO: We can distill this entire loop down to a single libxdo function
# call when we figure out how to properly user charcode_t entities from
# libxdo.
for event in events:
(key, down, timeout) = event
delay_micros = int(timeout * 1000.0)
key = KEY_TRANSLATION.get(key, key)
# Press/release the key, catching any errors.
try:
if down:
cls.libxdo.send_keysequence_window_down(
0, key, delay_micros)
else:
cls.libxdo.send_keysequence_window_up(0, key, delay_micros)
except Exception as e:
cls._log.exception("Failed to type key code %s: %s", key, e)
# Sleep after the keyboard event if necessary.
if timeout:
time.sleep(timeout)
except AttributeError:
pressSpecialKey(key)
if __name__ == '__main__':
# Connect to the device on port 6000 via ZXTouch tweak
s = socket.socket()
s.connect((device_ip, 6000)) # connect to the tweak
time.sleep(0.1) # please sleep after connection.
#Pick your UxPlay window to determine location
print("Please click on your UxPlay window or other screen mirroring tool")
xdo = xdo.Xdo()
win_id = xdo.select_window_with_click()
print("UxPlay window id selected: ", win_id)
generateKeymap()
#listen to keypresses without blocking
klistener = keyboard.Listener(
on_press=on_press
)
klistener.start()
with Listener( on_move=on_move,
on_click=on_click,
on_scroll=on_scroll) as listener:
listener.join()
def __init__(self, *args, **kwargs) -> None:
super(LinuxSpy, self).__init__(*args, **kwargs)
self.__xdo = xdo.Xdo() # type: ignore
print('Please click on the OrbtXL window')
self.__window = self.__xdo.select_window_with_click()
def AjTrak (modelId='last',moveMouse=False, displayCascade = False):
targetEye = (48,32)
dimVector = (1,)+targetEye+(3,)
recurrent = False
model = getTrainedModel (modelId)
model.summary()
if recurrent:
config = model.get_config()
config = configForwardPass (config)
model.from_config (config)
model.summary()
xContext = xdo.Xdo()
capture = cv2.VideoCapture (0)
try:
faceCascade= cv2.CascadeClassifier ("./data/haarcascade_frontalface_alt.xml")
eyeCascade = cv2.CascadeClassifier ("./data/haarcascade_eye_tree_eyeglasses.xml")
eye0Recent = LastAverage (10)
except Exception as e:
print (e)
return
try:
if displayCascade:
cv2.namedWindow ("test")
# cv2.namedWindow ("test2")
while True:
# wyjście z głównej pętli wdrażana przez openCV - nie działa bez okien...
# ch = cv2.waitKey(1) & 0xFF
# if ch == ord ('q'):
# break
image = capture.read ()[1]
imageGrayscale = cv2.cvtColor (image, cv2.COLOR_BGR2GRAY)
facesFound = faceCascade.detectMultiScale (imageGrayscale, 1.3, 5) # współczynniki ?
if len(facesFound) == 1:
# print (facesFound)
x,y,w,h = facesFound[0]
faceInput = np.array(facesFound[0]).reshape((1,4))
imageFace = image[y:y+h, x:x+w]
faceGrayscale = imageGrayscale[y:y+h, x:x+w]
cv2.rectangle (image,(x,y), (x+w, y+h), (255,0,0), 2)
eyesFound = eyeCascade.detectMultiScale (faceGrayscale, 1.3, 5)
if len (eyesFound) == 2:
# print ("Possible face detected\t", facesFound)
e0, e1 = eyesFound[0], eyesFound[1]
if e0[0] > e1[0]:
e0, e1 = e1, e0
e0, e1 = scale (e0, targetEye), scale (e1, targetEye)
# experiment with local averaging
# eye0Recent.push (e0)
# e0 = eye0Recent.average ()
(x0,y0,w0,h0,x1,y1,w1,h1) = np.concatenate([e0,e1])
cv2.rectangle (imageFace,(x0,y0), (x0+w0, y0+h0), (0,0,255), 2)
cv2.rectangle (imageFace,(x1,y1), (x1+w1, y1+h1), (0,255,0), 2)
imageEye0 = imageFace[y0:y0+h0, x0:x0+w0]
imageEye1 = imageFace[y1:y1+h1, x1:x1+w1]
eyeInput = np.concatenate (
(imageEye0.reshape (dimVector).transpose(0, 2,1,3),
imageEye1.reshape (dimVector).transpose(0, 2,1,3)),
axis=3
)
# forward pass
modelPrediction = model.predict (x=[faceInput,eyeInput])
modelPrediction = modelPrediction.flatten()
mouseLoc = getMousePos ()
predictionError = np.linalg.norm (modelPrediction-mouseLoc)
print ("Delta: {}\tPredicted: {}\tActual: {}".format(
"%.2f" % predictionError,modelPrediction,mouseLoc))
if moveMouse:
xContext.move_mouse (modelPrediction[0],modelPrediction[1],0)
else:
# print ("{} eyes found".format (len(eyesFound)))
pass
else:
print ("{} faces found".format (len(facesFound)))
pass
if displayCascade:
cv2.imshow ("test", image )
except Exception as e:
print (e)
pass
capture.release()
cv2.destroyAllWindows()
return
