def action(self):
tts.say("Scanning for humans")
cascPath = constants.CLASSIFIER_FILE
faceCascade = cv2.CascadeClassifier(cascPath)
video = Pyro4.Proxy("PYRONAME:video.frame")
Pyro4.config.SERIALIZERS_ACCEPTED = set(
['pickle', 'json', 'marshal', 'serpent'])
Pyro4.config.SERIALIZER = 'pickle'
frame = video.get_frame()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=constants.SCALE_FACTOR,
minNeighbors=constants.MIN_NEIGHBORS,
minSize=(constants.MIN_WIDTH, constants.MIN_HEIGHT),
flags=cv2.CASCADE_SCALE_IMAGE)
video._pyroRelease()
if (type(faces) != tuple and faces.size > 0):
tts.say("Face detected")
self.next = recognizeState.RecognizeState(
data=[frame, faces, self.data])
else:
self.next = wanderState.WanderState()
def action(self):
Pyro4.config.SERIALIZERS_ACCEPTED = set(['pickle','json', 'marshal', 'serpent'])
Pyro4.config.SERIALIZER = 'pickle'
audio = Pyro4.Proxy("PYRONAME:audio.text")
test = ""
if(type(audio) != type(None) and audio != None):
try:
text = audio.get_text()
print(text)
except UnboundLocalError as ule:
print("No text received")
except NameError as ne:
print("No text received")
else:
print("No text received")
if(text == "yes"):
tts.say("Good")
time.sleep(1)
tts.say("Fix it or be destroyed")
time.sleep(1)
self.next = waitState.WaitState()
else:
time.sleep(1)
self.next = requestAckState.RequestAckState()
def action(self):
for i in range(3):
r = sr.Recognizer()
with sr.Microphone() as source:
audio = r.listen(source)
try:
text = r.recognize_google(audio)
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print(
"Could not request results from Google Speech Recognition service; {0}"
.format(e))
if (text == "yes"):
break
if (text == "yes"):
tts.say("Good")
time.sleep(1)
tts.say("Fix it or be destroyed")
time.sleep(1)
self.next = waitState.WaitState()
else:
time.sleep(1)
self.next = requestAckState.RequestAckState()
def action(self):
tts.say("Scanning for humans")
cascPath = constants.CLASSIFIER_FILE
faceCascade = cv2.CascadeClassifier(cascPath)
video_capture = cv2.VideoCapture(0)
# Throw away the first few frames, they can be blank
for i in range(5):
video_capture.read()
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=constants.SCALE_FACTOR,
minNeighbors=constants.MIN_NEIGHBORS,
minSize=(constants.MIN_WIDTH, constants.MIN_HEIGHT),
flags=cv2.CASCADE_SCALE_IMAGE
)
video_capture.release()
if(type(faces) != tuple and faces.size > 0):
tts.say("Face detected")
self.next = recognizeState.RecognizeState(data=[frame, faces, self.data])
else:
self.next = wanderState.WanderState()
def action(self):
tts.say("Watching for build failures")
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
app = Flask(__name__)
app.add_url_rule("/", view_func=self.wait, methods=[
'POST',
])
app.run()
self.next = wanderState.WanderState(data=self.post_body)
def action(self):
tts.say("Identifying human")
recognized = False
frame, faces, post_body = self.data
for (x, y, w, h) in faces:
# This looks like what I would need if I wasn't writing the file
#x, y, w, h = vision.resize(x, y, w, h)
#face = frame[y:y+h, x:x+w]
#resized = cv2.resize(face, (constants.WIDTH, constants.HEIGHT))
#grayscale = cv2.cvtColor(resized, cv2.COLOR_RGB2GRAY)
person = vision.find_person(self.Y, self.pca, self.X_pca)
print("Recognized person as '", person, "'. Culprit was '", post_body, "'.")
if(person == post_body):
recognized = True
if(recognized):
self.next = requestIdentityState.RequestIdentityState(data=[person, self.data])
else:
self.next = wanderState.WanderState()
def action(self):
tts.say("I know, I was just being polite")
time.sleep(1)
tts.say("You have broken the build")
time.sleep(1)
self.next = requestAckState.RequestAckState()
print("Please enter a name")
exit()
NAME = sys.argv[1]
faceCascade = cv2.CascadeClassifier(constants.CASC_PATH)
video_capture = cv2.VideoCapture(0)
if not os.path.exists(constants.TRAIN_DIR):
os.makedirs(constants.TRAIN_DIR)
if not os.path.exists(constants.TRAIN_DIR + '/' + NAME):
os.makedirs(constants.TRAIN_DIR + '/' + NAME)
tts.say("Hello, I am the build butler")
tts.say("Welcome to the team")
tts.say("To begin, please look at the camera with a neutral expression")
tts.say(
"Once your face is selected, slowly change your expression, lighting and tilt"
)
i = 0
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=constants.SCALE_FACTOR,
def action(self):
person, data = self.data
tts.say("Are you " + person)
self.next = pyroConfirmIdentityState.PyroConfirmIdentityState(data=self.data)
def action(self):
tts.say("Initiated pathfinding")
time.sleep(1)
self.next = pyroDetectState.PyroDetectState(data=self.data)
def wait(self):
self.shutdown_server()
tts.say("Received build failure alert")
self.post_body = "chris"
return "Alert received"
