def train_char(training_char):
controller = Leap.Controller()
for t in range(NUM_SAMPLES):
time.sleep(SAMPLE_DELAY)
sample = get_hand_position(controller, True)
while len(sample) != NUM_FEATURES:
print "Please place only right hand in view"
sample = get_hand_position(controller, True)
print sample
add_data(sign=training_char, **sample)
print "Done training"
def train_char(training_char):
controller = Leap.Controller()
for t in range(NUM_SAMPLES):
time.sleep(SAMPLE_DELAY)
sample = get_hand_position(controller, True)
while len(sample) != NUM_FEATURES:
print "Please place only right hand in view"
sample = get_hand_position(controller, True)
print sample
add_data(sign=training_char, **sample)
print "Done training"
def train_char(model_name, training_word):
for t in range(NUM_SAMPLES):
time.sleep(SAMPLE_DELAY)
sample = get_hand_position(controller, True)
while len(sample) != NUM_FEATURES:
emit(ACTION_TRAINING_ERROR, "Please place only right hand in view")
# print "Please place only right hand in view"
sample = get_hand_position(controller, True)
print sample
if t % 10 == 0:
emit(ACTION_TRAINING_INPROGRESS, str(t) + "/" + str(NUM_SAMPLES))
add_data(model_name=model_name, sign=training_word, **sample)
emit(ACTION_TRAINING_DONE, "Done training")
def train():
train_gui = TrainGUI()
controller = Leap.Controller()
print("Please show your hand")
for t in range(NUM_SAMPLES):
print('Getting hand data..')
sample = get_hand_position(controller, True)
while len(sample) != NUM_FEATURES:
print "Please place only right hand in view"
sample = get_hand_position(controller, True)
print(sample)
print "Please submit and try again.."
train_gui.run(sample)
time.sleep(SAMPLE_DELAY)
def current_symbol():
global past_symbol
global prev_prediction
hand_pos = get_hand_position(controller)
if not hand_pos:
new = past_symbol != ' '
past_symbol = ' '
features = [v for k, v in hand_pos.iteritems()]
prediction = ''.join(clf.predict(features))
if prediction != prev_prediction:
text.append(prediction)
prev_prediction = prediction
def guess_char():
global prev
controller = Leap.Controller()
controller.set_policy(Leap.Controller.POLICY_BACKGROUND_FRAMES)
classes = clf.classes_
probs = zip(
classes,
clf.predict_proba([
v for k, v in get_hand_position(controller, True).iteritems()
])[0])
alpha = max([score for sym, score in probs])
most_probable = sorted([(sym, alpha * score + (1 - alpha))
for sym, score in probs],
key=lambda t: t[1],
reverse=True)
print most_probable[:3]
prev = most_probable[0][0]
print prev
def guess_char():
global prev
controller = Leap.Controller()
controller.set_policy(Leap.Controller.POLICY_BACKGROUND_FRAMES)
classes = clf.classes_
probs = zip(classes, clf.predict_proba([v for k, v in
get_hand_position(controller, True).iteritems()])[0])
alpha = max([score for sym, score in probs])
most_probable = sorted([(sym, alpha * score + (1 - alpha))
for sym, score in probs], key=lambda t: t[1],
reverse=True)
print most_probable[:3]
prev = most_probable[0][0]
print prev
def current_symbol():
global past_symbol
global prev_prediction
# Is there a hand?
hand_pos = get_hand_position(controller)
if not hand_pos:
new = past_symbol != ' '
past_symbol = ' '
return jsonify(symbol=' ', new=new)
features = [v for k, v in hand_pos.iteritems()]
# Do we have a new symbol?
prediction = ''.join(clf.predict(features))
if prediction == prev_prediction:
# We good fam
return jsonify(new=False, symbol=prediction)
else:
prev_prediction = prediction
return jsonify(new=True, symbol=prediction)
def current_symbol():
global past_symbol
global prev_prediction
# Is there a hand?
hand_pos = get_hand_position(controller)
if not hand_pos:
new = past_symbol != ' '
past_symbol = ' '
return jsonify(symbol=' ', new=new)
features = [v for k, v in hand_pos.iteritems()]
# Do we have a new symbol?
prediction = ''.join(clf.predict(features))
if prediction == prev_prediction:
# We good fam
return jsonify(new=False, symbol=prediction)
else:
prev_prediction = prediction
return jsonify(new=True, symbol=prediction)
def current_symbol():
global past_symbol
global prev_prediction
#detect if there is a hand
hand_pos = get_hand_position(controller)
if not hand_pos:
new = past_symbol != '%'
past_symbol = ' '
return jsonify(symbol='bleh', new=new)
features = np.array([v for k, v in hand_pos.iteritems()]).reshape(1, -1)
prediction = ''.join(clf.predict(features))
if prediction == prev_prediction:
return jsonify(new=False, symbol=prediction)
else:
prev_prediction = prediction
return jsonify(new=True, symbol=prediction)
def send_data(data):
print data
h = httplib.HTTPConnection(IP_ADDR)
headers = {"Content-Type": "application/x-www-form-urlencoded"}
h.request('POST', '/', urllib.urlencode({'data': data}), headers)
r = h.getresponse()
time.sleep(2)
p = vlc.MediaPlayer(
"file:///home/gautham/temp/sign-language-tutor/alexa_call.m4a")
p.play()
while True:
try:
hand_pos = get_hand_position(controller)
if hand_pos is None:
continue
features = [v for k, v in hand_pos.iteritems()]
prediction = ''.join(clf.predict(features))
time.sleep(1)
if prediction != prev_prediction:
prev_prediction = prediction
send_data(dictmapping[prediction])
time.sleep(10)
# print dictmapping[prediction]
except Exception as ex:
print(ex.message)
continue
def worker():
data = get_hand_position(controller, blocking=True)
return model.predict(data)[0]