def post_speech_data():
data = request.form.to_dict()
data["time"] = float(data["time"])
db.speech.insert_one(data)
cm.trigger_controllers(data['user_name'], "speech", data)
data.pop("_id")
return jsonify(data), 201
def post_confirm_data():
data = request.get_json(force=True)
#data["time"] = time.time()
#data["action"] = json.loads(data["action"])
db.confirmation.insert_one(data)
cm.trigger_controllers(data['user_name'], "confirmation", data)
data.pop("_id")
return jsonify(data), 201
def post_hue_data():
data = request.form.to_dict()
data["time"] = float(data["time"])
#data["last_manual"] = float(data["last_manual"])
data['light_states'] = json.loads(data['lights'])
db.hue.insert_one(data)
cm.trigger_controllers(data['user_name'], "hue", data)
data.pop("_id")
return jsonify(data), 201
def handle_messages():
payload = request.get_json()
for event in messaging_events(payload):
fb_id = event["sender"]["id"]
username = None
result = db.fb_users.find_one({"fb_id": fb_id})
if result:
username = result["username"]
if fb_id != bot_id:
cm.trigger_controllers(username, "chat", event)
return "ok", 200
def process(img_data):
try:
with current_app.test_request_context('/'):
if data["motion_update"]:
db.images.update_one({"filename": img_data['filename']}, {
'$set': {
"history.image_processing_start": time.time()
}
},
upsert=False)
cm.trigger_controllers(data['user_name'], "image",
img_data)
db.images.update_one({"filename": img_data['filename']}, {
'$set': {
"history.image_processing_finish": time.time()
}
},
upsert=False)
except:
traceback.print_exc()
def on_event(self, event, data):
if event == "image":
# request update
self.update = True
self.current_images = get_newest_images(self.username, self.cams)
# if model is ready
if "activity" in self.learner.models and self.classes is not None:
pred, pred_probs = self.learner.predict(
"activity", [self.current_images])
self.current_predictions = pred_probs[0].tolist()
self.current_activity = self.classes[np.argmax(
self.current_predictions)]
cm.trigger_controllers(self.username, "activity",
self.current_activity)
conf_mean = np.mean(self.confidences[-1000:])
conf_std = np.std(self.confidences[-1000:])
sigma = 1.0
current_conf = np.max(pred_probs[0])
patience = 60 * 60 # actually 1 hour or more is probably good
if current_conf < conf_mean - conf_std * sigma:
logger.debug(
"current confidence: {}; mean: {}, std: {}".format(
current_conf, conf_mean, conf_std))
if time.time() - self.last_asked > patience:
ask = {
"platform": "",
"data": "",
"confirmation": {
"question": "今は何をしてるんですか？",
"open": "",
"data": "activity"
}
}
if ask not in self.re:
self.re.append(ask)
self.last_asked = time.time()
elif event == "speech" and data["type"] == "speech":
msg = "".join(data["text"].split())
if ("という行動" in msg or
("今" in msg and "してる" in msg)) and self.classes is not None:
self.confirm_label(msg)
elif event == "confirmation":
if "data" in data and "label" in data["data"]:
phase = data["data"]["phase"]
ans = data["answer"]
if ans and self.classes is not None:
label = data["data"]["label"]
db.labels.insert_one({
"username": self.username,
"time": data['response_time'],
"label": label
})
#if label in self.classes:
# self.re.append({"platform": "tts", "data": label + "をデータベースに追加しました．"})
#else:
# self.re.append({"platform": "tts", "data": label + "をデータベースに新しいクラスとして追加しました．"})
if "data" in data and data["data"] == "activity":
msg = data["answer"]
self.confirm_label(msg)