def registerFlight():
session = request.environ.get("beaker.session")
username = session["username"]
tailId = request.json["tailId"]
flightDate = datetime.datetime.strptime(request.json["departure"], "%m/%d/%Y %I:%M %p")
departTz = pytz.timezone(request.json["timezone"])
localized = departTz.localize(flightDate)
normalized = localized.astimezone(pytz.utc)
obj = {
"tailId": tailId,
"departureTime": normalized,
"departureStr": flightDate.strftime("%Y-%m-%d %H:%M"),
"departureDay": normalized.strftime("%Y-%m-%d"),
"status": "INIT",
"searchCount": 0,
"user": username,
}
emuDb = mongoConnect("localhost", 27017, "emu", "accounts")
result = emuDb.find_one({"username": username}, {"_id": 0})
result["flights"].append(obj)
emuDb.update(
{"username": username}, {"$set": {"flights": result["flights"], "lastActive": datetime.datetime.now()}}
)
preflight = mongoConnect("localhost", 27017, "emu", "preflights")
preflight.insert(obj)
return {"success": True}
def kafka_consumer():
#Path of the classes file
path_names = 'data/YOLO/rehoboam.names'
#Path of the Avro scheme
path_avro = "data/scheme.avsc"
#Read the class file and transform in dictionary
with open(path_names) as f:
names_dict = {i: line.split('\n')[0] for i,line in enumerate(f)}
#Connection to the Mongo collection to write in
mongo_collection = mongoConnect()
#Load the tf model
saved_model_loaded = tf.saved_model.load(
config['weights_tf'], tags=[tag_constants.SERVING])
infer = saved_model_loaded.signatures['serving_default']
#logging.basicConfig(level=logging.INFO)
#Read the Avro schema and create the Kafka consumer
schema = avro.schema.parse(open(path_avro, "r").read())
reader = DatumReader(schema)
consumer = KafkaConsumer('input_image', bootstrap_servers=['localhost:9094', 'localhost:9095'],
group_id="rehoboam", value_deserializer=lambda m: decode_avro(m, reader))
for msg in consumer:
#Inicialize a dictionary with the class and the number of apperence to 0
default_document = {i: 0 for i in sorted(names_dict.values(), key=lambda x: x)}
#Transform the bytes to image
img = io.BytesIO(msg.value['image'])
img_pil = Image.open(img)
img_cv2 = np.array(img_pil)
#Call the model to detect the classes
scores, classes = detect(
parameters=config, infer_model=infer, image=img_cv2)
real_classes = [i for i,j in zip(classes[0], scores[0]) if j > 0.0]
unique, count = np.unique(real_classes, return_counts=True)
result_dict = {names_dict[i]: int(j) for i,j in zip(unique, count)}
#Store the number of incidences in the default dictionary
for k,v in result_dict.items():
default_document[k] = v
result = {
'district_id': int(msg.value['district']),
'camera_id': int(msg.value['camera']),
'timestamp': datetime.now(),
'results': default_document
}
#Save it in Mongo
mongo_collection.insert_one(result).inserted_id
consumer.commit()
def showFlights():
session = request.environ.get("beaker.session")
username = session["username"]
flights = []
emuDb = mongoConnect("localhost", 27017, "emu", "accounts")
result = emuDb.find_one({"username": username}, {"_id": 0})
for flight in result["flights"]:
if flight["departureTime"] > datetime.datetime.now():
flights.append(flight)
flights.sort(key=lambda x: x["departureStr"])
return {"flights": flights}
def showAccount():
session = request.environ.get('beaker.session')
username = session['username']
emuDb = mongoConnect("localhost", 27017, "emu", "accounts")
result = emuDb.find_one( {'username':username}, {'_id':0})
obj = {
'username': result['username'],
'firstActive': result['firstActive'].strftime('%Y-%m-%d %H:%M'),
'lastActive': result['lastActive'].strftime('%Y-%m-%d %H:%M')
}
return { 'account': obj }
input_topic = "input_image"
topic_list = []
consumer = KafkaConsumer(
bootstrap_servers=['localhost:9094', 'localhost:9095'],
group_id="rehoboam")
if input_topic not in consumer.topics():
topic_list.append(
NewTopic(name=input_topic, num_partitions=2, replication_factor=2))
admin_client.create_topics(new_topics=topic_list, validate_only=False)
# Font for the html style
font = 'Arial'
# Connection to the Mongo collection to write in
mongo_col = mongoConnect()
# Read the class file and transform in dictionary
with open(path_names) as f:
names_dict = {i: line.split('\n')[0] for i, line in enumerate(f)}
# Read the CSV file with the data of the cameras and transform it to Pandas dataframe
cameras_df = pd.read_excel(path_data, sheet_name='cameras')
cameras_df = cameras_df.set_index('id_district', drop=True)
cameras_df = cameras_df[cameras_df['readable'] == 1]
# Malaga's university logo
UMA_LOGO = "https://www.uma.es/servicio-comunicacion/navegador_de_ficheros/Marcas-UMA/descargar/Marca%20Universidad%20de%20M%C3%A1laga/marcauniversidaddemalagaVERTICAL.png"
# ==============================================================================
# Forms menus
