def test_database_model(self):
configs = [{}, {
"db_name": "name1",
"frequency": "wrong_freq",
"compress": True,
"verify": True
}, {
"frequency": "daily",
"compress": True,
"verify": True
}, {
"db_name": "name1",
"frequency": "daily",
"compress": 100,
"verify": True
}]
for config in configs:
with self.assertRaises(Exception) as context:
d = Database(config)
d.validate()
self.assertTrue(isinstance(context.exception, DataError))
# Should pass ok
d = Database({
"db_name": "name1",
"frequency": "daily",
"compress": True,
"verify": True
})
d.validate()
import sys
sys.path.append('../.')
from common.config import *
from methods.BlockMatchingOF import *
from common.Database import *
from methods.GaussianMethods import *
from common.extractPerformance import *
from common.metrics import *
from methods.Farneback import *
from methods.LucasKanade import *
from time import time
if __name__ == "__main__":
gt_db = Database(abs_dir_gt, start_frame=start_frame, end_frame=end_frame)
input_db = Database(abs_dir_input,
start_frame=start_frame,
end_frame=end_frame)
gt = gt_db.loadDB(im_color=cv2.IMREAD_UNCHANGED | cv2.IMREAD_ANYDEPTH)
input = input_db.loadDB(im_color=True)
dit_to_save = "output_{}".format(OF_TYPE)
if not os.path.exists(dit_to_save):
os.mkdir(dit_to_save)
# window_sizes = np.arange(2, 500, 10) # up to 150 because of "somthing strange in Lucas Kanade
# shift_sizes = np.arange(1, 2, 1)
window_sizes = [7, 15, 23, 31, 39, 47, 55, 63, 71]
shift_sizes = [7, 15, 23, 31, 39, 47, 55, 63, 71]
from common.Database import *
from common.metrics import *
from performance import *
from temporalanalysis import *
from frameperf import *
from FramePerformancedelay import *
if __name__ == "__main__":
if DATABASE == "changedetection":
start_frame = 1201
end_frame = 1400
gt_db = Database(abs_dir_gt,
start_frame=start_frame,
end_frame=end_frame)
input_db = Database(abs_dir_input,
start_frame=start_frame,
end_frame=end_frame)
results_db = Database(abs_dir_result, start_frame=0)
gt = gt_db.loadDB()
input = input_db.loadDB()
res = results_db.loadDB()
res_A = res[:200]
res_B = res[200:]
TP, FP, TN, FN, y_gt, y_test = performance(gt, res_A)
from common import Chart
from common import Database
import warnings
warnings.simplefilter("ignore")
pd.set_option('mode.chained_assignment', None)
plt.style.use('dark_background')
parser = argparse.ArgumentParser()
args = parser.parse_args("")
clean = Cleaning.cleaning()
chart = Chart.plot()
schedule = BackgroundScheduler()
db = Database.Database()
# =======================================CONFIGURATION=======================================*
args.host = 'http://127.0.0.1:'
# args.host ='http://mdfc-server:'
args.port = '5520'
args.url = args.host + args.port + '/predict'
args.gateway = 'a21908053003'
args.interval = 5
args.count = 60
args.date = '2021-01-01'
args.mode = 2
args.features = ['gateway_id', 'stack_power', 'stack_current', 'stack_voltage', 'stack_temp', 'meoh_concentration', 'fuel_tank_level']
# ============================================================================================*
def collect(gw):