from sklearn import svm
from sklearn.externals import joblib
import pickle
warnings.filterwarnings("ignore")
"""clf = joblib.load('input/detection_iris_new.pkl')## this is the vnear robust one"""
clf = joblib.load(
'input/detection_new18july.pkl') ## this is taken at the beach
#clm = joblib.load('input/detection_new18july.pkl')
#clf1 = joblib.load('input/dronedetectionfinal_new.pkl')
#discard this part----------------------------------------------------------
rows = 10
cols = 60
winlist = []
"""set this part to the number of logs you want to save before computing confidence level"""
log = logdata(
3) ##check getconfi.py for more details(in feature_extraction folder)
######################################################################################################
global itervalue #I used this when I needed to get only a certain number o iterations
itervalue = 0
"""this is the script which would record data"""
def record(time=1, fs=44100):
file = 'temp_out'
duration = time
recording = sd.rec(int(duration * fs),
samplerate=fs,
channels=1,
blocking=False)
for i in range(time):
warnings.filterwarnings("ignore")
"""set this true not to send any data to server"""
test = True
"""clf = joblib.load('input/detection_iris_new.pkl')## this is the vnear robust one"""
clf = joblib.load(
'input/detection_backyaardwithnoise.pkl') ## this is taken at the beach
#clm = joblib.load('input/detection_new18july.pkl')
#clf1 = joblib.load('input/dronedetectionfinal_new.pkl')
rows = 10
cols = 60
winlist = []
datacount = 4
"""set this part to the number of logs you want to save before computing confidence level"""
log = logdata(datacount)
######################################################################################################
global itervalue
itervalue = 0
"""this is the script which would record data"""
def record(time=1, fs=44100):
file = 'temp_out'
duration = time
recording = sd.rec(int(duration * fs),
samplerate=fs,
channels=1,
blocking=False)
from sklearn import svm
from sklearn.externals import joblib
import pickle
warnings.filterwarnings("ignore")
"""clf = joblib.load('input/detection_iris_new.pkl')## this is the vnear robust one"""
clf = joblib.load(
'input/detection_iris_new.pkl') ## this is taken at the beach
#clm = joblib.load('input/detection_new18july.pkl')
#clf1 = joblib.load('input/dronedetectionfinal_new.pkl')
rows = 10
cols = 60
winlist = []
log = logdata(3)
######################################################################################################
global itervalue
itervalue = 0
def record(time=1, fs=44100):
file = 'temp_out'
duration = time
recording = sd.rec(int(duration * fs),
samplerate=fs,
channels=1,
blocking=False)
for i in range(time):
i += 1
from sklearn import svm
from sklearn.externals import joblib
import pickle
warnings.filterwarnings("ignore")
"""clf = joblib.load('input/detection_iris_new.pkl')## this is the vnear robust one"""
clf = joblib.load(
'input/detection_backyaard.pkl') ## this is taken at the beach
#clm = joblib.load('input/detection_new18july.pkl')
#clf1 = joblib.load('input/dronedetectionfinal_new.pkl')
rows = 10
cols = 60
winlist = []
log = logdata(4)
######################################################################################################
global itervalue
itervalue = 0
def record(time=1, fs=44100):
file = 'temp_out'
duration = time
recording = sd.rec(int(duration * fs),
samplerate=fs,
channels=1,
blocking=False)
for i in range(time):
i += 1
elif value == 3:
label = "vfar"
elif value == 4:
label = "vnear"
return label
def drone_prediction_label(value):
if value == 1:
label = "drone"
elif value == 0:
label = "no drone"
return label
log = logdata(10)
log.insertdf(3, str(datetime.datetime.now())[:-7]) #dummy value
i = 0
bandpass = [600, 10000]
for root, dirs, files in os.walk(sys.argv[1]):
for file in files:
path = os.path.join(root, file)
try:
data, fs = rdt.readaudio(path)
ns = fil.bandpass_filter(data, bandpass)
try:
p, freq, b = hmn.psddetectionresults(data)
except IndexError:
pass
b = False
b = True