def insert_table_brightness(brightness_values):
colnames = [
"timestamp", "srgb", "vrgb", "vrgb_lin", "intensity", "luminance"
]
vals = []
for name in colnames:
vals.append(brightness_values[name])
vals_str_list = ["%s"] * len(vals)
vals_str = ", ".join(vals_str_list)
cols = ', '.join(colnames)
sql = """INSERT INTO public.brightness ({cols}) VALUES ({vals_str})""".format(
cols=cols, vals_str=vals_str)
db_mac.execute((sql, vals))
from _collections import defaultdict
import db_mac
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import MultipleLocator
sql = """select object, img_url from od2 where object is not null and timestamp::time > '07:59:00'::time and timestamp::time <= '16:40:00'::time and weather_status like 'Clouds'
"""
falses = defaultdict(list)
result = db_mac.execute(sql)
for row in result:
if len(row[0].split(",")) > 5:
falses["falses"].append(row[1])
falsepositves = []
for v in falses.values():
falsepositves = v
yes_step = defaultdict(int)
no_step = defaultdict(int)
percentage = defaultdict(int)
sql = """
select humidity,
count(*) as cnt
from od2 where humidity is not null and bound like 'in' and img_url not in """ + str(
tuple(falsepositves)
) + """ and timestamp::time > '07:59:00'::time and timestamp::time <= '16:40:00'::time and object is not null
group by 1
import matplotlib.pyplot as plt
import numpy as np
from _collections import defaultdict
import db_mac
from matplotlib.pyplot import MultipleLocator
sql = """select object, img_url from od2 where object is not null
"""
falses = defaultdict(list)
result = db_mac.execute(sql)
for row in result:
if len(row[0].split(",")) > 5:
falses["falses"].append(row[1])
falsepositves = []
for v in falses.values():
falsepositves = v
print(falsepositves)
sql = """with nn as (select DISTINCT(DATE(timestamp)) as date1,
count(*) as count1
from od2 where object is not null and bound = 'in' and img_url not in """ + str(
tuple(falsepositves)) + """
group by 1
order by 1 asc),
nn2 as
(select DISTINCT(DATE(timestamp)) as date2,
count(*) as count2
import db_mac
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import (MultipleLocator)
sql = """select det_dist_surf + 4191, altitude from od2 where bound like 'in'
"""
data = np.array(list(db_mac.execute(sql))).T
det_dist = [round(i) for i in data[0, :]]
alt = [round(i) for i in data[1, :]]
fig, ax = plt.subplots(figsize=(10, 4))
ax.grid(True)
ax.set_title('Detektionsentfernung im Geofence')
ax.margins(0)
ax.axvline(x=4191, color='red')
ax.set_ylim(min(alt), 1000)
ax.set_xlim(0, 11000)
ax.yaxis.set_major_locator(MultipleLocator(100))
ax.xaxis.set_major_locator(MultipleLocator(1000))
ax.set(ylabel="Höhe in Meter")
ax.set(
xlabel="Distanz zum Zentroid (E:382983 N:5824610 (UTM-33N WGS84) in Meter")
ax.scatter(det_dist, alt, 0.1)
plt.show()
# 1
# 20
# 21
from collections import OrderedDict
from collections import defaultdict
import db_mac
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import MultipleLocator
sql = """select object, img_url from od2 where object is not null and timestamp::time > '07:59:00'::time and timestamp::time <= '16:40:00'::time and weather_status like 'Clouds'
"""
falses = defaultdict(list)
result = db_mac.execute(sql)
for row in result:
if len(row[0].split(",")) > 5:
falses["falses"].append(row[1])
falsepositves = []
for v in falses.values():
falsepositves = v
false_pos2 = [
"/home/pi/Desktop/project/img/d_c__in_2020_01_08-08_04_53.png",
"/home/pi/Desktop/project/img/d_c__in_2020_01_09-17_38_02.png",
"/home/pi/Desktop/project/img/d_c__in_2020_01_09-17_40_03.png",
"/home/pi/Desktop/project/img/d_c__in_2020_01_09-17_42_49.png",
"/home/pi/Desktop/project/img/d_c__in_2020_01_13-16_27_44.png",
"/home/pi/Desktop/project/img/d_c__in_2020_01_13-16_36_43.png",
"/home/pi/Desktop/project/img/d_c__in_2020_01_09-17_35_53.png",
"/home/pi/Desktop/project/img/d_c__in_2020_01_13-16_34_46.png"
]
import db_mac
import numpy as np
import pandas as pd
from matplotlib import pyplot
# series = read_csv('daily-minimum-temperatures.csv', header=0, index_col=0, parse_dates=True, squeeze=True)
timestamps = []
intensity = []
result = db_mac.execute(
"select * from brightness where timestamp > '2020-01-10' and timestamp < '2020-01-11'"
)
for row in result:
timestamps.append(row[0])
intensity.append(float(row[-1]))
print(timestamps)
print(intensity)
data = np.array(intensity)
series = pd.Series(data, index=timestamps)
series.plot()
pyplot.show()
"https://machinelearningmastery.com/time-series-data-visualization-with-python/"
"https://www.geeksforgeeks.org/python-pandas-series/"
