def get_sensor_last_image(id):
processor = ImageProcessor()
print(f'requesting last image with id={id}')
scaled_up = request.args.get('scale_up')
scaled_up = 1 if scaled_up is None else int(scaled_up)
interpolate = request.args.get('interpolate')
interpolate = True if interpolate == "1" else False
simulated = request.args.get('simulate')
simulated = True if simulated == "1" else False
if simulated:
last_result = Measurement_test.query.filter(
Measurement_test.sensor_id == id).order_by(
Measurement_test.timestamp.desc()).first()
else:
last_result = Measurement.query.filter(
Measurement.sensor_id == int(id)).order_by(
Measurement.timestamp.desc()).first()
#get processed frame
processor.process(last_result.data)
cv2_data = processor.plot_frame()
img = fast_thermal_image(cv2_data, scale=scaled_up)
return Response(img)
This files convert a database (from pgadmin) to a csv that contains 3 columns: the centroids, epoch time and local time
"""
import csv
from localization.processing import ImageProcessor
from help_module.time_helper import convert_to_datetime, get_time_str
pros = ImageProcessor()
data_list = []
folder_location = '../../data/'
file_name = '19042019.csv'
with open(folder_location + file_name) as csvfile:
reader = csv.reader(csvfile, delimiter=',')
for index, row in enumerate(reader):
# print(row)
# print(index)
thermal_data = eval(row[1])
centroids = pros.process(thermal_data)
meas_datetime = convert_to_datetime(row[3])
epoch_time = meas_datetime.timestamp()
local_time = get_time_str(meas_datetime, microseconds=True)
data_list.append([centroids, epoch_time, local_time])
with open(folder_location + 'centroid_' + file_name, 'w+',
newline='') as csvfile:
writer = csv.writer(csvfile)
for row in data_list:
writer.writerow(row)