def load_data(self):
"""
This function takes a source and then looks at its type to find the function to get that data
:param source: A dict containing all the information to load the data
:return: list of measurements
"""
if self.sensor_type == 'csv':
self.meas_list = load_csv(self.file_name)
elif self.sensor_type == 'sensor':
self.meas_list = self.load_sensor()
else:
raise Exception('Source type was not recognized')
self.start_time = self.meas_list[0].timestamp
self.stop_time = self.meas_list[-1].timestamp
for index, meas in enumerate(self.meas_list):
meas.set_or_index(index)
meas.set_sensor(self)
meas.convert_to_numpy()
timing_list.append((time_diff, add_dict))
return timing_list
def send_request(timer_list):
if len(timer_list) > 0:
json_data = timer_list.pop(0)[1]
next_time = timer_list[0][0]
timer = threading.Timer(next_time, send_request, [timer_list])
timer.start()
r = requests.post(POST_url, json=json_data)
if r.status_code == 200:
print(f'OK: from sensor_id {json_data["device_id"]}')
else:
print(r.status_code)
if __name__ == "__main__":
csv_folder = 'E:/VOP_backup/2_personen/'
csv_file = "shortened.csv"
csv_data = load_csv(csv_folder + csv_file, to_numpy=False)
timing_list = create_timing_list(csv_data, speed_up=1)
timer = threading.Timer(0, send_request, [timing_list])
timer.start()
print("exit")
microseconds=True,
seconds=True)
pros_imgs = pros.get_imgs()
for img_index, img in enumerate(pros_imgs):
comp.paste(img, (img_index * (320 + margin), 20))
d = ImageDraw.Draw(comp)
d.text((0, 0), local_time, fill=(0, 0, 0))
img_name = f'{frame_folder}{sensor_id}_' + ('000000' +
str(index))[-6:] + '.png'
print(img_name)
comp.save(img_name)
csv_folder = "D:/VOP_scenarios/scenarios/2_personen/"
csv_file = 'data.csv'
frame_folder = csv_folder + 'pros_frames/'
if not os.path.exists(frame_folder):
os.mkdir(frame_folder)
data = load_csv(csv_folder + csv_file,
to_numpy=True,
split=True,
csv_tag=False)
for key, value in data.items():
save_video_frames(int(key), value)
import time as time_module
scenario_folder = 'D:/VOP_scenarios/scenarios/'
cur_scenario = '1_persoon/'
vol_scen_folder = scenario_folder + cur_scenario
rgb_folder = vol_scen_folder + 'rgb_frames/'
csv_file = vol_scen_folder + 'sensor_data.csv'
frame_folder = vol_scen_folder + 'comp_frames/'
if not os.path.exists(frame_folder):
os.mkdir(frame_folder)
measurements = load_csv(csv_file, to_numpy=True, split=False, csv_tag=False)
video_start = datetime.combine(measurements[0].timestamp.date(),
time(hour=12, minute=55,
second=18)).timestamp()
video_stop = datetime.combine(measurements[0].timestamp.date(),
time(hour=13, minute=00, second=2)).timestamp()
cur_time = video_start
time_diff = video_stop - video_start
time_jumps = 4229
time_jump = time_diff / time_jumps
meas_index = 0
index = start_index - 1
min_diff = abs_diff(ref_time, meas_list[index].timestamp)
min_index = index
index += 1
while index < len(meas_list):
diff = abs_diff(ref_time, meas_list[index].timestamp)
if diff < min_diff:
min_diff = diff
min_index = index
index += 1
else:
break
return min_index
measurements = [load_csv(f'files/csv/{sensor_id}.csv', csv_tag=False) for sensor_id in sensor_ids]
cur_indices = [0] * len(measurements)
start_time = max([meas[0].timestamp for meas in measurements])
end_time = min([meas[-1].timestamp for meas in measurements])
time_diff = (end_time - start_time).seconds - 1
FPS = 20
amount_sensors = len(sensor_ids)
amount_extra_webcams = 1
start_index = 424
frame_time_increase = timedelta(milliseconds=1000/FPS)
cur_time = start_time + start_index * frame_time_increase
