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Copy pathextract_triggers_prophesee.py
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extract_triggers_prophesee.py
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import argparse
from pathlib import Path
import numpy as np
from metavision_core.event_io import RawReader
def get_ext_trigger_timestamps(rawfile: Path):
assert rawfile.exists()
assert rawfile.suffix == '.raw'
rawreader = RawReader(str(rawfile))
while not rawreader.is_done():
rawreader.load_delta_t(10**5)
ext_trigger_list = rawreader.get_ext_trigger_events()
time = ext_trigger_list['t']
pol = ext_trigger_list['p']
return {'t': time, 'p': pol}
def get_reconstruction_timestamps(time: np.ndarray, pol: np.ndarray, use_avg_ts: bool=False, time_offset_us: int=0):
assert 0 <= pol.max() <= 1
assert np.all(np.abs(np.diff(pol)) == 1), 'polarity must alternate from trigger to trigger'
timestamps = None
if use_avg_ts:
assert pol[0] == 1, 'first ext trigger polarity must be positive'
assert pol[-1] == 0, 'last ext trigger polarity must be negative'
rising_ts = time[pol==1]
falling_ts = time[pol==0]
timestamps = ((rising_ts + falling_ts)/2).astype('int64')
else:
timestamps = time[pol==1]
timestamps = timestamps + time_offset_us
return timestamps
if __name__ == '__main__':
parser = argparse.ArgumentParser('Read trigger data from prophesee raw file')
parser.add_argument('rawfile')
parser.add_argument('output_file', help='Path to output text file with timestamps for reconstruction.')
parser.add_argument('--use_average_timestamp', '-avgt', action='store_true',
help='Take the average timestamp of rising and falling edge for the timestamp. '
'Otherwise, use the rising edge as timestamp.')
parser.add_argument('--time_offset_us', '-offset', type=int, default=0,
help='Add a constant time offset to the timestamp for reconstruction. '
'Can be useful to compensate for a constant exposure time of global shutter cameras.')
args = parser.parse_args()
rawfile = Path(args.rawfile)
outfile = Path(args.output_file)
assert not outfile.exists()
assert outfile.suffix == '.txt'
triggers = get_ext_trigger_timestamps(rawfile)
reconstruction_ts = get_reconstruction_timestamps(triggers['t'], triggers['p'], args.use_average_timestamp, args.time_offset_us)
np.savetxt(str(outfile), reconstruction_ts, '%i')