Converting PCAPs to Other Formats
PCAPs to CSV
Sometimes we want to get a point cloud (XYZ
+ other fields) as a CSV
file for further
analysis with other tools.
To convert the first 5
scans of our sample data from a pcap file, you can try:
$ python3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH $SAMPLE_DATA_JSON_PATH pcap-to-csv --scan-num 5
PS > py -3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH $SAMPLE_DATA_JSON_PATH pcap-to-csv --scan-num 5
The source code of an example below:
1from itertools import islice
2# precompute xyzlut to save computation in a loop
3xyzlut = client.XYZLut(metadata)
4
5# create an iterator of LidarScans from pcap and bound it if num is specified
6scans = iter(client.Scans(source))
7if num:
8 scans = islice(scans, num)
9
10for idx, scan in enumerate(scans):
11
12 # initialize the field names for csv header
13 if not field_names or not field_fmts:
14 field_names, field_fmts = get_fields_info(scan)
15
16 # copy per-column timestamps for each channel
17 timestamps = np.tile(scan.timestamp, (scan.h, 1))
18
19 # grab channel data
20 fields_values = [scan.field(ch) for ch in scan.fields]
21
22 # use integer mm to avoid loss of precision casting timestamps
23 xyz = (xyzlut(scan.field(client.ChanField.RANGE)) * 1000).astype(
24 np.int64)
25
26 if dual:
27 xyz2 = (xyzlut(scan.field(client.ChanField.RANGE2)) * 1000).astype(
28 np.int64)
29
30 # get all data as one H x W x num fields int64 array for savetxt()
31 frame = np.dstack((timestamps, *fields_values, xyz, xyz2))
32
33 else:
34 # get all data as one H x W x num fields int64 array for savetxt()
35 frame = np.dstack((timestamps, *fields_values, xyz))
36
37 # not necessary, but output points in "image" vs. staggered order
38 frame = client.destagger(metadata, frame)
39
40 # write csv out to file
41 csv_path = os.path.join(csv_dir, f'{csv_base}_{idx:06d}.{csv_ext}')
42 print(f'write frame #{idx}, to file: {csv_path}')
43
44 header = '\n'.join([f'frame num: {idx}', field_names])
45
46 np.savetxt(csv_path,
47 frame.reshape(-1, frame.shape[2]),
48 fmt=field_fmts,
49 delimiter=',',
50 header=header)
Because we stored the scan as structured 2D images, we can easily recover it by loading it back into
a numpy.ndarray
and continuing to use it as a 2D image.
import numpy as np
# read array from CSV
frame = np.loadtxt('my_frame_00000.csv', delimiter=',')
# convert back to "fat" 2D image [H x W x num_fields] shape
frame = frame.reshape((128, -1, frame.shape[1]))
We used 128
while restoring 2D image from a CSV file because it’s the number of channels of our
OS-1-128.pcap
sample data recording.
PCAPs to LAS
To convert to the first 5
scans of our sample data from a pcap file to LAS
, you can try:
$ python3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH $SAMPLE_DATA_JSON_PATH pcap-to-las --scan-num 5
PS > py -3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH $SAMPLE_DATA_JSON_PATH pcap-to-las --scan-num 5
Checkout the examples.pcap.pcap_to_las()
documentation for the example source code.
PCAPs to PCD
To convert to the first 5
scans of our sample data from a pcap file to PCD
, you can try:
$ python3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH $SAMPLE_DATA_JSON_PATH pcap-to-pcd --scan-num 5
PS > py -3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH $SAMPLE_DATA_JSON_PATH pcap-to-pcd --scan-num 5
Checkout the examples.pcap.pcap_to_pcd()
documentation for the example source code.
PCAPs to PLY
Here we will reuse the PCAP to PCD function that uses Open3d and will exploit the extensive Open3d
File IO that gives us an easy way to save the loaded point cloud to PLY
. Alternative ways are available via plyfile library.
To convert to the first 5
scans of our sample data from a pcap file to PLY
, you can try:
$ python3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH $SAMPLE_DATA_JSON_PATH pcap-to-ply --scan-num 5
PS > py -3 -m ouster.sdk.examples.pcap $SAMPLE_DATA_PCAP_PATH $SAMPLE_DATA_JSON_PATH pcap-to-ply --scan-num 5
Checkout the examples.pcap.pcap_to_ply()
documentation for the example source code.