def pcap_display_xyz_points(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0) -> None:
"""Plot point cloud using matplotlib."""
import matplotlib.pyplot as plt # type: ignore
# [doc-stag-pcap-plot-xyz-points]
from more_itertools import nth
scan = nth(client.Scans(source), num)
if not scan:
print(f"ERROR: Scan # {num} in not present in pcap file")
exit(1)
# set up figure
plt.figure()
ax = plt.axes(projection='3d')
r = 6
ax.set_xlim3d([-r, r])
ax.set_ylim3d([-r, r])
ax.set_zlim3d([-r, r])
plt.title("3D Points XYZ for scan")
# transform data to 3d points and graph
xyzlut = client.XYZLut(metadata)
xyz = xyzlut(scan)
key = scan.field(client.ChanField.SIGNAL)
[x, y, z] = [c.flatten() for c in np.dsplit(xyz, 3)]
ax.scatter(x, y, z, c=normalize(key.flatten()), s=0.2)
plt.show()
def test_scans_meta(packets: client.PacketSource) -> None:
"""Sanity check metadata and column headers of a batched scan."""
scans = iter(client.Scans(packets))
scan = next(scans)
assert scan.frame_id != -1
assert scan.h == packets.metadata.format.pixels_per_column
assert scan.w == packets.metadata.format.columns_per_frame
assert len(scan.timestamp) == scan.w
assert len(scan.measurement_id) == scan.w
assert len(scan.status) == scan.w
assert len(scan.header(ColHeader.ENCODER_COUNT)) == scan.w
assert scan._complete()
# all timestamps valid
assert np.count_nonzero(scan.timestamp) == scan.w
if (packets.metadata.format.udp_profile_lidar ==
client.UDPProfileLidar.PROFILE_LIDAR_LEGACY):
# check that all columns are valid
assert (scan.status == 0xffffffff).all()
# only first encoder count is zero
assert np.count_nonzero(scan.header(
ColHeader.ENCODER_COUNT)) == scan.w - 1
else:
# only lowest bit indicates valid
assert (scan.status & 0x1).all()
# encoder counts zeroed
assert (scan.header(ColHeader.ENCODER_COUNT) == 0).all()
def test_scans_closed(meta: client.SensorInfo) -> None:
"""Check reading from closed scans raises an exception."""
with closing(client.Sensor("os.invalid", 0, 0, metadata=meta)) as source:
scans = client.Scans(source)
scans.close()
with pytest.raises(ValueError):
next(iter(scans))
def test_scans_first_packet(packet: client.LidarPacket,
packets: client.PacketSource) -> None:
"""Check that data in the first packet survives batching to a scan."""
scans = iter(client.Scans(packets))
scan = next(scans)
h = packet._pf.pixels_per_column
w = packet._pf.columns_per_packet
assert np.array_equal(packet.field(ChanField.RANGE),
scan.field(ChanField.RANGE)[:h, :w])
assert np.array_equal(packet.field(ChanField.REFLECTIVITY),
scan.field(ChanField.REFLECTIVITY)[:h, :w])
assert np.array_equal(packet.field(client.ChanField.SIGNAL),
scan.field(client.ChanField.SIGNAL)[:h, :w])
assert np.array_equal(packet.field(client.ChanField.NEAR_IR),
scan.field(client.ChanField.NEAR_IR)[:h, :w])
assert np.all(packet.header(ColHeader.FRAME_ID) == scan.frame_id)
assert np.array_equal(packet.header(ColHeader.TIMESTAMP),
scan.header(ColHeader.TIMESTAMP)[:w])
assert np.array_equal(packet.header(ColHeader.ENCODER_COUNT),
scan.header(ColHeader.ENCODER_COUNT)[:w])
assert np.array_equal(packet.header(ColHeader.STATUS),
scan.header(ColHeader.STATUS)[:w])
def pcap_show_one_scan(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0,
destagger: bool = True) -> None:
"""Plot all channels of one scan in 2D using matplotlib."""
import matplotlib.pyplot as plt # type: ignore
scan = nth(client.Scans(source), num)
if not scan:
print(f"ERROR: Scan # {num} in not present in pcap file")
exit(1)
# [doc-stag-pcap-show-one]
fig = plt.figure(constrained_layout=True)
axs = fig.subplots(len(client.ChanField), 1, sharey=True)
for ax, field in zip(axs, client.ChanField):
img = normalize(scan.field(field))
if destagger:
img = client.destagger(metadata, img)
ax.set_title(str(field), fontdict={'fontsize': 10})
ax.imshow(img, cmap='gray', resample=False)
ax.set_yticklabels([])
ax.set_yticks([])
ax.set_xticks([0, scan.w])
plt.show()
def pcap_to_las(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0,
las_dir: str = ".",
las_base: str = "las_out",
las_ext: str = "las") -> None:
"Write scans from a pcap to las files (one per lidar scan)."
from itertools import islice
import laspy # type: ignore
# precompute xyzlut to save computation in a loop
xyzlut = client.XYZLut(metadata)
# create an iterator of LidarScans from pcap and bound it if num is specified
scans = iter(client.Scans(source))
if num:
scans = islice(scans, num)
for idx, scan in enumerate(scans):
xyz = xyzlut(scan)
las = laspy.create()
las.x = xyz[:, :, 0].flatten()
las.y = xyz[:, :, 1].flatten()
las.z = xyz[:, :, 2].flatten()
las_path = os.path.join(las_dir, f'{las_base}_{idx:06d}.{las_ext}')
print(f'write frame #{idx} to file: {las_path}')
las.write(las_path)
def test_scans_meta(packets: client.PacketSource) -> None:
"""Sanity check metadata and column headers of a batched scan."""
scans = iter(client.Scans(packets))
scan = next(scans)
assert scan.frame_id != -1
assert scan.h == packets.metadata.format.pixels_per_column
assert scan.w == packets.metadata.format.columns_per_frame
assert len(scan.header(ColHeader.TIMESTAMP)) == scan.w
assert len(scan.header(ColHeader.ENCODER_COUNT)) == scan.w
assert len(scan.header(ColHeader.STATUS)) == scan.w
assert not scan._complete(), "test data should have missing packet!"
# check that the scan is missing exactly one packet's worth of columns
valid_columns = list(scan.header(ColHeader.STATUS)).count(0xffffffff)
assert valid_columns == (packets.metadata.format.columns_per_frame -
packets.metadata.format.columns_per_packet)
missing_ts = list(scan.header(ColHeader.TIMESTAMP)).count(0)
assert missing_ts == packets.metadata.format.columns_per_packet
# extra zero encoder value for first column
zero_enc = list(scan.header(ColHeader.ENCODER_COUNT)).count(0)
assert zero_enc == packets.metadata.format.columns_per_packet + 1
def test_scans_simple(packets: client.PacketSource) -> None:
"""Test that the test data contains exactly one scan."""
scans = iter(client.Scans(packets))
assert next(scans) is not None
with pytest.raises(StopIteration):
next(scans)
def pcap_3d_one_scan(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0) -> None:
"""Render one scan from a pcap file in the Open3D viewer.
Args:
source: PacketSource from pcap
metadata: associated SensorInfo for PacketSource
num: scan number in a given pcap file (satrs from *0*)
"""
try:
import open3d as o3d # type: ignore
except ModuleNotFoundError:
print(
"This example requires open3d, which may not be available on all "
"platforms. Try running `pip3 install open3d` first.")
exit(1)
from more_itertools import nth
# get single scan by index
scan = nth(client.Scans(source), num)
if not scan:
print(f"ERROR: Scan # {num} in not present in pcap file")
exit(1)
# [doc-stag-open3d-one-scan]
# compute point cloud using client.SensorInfo and client.LidarScan
xyz = client.XYZLut(metadata)(scan)
# create point cloud and coordinate axes geometries
cloud = o3d.geometry.PointCloud(
o3d.utility.Vector3dVector(xyz.reshape((-1, 3)))) # type: ignore
axes = o3d.geometry.TriangleMesh.create_coordinate_frame(
1.0) # type: ignore
# [doc-etag-open3d-one-scan]
# initialize visualizer and rendering options
vis = o3d.visualization.Visualizer() # type: ignore
vis.create_window()
vis.add_geometry(cloud)
vis.add_geometry(axes)
ropt = vis.get_render_option()
ropt.point_size = 1.0
ropt.background_color = np.asarray([0, 0, 0])
# initialize camera settings
ctr = vis.get_view_control()
ctr.set_zoom(0.1)
ctr.set_lookat([0, 0, 0])
ctr.set_up([1, 0, 0])
# run visualizer main loop
print("Press Q or Excape to exit")
vis.run()
vis.destroy_window()
def test_scans_timeout(packets: client.PacketSource) -> None:
"""A zero timeout should deterministically throw.
TODO: should it, though?
"""
scans = iter(client.Scans(packets, timeout=0.0))
with pytest.raises(client.ClientTimeout):
next(scans)
def scan(stream_digest: digest.StreamDigest, meta: client.SensorInfo) -> client.LidarScan:
bin_path = path.join(DATA_DIR, "os-992011000121_data.bin")
with open(bin_path, 'rb') as b:
source = digest.LidarBufStream(b, meta)
scans = client.Scans(source)
scan = next(iter(scans))
return scan
def test_scans_one_field(packets: client.PacketSource) -> None:
"""Test batching scans with a single field."""
fields = {ChanField.FLAGS: np.uint8}
scan = next(iter(client.Scans(packets, fields=fields)))
assert set(scan.fields) == {ChanField.FLAGS}
assert scan.field(ChanField.FLAGS).dtype == np.uint8
with pytest.raises(ValueError):
scan.field(ChanField.RANGE)
def test_scans_complete(packets: client.PacketSource) -> None:
"""Test built-in filtering for complete scans.
The test dataset only contains a single incomplete frame. Check that
specifying ``complete=True`` discards it.
"""
scans = iter(client.Scans(packets, complete=True))
with pytest.raises(StopIteration):
next(scans)
def main() -> None:
descr = """Visualize pcap or sensor data using simple viz bindings."""
epilog = """When reading data from a sensor, this will autoconfigure the udp
destination unless -x is used."""
parser = argparse.ArgumentParser(description=descr, epilog=epilog)
required = parser.add_argument_group('one of the following is required')
group = required.add_mutually_exclusive_group(required=True)
group.add_argument('--sensor', metavar='HOST', help='sensor hostname')
group.add_argument('--pcap', metavar='PATH', help='path to pcap file')
parser.add_argument('--meta', metavar='PATH', help='path to metadata json')
parser.add_argument('--lidar-port', type=int, help='lidar port for sensor')
parser.add_argument('-x',
'--no-auto-dest',
action='store_true',
help='do not auto configure udp destination')
args = parser.parse_args()
if args.sensor:
hostname = args.sensor
if args.lidar_port or (not args.no_auto_dest):
config = client.SensorConfig()
if args.lidar_port:
config.udp_port_lidar = args.lidar_port
print("Configuring sensor...")
client.set_config(hostname,
config,
udp_dest_auto=(not args.no_auto_dest))
config = client.get_config(hostname)
print("Initializing...")
scans = client.Scans.stream(hostname,
config.udp_port_lidar or 7502,
complete=False)
rate = None
elif args.pcap:
import ouster.pcap as pcap
if args.meta:
metadata_path = args.meta
else:
print("Deducing metadata based on pcap name. "
"To provide a different metadata path, use --meta")
metadata_path = os.path.splitext(args.pcap)[0] + ".json"
with open(metadata_path) as json:
info = client.SensorInfo(json.read())
scans = client.Scans(pcap.Pcap(args.pcap, info))
rate = 1.0
SimpleViz(scans.metadata, rate).run(scans)
def test_scans_complete(packets: client.PacketSource) -> None:
"""Test built-in filtering for complete scans."""
# make new packet source missing a packet
ps = list(packets)
del ps[5]
dropped = client.Packets(ps, packets.metadata)
scans = iter(client.Scans(dropped, complete=True))
with pytest.raises(StopIteration):
next(scans)
def pcap_3d_one_scan(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0) -> None:
"""Render one scan from a pcap file in the Open3D viewer.
Args:
pcap_path: path to the pcap file
metadata_path: path to the .json with metadata (aka :class:`.SensorInfo`)
num: scan number in a given pcap file (satrs from *0*)
"""
import open3d as o3d
# get single scan by index
scan = nth(client.Scans(source), num)
if not scan:
print(f"ERROR: Scan # {num} in not present in pcap file")
exit(1)
# [doc-stag-open3d-one-scan]
# compute point cloud using client.SensorInfo and client.LidarScan
xyz = client.XYZLut(metadata)(scan)
# create point cloud and coordinate axes geometries
cloud = o3d.geometry.PointCloud(
o3d.utility.Vector3dVector(xyz.reshape((-1, 3))))
axes = o3d.geometry.TriangleMesh.create_coordinate_frame(1.0)
# [doc-etag-open3d-one-scan]
# initialize visualizer and rendering options
vis = o3d.visualization.Visualizer()
vis.create_window()
vis.add_geometry(cloud)
vis.add_geometry(axes)
ropt = vis.get_render_option()
ropt.point_size = 1.0
ropt.background_color = np.asarray([0, 0, 0])
# initialize camera settings
ctr = vis.get_view_control()
ctr.set_zoom(0.1)
ctr.set_lookat([0, 0, 0])
ctr.set_up([1, 0, 0])
# run visualizer main loop
print("Press Q or Excape to exit")
vis.run()
vis.destroy_window()
def pcap_show_one_scan(pcap_path: str,
metadata_path: str,
num: int = 0,
destagger: bool = True) -> None:
"""Show all 4 channels of one scan (*num*) form pcap file (*pcap_path*)
Args:
pcap_path: path to the pcap file
metadata_path: path to the .json with metadata (aka :class:`.SensorInfo`)
num: scan number in a given pcap file (satrs from *0*)
"""
import matplotlib.pyplot as plt # type: ignore
# [doc-stag-pcap-show-one]
metadata = read_metadata(metadata_path)
def prepare_field_image(scan, key, metadata, destagger=True):
f = ae(scan.field(key))
if destagger:
return client.destagger(metadata, f)
return f
show_fields = [('range', client.ChanField.RANGE),
('signal', client.ChanField.SIGNAL),
('near_ir', client.ChanField.NEAR_IR),
('reflectivity', client.ChanField.REFLECTIVITY)]
with closing(pcap.Pcap(pcap_path, metadata)) as source:
scan = nth(client.Scans(source), num)
if not scan:
return
fields_images = [(sf[0],
prepare_field_image(scan, sf[1], source.metadata))
for sf in show_fields]
fig = plt.figure(constrained_layout=True)
axs = fig.subplots(len(fields_images), 1, sharey=True)
for ax, field in zip(axs, fields_images):
ax.set_title(field[0], fontdict={'fontsize': 10})
ax.imshow(field[1], cmap='gray', resample=False)
ax.set_yticklabels([])
ax.set_yticks([])
ax.set_xticks([0, scan.w])
plt.show()
def pcap_2d_viewer(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0) -> None:
"""Visualize channel fields in 2D using opencv."""
import cv2 # type: ignore
# [doc-stag-pcap-display-live]
print("press ESC from visualization to exit")
quit = False
paused = False
destagger = True
num = 0
for scan in client.Scans(source):
print("frame id: {}, num = {}".format(scan.frame_id, num))
fields = [scan.field(ch) for ch in client.ChanField]
if destagger:
fields = [client.destagger(metadata, f) for f in fields]
combined_images = np.vstack(
[np.pad(normalize(f), 2, constant_values=1.0) for f in fields])
cv2.imshow("4 channels: ", combined_images)
# handle keys presses
while True:
key = cv2.waitKey(1) & 0xFF
# 100 is d
if key == 100:
destagger = not destagger
# 32 is SPACE
if key == 32:
paused = not paused
# 27 is ESC
elif key == 27:
quit = True
if not paused:
break
time.sleep(0.1)
if quit:
break
num += 1
cv2.destroyAllWindows()
def main() -> None:
import argparse
import os
import ouster.pcap as pcap
descr = """Example visualizer using the open3d library.
Visualize either pcap data (specified using --pcap) or a running sensor
(specified using --sensor). If no metadata file is specified, this will look
for a file with the same name as the pcap with the '.json' extension, or
query it directly from the sensor.
Visualizing a running sensor requires the sensor to be configured and
sending lidar data to the default UDP port (7502) on the host machine.
"""
parser = argparse.ArgumentParser(description=descr)
parser.add_argument('--pause', action='store_true', help='start paused')
parser.add_argument('--start', type=int, help='skip to frame number')
parser.add_argument('--meta', metavar='PATH', help='path to metadata json')
required = parser.add_argument_group('one of the following is required')
group = required.add_mutually_exclusive_group(required=True)
group.add_argument('--sensor', metavar='HOST', help='sensor hostname')
group.add_argument('--pcap', metavar='PATH', help='path to pcap file')
args = parser.parse_args()
if args.sensor:
scans = client.Scans.stream(args.sensor, metadata=args.meta)
elif args.pcap:
pcap_path = args.pcap
metadata_path = args.meta or os.path.splitext(pcap_path)[0] + ".json"
with open(metadata_path, 'r') as f:
metadata = client.SensorInfo(f.read())
source = pcap.Pcap(pcap_path, metadata)
scans = client.Scans(source)
consume(scans, args.start or 0)
try:
viewer_3d(scans, paused=args.pause)
except (KeyboardInterrupt, StopIteration):
pass
finally:
scans.close()
def pcap_query_scan(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0) -> None:
"""
Example: Query available fields in LidarScan
Args:
source: PacketSource from pcap
metadata: associated SensorInfo for PacketSource
num: scan number in a given pcap file (satrs from *0*)
"""
scans = iter(client.Scans(source))
# [doc-stag-pcap-query-scan]
scan = next(scans)
print("Available fields and corresponding dtype in LidarScan")
for field in scan.fields:
print('{0:15} {1}'.format(str(field), scan.field(field).dtype))
def test_scans_raw(packets: client.PacketSource) -> None:
"""Smoke test reading raw channel field data."""
fields = {
ChanField.RAW32_WORD1: np.uint32,
ChanField.RAW32_WORD2: np.uint32,
ChanField.RAW32_WORD3: np.uint32
}
scans = client.Scans(packets, fields=fields)
ls = list(scans)
assert len(ls) == 1
assert set(ls[0].fields) == {
ChanField.RAW32_WORD1, ChanField.RAW32_WORD2, ChanField.RAW32_WORD3
}
# just check that raw fields are populated?
for f in ls[0].fields:
assert np.count_nonzero(ls[0].field(f)) != 0
def pcap_display_xyz_points(pcap_path: str,
metadata_path: str,
num: int = 0) -> None:
"""Display range from a specified scan number (*num*) as 3D points from
pcap file located at *pcap_path*
Args:
pcap_path: path to the pcap file
metadata_path: path to the .json with metadata (aka :class:`.SensorInfo`)
num: scan number in a given pcap file (satrs from *0*)
"""
import matplotlib.pyplot as plt # type: ignore
# [doc-stag-pcap-plot-xyz-points]
metadata = read_metadata(metadata_path)
source = pcap.Pcap(pcap_path, metadata)
# get single scan
scans = client.Scans(source)
scan = nth(scans, num)
if not scan:
print(f'ERROR: Scan # {num} in not present in pcap file: {pcap_path}')
return
# set up figure
plt.figure()
ax = plt.axes(projection='3d')
r = 6
ax.set_xlim3d([-r, r])
ax.set_ylim3d([-r, r])
ax.set_zlim3d([-r, r])
plt.title("3D Points XYZ for scan")
# transform data to 3d points and graph
xyzlut = client.XYZLut(metadata)
xyz = xyzlut(scan)
key = scan.field(client.ChanField.SIGNAL)
[x, y, z] = [c.flatten() for c in np.dsplit(xyz, 3)]
ax.scatter(x, y, z, c=ae(key.flatten()), s=0.2)
plt.show()
def test_scans_dual(packets: client.PacketSource) -> None:
"""Test scans from dual returns data."""
scans = client.Scans(packets, complete=True)
assert (scans.metadata.format.udp_profile_lidar ==
client.UDPProfileLidar.PROFILE_LIDAR_RNG19_RFL8_SIG16_NIR16_DUAL)
ls = list(scans)
assert len(ls) == 1
assert set(ls[0].fields) == {
ChanField.RANGE,
ChanField.RANGE2,
ChanField.REFLECTIVITY,
ChanField.REFLECTIVITY2,
ChanField.SIGNAL,
ChanField.SIGNAL2,
ChanField.NEAR_IR,
}
def pcap_to_pcd(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0,
pcd_dir: str = ".",
pcd_base: str = "pcd_out",
pcd_ext: str = "pcd") -> None:
"Write scans from a pcap to pcd files (one per lidar scan)."
from itertools import islice
try:
import open3d as o3d # type: ignore
except ModuleNotFoundError:
print(
"This example requires open3d, which may not be available on all "
"platforms. Try running `pip3 install open3d` first.")
exit(1)
if not os.path.exists(pcd_dir):
os.makedirs(pcd_dir)
# precompute xyzlut to save computation in a loop
xyzlut = client.XYZLut(metadata)
# create an iterator of LidarScans from pcap and bound it if num is specified
scans = iter(client.Scans(source))
if num:
scans = islice(scans, num)
for idx, scan in enumerate(scans):
xyz = xyzlut(scan)
pcd = o3d.geometry.PointCloud() # type: ignore
pcd.points = o3d.utility.Vector3dVector(xyz.reshape(-1,
3)) # type: ignore
pcd_path = os.path.join(pcd_dir, f'{pcd_base}_{idx:06d}.{pcd_ext}')
print(f'write frame #{idx} to file: {pcd_path}')
o3d.io.write_point_cloud(pcd_path, pcd) # type: ignore
def main():
"""PointViz visualizer examples."""
parser = argparse.ArgumentParser(
description=main.__doc__,
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('pcap_path',
nargs='?',
metavar='PCAP',
help='path to pcap file')
parser.add_argument('meta_path',
nargs='?',
metavar='METADATA',
help='path to metadata json')
args = parser.parse_args()
pcap_path = os.getenv("SAMPLE_DATA_PCAP_PATH", args.pcap_path)
meta_path = os.getenv("SAMPLE_DATA_JSON_PATH", args.meta_path)
if not pcap_path or not meta_path:
print("ERROR: Please add SAMPLE_DATA_PCAP_PATH and SAMPLE_DATA_JSON_PATH to" +
" environment variables or pass <pcap_path> and <meta_path>")
sys.exit()
print(f"Using:\n\tjson: {meta_path}\n\tpcap: {pcap_path}")
# Getting data sources
meta = client.SensorInfo(open(meta_path).read())
packets = pcap.Pcap(pcap_path, meta)
scans = iter(client.Scans(packets))
# ==============================
print("Ex 0: Empty Point Viz")
# [doc-stag-empty-pointviz]
# Creating a point viz instance
point_viz = viz.PointViz("Example Viz")
viz.add_default_controls(point_viz)
# ... add objects here
# update internal objects buffers and run visualizer
point_viz.update()
point_viz.run()
# [doc-etag-empty-pointviz]
# =========================================================================
print("Ex 1.0:\tImages and Labels: the Image object and 2D Image set_position() - height-normalized screen coordinates")
label_top = viz.Label("[0, 1]", 0.5, 0.0, align_top=True)
label_top.set_scale(2)
point_viz.add(label_top)
label_bot = viz.Label("[0, -1]", 0.5, 1, align_top=False)
label_bot.set_scale(2)
point_viz.add(label_bot)
# [doc-stag-image-pos-center]
img = viz.Image()
img.set_image(np.full((10, 10), 0.5))
img.set_position(-0.5, 0.5, -0.5, 0.5)
point_viz.add(img)
# [doc-etag-image-pos-center]
# visualize
point_viz.update()
point_viz.run()
# =========================================================================
print("Ex 1.1:\tImages and Labels: Window-aligned images with 2D Image set_hshift() - width-normalized [-1, 1] horizontal shift")
# [doc-stag-image-pos-left]
# move img to the left
img.set_position(0, 1, -0.5, 0.5)
img.set_hshift(-1)
# [doc-etag-image-pos-left]
# visualize
point_viz.update()
point_viz.run()
# [doc-stag-image-pos-right]
# move img to the right
img.set_position(-1, 0, -0.5, 0.5)
img.set_hshift(1)
# [doc-etag-image-pos-right]
# visualize
point_viz.update()
point_viz.run()
# [doc-stag-image-pos-right-bottom]
# move img to the right bottom
img.set_position(-1, 0, -1, 0)
img.set_hshift(1)
# [doc-etag-image-pos-right-bottom]
# visualize
point_viz.update()
point_viz.run()
# remove_objs(point_viz, [label_top, label_mid, label_bot, img])
remove_objs(point_viz, [label_top, label_bot, img])
# =======================================
print("Ex 1.2:\tImages and Labels: Lidar Scan Fields as Images")
# [doc-stag-scan-fields-images]
scan = next(scans)
img_aspect = (meta.beam_altitude_angles[0] -
meta.beam_altitude_angles[-1]) / 360.0
img_screen_height = 0.4 # [0..2]
img_screen_len = img_screen_height / img_aspect
# prepare field data
ranges = scan.field(client.ChanField.RANGE)
ranges = client.destagger(meta, ranges)
ranges = np.divide(ranges, np.amax(ranges), dtype=np.float32)
signal = scan.field(client.ChanField.SIGNAL)
signal = client.destagger(meta, signal)
signal = np.divide(signal, np.amax(signal), dtype=np.float32)
# creating Image viz elements
range_img = viz.Image()
range_img.set_image(ranges)
# top center position
range_img.set_position(-img_screen_len / 2, img_screen_len / 2,
1 - img_screen_height, 1)
point_viz.add(range_img)
signal_img = viz.Image()
signal_img.set_image(signal)
img_aspect = (meta.beam_altitude_angles[0] -
meta.beam_altitude_angles[-1]) / 360.0
img_screen_height = 0.4 # [0..2]
img_screen_len = img_screen_height / img_aspect
# bottom center position
signal_img.set_position(-img_screen_len / 2, img_screen_len / 2, -1,
-1 + img_screen_height)
point_viz.add(signal_img)
# [doc-etag-scan-fields-images]
# visualize
point_viz.update()
point_viz.run()
print("Ex 1.3:\tImages and Labels: Adding labels")
# [doc-stag-scan-fields-images-labels]
range_label = viz.Label(str(client.ChanField.RANGE), 0.5, 0, align_top=True)
range_label.set_scale(1)
point_viz.add(range_label)
signal_label = viz.Label(str(client.ChanField.SIGNAL),
0.5, 1 - img_screen_height / 2,
align_top=True)
signal_label.set_scale(1)
point_viz.add(signal_label)
# [doc-etag-scan-fields-images-labels]
# visualize
point_viz.update()
point_viz.run()
# ===============================================================
print("Ex 2.0:\tPoint Clouds: As Structured Points")
# [doc-stag-scan-structured]
cloud_scan = viz.Cloud(meta)
cloud_scan.set_range(scan.field(client.ChanField.RANGE))
cloud_scan.set_key(signal)
point_viz.add(cloud_scan)
# [doc-etag-scan-structured]
# visualize
point_viz.update()
point_viz.run()
remove_objs(point_viz, [cloud_scan])
# ===============================================================
print("Ex 2.1:\tPoint Clouds: As Unstructured Points")
# [doc-stag-scan-unstructured]
# transform scan data to 3d points
xyzlut = client.XYZLut(meta)
xyz = xyzlut(scan.field(client.ChanField.RANGE))
cloud_xyz = viz.Cloud(xyz.shape[0] * xyz.shape[1])
cloud_xyz.set_xyz(np.reshape(xyz, (-1, 3)))
cloud_xyz.set_key(signal.ravel())
point_viz.add(cloud_xyz)
# [doc-etag-scan-unstructured]
point_viz.camera.dolly(150)
# visualize
point_viz.update()
point_viz.run()
# =======================================================
print("Ex 2.2:\tPoint Clouds: Custom Axes Helper as Unstructured Points")
# [doc-stag-axes-helper]
# basis vectors
x_ = np.array([1, 0, 0]).reshape((-1, 1))
y_ = np.array([0, 1, 0]).reshape((-1, 1))
z_ = np.array([0, 0, 1]).reshape((-1, 1))
axis_n = 100
line = np.linspace(0, 1, axis_n).reshape((1, -1))
# basis vector to point cloud
axis_points = np.hstack((x_ @ line, y_ @ line, z_ @ line)).transpose()
# colors for basis vectors
axis_color_mask = np.vstack((
np.full((axis_n, 4), [1, 0.1, 0.1, 1]),
np.full((axis_n, 4), [0.1, 1, 0.1, 1]),
np.full((axis_n, 4), [0.1, 0.1, 1, 1])))
cloud_axis = viz.Cloud(axis_points.shape[0])
cloud_axis.set_xyz(axis_points)
cloud_axis.set_key(np.full(axis_points.shape[0], 0.5))
cloud_axis.set_mask(axis_color_mask)
cloud_axis.set_point_size(3)
point_viz.add(cloud_axis)
# [doc-etag-axes-helper]
point_viz.camera.dolly(50)
# visualize
point_viz.update()
point_viz.run()
remove_objs(point_viz, [
range_img, range_label, signal_img, signal_label, cloud_axis, cloud_xyz
])
# ===============================================================
print("Ex 2.3:\tPoint Clouds: the LidarScanViz class")
# [doc-stag-lidar-scan-viz]
# Creating LidarScan visualizer (3D point cloud + field images on top)
ls_viz = viz.LidarScanViz(meta, point_viz)
# adding scan to the lidar scan viz
ls_viz.scan = scan
# refresh viz data
ls_viz.draw()
# visualize
# update() is not needed for LidatScanViz because it's doing it internally
point_viz.run()
# [doc-etag-lidar-scan-viz]
# ===================================================
print("Ex 3.0:\tAugmenting point clouds with 3D Labels")
# [doc-stag-lidar-scan-viz-labels]
# Adding 3D Labels
label1 = viz.Label("Label1: [1, 2, 4]", 1, 2, 4)
point_viz.add(label1)
label2 = viz.Label("Label2: [2, 1, 4]", 2, 1, 4)
label2.set_scale(2)
point_viz.add(label2)
label3 = viz.Label("Label3: [4, 2, 1]", 4, 2, 1)
label3.set_scale(3)
point_viz.add(label3)
# [doc-etag-lidar-scan-viz-labels]
point_viz.camera.dolly(-100)
# visualize
point_viz.update()
point_viz.run()
# ===============================================
print("Ex 4.0:\tOverlay 2D Images and 2D Labels")
# [doc-stag-overlay-images-labels]
# Adding image 1 with aspect ratio preserved
img = viz.Image()
img_data = make_checker_board(10, (2, 4))
mask_data = np.zeros((30, 30, 4))
mask_data[:15, :15] = np.array([1, 0, 0, 1])
img.set_mask(mask_data)
img.set_image(img_data)
ypos = (0, 0.5)
xlen = (ypos[1] - ypos[0]) * img_data.shape[1] / img_data.shape[0]
xpos = (0, xlen)
img.set_position(*xpos, *ypos)
img.set_hshift(-0.5)
point_viz.add(img)
# Adding Label for image 1: positioned at bottom left corner
img_label = viz.Label("ARRrrr!", 0.25, 0.5)
img_label.set_rgba((1.0, 1.0, 0.0, 1))
img_label.set_scale(2)
point_viz.add(img_label)
# Adding image 2: positioned to the right of the window
img2 = viz.Image()
img_data2 = make_checker_board(10, (4, 2))
mask_data2 = np.zeros((30, 30, 4))
mask_data2[15:25, 15:25] = np.array([0, 1, 0, 0.5])
img2.set_mask(mask_data2)
img2.set_image(img_data2)
ypos2 = (0, 0.5)
xlen2 = (ypos2[1] - ypos2[0]) * img_data2.shape[1] / img_data2.shape[0]
xpos2 = (-xlen2, 0)
img2.set_position(*xpos2, *ypos2)
img2.set_hshift(1.0)
point_viz.add(img2)
# Adding Label for image 2: positioned at top left corner
img_label2 = viz.Label("Second", 1.0, 0.25, align_top=True, align_right=True)
img_label2.set_rgba((0.0, 1.0, 1.0, 1))
img_label2.set_scale(1)
point_viz.add(img_label2)
# [doc-etag-overlay-images-labels]
# visualize
point_viz.update()
point_viz.run()
# ===============================================================
print("Ex 5.0:\tAdding key handlers: 'R' for random camera dolly")
# [doc-stag-key-handlers]
def handle_dolly_random(ctx, key, mods) -> bool:
if key == 82: # key R
dolly_num = random.randrange(-15, 15)
print(f"Random Dolly: {dolly_num}")
point_viz.camera.dolly(dolly_num)
point_viz.update()
return True
point_viz.push_key_handler(handle_dolly_random)
# [doc-etag-key-handlers]
# visualize
point_viz.update()
point_viz.run()
def scan(meta) -> client.LidarScan:
bin_path = path.join(DATA_DIR, "os-992011000121_data.bin")
with open(bin_path, 'rb') as b:
source = digest.LidarBufStream(b, meta)
scans = client.Scans(source)
return next(iter(scans))
def pcap_to_csv(source: client.PacketSource,
metadata: client.SensorInfo,
num: int = 0,
csv_dir: str = ".",
csv_base: str = "pcap_out",
csv_ext: str = "csv") -> None:
"""Write scans from a pcap to csv files (one per lidar scan).
The number of saved lines per csv file is always H x W, which corresponds to
a full 2D image representation of a lidar scan.
Each line in a csv file is (for LEGACY profile):
TIMESTAMP, RANGE (mm), SIGNAL, NEAR_IR, REFLECTIVITY, X (mm), Y (mm), Z (mm)
If ``csv_ext`` ends in ``.gz``, the file is automatically saved in
compressed gzip format. :func:`.numpy.loadtxt` can be used to read gzipped
files transparently back to :class:`.numpy.ndarray`.
Args:
source: PacketSource from pcap
metadata: associated SensorInfo for PacketSource
num: number of scans to save from pcap to csv files
csv_dir: path to the directory where csv files will be saved
csv_base: string to use as the base of the filename for pcap output
csv_ext: file extension to use, "csv" by default
"""
# ensure that base csv_dir exists
if not os.path.exists(csv_dir):
os.makedirs(csv_dir)
# construct csv header and data format
def get_fields_info(scan: client.LidarScan) -> Tuple[str, List[str]]:
field_names = 'TIMESTAMP (ns)'
field_fmts = ['%d']
for chan_field in scan.fields:
field_names += f', {chan_field}'
if chan_field in [client.ChanField.RANGE, client.ChanField.RANGE2]:
field_names += ' (mm)'
field_fmts.append('%d')
field_names += ', X (mm), Y (mm), Z (mm)'
field_fmts.extend(3 * ['%d'])
return field_names, field_fmts
field_names: str = ''
field_fmts: List[str] = []
# [doc-stag-pcap-to-csv]
from itertools import islice
# precompute xyzlut to save computation in a loop
xyzlut = client.XYZLut(metadata)
# create an iterator of LidarScans from pcap and bound it if num is specified
scans = iter(client.Scans(source))
if num:
scans = islice(scans, num)
for idx, scan in enumerate(scans):
# initialize the field names for csv header
if not field_names or not field_fmts:
field_names, field_fmts = get_fields_info(scan)
# copy per-column timestamps for each channel
timestamps = np.tile(scan.timestamp, (scan.h, 1))
# grab channel data
fields_values = [scan.field(ch) for ch in scan.fields]
# use integer mm to avoid loss of precision casting timestamps
xyz = (xyzlut(scan) * 1000).astype(np.int64)
# get all data as one H x W x 8 int64 array for savetxt()
frame = np.dstack((timestamps, *fields_values, xyz))
# not necessary, but output points in "image" vs. staggered order
frame = client.destagger(metadata, frame)
# write csv out to file
csv_path = os.path.join(csv_dir, f'{csv_base}_{idx:06d}.{csv_ext}')
print(f'write frame #{idx}, to file: {csv_path}')
header = '\n'.join([f'frame num: {idx}', field_names])
np.savetxt(csv_path,
frame.reshape(-1, frame.shape[2]),
fmt=field_fmts,
delimiter=',',
header=header)
- a proxy run()/quit() on ls_viz would be useful
- maybe: ls_viz could initialize underlying viz + expose it
- point_viz.run() twice is broken
- ideally, run() would open/close window
- auto camera movement example?
"""
from ouster import client, pcap
from ouster.sdk import viz
meta_path = "/mnt/aux/test_drives/OS1_128_2048x10.json"
pcap_path = "/mnt/aux/test_drives/OS1_128_2048x10.pcap"
meta = client.SensorInfo(open(meta_path).read())
packets = pcap.Pcap(pcap_path, meta)
scans = iter(client.Scans(packets))
point_viz = viz.PointViz("Example Viz")
ls_viz = viz.LidarScanViz(meta, point_viz)
ls_viz.scan = next(scans)
ls_viz.draw()
print("Showing first frame, close visuzlier window to continue")
point_viz.run()
ls_viz.scan = next(scans)
ls_viz.draw()
print("Showing second frame, close visuzlier window to continue")
point_viz.run()
# won't work on macos, but convenient:
def test_scans_multi(lidar_stream: client.PacketSource) -> None:
"""Test batching multiple scans."""
scans = take(10, client.Scans(lidar_stream))
assert list(map(lambda s: s.frame_id, scans)) == list(range(10))
def pcap_to_csv(pcap_path: str,
metadata_path: str,
num: int = 0,
csv_dir: str = ".",
csv_prefix: str = "pcap_out",
csv_ext: str = "csv") -> None:
"""Write scans from pcap file (*pcap_path*) to plain csv files (one per
lidar scan).
If the *csv_ext* ends in ``.gz``, the file is automatically saved in
compressed gzip format. :func:`.numpy.loadtxt` can be used to read gzipped
files transparently back to :class:`.numpy.ndarray`.
Number of saved lines per csv file is always [H x W], which corresponds
to a full 2D image representation of a lidar scan.
Each line in a csv file is:
RANGE (mm), SIGNAL, NEAR_IR, REFLECTIVITY, X (m), Y (m), Z (m)
Args:
pcap_path: path to the pcap file
metadata_path: path to the .json with metadata (aka :class:`.SensorInfo`)
num: number of scans to save from pcap to csv files
csv_dir: path to the directory where csv files will be saved
csv_prefix: the filename prefix that will be appended with frame number
and *csv_ext*
csv_ext: file extension to use. If it ends with ``.gz`` the output is
gzip compressed
"""
from itertools import islice
# ensure that base csv_dir exists
if not os.path.exists(csv_dir):
os.makedirs(csv_dir)
metadata = read_metadata(metadata_path)
source = pcap.Pcap(pcap_path, metadata)
# [doc-stag-pcap-to-csv]
field_names = 'RANGE (mm), SIGNAL, NEAR_IR, REFLECTIVITY, X (m), Y (m), Z (m)'
field_fmts = ['%d', '%d', '%d', '%d', '%.8f', '%.8f', '%.8f']
channels = [
client.ChanField.RANGE, client.ChanField.SIGNAL,
client.ChanField.NEAR_IR, client.ChanField.REFLECTIVITY
]
with closing(pcap.Pcap(pcap_path, metadata)) as source:
# precompute xyzlut to save computation in a loop
xyzlut = client.XYZLut(metadata)
# create an iterator of LidarScans from pcap and bound it if num is specified
scans = iter(client.Scans(source))
if num:
scans = islice(scans, num)
for idx, scan in enumerate(scans):
fields_values = [scan.field(ch) for ch in channels]
xyz = xyzlut(scan)
# get lidar data as one frame of [H x W x 7], "fat" 2D image
frame = np.dstack((*fields_values, xyz))
frame = client.destagger(metadata, frame)
csv_path = os.path.join(csv_dir,
f'{csv_prefix}_{idx:06d}.{csv_ext}')
header = '\n'.join([
f'pcap file: {pcap_path}', f'frame num: {idx}',
f'metadata file: {metadata_path}', field_names
])
print(f'write frame #{idx}, to file: {csv_path}')
np.savetxt(csv_path,
np.reshape(frame, (-1, frame.shape[2])),
fmt=field_fmts,
delimiter=',',
header=header)