def test_destagger_type_good(meta, dtype) -> None:
"""Check that destaggering preserves dtype."""
h = meta.format.pixels_per_column
w = meta.format.columns_per_frame
assert client.destagger(meta, np.zeros((h, w), dtype)).dtype == dtype
assert client.destagger(meta, np.zeros((h, w, 2), dtype)).dtype == dtype
def plot_all_channels(hostname: str,
lidar_port: int = 7502,
n_scans: int = 5) -> None:
"""Display all channels of n consecutive lidar scans taken live from sensor
Args:
hostname: hostname of the sensor
lidar_port: UDP port to listen on for lidar data
n_scans: number of scans to show
"""
import matplotlib.pyplot as plt # type: ignore
# [doc-stag-display-all-2d]
# take sample of n scans from sensor
metadata, sample = client.Scans.sample(hostname, n_scans, lidar_port)
# initialize and configure subplots
fig, axarr = plt.subplots(n_scans,
4,
sharex=True,
sharey=True,
figsize=(12.0, n_scans * .75),
tight_layout=True)
fig.suptitle("{} consecutive scans from {}".format(n_scans, hostname))
fig.canvas.set_window_title("example: display_all_2D")
# set row and column titles of subplots
column_titles = ["range", "reflectivity", "near_ir", "signal"]
row_titles = ["Scan {}".format(i) for i in list(range(n_scans))]
for ax, column_title in zip(axarr[0], column_titles):
ax.set_title(column_title)
for ax, row_title in zip(axarr[:, 0], row_titles):
ax.set_ylabel(row_title)
# plot 2D scans
for count, scan in enumerate(next(sample)):
axarr[count, 0].imshow(
client.destagger(metadata, scan.field(client.ChanField.RANGE)))
axarr[count, 1].imshow(
client.destagger(metadata,
scan.field(client.ChanField.REFLECTIVITY)))
axarr[count, 2].imshow(
client.destagger(metadata, scan.field(client.ChanField.NEAR_IR)))
axarr[count, 3].imshow(
client.destagger(metadata, scan.field(client.ChanField.SIGNAL)))
# [doc-etag-display-all-2d]
# configure and show plot
[ax.get_xaxis().set_visible(False) for ax in axarr.ravel()]
[ax.set_yticks([]) for ax in axarr.ravel()]
[ax.set_yticklabels([]) for ax in axarr.ravel()]
plt.show()
def test_destagger_inverse(meta) -> None:
"""Check that stagger/destagger are inverse operations."""
h = meta.format.pixels_per_column
w = meta.format.columns_per_frame
a = np.arange(h * w).reshape((h, w))
b = client.destagger(meta, a, inverse=True)
c = client.destagger(meta, b)
assert np.array_equal(a, c)
d = client.destagger(meta, a)
e = client.destagger(meta, d, inverse=True)
assert np.array_equal(a, e)
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 live_plot_signal(hostname: str, lidar_port: int = 7502) -> None:
"""Display signal from live sensor
Args:
hostname: hostname of the sensor
lidar_port: UDP port to listen on for lidar data
"""
import cv2 # type: ignore
print("press ESC from visualization to exit")
# [doc-stag-live-plot-signal]
# establish sensor connection
with closing(client.Scans.stream(hostname, lidar_port,
complete=False)) as stream:
show = True
while show:
for scan in stream:
# uncomment if you'd like to see frame id printed
# print("frame id: {} ".format(scan.frame_id))
signal = client.destagger(stream.metadata,
scan.field(client.ChanField.SIGNAL))
signal = (signal / np.max(signal) * 255).astype(np.uint8)
cv2.imshow("scaled signal", signal)
key = cv2.waitKey(1) & 0xFF
# [doc-etag-live-plot-signal]
# 27 is esc
if key == 27:
show = False
break
cv2.destroyAllWindows()
def plot_range_image(hostname: str, lidar_port: int = 7502) -> None:
"""Display range data taken live from sensor as an image
Args:
hostname: hostname of the sensor
lidar_port: UDP port to listen on for lidar data
"""
import matplotlib.pyplot as plt # type: ignore
# get single scan [doc-stag-single-scan]
metadata, sample = client.Scans.sample(hostname, 1, lidar_port)
scan = next(sample)[0]
# [doc-etag-single-scan]
# initialize plot
fig, ax = plt.subplots()
fig.canvas.set_window_title("example: plot_range_image")
# plot using imshow
range = scan.field(client.ChanField.RANGE)
plt.imshow(client.destagger(metadata, range), resample=False)
# configure and show plot
plt.title("Range Data from {}".format(hostname))
plt.axis('off')
plt.show()
def filter_3d_by_range_and_azimuth(hostname: str,
lidar_port: int = 7502,
range_min: int = 2) -> None:
"""Easily filter 3D Point Cloud by Range and Azimuth Using the 2D Representation
Args:
hostname: hostname of sensor
lidar_port: UDP port to listen on for lidar data
range_min: range minimum in meters
"""
import matplotlib.pyplot as plt # type: ignore
import math
# set up figure
plt.figure()
ax = plt.axes(projection='3d')
r = 3
ax.set_xlim3d([-r, r])
ax.set_ylim3d([-r, r])
ax.set_zlim3d([-r, r])
plt.title("Filtered 3D Points from {}".format(hostname))
metadata, sample = client.Scans.sample(hostname, 2, lidar_port)
scan = next(sample)[1]
# [doc-stag-filter-3d]
# obtain destaggered range
range_destaggered = client.destagger(metadata,
scan.field(client.ChanField.RANGE))
# obtain destaggered xyz representation
xyzlut = client.XYZLut(metadata)
xyz_destaggered = client.destagger(metadata, xyzlut(scan))
# select only points with more than min range using the range data
xyz_filtered = xyz_destaggered * (range_destaggered[:, :, np.newaxis] >
(range_min * 1000))
# get first 3/4 of scan
to_col = math.floor(metadata.mode.cols * 3 / 4)
xyz_filtered = xyz_filtered[:, 0:to_col, :]
# [doc-etag-filter-3d]
[x, y, z] = [c.flatten() for c in np.dsplit(xyz_filtered, 3)]
ax.scatter(x, y, z, c=z / max(z), s=0.2)
plt.show()
def test_destagger_xyz(meta, scan) -> None:
"""Check that we can destagger the output of xyz projection."""
h = meta.format.pixels_per_column
w = meta.format.columns_per_frame
xyz = client.XYZLut(meta)(scan)
destaggered = client.destagger(meta, xyz)
assert destaggered.shape == (h, w, 3)
def test_destagger_correct(meta, scan) -> None:
"""Compare client destagger function to reference implementation."""
# get destaggered range field using reference implementation
destagger_ref = reference.destagger(meta.format.pixel_shift_by_row,
scan.field(client.ChanField.RANGE))
# obtain destaggered range field using client implemenation
destagger_client = client.destagger(meta,
scan.field(client.ChanField.RANGE))
assert np.array_equal(destagger_ref, destagger_client)
def test_destagger_correct_multi(meta, scan) -> None:
"""Compare client destagger function to reference on stacked fields."""
near_ir = scan.field(client.ChanField.NEAR_IR)
near_ir_stacked = np.repeat(near_ir[..., None], 5, axis=2)
ref = reference.destagger(meta.format.pixel_shift_by_row, near_ir)
ref_stacked = np.repeat(ref[..., None], 5, axis=2)
destaggered_stacked = client.destagger(meta, near_ir_stacked)
assert near_ir_stacked.dtype == np.uint32
assert destaggered_stacked.dtype == np.uint32
assert np.array_equal(ref_stacked, destaggered_stacked)
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 update_data(vis: o3d.visualization.Visualizer):
xyz = xyzlut(scan.field(range_for_field(fields[field_ind])))
key = scan.field(fields[field_ind]).astype(float)
# apply colormap to field values
aes[field_ind](key)
color_img = colorize(key)
# prepare point cloud for Open3d Visualiser
cloud.points = o3d.utility.Vector3dVector(xyz.reshape((-1, 3)))
cloud.colors = o3d.utility.Vector3dVector(color_img.reshape((-1, 3)))
# prepare canvas for 2d image
gray_img = np.dstack([key] * 3)
canvas_set_image_data(image, client.destagger(metadata, gray_img))
# signal that point cloud and needs to be re-rendered
vis.update_geometry(cloud)
def pcap_2d_viewer(
pcap_path: str,
metadata_path: str,
num: int = 0, # not used in this example
rate: float = 0.0) -> None:
"""Simple sensor field visualization pipeline as 2D images from pcap file
(*pcap_path*)
Args:
pcap_path: path to the pcap file
metadata_path: path to the .json with metadata (aka :class:`.SensorInfo`)
rate: read speed of packets from the pcap file (**1.0** - corresponds to
real-time by packets timestamp, **0.0** - as fast as it reads from
file without any delay)
"""
import cv2 # type: ignore
# [doc-stag-pcap-display-live]
metadata = read_metadata(metadata_path)
source = pcap.Pcap(pcap_path, metadata, rate=rate)
with closing(source) as source:
scans = iter(client.Scans(source))
print("press ESC from visualization to exit")
channels = [
client.ChanField.RANGE, client.ChanField.SIGNAL,
client.ChanField.NEAR_IR, client.ChanField.REFLECTIVITY
]
paused = False
destagger = True
num = 0
scan = next(scans, None)
while scan:
print("frame id: {}, num = {}".format(scan.frame_id, num))
fields_values = [scan.field(ch) for ch in channels]
if destagger:
fields_values = [
client.destagger(metadata, field_val)
for field_val in fields_values
]
fields_images = [ae(field_val) for field_val in fields_values]
combined_images = np.vstack(
[np.pad(img, 2, constant_values=1.0) for img in fields_images])
cv2.imshow("4 channels: ", combined_images)
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:
break
if not paused:
scan = next(scans, None)
num += 1
cv2.destroyAllWindows()
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)
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 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)
def prepare_field_image(scan, key, metadata, destagger=True):
f = ae(scan.field(key))
if destagger:
return client.destagger(metadata, f)
return f
def test_destagger_shape_bad(meta) -> None:
"""Check that arrays of the wrong shape are rejected."""
h = meta.format.pixels_per_column
w = meta.format.columns_per_frame
with pytest.raises(ValueError):
client.destagger(meta, np.zeros((0, w)))
with pytest.raises(ValueError):
client.destagger(meta, np.zeros((h, 0, 2)))
with pytest.raises(ValueError):
client.destagger(meta, np.zeros((h, w + 1)))
with pytest.raises(ValueError):
client.destagger(meta, np.zeros((h - 1, w)))
with pytest.raises(ValueError):
client.destagger(meta, np.zeros((h, w - 1, 1)))
with pytest.raises(ValueError):
client.destagger(meta, np.zeros((h + 1, w, 2)))
def test_destagger_shape_good(meta, shape) -> None:
"""Check that (de)staggering preserves shape."""
assert client.destagger(meta, np.zeros(shape)).shape == shape
assert client.destagger(meta, np.zeros(shape), inverse=True).shape == shape
