"""
Copyright (c) 2021, Ouster, Inc.
All rights reserved.
Reference implementations of common operations.
"""
from itertools import product
from math import cos, pi, sin, sqrt
from typing import List
import numpy as np
from ouster import client
# TODO: replace link when new FW 2.5/3.0 manual is up
[docs]def xyz_proj_origin_to_origin_mm(metadata: client.SensorInfo,
scan: client.LidarScan) -> np.ndarray:
"""Computes a point cloud from a scan as numpy array
This is the old reference implementation that follows the calculations from
`Section 3.1.2`_ of the FW 2.0 Software User Manual exactly for OS-0, OS-1,
and OS-2 sensors. The output is a point cloud in the *sensor frame* with
points arranged in column-major order, with coordinates in meters.
Args:
metadata: Sensor metadata associated with the scan
scan: A frame of lidar data
Returns:
A H x W x 3 array of point coordinates
.. _Section 3.1.2: https://data.ouster.io/downloads/software-user-manual/software-user-manual-v2p0.pdf#a
"""
# use homogeneous coordinates for convenient transformation
xyz = np.zeros((scan.w * scan.h, 4))
# return 0s since this method is not valid for OS-DOME
if "OS-DOME" in metadata.prod_line:
return xyz
# iterate over each measurement channel/row and measurement block/column
for u, v in product(range(scan.h), range(scan.w)):
r = scan.field(client.ChanField.RANGE)[u, v]
n = metadata.lidar_origin_to_beam_origin_mm
# scans are always a full frame, so the measurement id is also the index
assert scan.measurement_id[v] == v
theta_encoder = 2.0 * pi * (1.0 - v / scan.w)
theta_azimuth = -2.0 * pi * (metadata.beam_azimuth_angles[u] / 360.0)
phi = 2.0 * pi * (metadata.beam_altitude_angles[u] / 360.0)
# zero ranges represent no return; avoid applying offsets to these
if r == 0.0:
continue
# compute point coordinates in the lidar frame
x = (r - n) * cos(theta_encoder +
theta_azimuth) * cos(phi) + n * cos(theta_encoder)
y = (r - n) * sin(theta_encoder +
theta_azimuth) * cos(phi) + n * sin(theta_encoder)
z = (r - n) * sin(phi)
# insert into xyz; point order is row-major to match input scan
xyz[u * scan.w + v] = [x, y, z, 1]
# transform from lidar to sensor frame and scale to meters from millimeters
xyz_sensor = xyz @ metadata.lidar_to_sensor_transform.T
return xyz_sensor[:, :3].reshape(scan.h, scan.w, 3) * 0.001
[docs]def destagger(pixel_shift_by_row: List[int], field: np.ndarray) -> np.ndarray:
"""Reference implementation for destaggering a field of data.
In the default staggered representation, each column corresponds to a
single timestamp. In the destaggered representation, each column
corresponds to a single azimuth angle, compensating for the azimuth offset
of each beam.
Destaggering is used for visualizing lidar data as an image or for
algorithms that exploit the structure of the lidar data, such as
beam_uniformity in ouster_viz, or computer vision algorithms.
Args:
pixel_shift_by_row: List of pixel shifts by row from sensor metadata
field: Staggered data as a H x W numpy array
Returns:
Destaggered data as a H x W numpy array
"""
destaggered = np.zeros(field.shape)
nrows = field.shape[0]
# iterate over every row and apply pixel shift
for u in range(nrows):
destaggered[u, :] = np.roll(field[u, :], pixel_shift_by_row[u])
return destaggered