"""
Object shape recognition with L-shape fitting
author: Atsushi Sakai (@Atsushi_twi)
Ref:
- Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners -
The Robotics Institute Carnegie Mellon University
https://www.ri.cmu.edu/publications/
efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners/
"""
import sys
import pathlib
sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
import matplotlib.pyplot as plt
import numpy as np
import itertools
from enum import Enum
from utils.angle import rot_mat_2d
from Mapping.rectangle_fitting.simulator \
import VehicleSimulator, LidarSimulator
show_animation = True
[docs]class LShapeFitting:
"""
LShapeFitting class. You can use this class by initializing the class and
changing the parameters, and then calling the fitting method.
"""
[docs] class Criteria(Enum):
AREA = 1
CLOSENESS = 2
VARIANCE = 3
def __init__(self):
"""
Default parameter settings
"""
#: Fitting criteria parameter
self.criteria = self.Criteria.VARIANCE
#: Minimum distance for closeness criteria parameter [m]
self.min_dist_of_closeness_criteria = 0.01
#: Angle difference parameter [deg]
self.d_theta_deg_for_search = 1.0
#: Range segmentation parameter [m]
self.R0 = 3.0
#: Range segmentation parameter [m]
self.Rd = 0.001
[docs] def fitting(self, ox, oy):
"""
Fitting L-shape model to object points
Parameters
----------
ox : x positions of range points from an object
oy : y positions of range points from an object
Returns
-------
rects: Fitting rectangles
id_sets: id sets of each cluster
"""
# step1: Adaptive Range Segmentation
id_sets = self._adoptive_range_segmentation(ox, oy)
# step2 Rectangle search
rects = []
for ids in id_sets: # for each cluster
cx = [ox[i] for i in range(len(ox)) if i in ids]
cy = [oy[i] for i in range(len(oy)) if i in ids]
rects.append(self._rectangle_search(cx, cy))
return rects, id_sets
@staticmethod
def _calc_area_criterion(c1, c2):
c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2)
alpha = -(c1_max - c1_min) * (c2_max - c2_min)
return alpha
def _calc_closeness_criterion(self, c1, c2):
c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2)
# Vectorization
d1 = np.minimum(c1_max - c1, c1 - c1_min)
d2 = np.minimum(c2_max - c2, c2 - c2_min)
d = np.maximum(np.minimum(d1, d2), self.min_dist_of_closeness_criteria)
beta = (1.0 / d).sum()
return beta
@staticmethod
def _calc_variance_criterion(c1, c2):
c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2)
# Vectorization
d1 = np.minimum(c1_max - c1, c1 - c1_min)
d2 = np.minimum(c2_max - c2, c2 - c2_min)
e1 = d1[d1 < d2]
e2 = d2[d1 >= d2]
v1 = - np.var(e1) if len(e1) > 0 else 0.
v2 = - np.var(e2) if len(e2) > 0 else 0.
gamma = v1 + v2
return gamma
@staticmethod
def _find_min_max(c1, c2):
c1_max = max(c1)
c2_max = max(c2)
c1_min = min(c1)
c2_min = min(c2)
return c1_max, c1_min, c2_max, c2_min
def _rectangle_search(self, x, y):
xy = np.array([x, y]).T
d_theta = np.deg2rad(self.d_theta_deg_for_search)
min_cost = (-float('inf'), None)
for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta):
c = xy @ rot_mat_2d(theta)
c1 = c[:, 0]
c2 = c[:, 1]
# Select criteria
cost = 0.0
if self.criteria == self.Criteria.AREA:
cost = self._calc_area_criterion(c1, c2)
elif self.criteria == self.Criteria.CLOSENESS:
cost = self._calc_closeness_criterion(c1, c2)
elif self.criteria == self.Criteria.VARIANCE:
cost = self._calc_variance_criterion(c1, c2)
if min_cost[0] < cost:
min_cost = (cost, theta)
# calc best rectangle
sin_s = np.sin(min_cost[1])
cos_s = np.cos(min_cost[1])
c1_s = xy @ np.array([cos_s, sin_s]).T
c2_s = xy @ np.array([-sin_s, cos_s]).T
rect = RectangleData()
rect.a[0] = cos_s
rect.b[0] = sin_s
rect.c[0] = min(c1_s)
rect.a[1] = -sin_s
rect.b[1] = cos_s
rect.c[1] = min(c2_s)
rect.a[2] = cos_s
rect.b[2] = sin_s
rect.c[2] = max(c1_s)
rect.a[3] = -sin_s
rect.b[3] = cos_s
rect.c[3] = max(c2_s)
return rect
def _adoptive_range_segmentation(self, ox, oy):
# Setup initial cluster
segment_list = []
for i, _ in enumerate(ox):
c = set()
r = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]])
for j, _ in enumerate(ox):
d = np.hypot(ox[i] - ox[j], oy[i] - oy[j])
if d <= r:
c.add(j)
segment_list.append(c)
# Merge cluster
while True:
no_change = True
for (c1, c2) in list(itertools.permutations(range(len(segment_list)), 2)):
if segment_list[c1] & segment_list[c2]:
segment_list[c1] = (segment_list[c1] | segment_list.pop(c2))
no_change = False
break
if no_change:
break
return segment_list
class RectangleData:
def __init__(self):
self.a = [None] * 4
self.b = [None] * 4
self.c = [None] * 4
self.rect_c_x = [None] * 5
self.rect_c_y = [None] * 5
def plot(self):
self.calc_rect_contour()
plt.plot(self.rect_c_x, self.rect_c_y, "-r")
def calc_rect_contour(self):
self.rect_c_x[0], self.rect_c_y[0] = self.calc_cross_point(
self.a[0:2], self.b[0:2], self.c[0:2])
self.rect_c_x[1], self.rect_c_y[1] = self.calc_cross_point(
self.a[1:3], self.b[1:3], self.c[1:3])
self.rect_c_x[2], self.rect_c_y[2] = self.calc_cross_point(
self.a[2:4], self.b[2:4], self.c[2:4])
self.rect_c_x[3], self.rect_c_y[3] = self.calc_cross_point(
[self.a[3], self.a[0]], [self.b[3], self.b[0]], [self.c[3], self.c[0]])
self.rect_c_x[4], self.rect_c_y[4] = self.rect_c_x[0], self.rect_c_y[0]
@staticmethod
def calc_cross_point(a, b, c):
x = (b[0] * -c[1] - b[1] * -c[0]) / (a[0] * b[1] - a[1] * b[0])
y = (a[1] * -c[0] - a[0] * -c[1]) / (a[0] * b[1] - a[1] * b[0])
return x, y
def main():
# simulation parameters
sim_time = 30.0 # simulation time
dt = 0.2 # time tick
angle_resolution = np.deg2rad(3.0) # sensor angle resolution
v1 = VehicleSimulator(-10.0, 0.0, np.deg2rad(90.0),
0.0, 50.0 / 3.6, 3.0, 5.0)
v2 = VehicleSimulator(20.0, 10.0, np.deg2rad(180.0),
0.0, 50.0 / 3.6, 4.0, 10.0)
l_shape_fitting = LShapeFitting()
lidar_sim = LidarSimulator()
time = 0.0
while time <= sim_time:
time += dt
v1.update(dt, 0.1, 0.0)
v2.update(dt, 0.1, -0.05)
ox, oy = lidar_sim.get_observation_points([v1, v2], angle_resolution)
rects, id_sets = l_shape_fitting.fitting(ox, oy)
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect(
'key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.axis("equal")
plt.plot(0.0, 0.0, "*r")
v1.plot()
v2.plot()
# Plot range observation
for ids in id_sets:
x = [ox[i] for i in range(len(ox)) if i in ids]
y = [oy[i] for i in range(len(ox)) if i in ids]
for (ix, iy) in zip(x, y):
plt.plot([0.0, ix], [0.0, iy], "-og")
plt.plot([ox[i] for i in range(len(ox)) if i in ids],
[oy[i] for i in range(len(ox)) if i in ids],
"o")
for rect in rects:
rect.plot()
plt.pause(0.1)
print("Done")
if __name__ == '__main__':
main()