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fit_ransac.py
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fit_ransac.py
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import numpy as np
from rl_config import *
import itertools
def fit_plane(points):
'''
Fit plane to points
:param points: 3d points (N, 4)
:return: plane (4,)
'''
assert points.shape[0] >= 3 # at least 3 points needed
_, _, vh = np.linalg.svd(points)
plane = vh[-1, :]
return plane
def get_point2plane_dist(points, plane):
'''
Get distance of points to the plane
:param points: 3d points (N, 4)
:param plane: plane (4,)
:return: distances (N,)
'''
dists = np.abs(points @ plane) / np.sqrt(plane[0] ** 2 + plane[1] ** 2 + plane[2] ** 2)
return dists
def fit_plane_RANSAC(points, inlier_thresh=0.05, iters=3000):
'''
Fit plane using RANSAC
:param points: 3d points (N, 4)
:param inlier_thresh: Inlier threshold value
:param return_outlier_list: If True, the function return the outlier list
:return:
'''
max_inlier_num = -1
max_inlier_list = None
best_plane = None
points_ransac = np.round(points, decimals=2)
points_ransac = np.unique(points_ransac, axis=0)
cand_points_ind = np.arange(points_ransac.shape[0])
points_fit = np.round(points, decimals=2)
points_fit = np.unique(points_fit, axis=0)
N = points_ransac.shape[0]
assert N >= 3
for i in range(iters):
chose_id = np.random.choice(cand_points_ind, 3, replace=False)
chose_points = points_ransac[chose_id, :]
tmp_plane = fit_plane(chose_points)
dists = get_point2plane_dist(points_fit, tmp_plane)
tmp_inlier_list = np.where(dists < inlier_thresh)[0]
tmp_inliers = points_fit[tmp_inlier_list, :]
num_inliers = tmp_inliers.shape[0]
if num_inliers > max_inlier_num:
max_inlier_num = num_inliers
max_inlier_list = tmp_inlier_list
best_plane = tmp_plane
# print('iter %d, %d inliers' % (i, max_inlier_num))
plane = best_plane
dists = get_point2plane_dist(points_fit, plane)
inlier_list = np.where(dists < inlier_thresh)[0]
outlier_list = np.where(dists >= inlier_thresh)[0]
return plane, inlier_list, outlier_list
#LINE-----RANSAC--------------------------------------------------------------------------------------------------------
def fit_line(points):
'''
Fit line to points
:param points: 2d points (N, 3)
:return: line (3,)
'''
assert points.shape[0] >= 2 # at least 3 points needed
_, _, vh = np.linalg.svd(points)
line = vh[-1, :]
return line
def get_point2line_dist(points, line):
'''
Get distance of points to the line
:param points: 2d points (N, 3)
:param line: line (3,)
:return: distances (N,)
'''
dists = np.abs(points @ line) / np.sqrt(line[0] ** 2 + line[1] ** 2 + line[2] ** 2)
return dists
def fit_line_RANSAC(points, inlier_thresh=0.05):
'''
Fit line using RANSAC
:param points: 2d points (N, 3)
:param inlier_thresh: Inlier threshold value
:param return_outlier_list: If True, the function return the outlier list
:return:
'''
max_inlier_num = -1
max_inlier_list = None
best_line = None
stride = max(int(points.shape[0] / (ransac_line_points_n)), 1)
# stride = 1
points_ransac = points[::stride, :]
stride = 1
points_fit = points[::stride, :]
N = points_ransac.shape[0]
assert N >= 2
chose_ids = itertools.permutations(list(range(N)), 2)
for chose_id in chose_ids:
chose_points = points_ransac[chose_id, :]
tmp_line = fit_line(chose_points)
tmp_m = - tmp_line[0] / tmp_line[1]
if np.abs(tmp_m) < 5:
continue
dists = get_point2line_dist(points_fit, tmp_line)
tmp_inlier_list = np.where(dists < inlier_thresh)[0]
tmp_inliers = points_fit[tmp_inlier_list, :]
num_inliers = tmp_inliers.shape[0]
if num_inliers > max_inlier_num:
max_inlier_num = num_inliers
max_inlier_list = tmp_inlier_list
best_line = tmp_line
if max_inlier_num < 2:
return (0, 0), [[0,0],[1,1]]
# final_points = points_fit[max_inlier_list, :]
# line = fit_line(final_points)
# fit_variance = np.var(get_point2line_dist(final_points, line))
# print('RANSAC fit variance: %f' % fit_variance)
line = best_line
dists = get_point2line_dist(points, line)
m = - line[0] / line[1]
b = - line[2] / line[1]
inlier_list = np.where(dists < inlier_thresh)[0]
outlier_list = np.where(dists >= inlier_thresh)[0]
return (m, b), inlier_list, outlier_list