/
BoxEvaluationClasses.py
671 lines (544 loc) · 27.4 KB
/
BoxEvaluationClasses.py
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#
# This file is part of PLCC.
#
# Copyright 2016 Johannes Graeter <johannes.graeter@kit.edu (Karlsruhe Institute of Technology)
#
# PLCC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# PLCC is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
import glob
import numpy as np
from scipy.optimize import leastsq
import cv2
import math3d
from matplotlib import pyplot as plt
from Plotting import get_color, get_color_cv, plot_mean_standard_dev_bins
class PclBoxSegmentor(object):
"""this class finds the card box in the scan"""
def __init__(self, gradient_thres):
"""Constructor for PclBoxSegmentor"""
self.gradient_thres = gradient_thres
self.__box_depth_tol = 0.3
self.__width_tol = 0.05
self.__box_width_left = 0.16
self.__box_width_middle = 0.12
self.__box_width_right = 0.16
self.__box_depth = 0.4
# vr_diff: soft threshold on range value change
# vr_ndiff: soft threshold on neighbor-relative change in range values
# slope_gain* exp(-dist*slope_exp)+slop_offest =tangent slope at vr_niff (sigmoid function)
# if dist<diff_min points are seen directly as one cluster
# self.__linkage_params = {"vr_diff": 0.2, "vr_ndiff": 1.90782, "slope_gain": 2., "slope_exp": 0.14,
# "slope_offset": 0.25, "diff_min": 0.1}
self.__linkage_params = {"vr_diff": 2.0, "vr_ndiff": 1.90782, "slope_gain": 2., "slope_exp": 0.14,
"slope_offset": 0.5, "diff_min": 0.07}
self.__linkage_thres = 0.1 # between 0 and one, the bigger, the more less segments
def __calc_err_line(self, x_y_yaw, pcl, line):
x, y, yaw = x_y_yaw
p0 = np.array(line[0])
p1 = np.array(line[1])
direction = p1 - p0
t = math3d.Transform([0., 0., yaw], [x, y, 0.])
p0 = np.array(t.get_pos().list) + np.array([p0[0], p0[1], 0])
direction = np.array((t.get_orient() * math3d.Vector([direction[0], direction[1], 0.])).list)
s1 = np.sign(np.dot(direction, np.array([0, 1, 0.]))) * np.linalg.norm(direction)
direction /= np.linalg.norm(direction)
s0 = 0.
err = []
for p in pcl:
cur_s = np.dot(direction, p - p0)
if s0 < cur_s < s1 or s1 < cur_s < s0:
err.append(p - direction * cur_s + p0)
err = [el / len(err) for el in err]
return err
def __calc_error_lines(self, x_y_yaw):
total_err = []
for l in self.__template:
total_err.extend(self.__calc_err_line(x_y_yaw, self.__pcl, l))
def __template_matching(self, pcl,
template=[((0., 0., 0.), (0., 0.16, 0.)), ((0., 0.19, 0.), (0., 0.31, 0.)),
((0., 0.34, 0.), (0., 0.5, 0.))]):
self.__template = template
self.__pcl = pcl
print np.array(pcl).shape
result, cov_x, infodict, mesg, ler = leastsq(self.__calc_error_lines, np.array([0., 0., 0]), full_output=True)
print result
def __calc_segments_gradient(self, pcl):
pcl_segments = []
cur_segment = []
for i in range(1, len(pcl)):
# grad = self.__double_sided_gradient(pcl[i - 1], pcl[i], pcl[i + 1])
depth_diff = np.linalg.norm(pcl[i - 1]) - np.linalg.norm(pcl[i])
# if difference in depth is bigger than threshold add segment and fill new one
if abs(depth_diff) > self.gradient_thres:
if not cur_segment == []:
pcl_segments.append(cur_segment)
cur_segment = []
cur_segment.append(pcl[i])
return pcl_segments
def __calc_segments_linkage(self, pcl):
pcl_segments = []
cur_segment = []
for i in range(1, len(pcl) - 2):
linkage = self.__calc_linkage(np.array([pcl[i - 1], pcl[i], pcl[i + 1], pcl[i + 2]]))
if abs(linkage) < self.__linkage_thres:
if not cur_segment == []:
pcl_segments.append(cur_segment)
cur_segment = []
cur_segment.append(pcl[i])
return pcl_segments
@staticmethod
def __sigmoid_like_soft_threshold(x, theta, m):
return 0.5 - ((0.5 * (x - theta) * m) / (1 + (x - theta) * (x - theta) * m * m) ** 0.5)
def __compute_forward_linkage_measure(self, d_i, d_j, abs_diff_hi, abs_diff_ij, abs_diff_jk, vr_diff, vr_ndiff,
slope_gain, slope_exp, slope_offset, diff_min):
# Reject linkage if distances out of range
# if d_i < 0.01 or d_j < 0.01:
# return 0.
# Always accept a small connection, so only check further if sufficient
# large connection (avoids singularity if diff_hi / diff_jk are also small).
if abs_diff_ij < diff_min:
return 1.
# Always reject connection if it is not small but one neighboring
# connection is (avoids division by zero)
if abs_diff_hi < diff_min / 2. or abs_diff_jk < diff_min / 2.:
return 0
# Approximate distance (for normalization)
dist = min([d_i, d_j])
assert (dist > 0.)
# Determine the tangential slope
vr_nf = slope_gain * np.exp(-dist * slope_exp) + slope_offset
# Compute exact threshold value
keep = self.__sigmoid_like_soft_threshold(abs_diff_ij / dist, vr_diff, 2. / vr_diff)
keep = min([keep,
self.__sigmoid_like_soft_threshold(abs(abs_diff_ij - abs_diff_hi) / abs_diff_hi,
vr_ndiff, vr_nf)])
keep = min([keep,
self.__sigmoid_like_soft_threshold(abs(abs_diff_ij - abs_diff_jk) / abs_diff_jk,
vr_ndiff, vr_nf)]);
return keep
def __calc_linkage(self, point_quadruple):
d_h = np.linalg.norm(point_quadruple[0]) # Dist for point before
d_i = np.linalg.norm(point_quadruple[1]) # Distance of point for which linkage is calculated
d_j = np.linalg.norm(point_quadruple[2]) # Dist for point after that
d_k = np.linalg.norm(point_quadruple[3]) # Dist for point after that
linkage = self.__compute_forward_linkage_measure(d_i, d_j, abs(d_h - d_i), abs(d_i - d_j), abs(d_j - d_k),
self.__linkage_params["vr_diff"],
self.__linkage_params["vr_ndiff"],
self.__linkage_params["slope_gain"],
self.__linkage_params["slope_exp"],
self.__linkage_params["slope_offset"],
self.__linkage_params["diff_min"])
return linkage
@staticmethod
def __get_depth_diff(p1, p2):
return np.linalg.norm(p1) - np.linalg.norm(p2)
def __find_box_segment(self, s):
candidates = []
for i in range(4, len(s)):
s_left = s[i - 4]
s_hole_left = s[i - 3]
s_middle = s[i - 2]
s_hole_right = s[i - 1]
s_right = s[i]
w_left = self.__get_segment_width(s_left)
w_middle = self.__get_segment_width(s_middle)
w_right = self.__get_segment_width(s_right)
s_diff_1 = self.__get_depth_diff(s_left[-1], s_hole_left[0])
s_diff_2 = self.__get_depth_diff(s_hole_left[-1], s_middle[0])
s_diff_3 = self.__get_depth_diff(s_middle[-1], s_hole_right[0])
s_diff_4 = self.__get_depth_diff(s_hole_right[-1], s_right[0])
# noinspection PyTypeChecker
c = [np.isclose(w_left, self.__box_width_left, atol=self.__width_tol),
np.isclose(w_middle, self.__box_width_middle, atol=self.__width_tol),
np.isclose(w_right, self.__box_width_right, atol=self.__width_tol),
np.isclose(s_diff_1, -self.__box_depth, atol=self.__box_depth_tol),
np.isclose(s_diff_2, self.__box_depth, atol=self.__box_depth_tol),
np.isclose(s_diff_3, -self.__box_depth, atol=self.__box_depth_tol),
np.isclose(s_diff_4, self.__box_depth, atol=self.__box_depth_tol)]
if all(c):
candidates.append([s_left, s_hole_left, s_middle, s_hole_right, s_right])
return candidates
@staticmethod
def __get_segment_width(s):
return np.linalg.norm(s[0] - s[-1])
@staticmethod
def plot_segment(pcl_segments):
from matplotlib import pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
plt.xlabel("")
plt.ylabel("")
plt.axis("equal")
plt.title("")
plt.ioff()
for i, xyz in enumerate(pcl_segments):
xyz_np = np.array(xyz)
PlotHandle = plt.plot(xyz_np[:, 0], xyz_np[:, 1], xyz_np[:, 2], "." + get_color(i), label="")
plt.legend(loc="best")
plt.show()
def process(self, pcl_in):
"""main of class"""
# print "doing template matching"
# self.__template_matching(pcl)
# print "done"
pcl = self.__preprocess(pcl_in, thres=0.1)
pcl_segments = self.__calc_segments_gradient(pcl)
# self.plot_segment(pcl_segments)
chosen_segment = self.__find_box_segment(pcl_segments)
out = []
if len(chosen_segment) == 1:
out = chosen_segment[0]
elif len(chosen_segment) > 1:
print("Warning: there is more than one box segment, tune parameters")
return out
def __preprocess(self, pcl_in, thres):
# reject discontinuous points
pcl = []
for i in range(1, len(pcl_in) - 1):
diff0 = abs(self.__get_depth_diff(pcl_in[i - 1], pcl_in[i]))
diff1 = abs(self.__get_depth_diff(pcl_in[i], pcl_in[i + 1]))
if diff0 > thres and diff1 > thres:
continue
pcl.append(pcl_in[i])
return pcl
class BoxSegmentEvaluator(object):
"""given a segmented scan this class finds the corresponding border points
in the image by their image gradients and evaluates the distance between them"""
def __init__(self, intrinsics, extrinsics):
"""Constructor for ImageBoxSegmentor"""
# self.__maxima_binning_percentage = 0.08
# range in meters in which maxima are searched
# (if we use back projections as priors) or rejected (whole line search)
self.__dy = 0.03
self.__gradient_thres = 100 # if any of the maxima is lower than this threshold, no points will be returned
# self.__gauss_sigma = 1.0
# self.__gauss_kernel_size = (9, 9)
self.__gauss_sigma = 0.3
self.__gauss_kernel_size = (3, 3)
self.__intrinsics = intrinsics
self.__extrinsics = math3d.Transform(extrinsics[:3], extrinsics[3:])
# self.__ksize_gradient = 3
self.__ksize_gradient = 5
@staticmethod
def __draw_box(img, box_roi):
p_tl = (int(box_roi["left"]), int(box_roi["top"]))
p2 = (p_tl[0] + int(box_roi["width"]), p_tl[1] + int(box_roi["height"]))
cv2.rectangle(img, p_tl, p2, get_color_cv(5))
def __draw_back_projection(self, img, sub_segments):
for ind, sub_seg in enumerate(sub_segments):
col = get_color_cv(ind)
for p in sub_seg:
uv = self.__transform_and_back_project(p)
cv2.circle(img, (int(uv[0]), int(uv[1])), 1, col, -1)
def get_debug_image_back_projection(self, img, chosen_segment):
# box_roi = self.__get_box_roi(chosen_segment)
#
# v_max = min(box_roi["top"] + box_roi["height"], img.shape[0])
# u_max = min(box_roi["left"] + box_roi["width"], img.shape[1])
#
# sub_img = img[box_roi["top"]:v_max, box_roi["left"]:u_max]
plot_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
# self.__draw_box(plot_img, box_roi)
self.__draw_back_projection(plot_img, chosen_segment)
return plot_img
def get_debug_image_border_points(self, plot_img, border_points, back_projected_border_points):
"""calculates gradient igefroplot ie and shows extracted points on borders"""
out = self.__get_gradient_image().copy()
cur_min, cur_max, _, _ = cv2.minMaxLoc(out)
out = np.uint8((out * 255.0 / (cur_max - cur_min)) - 255 * cur_min / (cur_max - cur_min))
out = cv2.cvtColor(out, cv2.COLOR_GRAY2BGR)
for border_point, pp in zip(border_points, back_projected_border_points):
cv2.circle(out, (int(border_point[0]), int(border_point[1])), 9, get_color_cv(0), 2)
cv2.circle(out, (int(pp[0]), int(pp[1])), 7, get_color_cv(6), 2)
return out
def plot_debug_images(self, img, segment, border_points, back_projections):
deb_img_1 = self.get_debug_image_back_projection(img, segment)
deb_img_2 = self.get_debug_image_border_points(img, border_points, back_projections)
plt.subplot(2, 1, 1)
plt.imshow(deb_img_1)
plt.xticks([]), plt.yticks([])
plt.subplot(2, 1, 2)
plt.imshow(deb_img_2)
plt.xticks([]), plt.yticks([])
return deb_img_1, deb_img_2
def __write_image_txt(self, n, err_vec, img1, img2):
# write down image and errors
cv2.imwrite(n + "_img1.png", img1)
cv2.imwrite(n + "_img2.png", img2)
f = open(n + ".txt", "w")
for e in err_vec:
f.write(str(e) + "\n")
def process(self, img, segment, outlier_thres=10, debug_dir=""):
"""main of class"""
back_projections = self.__calc_border_projection(segment)
border_points_image = self.__get_border_points_image(img, segment)
if not border_points_image:
return []
if not len(back_projections) == len(border_points_image):
msg = "in BoxSegmentEvaluator: features have not same length: len(image)=" + str(
len(border_points_image)) + " len(back_proj)=" + str(len(back_projections))
# raise Exception(msg)
print(msg)
return []
err_vec = self.__calc_back_projection_errors(back_projections, border_points_image, self.__search_line)
# for debugging
is_outlier = any(np.array(err_vec) > outlier_thres)
if is_outlier:
num_png = len(glob.glob(debug_dir + "/outlier_*.png"))
n = debug_dir + "/outlier_" + str(num_png)
print("detected outlier writing to " + n)
else:
num_png = len(glob.glob(debug_dir + "/inlier_*.png"))
n = debug_dir + "/inlier_" + str(num_png)
if not debug_dir == "":
img1, img2 = self.plot_debug_images(img, segment, border_points_image, back_projections)
self.__write_image_txt(n, err_vec, img1, img2)
# plt.ioff()
# plt.show()
if is_outlier:
return []
else:
return err_vec
def __get_box_roi(self, chosen_segment):
p_left = chosen_segment[0][0]
p_right = chosen_segment[-1][-1]
uv_left = self.__transform_and_back_project(p_left)
uv_right = self.__transform_and_back_project(p_right)
width = uv_right[0] - uv_left[0]
assert (width > 1.)
height = width
return {"top": max(uv_left[1] - height / 2., 0.), "left": max(uv_left[0], 0.), "width": width, "height": height}
def __transform_and_back_project(self, p):
p_cam = np.array((self.__extrinsics * math3d.Vector(p)).list)
uv1 = np.dot(self.__intrinsics, p_cam)
uv1 /= p_cam[2]
return uv1[:2]
@staticmethod
def __get_border_points(s):
out = []
for i in range(1, len(s)):
out.append((s[i - 1][-1], s[i][0]))
return out
def __get_border_points_image(self, img, segment, back_projections=[]):
"""get point with maximum grey value diff between the projections of the border points"""
assert (len(segment) == 5)
ps = segment[0]
ps.extend(segment[2])
ps.extend(segment[4])
search_line = self.__calc_search_line(np.array(ps))
self.__search_line=search_line
deriv_img = self.__calc_gradient_image(img, cv2.CV_32F)
# get and right point of segment -> search_interval
p_left = self.__get_point_from_segment(segment[0], 0.33)
p_right = self.__get_point_from_segment(segment[4], -0.33)
# p_left = segment[0][1]
# p_right = segment[-1][-2]
uv_left = self.__transform_and_back_project(p_left)
uv_right = self.__transform_and_back_project(p_right)
cur_depth = np.linalg.norm(self.__get_point_from_segment(segment[2], 0.5))
binning_range = self.__intrinsics[0, 0] * self.__dy / cur_depth
if not back_projections:
grad_and_point = self.__get_border_point_in_proximity(deriv_img, uv_left, uv_right,
search_line, binning_range=binning_range)
else:
grad_and_point = []
for cur_b in back_projections:
cur_maximum = self.__get_border_point_in_proximity_of_point(deriv_img, cur_b, search_line,
search_range=(
-binning_range, binning_range),
number_points=1)
grad_and_point.extend(cur_maximum)
if any(np.array([p[0] for p in grad_and_point]) < self.__gradient_thres):
return []
# don't return gradient value
return [p[1] for p in grad_and_point]
def __get_point_from_segment(self, segment, proportion=0.33):
n = len(segment)
return segment[int((n - 1) * proportion)]
def __get_maxima_list(self, deriv_img, search_pixel_list):
# get highest gradient in next cur_var pixels in + and - direction use set to not get double maxima
val_spot = []
for cur_pix in self.set_from_list(search_pixel_list, conversion_func=lambda p: (int(p[0]), int(p[1]))):
if 0 < cur_pix[1] < deriv_img.shape[1] and deriv_img.shape[0] > cur_pix[0] > 0:
deriv = abs(deriv_img[cur_pix[1], cur_pix[0]])
val_spot.append((deriv, cur_pix))
return val_spot
def __get_border_point_in_proximity(self, deriv_img, p1, p2, search_line, binning_range=2, number_points=4):
"""we search for the border point in search direction by grey value diff"""
# determine pixels on box and search_line
search_pixel_list = self.__get_search_pixel_list(p1, p2, search_line)
val_spot = self.__get_maxima_list(deriv_img, search_pixel_list)
if not len(val_spot) >= number_points:
return []
# sort by gradient and return n max vals
unique_maxima = self.__get_unqiue_maxima(val_spot, binning_range)
return unique_maxima
def __get_border_point_in_proximity_of_point(self, deriv_img, p1, search_line, search_range=(-5, 5),
number_points=1):
"""we search for the border point in search direction by grey value diff"""
# determine pixels on box and search_line
search_pixel_list = self.__get_search_pixel_list_point_range(p1, search_line, search_range)
val_spot = self.__get_maxima_list(deriv_img, search_pixel_list)
if not len(val_spot) >= number_points:
return []
# sort by gradient and return n max vals
# unique_maxima = self.__get_unqiue_maxima(val_spot, self.__calc_maxima_binning_range(p1, p2))
vs_sorted = sorted(val_spot, key=lambda cur_var: cur_var[0])
vs_sorted = [x for x in reversed(vs_sorted)]
return vs_sorted[:number_points]
def __calc_search_line(self, ps):
"""calc dir in which we will search the border point"""
uvs = np.array([self.__transform_and_back_project(p) for p in ps])
# now we perform a pca to get the least square solution
# of the line fitting problem http://sebastianraschka.com/Articles/2014_pca_step_by_step.html
mean_uv = np.mean(uvs, 0)
assert (len(mean_uv) == 2)
cov_mat = np.cov(uvs.T)
assert (cov_mat.shape == (2, 2))
eig_val, eig_vec = np.linalg.eig(cov_mat)
# Sort the (eigenvalue, eigenvector) tuples from low to high
low_val, normal = sorted(zip(np.abs(eig_val), eig_vec))[0]
normal /= np.linalg.norm(normal)
direction = np.array([-normal[1], normal[0]])
return direction, mean_uv
@staticmethod
def __get_search_pixel_list(p1, p2, line):
"""get search interval from p1 to p2 on line is given as normal and distance in image coordinates"""
direction, p0 = line
direction /= np.linalg.norm(direction)
# get line vars according to extremities of box
s1 = int(np.dot(p1 - p0, direction))
s2 = int(np.dot(p2 - p0, direction))
# define a search range
search_range = range(s1, s2)
if not search_range:
search_range = range(s2, s1)
# calc corresponding pixels
out = [direction * i + p0 for i in search_range]
return out
@staticmethod
def __get_search_pixel_list_point_range(p1, line, range_minmax=(-5, 5)):
"""get search interval from p1 to p2 on line is given as normal and distance in image coordinates"""
direction, p0 = line
direction /= np.linalg.norm(direction)
# get line vars according to extremities of box
s = np.dot(p1 - p0, direction)
s1 = int(s + range_minmax[0])
s2 = int(s + range_minmax[1])
# define a search range
search_range = range(s1, s2)
if not search_range:
search_range = range(s2, s1)
# calc corresponding pixels
out = [direction * i + p0 for i in search_range]
return out
@staticmethod
def set_from_list(l, conversion_func=lambda p: p):
"""converts each element of list with conversion_func and generates a set from it"""
return {conversion_func(x) for x in l}
# @staticmethod
# def __get_unqiue_maxima(maxima, val_spot, proximity_range=10):
# """go through array and delete all maxima that are in proximity range -> binning"""
# unique_maxima = []
# for val, cur_var in maxima:
# diff = np.array([np.linalg.norm(np.array(cur_var) - np.array(cur_var)) for _, cur_var in maxima])
#
# # get points that are in the same range
# is_in_range = diff < proximity_range
# same_range = []
# for ir, vs in zip(is_in_range, val_spot):
# if ir:
# same_range.append(vs)
#
# # get maximum
# unique_maxima.append(max(same_range, key=lambda cur_var: cur_var[0]))
# # get rid of double values
# unique_maxima = list(set(unique_maxima))
#
# # fill with new values that are not in proximity
# while len(unique_maxima)<len(maxima):
# unique_maxima
# return unique_maxima
def __calc_border_projection(self, segment):
"""get projections of all points of interest assumes that scan is ordered"""
assert (len(segment) == 5)
pois = [segment[0][-1], segment[2][0], segment[2][-1], segment[4][0]]
return [self.__transform_and_back_project(x) for x in pois]
@staticmethod
def __get_unqiue_maxima(val_spot, proximity_range=10, number_elements=4):
"""get binned maxima"""
unique_maxima = []
vs_sorted = sorted(val_spot, key=lambda cur_var: cur_var[0])
for v, p in reversed(vs_sorted):
# test if cur_var is in range
diff = np.array([np.linalg.norm(np.array(p) - np.array(x)) for _, x in unique_maxima])
is_in_range = diff < proximity_range
# if non of them is add to maxima
if not any(is_in_range):
unique_maxima.append((v, p))
# break if we have enough elements
if len(unique_maxima) == number_elements:
break
if not len(unique_maxima) == number_elements:
return []
return unique_maxima
@staticmethod
def __calc_back_projection_errors(back_projections, border_points_image, line_projection=[]):
"""calculate back projection error"""
# calc error for both and take minimum since it is possible that points are not in the same order
# sort by v alternatively we can sort by line if the image is tilted too much
ordered_points = sorted(border_points_image, key=lambda x: x[0])
ordered_points_rev = [p for p in reversed(ordered_points)]
res1 = np.array(back_projections) - np.array(ordered_points)
res2 = np.array(back_projections) - np.array(ordered_points_rev)
if not line_projection:
bp_errors1 = np.linalg.norm(res1, axis=1)
bp_errors2 = np.linalg.norm(res2, axis=1)
else:
direction, p0 = line_projection
bp_errors1 = abs(np.dot(res1, direction))
bp_errors2 = abs(np.dot(res2, direction))
err_vec = min([bp_errors1, bp_errors2], key=lambda x: np.sum(x))
return err_vec
def __calc_gradient_image(self, img, image_depth=cv2.CV_8UC1):
deriv_img = cv2.GaussianBlur(img, self.__gauss_kernel_size, self.__gauss_sigma)
sx = cv2.Sobel(deriv_img, image_depth, 1, 0, ksize=self.__ksize_gradient)
sy = cv2.Sobel(deriv_img, image_depth, 0, 1, ksize=self.__ksize_gradient)
self.__deriv_img = cv2.sqrt(cv2.add(cv2.pow(sx, 2), cv2.pow(sy, 2)))
# self.__deriv_img = cv2.Laplacian(deriv_img, image_depth, ksize=self.__ksize_gradient)
return self.__deriv_img
def __get_gradient_image(self):
return self.__deriv_img
def __calc_maxima_binning_range(self, p1, p2):
"""returns the range in which a second maxima is not allowed, shall be proportional to total search length"""
length_line = np.linalg.norm(p2 - p1)
return int(length_line * self.__maxima_binning_percentage)
def calc_mean_depth_segments(segment):
depths = [np.linalg.norm(segment[0], axis=1), np.linalg.norm(segment[2], axis=1),
np.linalg.norm(segment[4], axis=1)]
mean = np.mean([d.mean() for d in depths])
return mean
def plot_results(result_in):
fig = plt.figure()
ax = fig.gca()
plt.xlabel("distance to object in meters")
plt.ylabel("back-projection error in pixels")
result = np.array([p[0] for p in result_in])
y = np.concatenate([result[:, 0], result[:, 1], result[:, 2], result[:, 3]])
depths = np.array([p[1] for p in result_in])
depths = np.concatenate([depths, depths, depths, depths])
plt.legend(loc="best")
PlotHandle = plt.plot(depths, y, ".b")
plot_mean_standard_dev_bins(depths, y, 10, 3)
# plt.show()