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3d_accuracy.py
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import numpy as np
import math
import cv2
import sys
import copy
import csv
import pandas as pd
from itertools import combinations
import time
import tkinter as tk
import tkinter.ttk
# from tkinter import *
from tkinter import filedialog
from tkinter import messagebox
# 12 GT점
# 548.0606, -0.9230, 202.5364, C
# 548.1446, -125.9743, 202.7360, N
# 548.1358, 123.9789, 202.4794,
# 658.2693, -0.8927, 203.8425, C
# 658.2218, -157.4343, 204.1222, N
# 658.4669, 155.4454, 203.8754,
# 768.5166, -0.9942, 205.2305, C
# 768.3877, -187.8320, 205.2665, N
# 768.5165, 186.0766, 205.2247, P
# 998.9362, -0.9115, 207.5076, C
# 999.4603, -223.7147, 208.0781, N(L)
# 998.7845, 222.0334, 207.5687 P(R)
#Automatic virtual position recovery using relative coordinates
# 최대 줄 수 설정
pd.set_option('display.max_rows', 1000)
# 최대 열 수 설정
pd.set_option('display.max_columns', 50)
# 표시할 가로의 길이
pd.set_option('display.width', 140)
# 출력값 소숫점4자리로 설정
pd.options.display.float_format = '{:.4f}'.format
C_TAB1 = 0
C_TAB2 = 1
C_TAB3 = 2
C_TAB4 = 3
C_TAB5 = 4
C_MAX_GAP = 2 # 중복좌표 체크 범위 (단위 mm)
C_MIN_VALUE_RIGID_CALC = 3
C_PRINT_ENABLE = 0
degreeToRadian = math.pi/180
radianToDegree = 180/math.pi
def isRotationMatrix(R):
Rt = np.transpose(R)
shouldBeIdentity = np.dot(Rt, R)
I = np.identity(3, dtype=R.dtype)
n = np.linalg.norm(I - shouldBeIdentity)
return n < 1e-6
# Calculates rotation matrix to euler angles
# The result is the same as MATLAB except the order
# of the euler angles ( x and z are swapped ).
def rotationMatrixToEulerAngles(R):
assert (isRotationMatrix(R))
# sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])
sy = math.sqrt(R[2, 1] * R[2, 1] + R[2, 2] * R[2, 2])
singular = sy < 1e-6
if not singular:
x = math.atan2(R[2, 1], R[2, 2])
y = math.atan2(-R[2, 0], sy)
z = math.atan2(R[1, 0], R[0, 0])
else:
x = math.atan2(-R[1, 2], R[1, 1])
y = math.atan2(-R[2, 0], sy)
z = 0
return np.array([ x, y, z])
# Calculates Rotation Matrix given euler angles.
def eulerAnglesToRotationMatrix(theta):
R_x = np.array([[1, 0, 0],
[0, math.cos(theta[0]), -math.sin(theta[0])],
[0, math.sin(theta[0]), math.cos(theta[0])]
])
R_y = np.array([[math.cos(theta[1]), 0, math.sin(theta[1])],
[0, 1, 0],
[-math.sin(theta[1]), 0, math.cos(theta[1])]
])
R_z = np.array([[math.cos(theta[2]), -math.sin(theta[2]), 0],
[math.sin(theta[2]), math.cos(theta[2]), 0],
[0, 0, 1]
])
R = np.dot(R_z, np.dot(R_y, R_x))
return R
def funcname():
return sys._getframe(1).f_code.co_name + "()"
def callername():
return sys._getframe(2).f_code.co_name + "()"
# a= np.array((0.00 ,0.00 ,0.00))
# b= np.array((3.00 ,0.00 ,0.00))
# c= np.array((3.00 ,3.00 ,0.00))
# d= np.array((0.00 ,3.00 ,0.00))
#
# e= np.array(( 9.00 ,3.00 ,0.00))
# f= np.array((18.00 ,6.00 ,0.00))
# g= np.array(( 6.00 ,21.00 ,0.00))
# print(math.acos(1/1.414) * 180/math.pi)
# h= b-a
# i= c-a
# j= d-a
# print('h',h)
# print('i',i)
# print('j',j)
# print(np.dot(h, i))
# print(np.cross(h, i))
# print(np.dot(h, j))
# print(np.cross(h, j))
# test_cross = np.cross(h, j)
# # 0 = dot product with cross vector using every vector in char
# print('%.10f'% np.dot(test_cross, a-e))
# print('%.10f'% np.dot(test_cross, b-a))
# print('%.10f'% np.dot(test_cross, c-a))
# print('%.10f'% np.dot(test_cross, d-a))
# print('%.10f'% np.dot(test_cross, e-a))
# print('%.10f'% np.dot(test_cross, f-a))
# print('%.10f'% np.dot(test_cross, g-a))
# print('%.10f'% np.dot(test_cross, f-e))
# print('%.10f'% np.dot(test_cross, g-c))
#
# print('각도',math.acos(np.dot(test_cross, a)) * 180 / math.pi)
# print('jawha chart','*'*50)
#
# a= np.array((0.079311 ,0.174912 ,0.755718))
# b= np.array((0.049689 ,0.175313 ,0.750517))
# c= np.array((0.049247 ,0.145295 ,0.750445))
# d= np.array((0.07886 ,0.144896, 0.75574))
#
# e= np.array((0.04876 ,0.115213 ,0.750144))
# f= np.array((0.048259 ,0.085176 ,0.749968))
# g= np.array((-0.159897 ,-0.001663 ,0.714354))
# print(math.acos(1/1.414) * 180/math.pi)
# h= b-a
# i= c-a
# j= d-a
# print('h',h)
# print('i',i)
# print('j',j)
# print(np.dot(h, i))
# print(np.cross(h, i))
# print(np.dot(h, j))
# print(np.cross(h, j))
# test_cross = np.cross(h, j)
# # 0 = dot product with cross vector using every vector in char
# print('%.10f'% np.dot(test_cross, a-e))
# print('%.10f'% np.dot(test_cross, b-a))
# print('%.10f'% np.dot(test_cross, c-a))
# print('%.10f'% np.dot(test_cross, d-a))
# print('%.10f'% np.dot(test_cross, e-a))
# print('%.10f'% np.dot(test_cross, f-a))
# print('%.10f'% np.dot(test_cross, g-a))
# print('%.10f'% np.dot(test_cross, f-e))
# print('%.10f'% np.dot(test_cross, g-c))
# print('각도',math.acos(np.dot(test_cross, a)) * 180 / math.pi)
# print('*'*50)
#
#
# print('sm chart - objectpoint3D_1.txt','*'*50)
#
# a= np.array((0.070856, -0.141501, 0.746554))
# b= np.array((0.082736, -0.139778, 0.746300))
# c= np.array((0.081050, -0.129107, 0.751628))
# d= np.array((0.069249, -0.130671, 0.751461))
#
# e= np.array((0.134306, -0.001547, 0.811589))
# f= np.array((0.176706, -0.039356, 0.791868))
# g= np.array((0.115216, -0.113509, 0.758401))
# h= b-a
# i= c-a
# j= d-a
# print('h',h)
# print('i',i)
# print('j',j)
# print(np.dot(h, i))
# print(np.cross(h, i))
# print(np.dot(h, j))
# print(np.cross(h, j))
# test_cross = np.cross(h, j)
# # 0 = dot product with cross vector using every vector in char
# print('%.10f'% np.dot(test_cross, a-e))
# print('%.10f'% np.dot(test_cross, b-a))
# print('%.10f'% np.dot(test_cross, c-a))
# print('%.10f'% np.dot(test_cross, d-a))
# print('%.10f'% np.dot(test_cross, e-a))
# print('%.10f'% np.dot(test_cross, f-a))
# print('%.10f'% np.dot(test_cross, g-a))
# print('%.10f'% np.dot(test_cross, f-e))
# print('%.10f'% np.dot(test_cross, g-c))
# print('각도',math.acos(np.dot(test_cross, a)) * 180 / math.pi)
# print('*'*50)
#
# print('9x9 circle grid chart - objectpoint3D_2.txt','*'*50)
#
# a= np.array((-0.059502, -0.155606, 0.853068))
# b= np.array((-0.029504, -0.155477, 0.853454))
# c= np.array((-0.029614, -0.125439, 0.853789))
# d= np.array((-0.059638, -0.125603, 0.853702))
#
# e= np.array((0.119562, 0.054919, 0.859973))
# f= np.array((0.059799, -0.005236, 0.857721))
# g= np.array((0.154781, -0.156280, 0.859369))
# h= b-a
# i= c-a
# j= d-a
# print('h',h)
# print('i',i)
# print('j',j)
# print(np.dot(h, i))
# print(np.cross(h, i))
# print(np.dot(h, j))
# print(np.cross(h, j))
# test_cross = np.cross(h, j)
# # 0 = dot product with cross vector using every vector in char
# print('%.10f'% np.dot(test_cross, a-e))
# print('%.10f'% np.dot(test_cross, b-a))
# print('%.10f'% np.dot(test_cross, c-a))
# print('%.10f'% np.dot(test_cross, d-a))
# print('%.10f'% np.dot(test_cross, e-a))
# print('%.10f'% np.dot(test_cross, f-a))
# print('%.10f'% np.dot(test_cross, g-a))
# print('%.10f'% np.dot(test_cross, f-e))
# print('%.10f'% np.dot(test_cross, g-c))
# print('각도',math.acos(np.dot(test_cross, a)) * 180 / math.pi)
# print('*'*50)
#
# print('projection plane - 9x9 circle grid chart - objectpoint3D_2.txt','*'*50)
#
# a= np.array((0.0607760881, -0.0301225983, 0.8439268045))
# b= np.array((0.0906037743, -0.0300211148, 0.8443397305))
# c= np.array((0.0904271574, -0.0002437271, 0.8448646253))
# d= np.array((0.0606141817, -0.0003490928, 0.8444518333))
#
# e= np.array((0.1496979683, 0.0594596900, 0.8467388733))
# f= np.array((0.0305134170, 0.0590399745, 0.8450886644))
# g= np.array((0.0899975801, 0.0890212626, 0.8464395024))
# h= b-a
# i= c-a
# j= d-a
# print('h',h)
# print('i',i)
# print('j',j)
# print(np.dot(h, i))
# print(np.cross(h, i))
# print(np.dot(h, j))
# print(np.cross(h, j))
# test_cross = np.cross(h, j)
# # 0 = dot product with cross vector using every vector in char
# print('%.10f'% np.dot(test_cross, a-e))
# print('%.10f'% np.dot(test_cross, b-a))
# print('%.10f'% np.dot(test_cross, c-a))
# print('%.10f'% np.dot(test_cross, d-a))
# print('%.10f'% np.dot(test_cross, e-a))
# print('%.10f'% np.dot(test_cross, f-a))
# print('%.10f'% np.dot(test_cross, g-a))
# print('%.10f'% np.dot(test_cross, f-e))
# print('%.10f'% np.dot(test_cross, g-c))
# print('각도',math.acos(np.dot(test_cross, a)) * 180 / math.pi)
# print('*'*50)
#
#
# print('projection plane - sm chart - objectpoint3D_1.txt','*'*50)
#
# a= np.array((0.0708468880, -0.1413858742, 0.7463172135))
# b= np.array((0.0827462198, -0.1399074094, 0.7465671366))
# c= np.array((0.0810573100, -0.1292012879, 0.7518220414))
# d= np.array((0.0692524020, -0.1307146590, 0.7515514985))
#
# e= np.array((0.1152056104, -0.1133864644, 0.7581475720))
# f= np.array((0.2085738696, -0.0893623254, 0.7661295196))
# g= np.array((0.0695950514, -0.0543322026, 0.7885565041))
# h= b-a
# i= c-a
# j= d-a
# print('h',h)
# print('i',i)
# print('j',j)
# print(np.dot(h, i))
# print(np.cross(h, i))
# print(np.dot(h, j))
# print(np.cross(h, j))
# test_cross = np.cross(h, j)
# # 0 = dot product with cross vector using every vector in char
# print('%.10f'% np.dot(test_cross, a-e))
# print('%.10f'% np.dot(test_cross, b-a))
# print('%.10f'% np.dot(test_cross, c-a))
# print('%.10f'% np.dot(test_cross, d-a))
# print('%.10f'% np.dot(test_cross, e-a))
# print('%.10f'% np.dot(test_cross, f-a))
# print('%.10f'% np.dot(test_cross, g-a))
# print('%.10f'% np.dot(test_cross, f-e))
# print('%.10f'% np.dot(test_cross, g-c))
# print('각도',math.acos(np.dot(test_cross, a)) * 180 / math.pi)
# print('*'*50)
def m_ProjectUserCoor(input_point, ref):
print("//////////", funcname(), "//////////")
# t_mean = np.mean(ref, axis= 0);
t_mean = np.mean((ref[0,:],ref[1,:],ref[3,:],ref[4,:]), axis=0)
# AE = t_mean - ref[0,:]
# BE = ref[1,:] - t_mean;
#
# XAxis = np.cross(AE, BE);
# YAxis = np.cross(AE, XAxis);
# ZAxis = np.cross(XAxis, YAxis);
P0 = ref[0,:] - t_mean
P1 = ref[1,:] - t_mean
YAxis = np.cross(P0, P1);
ZAxis = np.cross(P0, YAxis);
XAxis = np.cross(YAxis, ZAxis);
XAxis = XAxis / np.linalg.norm(XAxis);
YAxis = YAxis / np.linalg.norm(YAxis);
ZAxis = ZAxis / np.linalg.norm(ZAxis);
t_axis = np.eye(3)
t_axis[:, 0] = XAxis
t_axis[:, 1] = YAxis
t_axis[:, 2] = ZAxis
print('mean', t_mean, '\nt_axis', t_axis, sep='\n')
print(rotationMatrixToEulerAngles(t_axis) * radianToDegree)
# print(eulerAnglesToRotationMatrix(rotationMatrixToEulerAngles(t_axis)) )
print(cv2.Rodrigues(t_axis)[0] * radianToDegree)
# print(cv2.Rodrigues(cv2.Rodrigues(t_axis)[0]) )
# output_point = (input_point - np.tile(t_mean, (input_point.shape[0],1))) * t_axis
output_point = (input_point - np.tile(t_mean, (input_point.shape[0],1))) * t_axis
if (C_PRINT_ENABLE):
print('ret',*output_point, sep='\n')
return output_point, t_mean, t_axis
def m_ProjectAbsCoor(P_DPA_Pts, P_DPA_Ref1):
print("//////////", funcname(), "//////////")
# P_DPA_Pts = np.asmatrix([[391.139000000000, - 1399.38500000000, 706.558000000000],
# [318.651000000000, - 1399.80100000000, 481.105000000000],
# [183.801000000000, - 1399.43800000000, 525.289000000000],
# [203.573000000000, - 1459.04200000000, 645.357000000000],
# [259.687000000000, - 1399.01000000000, 749.782000000000]])
#
# P_DPA_Ref1 = np.asmatrix([[949.852000000000, - 51.6980000000000, 731.117000000000],
# [727.499000000000, - 53.3340000000000, 42.3240000000000],
# [451.291000000000, - 53.0250000000000, 133.457000000000],
# [646.216000000000, - 51.4270000000000, 831.055000000000]])
Centroid, Axis = m_MakeAbsAxis(P_DPA_Ref1);
# print(P_DPA_Pts, np.tile(Centroid, (P_DPA_Pts.shape[0],1)),'\n',Axis )
P_Ref1_Pts = (P_DPA_Pts - np.tile(Centroid, (P_DPA_Pts.shape[0],1))) * Axis
if (C_PRINT_ENABLE):
print('ret',*P_Ref1_Pts, sep='\n')
# P_Ref1_Pts =
# 1347.73296211896 404.477604521447 - 5.85905452814663
# 1347.61341605657 311.995327466233 212.156432418129
# 1347.38468857793 442.937470819739 266.846311762761
# 1407.27742564632 506.339726820845 163.150085194833
# 1347.48922525842 532.220488679192 47.3366148858555
# np.tile(T, (1, P_Ref.shape[0]))
return P_Ref1_Pts
def m_ProjectDispCoor(P_Ref1_Pts, P_Ref1_Disp):
print("//////////", funcname(), "//////////")
# P_Ref1_Pts = np.asmatrix(
# [[1261.29623616261, 239.985317917590, 51.5313160178364],
# [1261.53702655582, 154.082122583680, 257.056595465577],
# [1261.50379097605, 325.570613718301, - 154.102117931721],
# [1031.00674552654, 239.708176629555, 51.3230778612638],
# [1031.10349410293, 167.687936469370, 223.972390869635],
# [1030.97732105302, 311.481917956706, - 121.116009669177],
# [930.758616411388, 239.605145729361, 51.2910088561620],
# [930.840679549455, 179.348376193649, 195.831057431068],
# [930.703960598259, 299.751880130932, - 93.1282020090007],
# [820.687625912447, 239.565695359699, 51.3331018939299],
# [820.678623657056, 191.501615649785, 166.651377890795],
# [820.585820231081, 287.700179385780, - 64.0824243331095]]
# )
#
# P_Ref1_Disp = np.asmatrix(
# [[261.0658, 430.6803, - 15.0083],
# [261.1287, 326.2123, 234.2476],
# [260.3185, 407.3123, 268.2431],
# [260.2502, 511.8065, 18.9925],
# [260.6193, 418.9697, 126.6069]]
# )
Centroid, Axis = m_MakeDispAxis(P_Ref1_Disp);
# Centroid = np.asmatrix([260.6765, 418.9962, 126.6164])
# Axis = np.asmatrix(
# [[0.999957259960338, 2.426512344112703e-04, 0.009242267266263],
# [0.008617504541717, -0.386586756527017, -0.922224100117558],
# [0.003349159310558, 0.922253013439533, -0.386545326783471]]
# )
P_Disp = (P_Ref1_Pts - np.tile(Centroid, (P_Ref1_Pts.shape[0],1))) * Axis
if (C_PRINT_ENABLE):
print('ret',P_Disp)
return P_Disp, Centroid, Axis
def rigid_transform_3D(A, B):
print("//////////",funcname(),"//////////")
assert len(A) == len(B)
N = A.shape[0]; # total points
centroid_A = np.mean(A, axis=0)
centroid_B = np.mean(B, axis=0)
# centre the points
AA = A - np.tile(centroid_A, (N, 1))
BB = B - np.tile(centroid_B, (N, 1))
# dot is matrix multiplication for array
H = np.transpose(AA) * BB
U, S, Vt = np.linalg.svd(H)
R = Vt.T * U.T
# special reflection case
if np.linalg.det(R) < 0:
print("Reflection detected")
Vt[2, :] *= -1
R = Vt.T * U.T
t = -R * centroid_A.T + centroid_B.T
# print(t)
return R, t
def m_findTransformedPoints(P_Ref, P_Input, P_Target):
print("//////////",funcname(),"//////////")
# P_Ref = np.asmatrix(
# [[1206.58800000000, - 52.3840000000000, 845.141000000000],
# [869.521000000000, - 44.0830000000000, - 206.674000000000],
# [-25.2610000000000, - 59.8710000000000, 56.7040000000000],
# [308.079000000000, - 68.3820000000000, 1105.31600000000]]
# )
#
# P_Input = np.asmatrix(
# [[575.621000000000, - 53.3910000000000, 347.671000000000],
# [1296.75700000000, - 46.9340000000000, - 489.023000000000],
# [604.683000000000, - 44.9410000000000, - 1114.64600000000],
# [-115.650000000000, - 51.2140000000000, - 282.811000000000]]
# )
#
# P_Target = np.asmatrix([[943.3570, - 54.7900, 731.0520],
# [727.7780, - 48.9640, 40.1490],
# [450.7460, - 53.8990, 128.5110],
# [638.8010, - 60.2540, 827.9780]])
# print('P_Input.shape[0],P_Ref.shape[0]', P_Input.shape[0],P_Ref.shape[0] )
n_loop = P_Input.shape[0] / P_Ref.shape[0]
# print(n_loop, P_Input.shape[0],P_Ref.shape[0] )
# n_Ref = P_Ref.shape[0]
P_Transformed = [];
for i in range(int(n_loop)):
# [R, T] = rigid_transform_3D(P_Ref', P_Input(n_Ref*(i-1)+1:n_Ref*i,:)');
# R = np.asmatrix([[0.522094256732165, 0.015812569675465, -0.852741197390830],
# [-0.017819784736575, 0.999812105327660, 0.007629502750195],
# [0.852701613906782, 0.011212345005914, 0.522277934590614]])
#
# T = np.asmatrix([6.671829005434147e+02, 14.124131785083954, -1.122026213358952e+03])
R, T = rigid_transform_3D(P_Ref, P_Input)
print('R',*R, 'T',*T , sep='\n')
# print(P_Target, P_Target.shape)
# P_Transformed = [P_Transformed;(R * P_Target'+T)'];
P_Transformed = (R * P_Target.T + T).T
# P_Transformed = (R * P_Target.T) + np.tile(T, (1, P_Ref.shape[0]))
# P_Transformed = P_Transformed.T
# P_Transformed = (R * P_Target[0].T + T.T )
# P_Transformed[1] = (R * P_Target[1].T + T.T )
# P_Transformed[2] = (R * P_Target[2].T + T.T )
if(C_PRINT_ENABLE):
print('ret', *P_Transformed, sep='\n')
# print('ret', P_Transformed)
# P_Transformed =
# 535.439643764020, - 51.8884288910241, 63.5738272867725
# 1012.14066152380, - 47.4931985302768, - 481.028802658092
# 792.075892677580, - 46.8164625429134, - 671.160246228224
# 293.693512131688, - 51.1847826907368, - 145.560518603204
return P_Transformed, R, T
# OK
def m_MakeAbsAxis(P_DPA_Ref1):
print("//////////",funcname(),"//////////")
# P_DPA_Ref1 = np.array([[949.852000000000, - 51.6980000000000, 731.117000000000],
# [727.499000000000, - 53.3340000000000, 42.3240000000000],
# [451.291000000000, - 53.0250000000000, 133.457000000000],
# [646.216000000000, - 51.4270000000000, 831.055000000000]])
centroid = np.mean(P_DPA_Ref1, axis= 0);
AE = centroid - P_DPA_Ref1[0,:]
# -256.137500000000 -0.673000000000002 -296.628750000000
BE = P_DPA_Ref1[1,:] - centroid;
XAxis = np.cross(AE, BE);
YAxis = np.cross(AE, XAxis);
ZAxis = np.cross(XAxis, YAxis);
XAxis = XAxis / np.linalg.norm(XAxis);
YAxis = YAxis / np.linalg.norm(YAxis);
ZAxis = ZAxis / np.linalg.norm(ZAxis);
axis = np.eye(3)
axis[:, 0] = XAxis
axis[:, 1] = YAxis
axis[:, 2] = ZAxis
print('mean', centroid, '\naxis', axis, sep='\n')
# centroid 693.714500 -52.371000 434.488250
# XAxis -0.000197 -0.999997 0.002439
# YAxis -0.756877 0.001743 0.653555
# ZAxis -0.653558 -0.001717 -0.756875
return centroid, axis
def m_MakeDispAxis(P_Ref1_Disp):
print("//////////",funcname(),"//////////")
centroid = np.mean(P_Ref1_Disp, axis= 0)
L_C = (P_Ref1_Disp[0,:] + P_Ref1_Disp[3,:]) / 2
R_C = (P_Ref1_Disp[1,:] + P_Ref1_Disp[2,:]) / 2
C_T = (P_Ref1_Disp[0,:] + P_Ref1_Disp[1,:]) / 2
C_B = (P_Ref1_Disp[2,:] + P_Ref1_Disp[3,:]) / 2
YAxis = R_C - L_C
ZAxis = C_T - C_B
XAxis = np.cross(YAxis, ZAxis)
XAxis = XAxis / np.linalg.norm(XAxis)
YAxis = YAxis / np.linalg.norm(YAxis)
ZAxis = ZAxis / np.linalg.norm(ZAxis)
axis = np.eye(3)
axis[:, 0] = XAxis
axis[:, 1] = YAxis
axis[:, 2] = ZAxis
print('mean', centroid, '\naxis', axis, sep='\n')
return centroid, axis
# Centroid = np.asmatrix([260.6765, 418.9962, 126.6164])
# Axis = np.asmatrix(
# [[0.999957259960338, 2.426512344112703e-04, 0.009242267266263],
# [0.008617504541717, -0.386586756527017, -0.922224100117558],
# [0.003349159310558, 0.922253013439533, -0.386545326783471]]
# )
# a = np.random.randn(9, 6) + 1j * np.random.randn(9, 6)
# b = np.random.randn(2, 7, 8, 3) + 1j * np.random.randn(2, 7, 8, 3)
# u, s, vh = np.linalg.svd(a, full_matrices=True)
#
# print('\na', a,a.shape)
# print(u.shape, s.shape, vh.shape)
# print('\nu', u, '\ns', s, '\nvh', vh)
#
# print(np.allclose(a, np.dot(u[:, :6] * s, vh)))
#
# smat = np.zeros((9, 6), dtype=complex)
# smat[:6, :6] = np.diag(s)
# print(np.allclose(a, np.dot(u, np.dot(smat, vh))))
def relative_position_based_on_refer_point():
pMane_Eye_disp = np.asmatrix(
[[943.3565, -54.7895, 731.0518], # "ABS1_P1"
[727.7784, -48.9640, 40.1491], # "ABS1_P2"
[450.7456, -53.8988, 128.5115], # "ABS1_P3"
[638.8006, -60.2539, 827.9783], # "ABS1_P4"
[1206.5884, -52.3844, 845.1411], # "ABS2_P1"
[869.5210, -44.0835, -206.6740], # "ABS2_P2"
[-25.2612, -59.8706, 56.7044], # "ABS2_P3"
[308.0794, -68.3821, 1105.3155], # "ABS2_P4"
[413.1723, -323.2010, 748.0892], # "DISP_P1"
[308.8122, -320.1825, 417.1731], # "DISP_P2"
[189.7155, -320.8003, 455.8135], # "DISP_P3"
[240.2499, -322.3313, 631.7363], # "DISP_P4"
[293.9572, -323.7673, 785.1393], # "DISP_P5"
[479.4774, -1096.2672, 505.8886], # "EYE_L",
[497.3375, -1097.3506, 562.5359], # "EYE_R",
[400.4670, -1144.7768, 683.2921], # "MANE_P1"
[330.2323, -1142.8242, 457.1621], # "MANE_P2"
[194.9668, -1145.1624, 499.9952], # "MANE_P3"
[214.4585, -1206.3375, 619.2865], # "MANE_P4"
[268.6216, -1147.1164, 725.2041]] # "MANE_P5"
)
# print(pMane_Eye_disp[4:8]) #ABS2
# print(pMane_Eye_disp[8:13]) #DISP
# print(pMane_Eye_disp[13:15]) #EYE
# print(pMane_Eye_disp[16:19]) #MANE_2_3_4
pPattern_disp = np.asmatrix(
[[ 94.1548, -136.3431, -117.7738],#"DISP_P1",
[100.1195, -141.4383, -464.7347], #"DISP_P2",
[ -4.8633, -209.6786, -464.4936],#"DISP_P3",
[-12.0288, -209.4645, -281.5658], #"DISP_P4",
[-10.4274, -204.4850, -119.0935], #"DISP_P5",
[ 74.1477, -148.8633, -156.4257], #"PATTERN_P1"
[ 78.1896, -153.0675, -426.6260], #"PATTERN_P2"
[ 4.4950, -201.0521, -426.9864], #"PATTERN_P3"
[ 0.4278, -196.8570, -156.7813], #"PATTERN_P4"
[39.3048, -174.8807, -291.7078]] #"PATTERN_P5"
)
# print(pPattern_disp[0:5]) #DISP
# print(pPattern_disp[5:10]) #PATTERN
p550N_name= np.asmatrix(
[[ 575.5138, -53.6211, 347.2357 ], #"ABS_P1",
[1296.9180, -45.9604, -489.1645 ], #"ABS_P2",
[ 605.0637, -43.4795, -1115.0599 ], #"ABS_P3",
[-115.5342, -50.8216, -283.4880 ], #"ABS_P4",
[ 237.6775, -311.2039, -388.146455 ], #"DISP_P1",
[ 465.7575, -308.9572, -649.6110 ], #"DISP_P2",
[ 370.7342, -307.0648, -731.1023 ], #"DISP_P3",
[ 246.9093, -308.1309, -596.3134 ], #"DISP_P4",
[ 143.9568, -309.2506, -470.5816 ], #"DISP_P5",
[ 349.9752, -903.3901, -519.6430 ], #"MANE_P2",
[ 242.8371, -903.0786, -612.6906 ], #"MANE_P3",
[ 150.3462, -963.7766, -534.5120 ]] #"MANE_P4"
)
# print(p550N_name[0:4]) #ABS
# print(p550N_name[4:9]) #DISP
# print(p550N_name[9:12]) #MANE_2_3_4
# MANE_2_3_4 #MANE_2_3_4 #EYE
p550N_Eye, rr, tt = m_findTransformedPoints(pMane_Eye_disp[16:19], p550N_name[9:12], pMane_Eye_disp[13:15] )
print('p550N_Eye\n', p550N_Eye)
#DISP #DISP #PATTERN
p550N_Pattern, rr, tt = m_findTransformedPoints(pPattern_disp[0:5] , p550N_name[4:9], pPattern_disp[5:10] )
print('p550N_Pattern\n', p550N_Pattern)
m_ProjectDispCoor(p550N_Eye, p550N_Pattern)
m_ProjectDispCoor(np.mean(p550N_Eye, axis=0), p550N_Pattern)
return
def relative_position_based_on_many_points():
print("relative_position_based_on_many_points\n")
# m_MakeAbsAxis(1)
# m_findTransformedPoints(1,1,1)
# rigid_transform_3D(1,1)
# m_ProjectAbsCoor(1,1)
# m_ProjectDispCoor(1,1)
p660P_Full = np.asmatrix(
[[1046.714, -510.129, -1106.304 ],
[279.674, 2.586, 200.757 ],
[757.788, -93.557, -665.898 ],
[1083.243, -95.258, -356.687 ],
[381.708, -99.336, -132.29 ],
[1199.754, -628.631, -976.484 ],
[532.552, -93.484, -862.412 ],
[424.541, 2.715, 304.001 ],
[1013.188, -622.468, -1136.629 ],
[635.433, -99.613, 95.958 ],
[-0.148 , -0.918, -0.113 ],
[172.003, -0.157, -175.397 ],
[170.573, -1.146, -1.093 ],
[346.334, -0.113, -0.086 ],
[169.74 , -0.87, 175.04 ],
[1148.348, -21.663, -460.659 ],
[676.332, -26.053, -1093.847 ],
[488.76 , -402.101, 438.949 ],
[-115.618, -52.152, -283.089 ],
[575.677, -53.408, 347.341 ],
[1296.735, -44.493, -489.345 ],
[604.62 , -43.492, -1114.973 ],
[238.087, -311.84, -387.855],
[466.058, -309.238, -649.434],
[370.987, -307.535, -730.875],
[247.226, -308.814, -596.039],
[144.315, -310.087, -470.248],
[536.218, -1011.754, -732.754],
[429.053, -1011.639, -825.74],
[336.705, -1072.507, -747.525]])
p550N_Full = np.asmatrix(
[[1046.042, -511.207, -1106.107 ],
[279.699, 2.93, 200.531 ],
[757.938, -93.867, -665.943 ],
[1083.275, -96.31, -356.602 ],
[381.63, -99.014, -132.495 ],
[1198.847, -629.936, -976.362 ],
[532.802, -93.418, -862.55 ],
[424.51, 2.775, 303.84 ],
[1012.32, -623.404, -1136.485 ],
[635.274, -99.82, 95.876 ],
[-0.043, 0.052, -0.453 ],
[172.168, 0.587, -175.655 ],
[170.666, -0.437, -1.355 ],
[346.42, 0.152, -0.284 ],
[169.762, -0.278, 174.764 ],
[1148.593, -22.802, -460.521 ],
[676.842, -26.108, -1093.914 ],
[487.873, -402.049, 438.767 ],
[-115.534, -50.822, -283.488 ],
[575.514, -53.621, 347.236 ],
[1296.918, -45.96, -489.164 ],
[605.064, -43.479, -1115.06 ],
[237.678, -311.204, -388.146 ],
[465.757, -308.957, -649.611 ],
[370.734, -307.065, -731.102 ],
[246.909, -308.131, -596.313 ],
[143.957, -309.251, -470.582 ],
[349.975, -903.39, -519.643],
[242.837, -903.079, -612.691],
[150.346, -963.777, -534.512]])
p660P = np.asmatrix(
[[-0.148, -0.918, -0.113],
[172.003, -0.157, -175.397],
[170.573, -1.146, -1.093],
[346.334, -0.113, -0.086],
[169.74, -0.87, 175.04]]
)
p550N = np.asmatrix(
[[ -0.0430, 0.0524, -0.4531],
[172.1681, 0.5869, -175.6547],
[170.6656, -0.4372, -1.3547],
[346.4195, 0.1522, -0.2835],
[169.7624, -0.2780, 174.7639]]
)
p660P_base501 = np.asmatrix(
[[0, 0, 0],
[172.151, 0.761, -175.284],
[170.721, -0.228, -0.98],
[346.482, 0.805, 0.027],
[169.888, 0.048, 175.153]]
)
p660P_base503 = np.asmatrix(
[[-170.721, 0.228, 0.98],
[1.43, 0.989, -174.304],
[0, 0, 0],
[175.761, 1.033, 1.007],
[-0.833, 0.276, 176.133]]
)
# print(m_ProjectAbsCoor(p660P,p660P))
# print(m_ProjectAbsCoor(p550N,p550N))
primeP2, rr, tt = m_findTransformedPoints(p660P,p550N,p660P_Full)
primeP, rr, tt = m_findTransformedPoints(p550N,p660P,p550N_Full)
print('\n\n')
print('p550N_Full - primeP2', *(p550N_Full - primeP2), sep='\n')
print('p660P_Full - primeP', *(p660P_Full - primeP), sep='\n')
print('\n\n')
#
# primeP3, rr, tt = m_findTransformedPoints(p660P, p660P_base501, p660P)
#
# primeP4, rr, tt = m_findTransformedPoints(p660P, p660P_base503, p660P_Full)
print("기준점 계산")
P_DPA_Pts = np.asmatrix([[391.139000000000, - 1399.38500000000, 706.558000000000],
[318.651000000000, - 1399.80100000000, 481.105000000000],
[183.801000000000, - 1399.43800000000, 525.289000000000],
[203.573000000000, - 1459.04200000000, 645.357000000000],
[259.687000000000, - 1399.01000000000, 749.782000000000]])
P_DPA_Ref1 = np.asmatrix([[949.852000000000, - 51.6980000000000, 731.117000000000],
[727.499000000000, - 53.3340000000000, 42.3240000000000],
[451.291000000000, - 53.0250000000000, 133.457000000000],
[646.216000000000, - 51.4270000000000, 831.055000000000]])
# relative_position_based_on_refer_point()
#
# m_ProjectAbsCoor(P_DPA_Pts, P_DPA_Ref1)
print("********p660P_Full",*p660P_Full[10:15], sep='\n')
ret1 = m_ProjectAbsCoor(p660P_Full, p660P)
# m_ProjectAbsCoor(p660P_Full-np.asmatrix(np.ones((p660P_Full.shape[0],1))*[170.573,-1.146, -1.093]), p660P_base503)
ret2 = m_ProjectAbsCoor(p550N_Full, p550N)
print('ret1', ret1)
print('ret2', ret2)
print('Sub1', *(ret1 - ret2), sep='\n')
ret4 = m_ProjectAbsCoor(p660P_Full, p660P_Full[18:22])
ret5 = m_ProjectAbsCoor(p550N_Full, p550N_Full[18:22])
print('ret4', *ret4, sep='\n' )
print('ret5', *ret5, sep='\n')
print('Sub2', *(ret4 - ret5), sep='\n')
#recovery original position
ret6, rr, tt = m_findTransformedPoints(ret4[18:22], p660P_Full[18:22], ret4)
ret7, rr, tt = m_findTransformedPoints(ret5[18:22], p550N_Full[18:22], ret5)
print('ret6', *ret6, sep='\n' )
print('ret7', *ret7, sep='\n')
print('Sub3', *(ret6 - ret7), sep='\n')
def move_origin_from_refer_point():
print("move_origin_from_refer_point\n")
p660P_Full = np.asmatrix(
[[1046.714, -510.129, -1106.304 ],
[279.674, 2.586, 200.757 ],
[757.788, -93.557, -665.898 ],
[1083.243, -95.258, -356.687 ],
[381.708, -99.336, -132.29 ],
[1199.754, -628.631, -976.484 ],
[532.552, -93.484, -862.412 ],
[424.541, 2.715, 304.001 ],
[1013.188, -622.468, -1136.629 ],
[635.433, -99.613, 95.958 ],
[-0.148 , -0.918, -0.113 ],
[172.003, -0.157, -175.397 ],
[170.573, -1.146, -1.093 ],
[346.334, -0.113, -0.086 ],
[169.74 , -0.87, 175.04 ],
[1148.348, -21.663, -460.659 ],
[676.332, -26.053, -1093.847 ],
[488.76 , -402.101, 438.949 ],
[-115.618, -52.152, -283.089 ],
[575.677, -53.408, 347.341 ],
[1296.735, -44.493, -489.345 ],
[604.62 , -43.492, -1114.973 ],
[238.087, -311.84, -387.855],
[466.058, -309.238, -649.434],
[370.987, -307.535, -730.875],
[247.226, -308.814, -596.039],
[144.315, -310.087, -470.248],
[536.218, -1011.754, -732.754],
[429.053, -1011.639, -825.74],
[336.705, -1072.507, -747.525]])
p660P = np.asmatrix(
[[-0.148, -0.918, -0.113],
[172.003, -0.157, -175.397],
[170.573, -1.146, -1.093],
[346.334, -0.113, -0.086],
[169.74, -0.87, 175.04]]
)
p660P_4P = np.asmatrix(
[[-0.148, -0.918, -0.113],
[172.003, -0.157, -175.397],
# [170.573, -1.146, -1.093],
[346.334, -0.113, -0.086],
[169.74, -0.87, 175.04]]
)
p660P_to_origin = np.asmatrix(
[[-170, 0, 0],
[ 0, 0, -170],
[ 0, 0, 0],
[170, 0, 0],
[0, 0, 170]]
)
pTest = np.asmatrix(
[
[-3, 0, 0],
[ 0, 0,-3],
[ 0, 0, 0],
[ 0, 0,+3],
[ 3, 0, 0],
[0,2,2],
[2,2,0],
[2.5,0.5,2.5],
[2,0,4],
[4,0,2],
[1, 1, 1],
[2, 2, 2],
[3, 3, 3],
[4, 4, 4],
[5, 5, 5],
]
)
test_axis = np.asmatrix(
[[-0.99875234, 0., - 0.04993762],
[0., - 1., - 0.],
[-0.04993762, - 0., 0.99875234]]
)
# print(*p660P_Full,'\n\n', *p660P, sep='\n' )
# print(p660P.shape)
# print('mean', *np.mean(p660P_4P, axis = 0), sep='\n')
# print('point - mean\n', *(p660P - np.mean(p660P_4P, axis=0) ), sep='\n')
# - np.asmatrix([-1.376154e+02, 0.000000e+00, 7.140000e-02])
print('mean', *np.mean(pTest[0:5], axis = 0), sep='\n')
print('point - mean\n', *(pTest - np.mean(pTest[0:5], axis=0) ), sep='\n')
# ret, rr, tt = m_findTransformedPoints(pTest[0:5], p660P_to_origin, pTest)
# print('final_ret', *ret, sep='\n')
retData, t_mean2, test_axis2 = m_ProjectUserCoor(pTest, pTest[5:10])
print('retData', retData)
# m_ProjectUserCoor(p660P_Full, p660P)
# minus90 = np.asmatrix([[1.57], [1.57], [1.57]]) - np.asmatrix(cv2.Rodrigues(test_axis2)[0])
# print('t', minus90)
# print('min90', minus90 * radianToDegree, cv2.Rodrigues(test_axis2)[0] * radianToDegree)
# print(cv2.Rodrigues(minus90)[0])
# print(t_mean2)
print("transform\n", (pTest - np.asmatrix(t_mean2)) * test_axis2 )
# print("transform\n", (pTest - np.asmatrix(t_mean2)) * (cv2.Rodrigues(minus90)[0]))
#reference 기준으로 도출된 좌표를 다시 원점 좌표로 복구
print('test',(test_axis2 *retData[5:10].T).T + np.asmatrix(t_mean2))
# print('1',(test_axis2 *pTest[5:10].T).T)
# print('2',np.asmatrix(t_mean2))
t_matrix = np.eye(4)
t_matrix[0:3,0:3] = test_axis2
t_matrix[0:3,3] = t_mean2
print(t_matrix)
t_matrix_inv = np.linalg.inv(t_matrix)
print(t_matrix_inv)
#원점 좌표를 reference 좌표축으로 변환(이 축이 기준이됨 0,0,0) - 역변환
print('inverse',(t_matrix_inv[0:3,0:3] * pTest[5:10].T).T + t_matrix_inv[0:3,3])
def load_DPA_file(fname):
print("//////////", funcname(), "//////////")
# tData = []
# fp = open(fname, 'r', encoding='utf-8')
# for row in csv.reader(fp, skipinitialspace= True, delimiter=','):
# print(row)
# # print(row[0], row[1], row[2], row[3], row[4], row[5])
# tData.append([row[0], row[1], row[2], row[3], row[4], row[5]])
# fp.close()
#
# tData.sort(key=lambda x: (x[0], x[2], x[1]))
# # sorted(tData, key=lambda x: (x[0], -x[2], x[1]))
# for row in tData:
# # print(row)
# print(row[0], row[1], row[2], row[3], row[4], row[5])
# print(tData[0:-1])
df = pd.read_csv(fname, skipinitialspace = True, header=None)
# print(df.head())
df.columns = ['title', 'number', 'point_name', 'tx', 'ty', 'tz']
df = df.sort_values(['title','point_name','number'], ascending=(True,True,True))
# print(tData)
return df
def save_DPA_file(tdata, fname):
print("//////////", funcname(), "//////////")
# if(tdata == ""):
# print("내용이 존재하지 않습니다.")
# return
if(tdata.group_sub.any()):
tdata = tdata.drop(['group_sub'], axis=1)
print("is group_sub")
tdata.point_name = '"' + tdata.point_name + '"'
tdata.to_csv(fname+"ext", mode='w', index=False, header=False, sep=',', quotechar=" ", float_format='%.4f')
if(tdata.group_first.any()):
tdata = tdata.drop(['group_first'], axis=1)
print("is group_first")
if(tdata.seq.any()):
tdata = tdata.drop(['seq'], axis=1)
print("is seq")
tdata.to_csv(fname, mode='w', index=False, header=False, sep=',', quotechar=" ", float_format='%.4f')
# print(tdata)
print(fname)
pass
def auto_recovery_3d_points_on_each_of_coordinate_predebug(tData):
# print( str(df['point_name']).split("_") )
tData['group_first'] = tData.groupby('title').grouper.group_info[0] + 1
tData['group_sub'] = tData['point_name'].str.split('_').str[0]