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3d_recovery_core.py
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3d_recovery_core.py
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import numpy as np
import os
import math
import cv2
import sys
import copy
import csv
import pandas as pd
import threading
from itertools import combinations
import time
import datetime as dt
import tkinter as tk
import tkinter.ttk
# from tkinter import *
from tkinter import filedialog
from tkinter import messagebox
import openpyxl
from openpyxl import Workbook
from openpyxl.utils.dataframe import dataframe_to_rows
# 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', 2500)
# 최대 열 수 설정
pd.set_option('display.max_columns', 50)
# 표시할 가로의 길이
pd.set_option('display.width', 160)
# 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_ALGO_ENABLE = 0
C_PRINT_CTRL_ENABLE = 0
degreeToRadian = math.pi/180
radianToDegree = 180/math.pi
def print_current_time(text=''):
tnow = dt.datetime.now()
print('%s-%2s-%2s %2s:%2s:%2s \t%s' % (tnow.year, tnow.month, tnow.day, tnow.hour, tnow.minute, tnow.second, text))
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
#숫자 자릿수 리턴
def digit_length(n):
ans = 0
while n:
n //= 10
ans += 1
return ans
def funcname():
return sys._getframe(1).f_code.co_name + "()"
def callername():
return sys._getframe(2).f_code.co_name + "()"
class RecoveryAlgo():
def __del__(self):
print("*************delete RecoveryAlgo class***********\n")
def __init__(self):
self.debugflag = C_PRINT_ALGO_ENABLE
print("*************initialize RecoveryAlgo class***********\n")
def m_ProjectUserCoor(self, input_point, ref):
"""
A function to project user coordinates based on input points and a reference.
Parameters:
- input_point: array-like, shape (n, 3)
The input points to be projected.
- ref: array-like, shape (5, 3)
The reference points for the projection.
Returns:
- output_point: array-like, shape (n, 3)
The projected output points based on the input points and reference.
- t_mean: array-like, shape (3,)
The mean of the reference points.
- t_axis: array-like, shape (3, 3)
The transformation axis matrix.
"""
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 (self.debugflag):
print('ret',*output_point, sep='\n')
return output_point, t_mean, t_axis
def m_ProjectAbsCoor(self, P_DPA_Pts, P_DPA_Ref1):
"""
A function to calculate the absolute coordinates of points based on given reference points.
Parameters:
- P_DPA_Pts: numpy matrix representing points in DPA coordinates
- P_DPA_Ref1: numpy matrix representing the reference points in DPA coordinates
Returns:
- P_Ref1_Pts: numpy matrix containing the absolute coordinates of the points
"""
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 = self.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 (self.debugflag):
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(self, P_Ref1_Pts, P_Ref1_Disp):
"""
A function that calculates the displacement of reference points based on given reference points and displacements. Returns the displaced points, centroid, and axis.
Parameters:
- P_Ref1_Pts: A matrix of reference points.
- P_Ref1_Disp: A matrix of displacements for the reference points.
Returns:
- P_Disp: Displaced points based on reference points, centroid, and axis.
- Centroid: The centroid of the displaced points.
- Axis: The axis of the displaced points.
"""
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 = self.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 (self.debugflag):
print('ret',P_Disp)
return P_Disp, Centroid, Axis
def rigid_transform_3D(self, A, B):
"""
A function to calculate the rigid transformation between two sets of 3D points.
Parameters:
- A: numpy array of shape (N, 3) representing the first set of 3D points
- B: numpy array of shape (N, 3) representing the second set of 3D points
Returns:
- R: the rotation matrix for the transformation
- t: the translation vector for the transformation
"""
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(self, P_Ref, P_Input, P_Target):
"""
A function to find transformed points based on input and reference points.
Parameters:
- P_Ref: A numpy matrix representing reference points.
- P_Input: A numpy matrix representing input points.
- P_Target: A numpy matrix representing target points.
Returns:
- P_Transformed: A numpy matrix of transformed points.
- R: A numpy matrix representing rotation.
- T: A numpy matrix representing translation.
"""
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 = self.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(self.debugflag):
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(self, P_DPA_Ref1):
"""
A function to calculate the centroid and axis of a given array.
Parameters:
- P_DPA_Ref1: a numpy array representing points in space
Returns:
- centroid: a numpy array representing the centroid of the input points
- axis: a numpy array representing the axis of the input points
"""
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(self, P_Ref1_Disp):
"""
Generate the centroid and axis of a given set of points in 3D space.
Parameters:
- P_Ref1_Disp (numpy.ndarray): The set of points in 3D space.
Returns:
- centroid (numpy.ndarray): The centroid of the set of points.
- axis (numpy.ndarray): The axis of the set of points.
"""
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]]
# )
def example_relative_position_based_on_refer_point(self):
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 = self.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 = self.m_findTransformedPoints(pPattern_disp[0:5] , p550N_name[4:9], pPattern_disp[5:10] )
print('p550N_Pattern\n', p550N_Pattern)
self.m_ProjectDispCoor(p550N_Eye, p550N_Pattern)
self.m_ProjectDispCoor(np.mean(p550N_Eye, axis=0), p550N_Pattern)
return
def example_relative_position_based_on_many_points(self):
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 = self.m_findTransformedPoints(p660P,p550N,p660P_Full)
primeP, rr, tt = self.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 = self.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 = self.m_ProjectAbsCoor(p550N_Full, p550N)
print('ret1', ret1)
print('ret2', ret2)
print('Sub1', *(ret1 - ret2), sep='\n')
ret4 = self.m_ProjectAbsCoor(p660P_Full, p660P_Full[18:22])
ret5 = self.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 = self.m_findTransformedPoints(ret4[18:22], p660P_Full[18:22], ret4)
ret7, rr, tt = self.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 example_move_origin_from_refer_point(self):
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 = self.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 example_check_face_pos_GT_based_on_MRA2_CAD_displaycenter(self): #미완성
print("//////////{:s}//////////".format(sys._getframe().f_code.co_name))
# GT -> display center -> Camera position -> CAD display center -> modified GT
nGT = np.asmatrix(
[[548.0606, -0.9230 ,195],
[548.1446, -125.9743 ,195],
[548.1358, 123.9789 ,195],
[658.2693, -0.8927 ,195],
[658.2218, -157.4343 ,195],
[658.4669, 155.4454 ,195],
[768.5166, -0.9942 ,195],
[768.3877, -187.8320 ,195],
[768.5165, 186.0766 ,195],
[998.9362, -0.9115 ,195],
[998.4603, -223.7147 ,195],
[998.7845, 222.0334 ,195]]
)
#extract GT position based on display center axis
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 = self.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 = self.m_findTransformedPoints(pPattern_disp[0:5] , p550N_name[4:9], pPattern_disp[5:10] )
print('p550N_Pattern\n', p550N_Pattern)
p550N_merge = np.concatenate((p550N_name, p550N_Pattern, p550N_Eye, np.mean(p550N_Eye, axis=0) ))
pDCenter, Rc, Tc = self.m_ProjectDispCoor(p550N_merge, p550N_Pattern)
print('pDCenter', pDCenter)
# m_ProjectDispCoor(np.mean(p550N_Eye, axis=0), p550N_Pattern)
pDCenter_merge = np.concatenate((pDCenter, nGT, [[0 ,0, 0]], [[1 ,0, 0]], [[11.56351826, 46.22008096, 52.38411943]]))
print('pDCenter_merge', pDCenter_merge)
#camera to display center
T_sample_778569 = np.asmatrix([-13.253501, - 46.178298, - 52.019384])
R_sample_778569 = np.array([0.070953, 1.948159, 0.105582]) * degreeToRadian
# R_sample_778569_2 = np.asmatrix([-12.670593, - 45.960195, - 51.981639])
# T_sample_778569_2 = np.asmatrix([0.071625, 1.971692, 0.083147]) * degreeToRadian
T_CAD = np.asmatrix([0.00978, -0.04584, -0.04719])
R_CAD = np.asmatrix([0.0 , 3.0 , 0.0]) * degreeToRadian
# print(cv2.Rodrigues(R_sample_778569)[0])
# rest = cv2.Rodrigues(p550N_name[0:3])[0]
print('camera', *((cv2.Rodrigues(R_sample_778569)[0] * pDCenter_merge.T).T + np.asmatrix(T_sample_778569)), sep='\n')
# print('camera_inv', *((pDCenter_merge - np.asmatrix(T_sample_778569)) * cv2.Rodrigues(R_sample_778569)[0]), sep='\n')
print('camera_2', *( (pDCenter_merge - np.asmatrix(T_CAD)) * cv2.Rodrigues(R_CAD)[0]), sep='\n')
# print('camera_2', *((pDCenter_merge - np.asmatrix(T_CAD)) * cv2.Rodrigues(R_CAD)[0]), sep='\n')
# print(pDCenter_merge, 'ttt\n', T_sample_778569, )
# t_matrix = np.eye(4)
# t_matrix[0:3,0:3] = cv2.Rodrigues(R_sample_778569)[0]
# t_matrix[0:3,3] = T_sample_778569
# 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] * pDCenter_merge.T).T + t_matrix_inv[0:3,3])
return
def example_test(self):
mst_739106_01_NG = np.asmatrix(
[[0.08195928, 0.10489032, 0.69267075],
[0.17728222, 0.10925468, 0.69065354],
[0.08181136, 0.10193214, 0.65129581],
[0.17674347, 0.10588553, 0.64905204],
[0.08011092, 0.10417334, 0.65523652],
[0.17484469, 0.10845659, 0.65260795],
[0, 0, 0]
]
)
mst_739106_02_NG = np.asmatrix(
[[0.07992614, 0.10851276, 0.69048120],
[0.17564883, 0.11238760, 0.69422094],
[0.08002846, 0.10547352, 0.65082413],
[0.17480817, 0.10970294, 0.64958008],
[0.07812445, 0.10771254, 0.65515808],
[0.17242126, 0.11228639, 0.65107764],
[0, 0, 0]
]
)
mst_739106_03_NG = np.asmatrix(
[[0.08080904, 0.08298502, 0.69747400],
[0.17585388, 0.08760310, 0.69544796],
[0.08076227, 0.08173648, 0.65997013],
[0.17549017, 0.08614120, 0.65676111],
[0.07912509, 0.08359874, 0.66174847],
[ 0.17389188, 0.08795844, 0.65849488],
[0, 0, 0]
]
)
mst_739066_01_NG = np.asmatrix(
[[0.07987873, 0.12114454, 0.68806497],
[0.17597210, 0.12185083, 0.68512255],
[0.07997913, 0.11740157, 0.65030805],
[0.17579403, 0.11818989, 0.64548738],
[0.07804393, 0.11961369, 0.65077830],
[0.17341946, 0.12057583, 0.64491026],
[0, 0, 0]
]