Commit 58e2489c authored by Lars Bittrich's avatar Lars Bittrich
Browse files

Zeiss Importer with permutation variation on the marker positions to allow... 

Zeiss Importer with permutation variation on the marker positions to allow changing marker sequence between Zeiss measurement and at Raman microscope -> minimize human error; but at the cost of limiting maximum angle rotations to -45,45 degrees in each euler angle
parent 5fd1cb0c

zeissimporter.py

+27 −12
Original line number Original line Diff line number Diff line
@@ -26,6 +26,7 @@ from helperfunctions import cv2imread_fix, cv2imwrite_fix 
from ramancom.ramancontrol import defaultPath

from ramancom.ramancontrol import defaultPath

from dataset import DataSet

from dataset import DataSet

from scipy.optimize import least_squares

from scipy.optimize import least_squares

from itertools import permutations

import cv2

import cv2

import numpy as np

import numpy as np
@@ -228,20 +229,34 @@ class ZeissImporter(QtWidgets.QDialog): 
                           [s1*c3+c1*c2*s3, -s1*s3+c1*c2*c3, -c1*s2],

                           [s1*c3+c1*c2*s3, -s1*s3+c1*c2*c3, -c1*s2],

                           [s1*s3, s2*c3, c2]])

                           [s1*s3, s2*c3, c2]])

        

        

        # find the transformation matrix with best fit for small angles in 

        # [-45°,45°] for all permutation of markers

        permbest = None

        pointsbest = None

        for perm in permutations(range(points.shape[0])):

            ppoints = points[perm,:]

            

            def err(angles_shift):

            def err(angles_shift):

                T = getRotMat(angles_shift[:3]).T.A

                T = getRotMat(angles_shift[:3]).T.A

            return (np.dot(zpoints, T)-angles_shift[np.newaxis,3:]-points).ravel()

                return (np.dot(zpoints, T) - angles_shift[np.newaxis,3:] \



                        - ppoints).ravel()

        best = None



        for i in range(100):

            angle = np.zeros(3)

            angle = np.pi*np.random.rand(3)-np.pi/2.

            opt = least_squares(err, np.concatenate((angle, np.zeros(3))), 

            opt = least_squares(err, np.concatenate((angle, np.zeros(3))), method='lm')

                                bounds=(np.array([-np.pi/4]*3+[-np.inf]*3),

            if best is None or best.cost>opt.cost:

                                        np.array([np.pi/4]*3+[np.inf]*3)),

                best = opt

                                method='dogbox')

        optangles = best.x[:3]

            

        shift = best.x[3:]

            if permbest is None or \

                permbest.cost>opt.cost:

                print("Current best permutation:", perm, flush=True)

                permbest = opt

                pointsbest = ppoints

        

        optangles = permbest.x[:3]

        shift = permbest.x[3:]

        T = getRotMat(optangles).T.A

        T = getRotMat(optangles).T.A

        e = (np.dot(zpoints, T)-shift[np.newaxis,:]-points)

        e = (np.dot(zpoints, T)-shift[np.newaxis,:]-pointsbest)

        print("Transformation angles:", optangles, flush=True)

        print("Transformation angles:", optangles, flush=True)

        print("Transformation shift:", shift, flush=True)

        print("Transformation shift:", shift, flush=True)

        print("Transformation err:", e, flush=True)

        print("Transformation err:", e, flush=True)