# | Line 29 | Line 29 | |
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29 | * @date 10/11/2004 | |
30 | * @version 1.0 | |
31 | */ | |
32 | < | #ifndef MATH_SQUAREMATRIX3_HPP |
32 | > | #ifndef MATH_SQUAREMATRIX3_HPP |
33 | #define MATH_SQUAREMATRIX3_HPP | |
34 | ||
35 | #include "Quaternion.hpp" | |
# | Line 59 | Line 59 | namespace oopse { | |
59 | } | |
60 | ||
61 | SquareMatrix3(const Quaternion<Real>& q) { | |
62 | < | *this = q.toRotationMatrix3(); |
62 | > | setupRotMat(q); |
63 | > | |
64 | } | |
65 | ||
66 | SquareMatrix3(Real w, Real x, Real y, Real z) { | |
67 | < | Quaternion<Real> q(w, x, y, z); |
67 | < | *this = q.toRotationMatrix3(); |
67 | > | setupRotMat(w, x, y, z); |
68 | } | |
69 | ||
70 | /** copy assignment operator */ | |
# | Line 72 | Line 72 | namespace oopse { | |
72 | if (this == &m) | |
73 | return *this; | |
74 | SquareMatrix<Real, 3>::operator=(m); | |
75 | + | return *this; |
76 | } | |
77 | ||
78 | /** | |
# | Line 118 | Line 119 | namespace oopse { | |
119 | * @param quat | |
120 | */ | |
121 | void setupRotMat(const Quaternion<Real>& quat) { | |
122 | < | *this = quat.toRotationMatrix3(); |
122 | > | setupRotMat(quat.w(), quat.x(), quat.y(), quat.z()); |
123 | } | |
124 | ||
125 | /** | |
# | Line 126 | Line 127 | namespace oopse { | |
127 | * @param w the first element | |
128 | * @param x the second element | |
129 | * @param y the third element | |
130 | < | * @parma z the fourth element |
130 | > | * @param z the fourth element |
131 | */ | |
132 | void setupRotMat(Real w, Real x, Real y, Real z) { | |
133 | Quaternion<Real> q(w, x, y, z); | |
# | Line 195 | Line 196 | namespace oopse { | |
196 | * z-axis (again). | |
197 | */ | |
198 | Vector3<Real> toEulerAngles() { | |
199 | < | Vector<Real> myEuler; |
199 | > | Vector3<Real> myEuler; |
200 | Real phi,theta,psi,eps; | |
201 | Real ctheta,stheta; | |
202 | ||
203 | // set the tolerance for Euler angles and rotation elements | |
204 | ||
205 | < | theta = acos(min(1.0,max(-1.0,data_[2][2]))); |
205 | > | theta = acos(std::min(1.0, std::max(-1.0,data_[2][2]))); |
206 | ctheta = data_[2][2]; | |
207 | stheta = sqrt(1.0 - ctheta * ctheta); | |
208 | ||
# | Line 236 | Line 237 | namespace oopse { | |
237 | ||
238 | return myEuler; | |
239 | } | |
240 | + | |
241 | + | /** Returns the determinant of this matrix. */ |
242 | + | Real determinant() const { |
243 | + | Real x,y,z; |
244 | + | |
245 | + | x = data_[0][0] * (data_[1][1] * data_[2][2] - data_[1][2] * data_[2][1]); |
246 | + | y = data_[0][1] * (data_[1][2] * data_[2][0] - data_[1][0] * data_[2][2]); |
247 | + | z = data_[0][2] * (data_[1][0] * data_[2][1] - data_[1][1] * data_[2][0]); |
248 | + | |
249 | + | return(x + y + z); |
250 | + | } |
251 | ||
252 | /** | |
253 | * Sets the value of this matrix to the inversion of itself. | |
254 | * @note since simple algorithm can be applied to inverse the 3 by 3 matrix, we hide the | |
255 | * implementation of inverse in SquareMatrix class | |
256 | */ | |
257 | < | void inverse() { |
257 | > | SquareMatrix3<Real> inverse() { |
258 | > | SquareMatrix3<Real> m; |
259 | > | double det = determinant(); |
260 | > | if (fabs(det) <= oopse::epsilon) { |
261 | > | //"The method was called on a matrix with |determinant| <= 1e-6.", |
262 | > | //"This is a runtime or a programming error in your application."); |
263 | > | } |
264 | ||
265 | < | } |
265 | > | m(0, 0) = data_[1][1]*data_[2][2] - data_[1][2]*data_[2][1]; |
266 | > | m(1, 0) = data_[1][2]*data_[2][0] - data_[1][0]*data_[2][2]; |
267 | > | m(2, 0) = data_[1][0]*data_[2][1] - data_[1][1]*data_[2][0]; |
268 | > | m(0, 1) = data_[2][1]*data_[0][2] - data_[2][2]*data_[0][1]; |
269 | > | m(1, 1) = data_[2][2]*data_[0][0] - data_[2][0]*data_[0][2]; |
270 | > | m(2, 1) = data_[2][0]*data_[0][1] - data_[2][1]*data_[0][0]; |
271 | > | m(0, 2) = data_[0][1]*data_[1][2] - data_[0][2]*data_[1][1]; |
272 | > | m(1, 2) = data_[0][2]*data_[1][0] - data_[0][0]*data_[1][2]; |
273 | > | m(2, 2) = data_[0][0]*data_[1][1] - data_[0][1]*data_[1][0]; |
274 | ||
275 | < | void diagonalize() { |
276 | < | |
275 | > | m /= det; |
276 | > | return m; |
277 | } | |
278 | + | /** |
279 | + | * Extract the eigenvalues and eigenvectors from a 3x3 matrix. |
280 | + | * The eigenvectors (the columns of V) will be normalized. |
281 | + | * The eigenvectors are aligned optimally with the x, y, and z |
282 | + | * axes respectively. |
283 | + | * @param a symmetric matrix whose eigenvectors are to be computed. On return, the matrix is |
284 | + | * overwritten |
285 | + | * @param w will contain the eigenvalues of the matrix On return of this function |
286 | + | * @param v the columns of this matrix will contain the eigenvectors. The eigenvectors are |
287 | + | * normalized and mutually orthogonal. |
288 | + | * @warning a will be overwritten |
289 | + | */ |
290 | + | static void diagonalize(SquareMatrix3<Real>& a, Vector3<Real>& w, SquareMatrix3<Real>& v); |
291 | }; | |
292 | + | /*========================================================================= |
293 | + | |
294 | + | Program: Visualization Toolkit |
295 | + | Module: $RCSfile: SquareMatrix3.hpp,v $ |
296 | + | |
297 | + | Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen |
298 | + | All rights reserved. |
299 | + | See Copyright.txt or http://www.kitware.com/Copyright.htm for details. |
300 | + | |
301 | + | This software is distributed WITHOUT ANY WARRANTY; without even |
302 | + | the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR |
303 | + | PURPOSE. See the above copyright notice for more information. |
304 | + | |
305 | + | =========================================================================*/ |
306 | + | template<typename Real> |
307 | + | void SquareMatrix3<Real>::diagonalize(SquareMatrix3<Real>& a, Vector3<Real>& w, |
308 | + | SquareMatrix3<Real>& v) { |
309 | + | int i,j,k,maxI; |
310 | + | Real tmp, maxVal; |
311 | + | Vector3<Real> v_maxI, v_k, v_j; |
312 | + | |
313 | + | // diagonalize using Jacobi |
314 | + | jacobi(a, w, v); |
315 | + | // if all the eigenvalues are the same, return identity matrix |
316 | + | if (w[0] == w[1] && w[0] == w[2] ) { |
317 | + | v = SquareMatrix3<Real>::identity(); |
318 | + | return; |
319 | + | } |
320 | + | |
321 | + | // transpose temporarily, it makes it easier to sort the eigenvectors |
322 | + | v = v.transpose(); |
323 | + | |
324 | + | // if two eigenvalues are the same, re-orthogonalize to optimally line |
325 | + | // up the eigenvectors with the x, y, and z axes |
326 | + | for (i = 0; i < 3; i++) { |
327 | + | if (w((i+1)%3) == w((i+2)%3)) {// two eigenvalues are the same |
328 | + | // find maximum element of the independant eigenvector |
329 | + | maxVal = fabs(v(i, 0)); |
330 | + | maxI = 0; |
331 | + | for (j = 1; j < 3; j++) { |
332 | + | if (maxVal < (tmp = fabs(v(i, j)))){ |
333 | + | maxVal = tmp; |
334 | + | maxI = j; |
335 | + | } |
336 | + | } |
337 | + | |
338 | + | // swap the eigenvector into its proper position |
339 | + | if (maxI != i) { |
340 | + | tmp = w(maxI); |
341 | + | w(maxI) = w(i); |
342 | + | w(i) = tmp; |
343 | ||
344 | + | v.swapRow(i, maxI); |
345 | + | } |
346 | + | // maximum element of eigenvector should be positive |
347 | + | if (v(maxI, maxI) < 0) { |
348 | + | v(maxI, 0) = -v(maxI, 0); |
349 | + | v(maxI, 1) = -v(maxI, 1); |
350 | + | v(maxI, 2) = -v(maxI, 2); |
351 | + | } |
352 | + | |
353 | + | // re-orthogonalize the other two eigenvectors |
354 | + | j = (maxI+1)%3; |
355 | + | k = (maxI+2)%3; |
356 | + | |
357 | + | v(j, 0) = 0.0; |
358 | + | v(j, 1) = 0.0; |
359 | + | v(j, 2) = 0.0; |
360 | + | v(j, j) = 1.0; |
361 | + | |
362 | + | /** @todo */ |
363 | + | v_maxI = v.getRow(maxI); |
364 | + | v_j = v.getRow(j); |
365 | + | v_k = cross(v_maxI, v_j); |
366 | + | v_k.normalize(); |
367 | + | v_j = cross(v_k, v_maxI); |
368 | + | v.setRow(j, v_j); |
369 | + | v.setRow(k, v_k); |
370 | + | |
371 | + | |
372 | + | // transpose vectors back to columns |
373 | + | v = v.transpose(); |
374 | + | return; |
375 | + | } |
376 | + | } |
377 | + | |
378 | + | // the three eigenvalues are different, just sort the eigenvectors |
379 | + | // to align them with the x, y, and z axes |
380 | + | |
381 | + | // find the vector with the largest x element, make that vector |
382 | + | // the first vector |
383 | + | maxVal = fabs(v(0, 0)); |
384 | + | maxI = 0; |
385 | + | for (i = 1; i < 3; i++) { |
386 | + | if (maxVal < (tmp = fabs(v(i, 0)))) { |
387 | + | maxVal = tmp; |
388 | + | maxI = i; |
389 | + | } |
390 | + | } |
391 | + | |
392 | + | // swap eigenvalue and eigenvector |
393 | + | if (maxI != 0) { |
394 | + | tmp = w(maxI); |
395 | + | w(maxI) = w(0); |
396 | + | w(0) = tmp; |
397 | + | v.swapRow(maxI, 0); |
398 | + | } |
399 | + | // do the same for the y element |
400 | + | if (fabs(v(1, 1)) < fabs(v(2, 1))) { |
401 | + | tmp = w(2); |
402 | + | w(2) = w(1); |
403 | + | w(1) = tmp; |
404 | + | v.swapRow(2, 1); |
405 | + | } |
406 | + | |
407 | + | // ensure that the sign of the eigenvectors is correct |
408 | + | for (i = 0; i < 2; i++) { |
409 | + | if (v(i, i) < 0) { |
410 | + | v(i, 0) = -v(i, 0); |
411 | + | v(i, 1) = -v(i, 1); |
412 | + | v(i, 2) = -v(i, 2); |
413 | + | } |
414 | + | } |
415 | + | |
416 | + | // set sign of final eigenvector to ensure that determinant is positive |
417 | + | if (v.determinant() < 0) { |
418 | + | v(2, 0) = -v(2, 0); |
419 | + | v(2, 1) = -v(2, 1); |
420 | + | v(2, 2) = -v(2, 2); |
421 | + | } |
422 | + | |
423 | + | // transpose the eigenvectors back again |
424 | + | v = v.transpose(); |
425 | + | return ; |
426 | + | } |
427 | typedef SquareMatrix3<double> Mat3x3d; | |
428 | typedef SquareMatrix3<double> RotMat3x3d; | |
429 | ||
430 | } //namespace oopse | |
431 | #endif // MATH_SQUAREMATRIX_HPP | |
432 | + |
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