Example #1
0
 def unravel(clas, anim, shape, parents):
     nf, nj = shape
     rotations = anim[nf * nj * 0:nf * nj * 3]
     positions = anim[nf * nj * 3:nf * nj * 6]
     orients = anim[nf * nj * 6 + nj * 0:nf * nj * 6 + nj * 3]
     offsets = anim[nf * nj * 6 + nj * 3:nf * nj * 6 + nj * 6]
     return cls(Quaternions.exp(rotations), positions,
                Quaternions.exp(orients), offsets, parents.copy())
Example #2
0
    def __call__(self):

        children = AnimationStructure.children_list(self.animation.parents)

        for i in range(self.iterations):

            for j in AnimationStructure.joints(self.animation.parents):

                c = np.array(children[j])
                if len(c) == 0: continue

                anim_transforms = Animation.transforms_global(self.animation)
                anim_positions = anim_transforms[:, :, :3, 3]
                anim_rotations = Quaternions.from_transforms(anim_transforms)

                jdirs = anim_positions[:, c] - anim_positions[:, np.newaxis, j]
                ddirs = self.positions[:, c] - anim_positions[:, np.newaxis, j]

                jsums = np.sqrt(np.sum(jdirs**2.0, axis=-1)) + 1e-10
                dsums = np.sqrt(np.sum(ddirs**2.0, axis=-1)) + 1e-10

                jdirs = jdirs / jsums[:, :, np.newaxis]
                ddirs = ddirs / dsums[:, :, np.newaxis]

                angles = np.arccos(np.sum(jdirs * ddirs, axis=2).clip(-1, 1))
                axises = np.cross(jdirs, ddirs)
                axises = -anim_rotations[:, j, np.newaxis] * axises

                rotations = Quaternions.from_angle_axis(angles, axises)

                if rotations.shape[1] == 1:
                    averages = rotations[:, 0]
                else:
                    averages = Quaternions.exp(rotations.log().mean(axis=-2))

                self.animation.rotations[:,
                                         j] = self.animation.rotations[:,
                                                                       j] * averages

            if not self.silent:
                anim_positions = Animation.positions_global(self.animation)
                error = np.mean(np.sum((anim_positions - self.positions)**2.0,
                                       axis=-1)**0.5,
                                axis=-1)
                print('[BasicInverseKinematics] Iteration %i Error: %f' %
                      (i + 1, error))

        return self.animation