Python euler.euler2mat方法代碼示例

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def find_errors(gt_pose, final_pose):
	# Simple euler distand between translation part.
	gt_position = gt_pose[0:3]				
	predicted_position = final_pose[0:3]
	translation_error = np.sqrt(np.sum(np.square(gt_position - predicted_position)))

	# Convert euler angles rotation matrix.
	gt_euler = gt_pose[3:6]
	pt_euler = final_pose[3:6]
	gt_mat = t3d.euler2mat(gt_euler[2],gt_euler[1],gt_euler[0],'szyx')
	pt_mat = t3d.euler2mat(pt_euler[2],pt_euler[1],pt_euler[0],'szyx')

	# Multiply inverse of one rotation matrix with another rotation matrix.
	error_mat = np.dot(pt_mat,np.linalg.inv(gt_mat))
	_,angle = transforms3d.axangles.mat2axangle(error_mat)			# Convert matrix to axis angle representation and that angle is error.
	return translation_error, abs(angle*(180/np.pi))

# Store all the results.
# if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def find_errors(self, gt_pose, final_pose):
		import transforms3d
		gt_position = gt_pose[0,0:3]
		predicted_position = final_pose[0,0:3]

		translation_error = np.sqrt(np.sum(np.square(gt_position - predicted_position)))
		print("Translation Error: {}".format(translation_error))

		gt_euler = gt_pose[0,3:6]
		pt_euler = final_pose[0,3:6]

		gt_mat = t3d.euler2mat(gt_euler[2],gt_euler[1],gt_euler[0],'szyx')
		pt_mat = t3d.euler2mat(pt_euler[2],pt_euler[1],pt_euler[0],'szyx')

		error_mat = np.dot(pt_mat,np.linalg.inv(gt_mat))
		_,angle = transforms3d.axangles.mat2axangle(error_mat)
		print("Rotation Error: {}".format(abs(angle*(180/np.pi))))
		return translation_error, angle*(180/np.pi) 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def find_models(category, model, templates, case):
	# model:		No of models to be stored for a particular category.
	# category: 	Name of the category to be stored.
	# templates:	Array having templates (BxNx3)
	# case:			Which files to be used? (test/train)

	if case == 'test':
		FILES = TEST_FILES
	if case == 'train':
		FILES = TRAIN_FILES
	print(FILES)
	count = 0														# Counter to find number of models.
	for train_idx in range(len(FILES)):						# Loop over all the training files from ModelNet40 data.
		current_data, current_label = provider.loadDataFile(FILES[train_idx])		# Load data of from a file.
		for i in range(current_data.shape[0]):
			if count<model and shapes.index(category)==current_label[i]:
				# import transforms3d.euler as t3d 
				# rot = t3d.euler2mat(0*np.pi/1	80, 0*np.pi/180, 90*np.pi/180, 'szyx')
				# templates.append((np.dot(rot, current_data[i].T).T))
				templates.append(current_data[i]/2.0)				# Append data if it belongs to the category and less than given number of models.
				count += 1
	return templates 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def find_errors(gt_pose, final_pose):
	# Simple euler distand between translation part.
	gt_position = gt_pose[0:3]				
	predicted_position = final_pose[0:3]
	translation_error = np.sqrt(np.sum(np.square(gt_position - predicted_position)))

	# Convert euler angles rotation matrix.
	gt_euler = gt_pose[3:6]
	pt_euler = final_pose[3:6]
	gt_mat = t3d.euler2mat(gt_euler[2],gt_euler[1],gt_euler[0],'szyx')
	pt_mat = t3d.euler2mat(pt_euler[2],pt_euler[1],pt_euler[0],'szyx')

	# Multiply inverse of one rotation matrix with another rotation matrix.
	error_mat = np.dot(pt_mat,np.linalg.inv(gt_mat))
	_,angle = transforms3d.axangles.mat2axangle(error_mat)			# Convert matrix to axis angle representation and that angle is error.
	return translation_error, abs(angle*(180/np.pi)) 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def inverse_pose(pose):
	transformation_pose = np.zeros((4,4))
	transformation_pose[3,3]=1
	transformation_pose[0:3,0:3] = t3d.euler2mat(pose[5], pose[4], pose[3], 'szyx')
	transformation_pose[0,3] = pose[0]
	transformation_pose[1,3] = pose[1]
	transformation_pose[2,3] = pose[2]
	transformation_pose = np.linalg.inv(transformation_pose)
	pose_inv = np.zeros((1,6))[0]
	pose_inv[0] = transformation_pose[0,3]
	pose_inv[1] = transformation_pose[1,3]
	pose_inv[2] = transformation_pose[2,3]
	orient_inv = t3d.mat2euler(transformation_pose[0:3,0:3], 'szyx')
	pose_inv[3] = orient_inv[2]
	pose_inv[4] = orient_inv[1]
	pose_inv[5] = orient_inv[0]
	return pose_inv


###################### Shuffling Operations #########################

# Randomly shuffle given array of poses for training procedure. 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def get_cam_corners_and_edge( input_array ):
    center = input_array[:3]
    edge_lengths = input_array[-3:]
    axis = transforms3d.euler.euler2mat( *input_array[3:6], axes='sxyz' )
    y = axis[0] * edge_lengths[0] / 2
    x = axis[1] * edge_lengths[1] / 2
    z = axis[2] * edge_lengths[2] / 2
    corners_for_cam = np.empty((8,3))
    corners_for_cam[0] = center - x - y - z
    corners_for_cam[1] = center - x - y + z
    corners_for_cam[2] = center - x + y - z
    corners_for_cam[3] = center - x + y + z
    corners_for_cam[4] = center + x - y - z
    corners_for_cam[5] = center + x - y + z
    corners_for_cam[6] = center + x + y - z
    corners_for_cam[7] = center + x + y + z
    return corners_for_cam, edge_lengths 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def get_cam_corners_and_edge_ordered( input_array ):
    center = input_array[:3]
    edge_lengths = input_array[-3:]
    axis = transforms3d.euler.euler2mat( *input_array[3:6], axes='sxyz' )
    y = axis[0] * edge_lengths[0] / 2
    x = axis[1] * edge_lengths[1] / 2
    z = axis[2] * edge_lengths[2] / 2
    corners_for_cam = np.empty((8,3))
    corners_for_cam[0] = center - x + y - z
    corners_for_cam[1] = center + x + y - z
    corners_for_cam[2] = center + x - y - z
    corners_for_cam[3] = center - x - y - z
    corners_for_cam[4] = center - x + y + z
    corners_for_cam[5] = center + x + y + z
    corners_for_cam[6] = center + x - y + z
    corners_for_cam[7] = center - x - y + z
    return corners_for_cam, edge_lengths 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def get_xforms(xform_num, rotation_range=(0, 0, 0, 'u'), scaling_range=(0.0, 0.0, 0.0, 'u'), order='rxyz'):
    xforms = np.empty(shape=(xform_num, 3, 3))
    rotations = np.empty(shape=(xform_num, 3, 3))
    for i in range(xform_num):
        rx = rotation_angle(rotation_range[0], rotation_range[3])
        ry = rotation_angle(rotation_range[1], rotation_range[3])
        rz = rotation_angle(rotation_range[2], rotation_range[3])
        rotation = euler2mat(rx, ry, rz, order)

        sx = scaling_factor(scaling_range[0], scaling_range[3])
        sy = scaling_factor(scaling_range[1], scaling_range[3])
        sz = scaling_factor(scaling_range[2], scaling_range[3])
        scaling = np.diag([sx, sy, sz])

        xforms[i, :] = scaling * rotation
        rotations[i, :] = rotation
    return xforms, rotations 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def set_motion(self, motion):
    if self.name == 'root':
      self.coordinate = np.reshape(np.array(motion['root'][:3]), [3, 1])
      rotation = np.deg2rad(motion['root'][3:])
      self.matrix = self.C.dot(euler2mat(*rotation)).dot(self.Cinv)
    else:
      idx = 0
      rotation = np.zeros(3)
      for axis, lm in enumerate(self.limits):
        if not np.array_equal(lm, np.zeros(2)):
          rotation[axis] = motion[self.name][idx]
          idx += 1
      rotation = np.deg2rad(rotation)
      self.matrix = self.parent.matrix.dot(self.C).dot(euler2mat(*rotation)).dot(self.Cinv)
      self.coordinate = self.parent.coordinate + self.length * self.matrix.dot(self.direction)
    for child in self.children:
      child.set_motion(motion) 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def getRotationMatrix(angle_x, angle_y, angle_z):
    """
    Get rotation matrix
    :param angle_x: angle around x-axis in deg
    :param angle_y: angle around y-axis in deg
    :param angle_z: angle around z-axis in deg
    :return: 4x4 rotation matrix
    """
    alpha_x = angle_x * numpy.pi / 180.
    alpha_y = angle_y * numpy.pi / 180.
    alpha_z = angle_z * numpy.pi / 180.
    R = numpy.eye(4)
    from transforms3d.euler import euler2mat
    R[:3, :3] = euler2mat(alpha_x, alpha_y, alpha_z, 'rxyz')
    return R 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def apply_random_rotation(templates):
	# templates:		Array of templates or point clouds (BxNx3)
	templates = np.array(templates)
	for i in range(templates.shape[0]):
		# Random rotation in range [-45, 45] degrees.
		rot = t3d.euler2mat((np.pi/2)*np.random.random_sample()-np.pi/4, (np.pi/2)*np.random.random_sample()-np.pi/4, (np.pi/2)*np.random.random_sample()-np.pi/4, 'szyx')
		templates[i,:,:] = np.dot(rot, templates[i,:,:].T).T
	return templates 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def get_camera_matrix( view_dict, flip_xy=False ):
    position = view_dict[ 'camera_location' ]
    rotation_euler = view_dict[ 'camera_rotation_final' ]
    R = transforms3d.euler.euler2mat( *rotation_euler, axes='sxyz' )
    camera_matrix = transforms3d.affines.compose(  position, R, np.ones(3) )
    
    if flip_xy:
        # For some reason the x and y are flipped in room layout
        temp = np.copy(camera_matrix[0,:])
        camera_matrix[0,:] = camera_matrix[1,:]
        camera_matrix[1,:] = -temp
    return camera_matrix 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def rotate_quat_by_euler(xyzw, e_x, e_y, e_z):
        """
        wxyz: transforms3s array format
        xyzw: pybullet format
        """
        wxyz = PhysicsObject.quatXyzwToWxyz(xyzw)
        rot_mat = euler.euler2mat(e_x, e_y, e_z)
        wxyz = quaternions.qmult(rot_mat, wxyz)
        return PhysicsObject.quatWxyzToXyzw(wxyz) 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def get_random_transformation():
    T = [0, np.random.uniform(-8, 8), np.random.uniform(-8, 8)]
    R = euler2mat(np.random.uniform(-5, 5) / 180.0 * np.pi, 0, 0, 'sxyz')
    Z = [1, np.random.uniform(0.9, 1.1), np.random.uniform(0.9, 1.1)]
    A = compose(T, R, Z)
    return A 

# 需要導入模塊: from transforms3d import euler [as 別名]
# 或者: from transforms3d.euler import euler2mat [as 別名]
def read_label(path, is_training=True):
    seg = nib.load(glob.glob(os.path.join(path, '*_seg.nii.gz'))[0]).get_data().astype(np.float32)
    # Crop to 128*128*64
    crop_size = (128, 128, 64)
    crop = [int((seg.shape[0] - crop_size[0]) / 2), int((seg.shape[1] - crop_size[1]) / 2),
            int((seg.shape[2] - crop_size[2]) / 2)]
    seg = seg[crop[0] : crop[0] + crop_size[0], crop[1] : crop[1] + crop_size[1], crop[2] : crop[2] + crop_size[2]]
    label = np.zeros((seg.shape[0], seg.shape[1], seg.shape[2], 3), dtype=np.float32)
    label[seg == 1, 0] = 1
    label[seg == 2, 1] = 1
    label[seg == 4, 2] = 1
    
    final_label = np.empty((16, 16, 16, 3), dtype=np.float32)
    for z in range(label.shape[3]):
        final_label[..., z] = resize(label[..., z], (16, 16, 16), mode='constant')
        
    # Augmentation
    if is_training:
        im_size = final_label.shape[:-1]
        translation = [np.random.uniform(-2, 2), np.random.uniform(-2, 2), np.random.uniform(-2, 2)]
        rotation = euler2mat(0, 0, np.random.uniform(-5, 5) / 180.0 * np.pi, 'sxyz')
        scale = [1, 1, 1]
        warp_mat = compose(translation, rotation, scale)
        tform_coords = get_tform_coords(im_size)
        w = np.dot(warp_mat, tform_coords)
        w[0] = w[0] + im_size[0] / 2
        w[1] = w[1] + im_size[1] / 2
        w[2] = w[2] + im_size[2] / 2
        warp_coords = w[0:3].reshape(3, im_size[0], im_size[1], im_size[2])
        for z in range(label.shape[3]):
            final_label[..., z] = warp(final_label[..., z], warp_coords)

    return final_label