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Reference for pyronn/ct_reconstruction/helpers/trajectories/circular_trajectory.py


pyronn.ct_reconstruction.helpers.trajectories.circular_trajectory.circular_trajectory_2d 

circular_trajectory_2d(number_of_projections, angular_range, swap_detector_axis)

Generates the central ray vectors defining a circular trajectory for use with the 2d projection layers.

Args: geometry: 2d Geometry class including angular_range and number_of_projections Returns: Central ray vectors as np.array.

Source code in pyronn/ct_reconstruction/helpers/trajectories/circular_trajectory.py 
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def circular_trajectory_2d(number_of_projections, angular_range, swap_detector_axis):
    """
        Generates the central ray vectors defining a circular trajectory for use with the 2d projection layers.
    Args:
        geometry: 2d Geometry class including angular_range and number_of_projections
    Returns:
        Central ray vectors as np.array.
    """
    rays = np.zeros([number_of_projections, 2])
    angular_increment = (angular_range[1] - angular_range[0]) / number_of_projections
    for i in range(number_of_projections):
        if swap_detector_axis:
            rays[i] = [np.cos(angular_range[0] + i * angular_increment), -np.sin(angular_range[0] + i * angular_increment)]
        else:
            rays[i] = [np.cos(angular_range[0] + i * angular_increment), np.sin(angular_range[0] + i * angular_increment)]
    return rays




pyronn.ct_reconstruction.helpers.trajectories.circular_trajectory.circular_trajectory_3d 

circular_trajectory_3d(number_of_projections, angular_range, detector_spacing, detector_origin, source_isocenter_distance, source_detector_distance, swap_detector_axis, **kwargs)

Generates the projection matrices defining a circular trajectory around the z-axis
for use with the 3d projection layers.
Adapted from CONRAD Source code https://github.com/akmaier/CONRAD.

Args: geometry: 3d Geometry class including angular_range, number_of_projections, source_detector_distance, detector_spacing, volume_origin, volume_shape and volume_spacing. Returns: Projection matrices with shape (num_projections, 3, 4) as np.array.

Source code in pyronn/ct_reconstruction/helpers/trajectories/circular_trajectory.py 
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def circular_trajectory_3d(number_of_projections, angular_range, detector_spacing, detector_origin, source_isocenter_distance, source_detector_distance, swap_detector_axis, **kwargs):
    """
        Generates the projection matrices defining a circular trajectory around the z-axis
        for use with the 3d projection layers.
        Adapted from CONRAD Source code https://github.com/akmaier/CONRAD.
    Args:
        geometry: 3d Geometry class including angular_range, number_of_projections, source_detector_distance,
        detector_spacing, volume_origin, volume_shape and volume_spacing.
    Returns:
        Projection matrices with shape (num_projections, 3, 4) as np.array.
    """

    # init empty
    projection_matrices = np.zeros((number_of_projections, 3, 4))

    # axes for later use
    x_axis = np.array([1.0, 0.0, 0.0])
    y_axis = np.array([0.0, 1.0, 0.0])
    z_axis = np.array([0.0, 0.0, 1.0])

    # defining u and v directions by: main coord axes
    u_dir = y_axis
    if swap_detector_axis:
        v_dir = x_axis
    else:
        v_dir = -x_axis

    # configure intrinsic camera parameters
    intrinsic_params_mat = np.eye(3, 3)
    for i in range(2):
        intrinsic_params_mat[i, i] = source_detector_distance / detector_spacing[1-i]

    # calc and set detector origin
    # we need t_x and t_y, and follow the [z,y,x] convention

    intrinsic_params_mat[0,2] = detector_origin[-1] / detector_spacing[-1] *-1
    intrinsic_params_mat[1,2] = detector_origin[-2] / detector_spacing[-2] *-1
    # intrinsic_params_mat[0:2, 2] = detector_origin / detector_spacing *-1
    # configure extrinisc pararams and create projection_matrices
    current_angle = angular_range[0]
    angular_increment = (angular_range[1] - angular_range[0]) / number_of_projections
    for p in range(number_of_projections):
        # calculate extrinsic params
        extrinsic_params_mat = np.eye(4, 4)

        # rotation of axes from world system to plane of rotation system
        R_to_plane = np.eye(4, 4)
        R_to_plane[0:3, 0:3] = np.array([z_axis, np.cross(z_axis, x_axis), -x_axis])

        # rotation for u and v direction
        axis_align_R = np.eye(4, 4)
        # v_dir = z_axis
        axis_align_R[0:3, 0] = u_dir
        axis_align_R[0:3, 1] = v_dir
        axis_align_R[0:3, 2] = np.cross(u_dir, v_dir)
        axis_align_R = axis_align_R.T

        # rotation about x axis
        R_x_axis = np.eye(4, 4)
        R_x_axis[0:3, 0:3] = np.array([1, 0, 0,
                                       0, np.cos(-current_angle), -np.sin(-current_angle),
                                       0, np.sin(-current_angle), np.cos(-current_angle)]).reshape((3, 3))

        # R_x_axis = np.eye(4, 4)
        # R_x_axis[0:3, 0:3] = np.array([
        #                                np.cos(-current_angle), -np.sin(-current_angle),0,
        #                                np.sin(-current_angle), np.cos(-current_angle), 0,
        #                                 0, 0, 1]).reshape((3, 3))

        # translation of camera
        translation = np.eye(4, 4)
        translation[0:4, 3] = np.array([0, 0, -source_isocenter_distance, 1])

        # combine the above into 4x4 extrinsic params matrix
        extrinsic_params_mat = np.dot(np.dot(np.dot(axis_align_R, translation), R_x_axis), R_to_plane.T)
        extrinsic_params_mat = extrinsic_params_mat / extrinsic_params_mat[3, 3]

        # calculate projection matrix
        projection_matrices[p][0:3, 0:3] = np.dot(intrinsic_params_mat, extrinsic_params_mat[0:3, 0:3])
        projection_matrices[p][0:3, 3] = np.dot(intrinsic_params_mat, extrinsic_params_mat[0:3, 3])

        # next angle
        current_angle += angular_increment

    return projection_matrices




pyronn.ct_reconstruction.helpers.trajectories.circular_trajectory.multi_circular_trajectory_3d 

multi_circular_trajectory_3d(number_of_projections, longtitude, angular_range, detector_spacing, detector_origin, source_isocenter_distance, source_detector_distance, swap_detector_axis, **kwargs)

Generates the projection matrices defining a circular trajectory around the z-axis
for use with the 3d projection layers.
Adapted from CONRAD Source code https://github.com/akmaier/CONRAD.

Args: geometry: 3d Geometry class including angular_range, number_of_projections, source_detector_distance, detector_spacing, volume_origin, volume_shape and volume_spacing. Returns: Projection matrices with shape (num_projections, 3, 4) as np.array.

Source code in pyronn/ct_reconstruction/helpers/trajectories/circular_trajectory.py 
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def multi_circular_trajectory_3d(number_of_projections, longtitude, angular_range, detector_spacing, detector_origin, source_isocenter_distance, source_detector_distance, swap_detector_axis, **kwargs):
    """
        Generates the projection matrices defining a circular trajectory around the z-axis
        for use with the 3d projection layers.
        Adapted from CONRAD Source code https://github.com/akmaier/CONRAD.
    Args:
        geometry: 3d Geometry class including angular_range, number_of_projections, source_detector_distance,
        detector_spacing, volume_origin, volume_shape and volume_spacing.
    Returns:
        Projection matrices with shape (num_projections, 3, 4) as np.array.
    """

    # init empty
    projection_matrices = np.zeros((number_of_projections, 3, 4))

    # axes for later use
    x_axis = np.array([1.0, 0.0, 0.0])
    y_axis = np.array([0.0, 1.0, 0.0])
    z_axis = np.array([0.0, 0.0, 1.0])

    # defining u and v directions by: main coord axes
    u_dir = y_axis
    if swap_detector_axis:
        v_dir = x_axis
    else:
        v_dir = -x_axis

    # configure intrinsic camera parameters
    intrinsic_params_mat = np.eye(3, 3)
    for i in range(2):
        intrinsic_params_mat[i, i] = source_detector_distance / detector_spacing[1-i]

    # calc and set detector origin
    # we need t_x and t_y, and follow the [z,y,x] convention

    intrinsic_params_mat[0,2] = detector_origin[-1] / detector_spacing[-1] *-1
    intrinsic_params_mat[1,2] = detector_origin[-2] / detector_spacing[-2] *-1
    # intrinsic_params_mat[0:2, 2] = detector_origin / detector_spacing *-1
    # configure extrinisc pararams and create projection_matrices
    current_angle = angular_range[0]
    angular_increment = (angular_range[1] - angular_range[0]) / number_of_projections
    for p in range(number_of_projections):
        # calculate extrinsic params
        extrinsic_params_mat = np.eye(4, 4)

        # # rotation of axes from world system to plane of rotation system
        R_to_plane = np.eye(4, 4)
        R_to_plane[0:3, 0:3] = np.array([z_axis, np.cross(z_axis, x_axis), -x_axis])

        # rotation for u and v direction
        axis_align_R = np.eye(4, 4)
        axis_align_R[0:3, 0] = u_dir
        axis_align_R[0:3, 1] = v_dir
        axis_align_R[0:3, 2] = np.cross(u_dir, v_dir)
        axis_align_R = axis_align_R.T

        # rotation about x axis
        R_x_axis = np.eye(4, 4)
        R_x_axis[0:3, 0:3] = np.array([1, 0, 0,
                                       0, np.cos(-current_angle), -np.sin(-current_angle),
                                       0, np.sin(-current_angle), np.cos(-current_angle)]).reshape((3, 3))

        # rotation about z axis
        R_z_axis = np.eye(4, 4)
        R_z_axis[0:3, 0:3] = np.array([
                                       np.cos(-longtitude), -np.sin(-longtitude),0,
                                       np.sin(-longtitude), np.cos(-longtitude), 0,
                                        0, 0, 1]).reshape((3, 3))

        # translation of camera
        translation = np.eye(4, 4)
        translation[0:4, 3] = np.array([0, 0, -source_isocenter_distance, 1])

        # combine the above into 4x4 extrinsic params matrix
        extrinsic_params_mat = np.dot(np.dot(np.dot(np.dot(axis_align_R, translation), R_x_axis), R_z_axis), R_to_plane.T)
        extrinsic_params_mat = extrinsic_params_mat / extrinsic_params_mat[3, 3]

        # calculate projection matrix
        projection_matrices[p][0:3, 0:3] = np.dot(intrinsic_params_mat, extrinsic_params_mat[0:3, 0:3])
        projection_matrices[p][0:3, 3] = np.dot(intrinsic_params_mat, extrinsic_params_mat[0:3, 3])

        # next angle
        current_angle += angular_increment

    return projection_matrices