sksurgerysurfacematch.pipelines.register_cloud_to_stereo_mosaic module

Pipeline to register 3D point cloud to mosaic’ed surface reconstruction.

class sksurgerysurfacematch.pipelines.register_cloud_to_stereo_mosaic.Register3DToMosaicedStereoVideo(video_segmentor: sksurgerysurfacematch.interfaces.video_segmentor.VideoSegmentor, surface_reconstructor: sksurgerysurfacematch.algorithms.reconstructor_with_rectified_images.StereoReconstructorWithRectifiedImages, rigid_registration: sksurgerysurfacematch.interfaces.rigid_registration.RigidRegistration, left_camera_matrix: numpy.ndarray, right_camera_matrix: numpy.ndarray, left_to_right_rmat: numpy.ndarray, left_to_right_tvec: numpy.ndarray, min_number_of_keypoints: int = 25, max_fre_threshold=2, left_mask: numpy.ndarray = None, z_range: list = None, radius_removal: list = None, voxel_reduction: list = None)

Bases: object

Class to register a point cloud to a series of surfaces derived from stereo video, and stitched together.

grab(left_image: numpy.ndarray, right_image: numpy.ndarray)

Call this repeatedly to grab a surface and use ORM key points to match previous reconstruction to the current frame.

Parameters:

• left_image – undistorted, BGR image
• right_image – undistorted, BGR image

register(point_cloud: numpy.ndarray, initial_transform: numpy.ndarray = None)

Registers a point cloud to the internal mosaicc’ed reconstruction.

Parameters:

• point_cloud – [Nx3] points, each row, x,y,z, e.g. from CT/MR.
• initial_transform – [4x4] of initial rigid transform.

Returns:

residual, [4x4] transform, of point_cloud to left camera space,

and [Mx6] reconstructed point cloud, as [x, y, z, r, g, b] rows.

reset()

Reset’s internal data members, so that you can start accumulating data again.