def _reconstruction_from_rigs_and_assignments(data: DataSetBase): assignments = data.load_rig_assignments() models = data.load_rig_models() if not data.reference_lla_exists(): data.invent_reference_lla() base_rotation = np.zeros(3) reconstructions = [] for rig_id, instances in assignments.items(): rig_cameras = models[rig_id]["rig_cameras"] reconstruction = types.Reconstruction() reconstruction.cameras = data.load_camera_models() for instance in instances: for image, camera_id in instance: rig_camera = rig_cameras[camera_id] rig_pose = pygeometry.Pose(base_rotation) rig_pose.set_origin( orec.get_image_metadata(data, image).gps_position.value) rig_camera_pose = pygeometry.Pose(rig_camera["rotation"], rig_camera["translation"]) d = data.load_exif(image) shot = reconstruction.create_shot(image, d["camera"]) shot.pose = rig_camera_pose.compose(rig_pose) shot.metadata = orec.get_image_metadata(data, image) reconstructions.append(reconstruction) return reconstructions
def rig_statistics(data: DataSetBase, reconstructions): stats = {} permutation = np.argsort([-len(r.shots) for r in reconstructions]) for rig_model_id, rig_model in data.load_rig_models().items(): stats[rig_model_id] = { "initial_values": _rig_model_statistics(rig_model) } for idx in permutation: rec = reconstructions[idx] for rig_model in rec.rig_models.values(): if "optimized_values" in stats[rig_model.id]: continue stats[rig_model.id]["optimized_values"] = _rig_model_statistics( rig_model) for rig_model_id in data.load_rig_models(): if "optimized_values" not in stats[rig_model_id]: del stats[rig_model_id] return stats
def bootstrap_reconstruction(data: DataSetBase, tracks_manager, im1, im2, p1, p2): """Start a reconstruction using two shots.""" logger.info("Starting reconstruction with {} and {}".format(im1, im2)) report: Dict[str, Any] = { "image_pair": (im1, im2), "common_tracks": len(p1), } camera_priors = data.load_camera_models() camera1 = camera_priors[data.load_exif(im1)["camera"]] camera2 = camera_priors[data.load_exif(im2)["camera"]] threshold = data.config["five_point_algo_threshold"] min_inliers = data.config["five_point_algo_min_inliers"] iterations = data.config["five_point_refine_rec_iterations"] R, t, inliers, report[ "two_view_reconstruction"] = two_view_reconstruction_general( p1, p2, camera1, camera2, threshold, iterations) logger.info("Two-view reconstruction inliers: {} / {}".format( len(inliers), len(p1))) if len(inliers) <= 5: report["decision"] = "Could not find initial motion" logger.info(report["decision"]) return None, report rig_model_priors = data.load_rig_models() rig_assignments = data.load_rig_assignments_per_image() reconstruction = types.Reconstruction() reconstruction.reference = data.load_reference() reconstruction.cameras = camera_priors reconstruction.rig_models = rig_model_priors new_shots = add_shot(data, reconstruction, rig_assignments, im1, pygeometry.Pose()) new_shots += add_shot(data, reconstruction, rig_assignments, im2, pygeometry.Pose(R, t)) align_reconstruction(reconstruction, None, data.config) triangulate_shot_features(tracks_manager, reconstruction, new_shots, data.config) logger.info("Triangulated: {}".format(len(reconstruction.points))) report["triangulated_points"] = len(reconstruction.points) if len(reconstruction.points) < min_inliers: report["decision"] = "Initial motion did not generate enough points" logger.info(report["decision"]) return None, report to_adjust = {s for s in new_shots if s != im1} bundle_shot_poses(reconstruction, to_adjust, camera_priors, rig_model_priors, data.config) retriangulate(tracks_manager, reconstruction, data.config) if len(reconstruction.points) < min_inliers: report[ "decision"] = "Re-triangulation after initial motion did not generate enough points" logger.info(report["decision"]) return None, report bundle_shot_poses(reconstruction, to_adjust, camera_priors, rig_model_priors, data.config) report["decision"] = "Success" report["memory_usage"] = current_memory_usage() return reconstruction, report
def grow_reconstruction(data: DataSetBase, tracks_manager, reconstruction, images, gcp): """Incrementally add shots to an initial reconstruction.""" config = data.config report = {"steps": []} camera_priors = data.load_camera_models() rig_model_priors = data.load_rig_models() paint_reconstruction(data, tracks_manager, reconstruction) align_reconstruction(reconstruction, gcp, config) bundle(reconstruction, camera_priors, rig_model_priors, None, config) remove_outliers(reconstruction, config) paint_reconstruction(data, tracks_manager, reconstruction) should_bundle = ShouldBundle(data, reconstruction) should_retriangulate = ShouldRetriangulate(data, reconstruction) while True: if config["save_partial_reconstructions"]: paint_reconstruction(data, tracks_manager, reconstruction) data.save_reconstruction( [reconstruction], "reconstruction.{}.json".format( datetime.datetime.now().isoformat().replace(":", "_")), ) candidates = reconstructed_points_for_images(tracks_manager, reconstruction, images) if not candidates: break logger.info("-------------------------------------------------------") threshold = data.config["resection_threshold"] min_inliers = data.config["resection_min_inliers"] for image, _ in candidates: ok, new_shots, resrep = resect( data, tracks_manager, reconstruction, image, threshold, min_inliers, ) if not ok: continue new_shots = set(new_shots) images -= new_shots bundle_shot_poses(reconstruction, new_shots, camera_priors, rig_model_priors, data.config) logger.info( f"Adding {' and '.join(new_shots)} to the reconstruction") step = { "images": list(new_shots), "resection": resrep, "memory_usage": current_memory_usage(), } report["steps"].append(step) np_before = len(reconstruction.points) triangulate_shot_features(tracks_manager, reconstruction, new_shots, config) np_after = len(reconstruction.points) step["triangulated_points"] = np_after - np_before if should_retriangulate.should(): logger.info("Re-triangulating") align_reconstruction(reconstruction, gcp, config) b1rep = bundle(reconstruction, camera_priors, rig_model_priors, None, config) rrep = retriangulate(tracks_manager, reconstruction, config) b2rep = bundle(reconstruction, camera_priors, rig_model_priors, None, config) remove_outliers(reconstruction, config) step["bundle"] = b1rep step["retriangulation"] = rrep step["bundle_after_retriangulation"] = b2rep should_retriangulate.done() should_bundle.done() elif should_bundle.should(): align_reconstruction(reconstruction, gcp, config) brep = bundle(reconstruction, camera_priors, rig_model_priors, None, config) remove_outliers(reconstruction, config) step["bundle"] = brep should_bundle.done() elif config["local_bundle_radius"] > 0: bundled_points, brep = bundle_local(reconstruction, camera_priors, rig_model_priors, None, image, config) remove_outliers(reconstruction, config, bundled_points) step["local_bundle"] = brep break else: logger.info("Some images can not be added") break logger.info("-------------------------------------------------------") align_reconstruction(reconstruction, gcp, config) bundle(reconstruction, camera_priors, rig_model_priors, gcp, config) remove_outliers(reconstruction, config) paint_reconstruction(data, tracks_manager, reconstruction) return reconstruction, report