Function main_vis

main.py:49–77

(
    data_dir: str = "/home/kin/data/00",
    voxel_map: bool = True, # output voxel-level map or raw point-level.
)

        return res_dict

def main_vis(
    data_dir: str = "/home/kin/data/00",
    voxel_map: bool = True, # output voxel-level map or raw point-level.
):
    dataset = DynamicMapData(data_dir)

    # STEP 0: initialize 
    mydufo = dufomap(0.1, 0.2, 2, num_threads=12) # resolution, d_s, d_p same with paper.
    cloud_acc = np.zeros((0, 3), dtype=np.float32)
    for data_id in (pbar := tqdm(range(0, len(dataset)),ncols=100)):
        data = dataset[data_id]
        now_scene_id = data['scene_id']
        pbar.set_description(f"id: {data_id}, scene_id: {now_scene_id}, timestamp: {data['timestamp']}")
        norm_pc0 = np.linalg.norm(data['pc'][:, :3] - data['pose'][:3], axis=1)
        range_mask = (
                (norm_pc0>MIN_AXIS_RANGE) & 
                (norm_pc0<MAX_AXIS_RANGE)
        )
        # STEP 1: integrate point cloud into dufomap
        mydufo.run(data['pc'][range_mask], data['pose'], cloud_transform = False)
        # STEP 1: integrate point cloud into dufomap
        cloud_acc = np.concatenate((cloud_acc, data['pc']), axis=0)
    
    # STEP 2: propagate
    mydufo.oncePropagateCluster(if_propagate=True, if_cluster=False)
    # STEP 3: Map results; You can save the voxel map directly based on the resolution we set before:
    mydufo.outputMap(cloud_acc, voxel_map=voxel_map)
    
    mydufo.printDetailTiming()

if __name__ == "__main__":
    start_time = time.time()