How to use the open3d.visualization.draw_geometries function in open3d

mesh = o3d.io.read_triangle_mesh("../../TestData/knot.ply")
    print(mesh)
    print(np.asarray(mesh.vertices))
    print(np.asarray(mesh.triangles))
    print("")

    print("Try to render a mesh with normals (exist: " +
          str(mesh.has_vertex_normals()) + ") and colors (exist: " +
          str(mesh.has_vertex_colors()) + ")")
    o3d.visualization.draw_geometries([mesh])
    print("A mesh with no normals and no colors does not seem good.")

    print("Computing normal and rendering it.")
    mesh.compute_vertex_normals()
    print(np.asarray(mesh.triangle_normals))
    o3d.visualization.draw_geometries([mesh])

    print("We make a partial mesh of only the first half triangles.")
    mesh1 = copy.deepcopy(mesh)
    mesh1.triangles = o3d.utility.Vector3iVector(
        np.asarray(mesh1.triangles)[:len(mesh1.triangles) // 2, :])
    mesh1.triangle_normals = o3d.utility.Vector3dVector(
        np.asarray(mesh1.triangle_normals)[:len(mesh1.triangle_normals) //
                                           2, :])
    print(mesh1.triangles)
    o3d.visualization.draw_geometries([mesh1])

    print("Painting the mesh")
    mesh1.paint_uniform_color([1, 0.706, 0])
    o3d.visualization.draw_geometries([mesh1])

def draw_registration_result_original_color(source, target, transformation):
    source_temp = copy.deepcopy(source)
    source_temp.transform(transformation)
    o3d.visualization.draw_geometries([source_temp, target])

pcd.paint_uniform_color([0.5, 0.5, 0.5])
    pcd_tree = o3d.geometry.KDTreeFlann(pcd)

    print("Paint the 1500th point red.")
    pcd.colors[1500] = [1, 0, 0]

    print("Find its 200 nearest neighbors, paint blue.")
    [k, idx, _] = pcd_tree.search_knn_vector_3d(pcd.points[1500], 200)
    np.asarray(pcd.colors)[idx[1:], :] = [0, 0, 1]

    print("Find its neighbors with distance less than 0.2, paint green.")
    [k, idx, _] = pcd_tree.search_radius_vector_3d(pcd.points[1500], 0.2)
    np.asarray(pcd.colors)[idx[1:], :] = [0, 1, 0]

    print("Visualize the point cloud.")
    o3d.visualization.draw_geometries([pcd])
    print("")

def draw_registration_result(source, target, transformation):
    source_temp = copy.deepcopy(source)
    target_temp = copy.deepcopy(target)
    source_temp.paint_uniform_color([1, 0.706, 0])
    target_temp.paint_uniform_color([0, 0.651, 0.929])
    source_temp.transform(transformation)
    o3d.visualization.draw_geometries([source_temp, target_temp])

o3d.visualization.draw_geometries([mesh, bbox])
    o3d.visualization.draw_geometries([mesh.crop(bbox), bbox])

    pcd = mesh.sample_points_uniformly(500000)

    bbox = o3d.geometry.AxisAlignedBoundingBox(min_bound=(-30, 0, -10),
                                               max_bound=(10, 20, 10))
    o3d.visualization.draw_geometries([pcd, bbox])
    o3d.visualization.draw_geometries([pcd.crop(bbox), bbox])

    bbox = o3d.geometry.OrientedBoundingBox(
        center=(-10, 10, 0),
        R=bbox.get_rotation_matrix_from_xyz((2, 1, 0)),
        extent=(40, 20, 20),
    )
    o3d.visualization.draw_geometries([pcd, bbox])
    o3d.visualization.draw_geometries([pcd.crop(bbox), bbox])

def draw_registration_result(source, target, transformation):
    source_temp = copy.deepcopy(source)
    target_temp = copy.deepcopy(target)
    source_temp.paint_uniform_color([1, 0.706, 0])
    target_temp.paint_uniform_color([0, 0.651, 0.929])
    source_temp.transform(transformation)
    o3d.visualization.draw_geometries([source_temp, target_temp])

print(
        "Convet a numpy image to o3d.geometry.Image and show it with DrawGeomtries()."
    )
    y = mpimg.imread("../../TestData/lena_color.jpg")
    print(y.shape)
    yy = o3d.geometry.Image(y)
    print(yy)
    o3d.visualization.draw_geometries([yy])

    print("Render a channel of the previous image.")
    z = np.array(y[:, :, 1])
    print(z.shape)
    print(z.strides)
    zz = o3d.geometry.Image(z)
    print(zz)
    o3d.visualization.draw_geometries([zz])

    print("Write the previous image to file then load it with matplotlib.")
    o3d.io.write_image("test.jpg", zz, quality=100)
    zzz = mpimg.imread("test.jpg")
    plt.imshow(zzz)
    plt.show()

    print("Testing basic image processing module.")
    im_raw = mpimg.imread("../../TestData/lena_color.jpg")
    im = o3d.geometry.Image(im_raw)
    im_g3 = im.filter(o3d.geometry.ImageFilterType.Gaussian3)
    im_g5 = im.filter(o3d.geometry.ImageFilterType.Gaussian5)
    im_g7 = im.filter(o3d.geometry.ImageFilterType.Gaussian7)
    im_gaussian = [im, im_g3, im_g5, im_g7]
    pyramid_levels = 4
    pyramid_with_gaussian_filter = True

yield meshes.armadillo()


if __name__ == "__main__":
    for mesh in mesh_generator():
        mesh.compute_vertex_normals()
        hull, _ = mesh.compute_convex_hull()
        hull_ls = o3d.geometry.LineSet.create_from_triangle_mesh(hull)
        hull_ls.paint_uniform_color((1, 0, 0))
        o3d.visualization.draw_geometries([mesh, hull_ls])

        pcl = mesh.sample_points_poisson_disk(number_of_points=2000)
        hull, _ = pcl.compute_convex_hull()
        hull_ls = o3d.geometry.LineSet.create_from_triangle_mesh(hull)
        hull_ls.paint_uniform_color((1, 0, 0))
        o3d.visualization.draw_geometries([pcl, hull_ls])

print("Recompute the normal of the downsampled point cloud")
    downpcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(
        radius=0.1, max_nn=30))
    o3d.visualization.draw_geometries([downpcd])

    print("Print a normal vector of the 0th point")
    print(downpcd.normals[0])
    print("Print the normal vectors of the first 10 points")
    print(np.asarray(downpcd.normals)[:10, :])
    print("")

    print("Load a polygon volume and use it to crop the original point cloud")
    vol = o3d.visualization.read_selection_polygon_volume(
        "../../TestData/Crop/cropped.json")
    chair = vol.crop_point_cloud(pcd)
    o3d.visualization.draw_geometries([chair])
    print("")

    print("Paint chair")
    chair.paint_uniform_color([1, 0.706, 0])
    o3d.visualization.draw_geometries([chair])
    print("")