How to use the pyrealsense2.spatial_filter function in pyrealsense2

#vis.create_window("Pointcloud",640,480)
    vis.create_window("Pointcloud")
    pointcloud = PointCloud()
    i = 0

    try:
        while True:
            dt0 = datetime.now()
            frames = pipeline.wait_for_frames()
            aligned_frames = align.process(frames)
            color_frame = aligned_frames.get_color_frame()
            color_image = np.asanyarray(color_frame.get_data())
            depth_frame = aligned_frames.get_depth_frame()
            depth_frame = rs.decimation_filter(1).process(depth_frame)
            depth_frame = rs.disparity_transform(True).process(depth_frame)
            depth_frame = rs.spatial_filter().process(depth_frame)
            depth_frame = rs.temporal_filter().process(depth_frame)
            depth_frame = rs.disparity_transform(False).process(depth_frame)
            # depth_frame = rs.hole_filling_filter().process(depth_frame)

            depth_image = np.asanyarray(depth_frame.get_data())
            color_image1 = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)
            depth_color_frame = rs.colorizer().colorize(depth_frame)
            depth_color_image = np.asanyarray(depth_color_frame.get_data())

            cv2.imshow('Color Stream', color_image1)
            cv2.imshow('Depth Stream', depth_color_image )

            depth = Image(depth_image)
            color = Image(color_image)

            rgbd = create_rgbd_image_from_color_and_depth(color, depth, convert_rgb_to_intensity = False)

# temFeatureList,tem_new_xyr = registration.extractFeatures(temPoint2,tem_old_xyr,n = 3)

    while True:
        # Pt2 = []
        Point2 = o3d.geometry.PointCloud()

        frames = pipeline.wait_for_frames()
        aligned_frames = align.process(frames)

        color_frame = aligned_frames.get_color_frame()
        color_image = np.asanyarray(color_frame.get_data())
        depth_frame = aligned_frames.get_depth_frame()

        depth_frame = rs.decimation_filter(1).process(depth_frame)
        depth_frame = rs.disparity_transform(True).process(depth_frame)
        depth_frame = rs.spatial_filter().process(depth_frame)
        depth_frame = rs.temporal_filter().process(depth_frame)
        depth_frame = rs.disparity_transform(False).process(depth_frame)

        depth_image = np.asanyarray(depth_frame.get_data())
        color_image1 = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)

        xl,yl,rl = keyPoints.getCircles(color_image1)
        old_xyr = []
        currentImage = color_image1.copy()

        if i == 0:
            for ind,x in enumerate(xl):
                cv2.circle(currentImage, (xl[ind],yl[ind]), rl[ind], (0, 255, 0), -1)

        elif i == 1:
            a,b,c,d = keyPoints.calculatePlane(RealSense,depth_image,xl,yl,rl)

temporal_smooth_delta : double
                            The delta value for temporal filter based smoothening


    Return:
    ----------
    filtered_frame : rs.frame()
                       The post-processed depth frame
    """

    # Post processing possible only on the depth_frame
    assert (depth_frame.is_depth_frame())

    # Available filters and control options for the filters
    decimation_filter = rs.decimation_filter()
    spatial_filter = rs.spatial_filter()
    temporal_filter = rs.temporal_filter()

    filter_magnitude = rs.option.filter_magnitude
    filter_smooth_alpha = rs.option.filter_smooth_alpha
    filter_smooth_delta = rs.option.filter_smooth_delta

    # Apply the control parameters for the filter
    decimation_filter.set_option(filter_magnitude, decimation_magnitude)
    spatial_filter.set_option(filter_magnitude, spatial_magnitude)
    spatial_filter.set_option(filter_smooth_alpha, spatial_smooth_alpha)
    spatial_filter.set_option(filter_smooth_delta, spatial_smooth_delta)
    temporal_filter.set_option(filter_smooth_alpha, temporal_smooth_alpha)
    temporal_filter.set_option(filter_smooth_delta, temporal_smooth_delta)

    # Apply the filters
    filtered_frame = decimation_filter.process(depth_frame)

try:
        while True:
            time_start = time.time()
            pointcloud.clear()

            frames = pipeline.wait_for_frames()
            aligned_frames = align.process(frames)

            color_frame = aligned_frames.get_color_frame()
            color_image = np.asanyarray(color_frame.get_data())
            depth_frame = aligned_frames.get_depth_frame()

            depth_frame = rs.decimation_filter(1).process(depth_frame)
            depth_frame = rs.disparity_transform(True).process(depth_frame)
            depth_frame = rs.spatial_filter().process(depth_frame)
            depth_frame = rs.temporal_filter().process(depth_frame)
            depth_frame = rs.disparity_transform(False).process(depth_frame)
            # depth_frame = rs.hole_filling_filter().process(depth_frame)
            

            depth_image = np.asanyarray(depth_frame.get_data())
            color_image1 = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)

            cv2.namedWindow('color image', cv2.WINDOW_AUTOSIZE)
            cv2.imshow('color image', cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR))
            cv2.namedWindow('depth image', cv2.WINDOW_AUTOSIZE)
            cv2.imshow('depth image', depth_image )

            depth = Image(depth_image)
            color = Image(color_image)

FORMAT      = [rs.format.z16, rs.format.bgr8]     # rs2_format is identifies how binary data is encoded within a frame
WIDTH       = 640              # Defines the number of columns for each frame or zero for auto resolve
HEIGHT      = 480              # Defines the number of lines for each frame or zero for auto resolve
FPS         = 30               # Defines the rate of frames per second
DEPTH_RANGE = [0.1, 8.0]       # Replace with your sensor's specifics, in meter

USE_PRESET_FILE = True
PRESET_FILE  = "../cfg/d4xx-default.json"

# List of filters to be applied, in this order.
# https://github.com/IntelRealSense/librealsense/blob/master/doc/post-processing-filters.md
filters = [
    [True, "Decimation Filter",     rs.decimation_filter()],
    [True, "Threshold Filter",      rs.threshold_filter()],
    [True, "Depth to Disparity",    rs.disparity_transform(True)],
    [True, "Spatial Filter",        rs.spatial_filter()],
    [True, "Temporal Filter",       rs.temporal_filter()],
    [False, "Hole Filling Filter",   rs.hole_filling_filter()],
    [True, "Disparity to Depth",    rs.disparity_transform(False)]
]

######################################################
##  ArduPilot-related parameters - reconfigurable   ##
######################################################

# Default configurations for connection to the FCU
connection_string_default = '/dev/ttyUSB0'
connection_baudrate_default = 921600
connection_timeout_sec_default = 5

# Use this to rotate all processed data
camera_facing_angle_degree = 0

try:
        while True:
            # time_start = time.time()
            pointcloud.clear()

            frames = pipeline.wait_for_frames()
            aligned_frames = align.process(frames)

            color_frame = aligned_frames.get_color_frame()
            color_image = np.asanyarray(color_frame.get_data())
            depth_frame = aligned_frames.get_depth_frame()

            depth_frame = rs.decimation_filter(1).process(depth_frame)
            depth_frame = rs.disparity_transform(True).process(depth_frame)
            depth_frame = rs.spatial_filter().process(depth_frame)
            depth_frame = rs.temporal_filter().process(depth_frame)
            depth_frame = rs.disparity_transform(False).process(depth_frame)
            # depth_frame = rs.hole_filling_filter().process(depth_frame)
            
            depth_color_frame = rs.colorizer().colorize(depth_frame)
            depth_color_image = np.asanyarray(depth_color_frame.get_data())

            depth_image = np.asanyarray(depth_frame.get_data())
            color_image1 = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)

            cv2.namedWindow('color image', cv2.WINDOW_AUTOSIZE)
            cv2.imshow('color image', cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR))
            cv2.namedWindow('depth image', cv2.WINDOW_AUTOSIZE)
            cv2.imshow('depth image', depth_color_image )

            depth = o3d.geometry.Image(depth_image)