How to use the photutils.segmentation.core.SegmentationImage function in photutils

def __init__(self, data, segment_img, label, filtered_data=None,
                 error=None, mask=None, background=None, wcs=None):

        if not isinstance(segment_img, SegmentationImage):
            segment_img = SegmentationImage(segment_img)

        if segment_img.shape != data.shape:
            raise ValueError('segment_img and data must have the same shape.')

        inputs = (data, filtered_data, error, background)
        has_unit = [hasattr(x, 'unit') for x in inputs if x is not None]
        use_units = all(has_unit)
        if any(has_unit) and not use_units:
            raise ValueError('If any of data, filtered_data, error, or '
                             'background has units, then they all must have '
                             'the same units.')

        if use_units:
            self._data_unit = data.unit
        else:

def __init__(self, data, segment_img, label, filtered_data=None,
                 error=None, mask=None, background=None, wcs=None):

        if not isinstance(segment_img, SegmentationImage):
            segment_img = SegmentationImage(segment_img)

        if segment_img.shape != data.shape:
            raise ValueError('segment_img and data must have the same shape.')

        inputs = (data, filtered_data, error, background)
        has_unit = [hasattr(x, 'unit') for x in inputs if x is not None]
        use_units = all(has_unit)
        if any(has_unit) and not use_units:
            raise ValueError('If any of data, filtered_data, error, or '
                             'background has units, then they all must have '
                             'the same units.')

        if use_units:
            self._data_unit = data.unit
        else:
            self._data_unit = 1

relabel = False
            # if the are more sources at the upper level, then
            # remove the parent source(s) from the lower level,
            # but keep any sources in the lower level that do not have
            # multiple children in the upper level
            for label in segments[i].labels:
                mask = (segm_lower == label)
                # checks for 1-to-1 label mapping n -> m (where m >= 0)
                upper_labels = segm_upper[mask]
                upper_labels = np.unique(upper_labels[upper_labels != 0])
                if upper_labels.size >= 2:
                    relabel = True
                    segm_lower[mask] = segm_upper[mask]

            if relabel:
                segm_new = object.__new__(SegmentationImage)
                segm_new._data = ndilabel(segm_lower, structure=selem)[0]
                segments[i + 1] = segm_new
            else:
                segments[i + 1] = segments[i]

        # Deblend using watershed.  If any sources do not meet the
        # contrast criterion, then remove the faintest such source and
        # repeat until all sources meet the contrast criterion.
        markers = segments[-1].data
        mask = segment_img.data.astype(bool)
        remove_marker = True
        while remove_marker:
            markers = watershed(-data, markers, mask=mask, connectivity=selem)

            labels = np.unique(markers[markers != 0])
            flux_frac = np.array([np.sum(data[markers == label])

Print some properties of the second object (labeled with ``2`` in
    the segmentation image):

    >>> props[1].id  # id corresponds to segment label number
    2
    >>> props[1].centroid  # doctest: +FLOAT_CMP
    
    >>> props[1].perimeter  # doctest: +FLOAT_CMP
    
    >>> props[1].orientation  # doctest: +FLOAT_CMP
    
    """

    if not isinstance(segment_img, SegmentationImage):
        segment_img = SegmentationImage(segment_img)

    if segment_img.shape != data.shape:
        raise ValueError('segment_img and data must have the same shape.')

    # filter the data once, instead of repeating for each source
    if filter_kernel is not None:
        filtered_data = _filter_data(data, filter_kernel, mode='constant',
                                     fill_value=0.0, check_normalization=True)
    else:
        filtered_data = None

    if labels is None:
        labels = segment_img.labels
    labels = np.atleast_1d(labels)

    sources_props = []

4.0

    Print some properties of the second object (labeled with ``2`` in
    the segmentation image):

    >>> props[1].id  # id corresponds to segment label number
    2
    >>> props[1].centroid  # doctest: +FLOAT_CMP
    
    >>> props[1].perimeter  # doctest: +FLOAT_CMP
    
    >>> props[1].orientation  # doctest: +FLOAT_CMP
    
    """

    if not isinstance(segment_img, SegmentationImage):
        segment_img = SegmentationImage(segment_img)

    if segment_img.shape != data.shape:
        raise ValueError('segment_img and data must have the same shape.')

    # filter the data once, instead of repeating for each source
    if filter_kernel is not None:
        filtered_data = _filter_data(data, filter_kernel, mode='constant',
                                     fill_value=0.0, check_normalization=True)
    else:
        filtered_data = None

    if labels is None:
        labels = segment_img.labels
    labels = np.atleast_1d(labels)

relabeled such that the labels are in consecutive order starting
        from 1.

    Returns
    -------
    segment_image : `~photutils.segmentation.SegmentationImage`
        A segmentation image, with the same shape as ``data``, where
        sources are marked by different positive integer values.  A
        value of zero is reserved for the background.

    See Also
    --------
    :func:`photutils.segmentation.detect_sources`
    """

    if not isinstance(segment_img, SegmentationImage):
        segment_img = SegmentationImage(segment_img)

    if segment_img.shape != data.shape:
        raise ValueError('The data and segmentation image must have '
                         'the same shape')

    if labels is None:
        labels = segment_img.labels
    labels = np.atleast_1d(labels)
    segment_img.check_labels(labels)

    if filter_kernel is not None:
        data = _filter_data(data, filter_kernel, mode='constant',
                            fill_value=0.0)

    last_label = segment_img.max_label

from 1.

    Returns
    -------
    segment_image : `~photutils.segmentation.SegmentationImage`
        A segmentation image, with the same shape as ``data``, where
        sources are marked by different positive integer values.  A
        value of zero is reserved for the background.

    See Also
    --------
    :func:`photutils.segmentation.detect_sources`
    """

    if not isinstance(segment_img, SegmentationImage):
        segment_img = SegmentationImage(segment_img)

    if segment_img.shape != data.shape:
        raise ValueError('The data and segmentation image must have '
                         'the same shape')

    if labels is None:
        labels = segment_img.labels
    labels = np.atleast_1d(labels)
    segment_img.check_labels(labels)

    if filter_kernel is not None:
        data = _filter_data(data, filter_kernel, mode='constant',
                            fill_value=0.0)

    last_label = segment_img.max_label
    segm_deblended = object.__new__(SegmentationImage)

segm_slices = ndimage.find_objects(segm_img)
        for i, slices in enumerate(segm_slices):
            cutout = segm_img[slices]
            segment_mask = (cutout == (i+1))
            if np.count_nonzero(segment_mask) < npixels:
                cutout[segment_mask] = 0

        if np.count_nonzero(segm_img) == 0:
            warnings.warn('No sources were found.', NoDetectionsWarning)
            if deblend_skip:
                continue
            else:
                segms.append(None)
                continue

        segm = object.__new__(SegmentationImage)
        segm._data = segm_img

        if deblend_skip and segm.nlabels == 1:
            continue
        else:
            segm.relabel_consecutive()
            segms.append(segm)

    return segms