class_docstr: image - a Numpy array
  
  mask - a binary image indicating the points of interest in the image. The
  mask is the same size as the child image.
  
  crop_mask - the binary image used to crop the parent image to the
  dimensions of the child (this) image. The crop_mask is the same size as
  the parent image.
  
  parent_image - for derived images, the parent that was used to create this
  image. This image may inherit attributes from the parent image, such as
  the masks used to create the parent
  
  masking_objects - the labels matrix from these objects is used to mask and
  crop the parent image to make this image. The labels are available as
  mask_labels and crop_labels.
  
  convert - true to try to coerce whatever dtype passed (other than bool or
  float) to a scaled image.
  
  path_name - the path name to the file holding the image or None for a
  derived image
  
  file_name - the file name of the file holding the image or None for a
  derived image
  
  scale - the scaling suggested by the initial image format (e.g., 4095 for
  a 12-bit a/d converter).
  
  Resolution of mask and cropping_mask properties:
  
  The Image class looks for the mask and cropping_mask in the following
  places:
  
  * self: if set using the properties or specified in the initializer
  
  * masking_objects: if set using the masking_object property or specified
  in the initializer. The crop_mask and mask are composed of all of the
  labeled points.
  
  * parent_image: if set using the initializer. The child image inherits the
  mask and cropping mask of the parent.
  
  Otherwise, the image has no mask or cropping mask and all pixels are
  significant.
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docstr: Get the segmentation labels from the masking objects
  
  returns the "segmented" labels: others are available through
  the masking_object.
docstr_tok: ["Get","the","segmentation","labels","from","the","masking","objects","returns","the","``","segmented","''","labels",":","others","are","available","through","the","masking_object","."]
code: def labels(self):
      """Get the segmentation labels from the masking objects
  
      returns the "segmented" labels: others are available through
      the masking_object.
      """
      if not self.has_masking_objects:
          return None
  
      return self.crop_image_similarly(self.masking_objects.segmented)
  
imports: import math
  import numpy
  from ..utilities.image import crop_image
label_desc: Use image node_id 10 for calling the Image obj's underlying member method code with example usage: obj.labels() and returns: self, None