class_docstr: Represents a segmentation of an image.
  
  IdentityPrimAutomatic produces three variants of its segmentation
  result. This object contains all three.
  
  There are three formats for segmentation, two of which support
  overlapping objects:
  
  get/set_segmented - Legacy, a single plane of labels that does not
                      support overlapping objects.
  
  (see cellprofiler_library.functions.segmentation._validate_labels)
  
  
  get_labels        - Supports overlapping objects, returns one or more planes
                      along with indices. A typical usage is to perform an
                      operation per-plane as if the objects did not overlap.
  
  (see cellprofiler_library.functions.segmentation.convert_dense_to_label_set)
  
  
  get/set_ijv       - Supports overlapping objects, returns a sparse
                      representation in which the first two columns are the
                      coordinates and the last is the object number. This
                      is efficient for doing things like calculating intensity
                      per-object.
  
  (see cellprofiler_library.functions.segmentation._validate_ijv)
  
  
  You can set one of the types and then get any of the types (except that
  get_segmented will raise an exception if objects overlap).
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docstr: Find per pixel overlap of parent labels and child labels,
  stored in ijv format.
  
  parent_ijv - the parents which contain the children
  child_ijv - the children to be mapped to a parent
  
  Returns a sparse matrix of overlap between each parent and child.
  Note that the first row and column are empty, as these
  correspond to parent and child labels of 0.
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code: def histogram_from_ijv(parent_ijv, child_ijv):
      """Find per pixel overlap of parent labels and child labels,
      stored in ijv format.
  
      parent_ijv - the parents which contain the children
      child_ijv - the children to be mapped to a parent
  
      Returns a sparse matrix of overlap between each parent and child.
      Note that the first row and column are empty, as these
      correspond to parent and child labels of 0.
      """
      return find_ijv_overlaps(parent_ijv, child_ijv, validate=True)
  
imports: import numpy
  from cellprofiler_library.functions.segmentation import convert_dense_to_label_set
  from cellprofiler_library.functions.segmentation import convert_sparse_to_ijv
  from cellprofiler_library.functions.segmentation import indices_from_ijv
  from cellprofiler_library.functions.segmentation import count_from_ijv
  from cellprofiler_library.functions.segmentation import areas_from_ijv
  from cellprofiler_library.functions.segmentation import convert_labels_to_dense
  from cellprofiler_library.functions.segmentation import convert_ijv_to_sparse
  from cellprofiler_library.functions.segmentation import make_rgb_outlines
  from cellprofiler_library.functions.segmentation import find_label_overlaps
  from cellprofiler_library.functions.segmentation import find_ijv_overlaps
  from cellprofiler_library.functions.segmentation import center_of_labels_mass
  from ._segmentation import Segmentation
label_desc: Use image node_id 31 for calling the Objects obj's underlying member method code with example usage: obj.histogram_from_ijv(parent_ijv, child_ijv) and returns: find_ijv_overlaps