class_docstr: MediaPipe Selfie Segmentation.
  
  MediaPipe Selfie Segmentation processes an RGB image and returns a
  segmentation mask.
  
  Please refer to
  https://solutions.mediapipe.dev/selfie_segmentation#python-solution-api for
  usage examples.
class_docstr_tok: ["MediaPipe","Selfie","Segmentation",".","MediaPipe","Selfie","Segmentation","processes","an","RGB","image","and","returns","a","segmentation","mask",".","Please","refer","to","https",":","\/\/solutions.mediapipe.dev\/selfie_segmentation","#","python-solution-api","for","usage","examples","."]
docstr: Initializes a MediaPipe Selfie Segmentation object.
  
  Args:
    model_selection: 0 or 1. 0 to select a general-purpose model, and 1 to
      select a model more optimized for landscape images. See details in
      https://solutions.mediapipe.dev/selfie_segmentation#model_selection.
docstr_tok: ["Initializes","a","MediaPipe","Selfie","Segmentation","object",".","Args",":","model_selection",":","0","or","1",".","0","to","select","a","general-purpose","model",",","and","1","to","select","a","model","more","optimized","for","landscape","images",".","See","details","in","https",":","\/\/solutions.mediapipe.dev\/selfie_segmentation","#","model_selection","."]
code: def __init__(self, model_selection=0):
      """Initializes a MediaPipe Selfie Segmentation object.
  
      Args:
        model_selection: 0 or 1. 0 to select a general-purpose model, and 1 to
          select a model more optimized for landscape images. See details in
          https://solutions.mediapipe.dev/selfie_segmentation#model_selection.
      """
      super().__init__(
          binary_graph_path=_BINARYPB_FILE_PATH,
          side_inputs={
              "model_selection": model_selection,
          },
          outputs=["segmentation_mask"],
      )
  
file_path: mediapipe/mediapipe/python/solutions/selfie_segmentation.py
imports: from typing import NamedTuple
  import numpy
  from mediapipe.calculators.core import constant_side_packet_calculator_pb2
  from mediapipe.calculators.tensor import image_to_tensor_calculator_pb2
  from mediapipe.calculators.tensor import inference_calculator_pb2
  from mediapipe.calculators.tensor import tensors_to_segmentation_calculator_pb2
  from mediapipe.calculators.util import local_file_contents_calculator_pb2
  from mediapipe.framework.tool import switch_container_pb2
  from mediapipe.python.solution_base import SolutionBase
label_desc: Use image node_id 1 to create a new SelfieSegmentation object from inherited base classes: SolutionBase with example: obj = SelfieSegmentation(model_selection)