code: def __init__(self):
      #
      ALP_UV = loadmat(
          os.path.join(
              os.path.dirname(__file__),
              "../../DensePoseData/UV_data/UV_Processed.mat",
          )
      )
      self.FaceIndices = np.array(ALP_UV["All_FaceIndices"]).squeeze()
      self.FacesDensePose = ALP_UV["All_Faces"] - 1
      self.U_norm = ALP_UV["All_U_norm"].squeeze()
      self.V_norm = ALP_UV["All_V_norm"].squeeze()
      self.All_vertices = ALP_UV["All_vertices"][0]
      ## Info to compute symmetries.
      self.SemanticMaskSymmetries = [
          0,
          1,
          3,
          2,
          5,
          4,
          7,
          6,
          9,
          8,
          11,
          10,
          13,
          12,
          14,
      ]
      self.Index_Symmetry_List = [
          1,
          2,
          4,
          3,
          6,
          5,
          8,
          7,
          10,
          9,
          12,
          11,
          14,
          13,
          16,
          15,
          18,
          17,
          20,
          19,
          22,
          21,
          24,
          23,
      ]
      UV_symmetry_filename = os.path.join(
          os.path.dirname(__file__),
          "../../DensePoseData/UV_data/UV_symmetry_transforms.mat",
      )
      self.UV_symmetry_transformations = loadmat(UV_symmetry_filename)
  
file_path: dense-pose/detectron/utils/densepose_methods.py
imports: import numpy
  import copy
  import cv2
  from scipy.io import loadmat
  import scipy.spatial.distance
  import os
label_desc: Use image node_id 1 to create a new DensePoseMethods object  with example: obj = DensePoseMethods()