class_docstr: Get connection for selected estimation points.
  
  Args:
      mapIdx(list): Part Affinity Fields map index, default is None.
      limbSeq(list): Peak candidate map index, default is None.
code: def __call__(
      self, all_peaks: list, paf_avg: np.ndarray, orgimg: np.ndarray
  ):
      connection_all = []
      special_k = []
      for k in range(len(self.mapIdx)):
          score_mid = paf_avg[:, :, [x - 19 for x in self.mapIdx[k]]]
          candA = all_peaks[self.limbSeq[k][0] - 1]
          candB = all_peaks[self.limbSeq[k][1] - 1]
          nA = len(candA)
          nB = len(candB)
          if nA != 0 and nB != 0:
              connection_candidate = self.caculate_vector(
                  candA, candB, nA, nB, score_mid, orgimg
              )
              connection_candidate = sorted(
                  connection_candidate, key=lambda x: x[2], reverse=True
              )
              connection = np.zeros((0, 5))
              for c in range(len(connection_candidate)):
                  i, j, s = connection_candidate[c][0:3]
                  if (
                      i not in connection[:, 3]
                      and j not in connection[:, 4]
                  ):
                      connection = np.vstack(
                          [
                              connection,
                              [candA[i][3], candB[j][3], s, i, j],
                          ]
                      )
                      if len(connection) >= min(nA, nB):
                          break
  
              connection_all.append(connection)
          else:
              special_k.append(k)
              connection_all.append([])
  
      return connection_all, special_k
  
imports: import base64
  import math
  from typing import Callable
  import cv2
  import numpy
  from scipy.ndimage import gaussian_filter
label_desc: Use image node_id 2 for calling the Connection obj's underlying member method code with example usage: obj.__call__(all_peaks, paf_avg, orgimg) and returns: connection_all,  special_k