class_docstr: Converts SciPy-style contours (generated by skimage.measure.find_contours) to a more succinct version which only includes
  the pixels to which lines need to be drawn (i.e. not the intervening pixels along each line).
  
  Args:
      height: height of bounding box, used to detect direction of line segment.
      width: width of bounding box, used to detect direction of line segment.
  
  Returns:
      the pixels that need to be joined by straight lines to describe the outmost pixels of the foreground similar to
          OpenCV's cv.CHAIN_APPROX_SIMPLE (counterclockwise)
class_docstr_tok: ["Converts","SciPy-style","contours","(","generated","by","skimage.measure.find_contours",")","to","a","more","succinct","version","which","only","includes","the","pixels","to","which","lines","need","to","be","drawn","(","i.e",".","not","the","intervening","pixels","along","each","line",")",".","Args",":","height",":","height","of","bounding","box",",","used","to","detect","direction","of","line","segment",".","width",":","width","of","bounding","box",",","used","to","detect","direction","of","line","segment",".","Returns",":","the","pixels","that","need","to","be","joined","by","straight","lines","to","describe","the","outmost","pixels","of","the","foreground","similar","to","OpenCV","'s","cv.CHAIN_APPROX_SIMPLE","(","counterclockwise",")"]
docstr: Args:
      contours: list of (n, 2)-ndarrays, scipy-style clockwise line segments, with lines separating foreground/background.
          Each contour is an ndarray of shape (n, 2), consisting of n (row, column) coordinates along the contour.
docstr_tok: ["Args",":","contours",":","list","of","(","n",",","2",")","-ndarrays",",","scipy-style","clockwise","line","segments",",","with","lines","separating","foreground\/background",".","Each","contour","is","an","ndarray","of","shape","(","n",",","2",")",",","consisting","of","n","(","row",",","column",")","coordinates","along","the","contour","."]
code: def __call__(self, contours: list[np.ndarray]) -> np.ndarray:
      """
      Args:
          contours: list of (n, 2)-ndarrays, scipy-style clockwise line segments, with lines separating foreground/background.
              Each contour is an ndarray of shape (n, 2), consisting of n (row, column) coordinates along the contour.
      """
      pixels: list[tuple[int, int]] = []
      sequences = []
      corners = [False, False, False, False]
  
      for group in contours:
          sequence: list[tuple[int, int]] = []
          last_added = None
          prev = None
          corner = -1
  
          for i, coord in enumerate(group):
              if i == 0:
                  # originating from the top, so must be heading south east
                  if coord[0] == 0.0:
                      corner = 1
                      pixel = (0, int(coord[1] - 0.5))
                      if pixel[1] == self.width - 1:
                          corners[1] = True
                      elif pixel[1] == 0.0:
                          corners[0] = True
                  # originating from the left, so must be heading north east
                  elif coord[1] == 0.0:
                      corner = 0
                      pixel = (int(coord[0] + 0.5), 0)
                  # originating from the bottom, so must be heading north west
                  elif coord[0] == self.height - 1:
                      corner = 3
                      pixel = (int(coord[0]), int(coord[1] + 0.5))
                      if pixel[1] == self.width - 1:
                          corners[2] = True
                  # originating from the right, so must be heading south west
                  elif coord[1] == self.width - 1:
                      corner = 2
                      pixel = (int(coord[0] - 0.5), int(coord[1]))
                  else:
                      warnings.warn(
                          f"Invalid contour coord {coord} is generated, skip this instance."
                      )
                      return None  # type: ignore
                  sequence.append(pixel)
                  last_added = pixel
              elif i == len(group) - 1:
                  # add this point
                  pixel = self._generate_contour_coord(coord, prev)  # type: ignore
                  if pixel != last_added:
                      sequence.append(pixel)
                      last_added = pixel
              elif np.any(coord - prev != group[i + 1] - coord):
                  pixel = self._generate_contour_coord(coord, prev)  # type: ignore
                  if pixel != last_added:
                      sequence.append(pixel)
                      last_added = pixel
  
              # flag whether each corner has been crossed
              if i == len(group) - 1:
                  if corner == 0:
                      if coord[0] == 0:
                          corners[corner] = True
                  elif corner == 1:
                      if coord[1] == self.width - 1:
                          corners[corner] = True
                  elif corner == 2:
                      if coord[0] == self.height - 1:
                          corners[corner] = True
                  elif corner == 3:
                      if coord[1] == 0.0:
                          corners[corner] = True
  
              prev = coord
          dist = self._calculate_distance_from_top_left(sequence)
  
          sequences.append({"distance": dist, "sequence": sequence})
  
      # check whether we need to insert any missing corners
      if corners[0] is False:
          sequences.append({"distance": 0, "sequence": [(0, 0)]})
      if corners[1] is False:
          sequences.append(
              {
                  "distance": self.width,
                  "sequence": [(0, self.width - 1)],
              }
          )
      if corners[2] is False:
          sequences.append(
              {
                  "distance": self.width + self.height,
                  "sequence": [(self.height - 1, self.width - 1)],
              }
          )
      if corners[3] is False:
          sequences.append(
              {
                  "distance": 2 * self.width + self.height,
                  "sequence": [(self.height - 1, 0)],
              }
          )
  
      # join the sequences into a single contour
      # starting at top left and rotating clockwise
      sequences.sort(key=lambda x: x.get("distance"))  # type: ignore
  
      last = (-1, -1)
      for _sequence in sequences:
          if _sequence["sequence"][0] == last:  # type: ignore
              pixels.pop()
          if pixels:
              pixels = [*pixels, *_sequence["sequence"]]  # type: ignore
          else:
              pixels = _sequence["sequence"]  # type: ignore
          last = pixels[-1]
  
      if pixels[0] == last:
          pixels.pop(0)
  
      if pixels[0] == (0, 0):
          pixels.append(pixels.pop(0))
  
      return np.flip(convert_to_numpy(pixels, dtype=np.int32))
  
imports: from __future__ import annotations
  import warnings
  from typing import Callable, Sequence
  import numpy
  import torch
  from monai.config.type_definitions import DtypeLike, NdarrayOrTensor
  from monai.transforms import Activations, AsDiscrete, BoundingRect, FillHoles, GaussianSmooth, RemoveSmallObjects, SobelGradients
  from monai.transforms.transform import Transform
  from monai.transforms.utils_pytorch_numpy_unification import max, maximum, min, sum, unique
  from monai.utils import TransformBackends, convert_to_numpy, optional_import
  from monai.utils.misc import ensure_tuple_rep
  from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor
label_desc: Use image node_id 4 for calling the GenerateSuccinctContour obj's underlying member method code with example usage: obj.__call__(contours) and returns: np, None