class_docstr: A dataset for segmentation tasks.
class_docstr_tok: ["A","dataset","for","segmentation","tasks","."]
code: def __getitem__(self, idx: int):
      img_path = os.path.join(self.image_folder, self.imgs[idx])
      mask_path = os.path.join(self.mask_folder, self.masks[idx])
  
      # img = Image.open(img_path).convert("RGB")
      # note that we haven't converted the mask to RGB,
      # because each color corresponds to a different instance
      # with 0 being background
      if self.jpeg is not None and (
          img_path.endswith(".jpg") or img_path.endswith(".jpeg")
      ):
          with open(img_path, "rb") as fd:
              img = self.jpeg.decode(fd.read())
      else:
          img = cv2.imread(img_path)
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  
      if self.jpeg is not None and (
          mask_path.endswith(".jpg") or mask_path.endswith(".jpeg")
      ):
          with open(mask_path, "rb") as fd:
              mask = self.jpeg.decode(fd.read(), pixel_format=TJPF_GRAY)
      else:
          mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
      mask = np.array(mask)
  
      # instances are encoded as different colors
      obj_ids = np.unique(mask)
      # first id is the background, so remove it
      obj_ids = obj_ids[1:]
  
      # split the color-encoded mask into a set
      # of binary masks
      masks = mask == obj_ids[:, None, None]
  
      # get bounding box coordinates for each mask
      num_objs = len(obj_ids)
      boxes = []
      area = []
      for i in range(num_objs):
          pos = np.where(masks[i])
          xmin = np.min(pos[1])
          xmax = np.max(pos[1])
          ymin = np.min(pos[0])
          ymax = np.max(pos[0])
          boxes.append([xmin, ymin, xmax, ymax])
          area.append((ymax - ymin) * (xmax - xmin))
  
      target = {}
      target["boxes"] = torch.as_tensor(boxes, dtype=torch.float32)
      # there is only one class
      target["labels"] = torch.ones((num_objs,), dtype=torch.int64)
      target["masks"] = torch.as_tensor(masks, dtype=torch.uint8)
      target["image_id"] = torch.tensor([idx])
      target["area"] = torch.as_tensor(area, dtype=torch.float32)
      # suppose all instances are not crowd
      target["iscrowd"] = torch.zeros((num_objs,), dtype=torch.int64)
  
      if self.transforms is not None:
          img, target = self.transforms(img, target)
  
      return img, target
  
file_path: BigDL/python/nano/src/bigdl/nano/pytorch/vision/datasets/datasets.py
imports: from typing import Union, Callable, Optional
  from turbojpeg import TurboJPEG, TJPF_GRAY
  import torchvision
  import cv2
  import os
  import numpy
  import torch
  from logging import warning
  from os.path import split, join, realpath
label_desc: Use image node_id 2 for calling the SegmentationImageFolder obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: img,  target