text: import os.path
  import typing
  import warnings
  from abc import abstractmethod
  from contextlib import suppress
  from io import BytesIO
  from pathlib import Path
  from threading import Lock
  
  import numpy as np
  import tifffile.tifffile
  from czifile.czifile import CziFile
  from defusedxml import ElementTree
  from oiffile import OifFile
  from tifffile import TiffFile
  
  from .image import Image
  
  
  class TiffFileException(Exception):
      """
      exception raised if reading tiff file fails. Created for distinguish exceptions which should
      reported as warning message (not for report)
      """
  
  
  class BaseImageReader:
      """
      Base class for reading image using Christopher Gholike libraries
  
      :cvar typing.Type[Image] ~.image_class: image class to return
      """
  
      image_class = Image
  
      @classmethod
      def return_order(cls) -> str:
          """
          Order to which image axes should be rearranged before pass to :py:attr:`image_class` constructor.
          Default is :py:attr:`image_class.return_order`
          """
          if hasattr(cls.image_class, "return_order"):  # pragma: no cover
              warnings.warn(
                  "Using return_order is deprecated since PartSeg 0.12.0. Please fix your image_class", DeprecationWarning
              )
              return cls.image_class.return_order
          return cls.image_class.axis_order
  
      def __init__(self, callback_function=None):
          self.default_spacing = 10 ** -6, 10 ** -6, 10 ** -6
          self.spacing = self.default_spacing
          if callback_function is None:
              self.callback_function = lambda x, y: 0
          else:
              self.callback_function = callback_function
  
      def set_default_spacing(self, spacing):
          spacing = tuple(spacing)
          if len(spacing) == 2:
              # one micrometer
              spacing = (10 ** -6,) + spacing
          if len(spacing) != 3:
              raise ValueError(f"wrong spacing {spacing}")
          self.default_spacing = spacing
  
      @abstractmethod
      def read(self, image_path: typing.Union[str, BytesIO, Path], mask_path=None, ext=None) -> Image:
          """
          Main function to read image. If ext is not set then it may be deduced from path to file.
          If BytesIO is given and non default data file type is needed then ext need to be set
  
          :param image_path: path to image or buffer
          :param mask_path: path to mask or buffer
          :param ext: extension if need to decide algorithm, if absent and image_path is path then
              should be deduced from path
          :return: image structure
          """
          raise NotImplementedError()
  
      @classmethod
      def read_image(
          cls,
          image_path: typing.Union[str, BytesIO, Path],
          mask_path=None,
          callback_function: typing.Optional[typing.Callable] = None,
          default_spacing: typing.Tuple[float, float, float] = None,
      ) -> Image:
          """
          read image file with optional mask file
  
          :param image_path: path or opened file contains image
          :param mask_path:
          :param callback_function: function for provide information about progress in reading file (for progressbar)
          :param default_spacing: used if file do not contains information about spacing
              (or metadata format is not supported)
          :return: image
          """
          # TODO add generic description of callback function
          instance = cls(callback_function)
          if default_spacing is not None:
              instance.set_default_spacing(default_spacing)
          return instance.read(image_path, mask_path)
  
      @classmethod
      def update_array_shape(cls, array: np.ndarray, axes: str):
          """
          Rearrange order of array axes to get proper internal axes order
  
          :param array: array to reorder
          :param axes: current order of array axes as string like "TZYXC"
          """
          try:
              final_mapping_dict = {l: i for i, l in enumerate(cls.return_order())}
              for let1, let2 in [("Z", "I"), ("Z", "Q"), ("C", "S")]:
                  if let1 in final_mapping_dict and let2 not in final_mapping_dict:
                      final_mapping_dict[let2] = final_mapping_dict[let1]
              axes = list(axes)
              # Fixme; workaround for old saved segmentation
              if axes[0] == "Q" and axes[1] == "Q":
                  axes[0] = "T"
                  axes[1] = "Z"
              i = 0
              while i < len(axes):
                  name = axes[i]
                  if name not in final_mapping_dict and array.shape[i] == 1:
                      array = array.take(0, i)
                      axes.pop(i)
                  else:
                      i += 1
  
              final_mapping = [final_mapping_dict[letter] for letter in axes]
          except KeyError as e:  # pragma: no cover
              raise NotImplementedError(
                  f"Data type not supported ({e.args[0]}). Please contact with author for update code"
              )
          if len(final_mapping) != len(set(final_mapping)):
              raise NotImplementedError("Data type not supported. Please contact with author for update code")
          if len(array.shape) < len(cls.return_order()):
              array = np.reshape(array, array.shape + (1,) * (len(cls.return_order()) - len(array.shape)))
  
          array = np.moveaxis(array, list(range(len(axes))), final_mapping)
          return array
  
  
  class GenericImageReader(BaseImageReader):
      """This class try to decide which method use base on path"""
  
      def read(self, image_path: typing.Union[str, BytesIO, Path], mask_path=None, ext=None) -> Image:
          if ext is None:
              if isinstance(image_path, (str, Path)):
                  ext = os.path.splitext(image_path)[1]
              else:
                  ext = ".tif"
          ext = ext.lower()
          if ext == ".czi":
              return CziImageReader.read_image(image_path, mask_path, self.callback_function, self.default_spacing)
          if ext in [".oif", ".oib"]:
              return OifImagReader.read_image(image_path, mask_path, self.callback_function, self.default_spacing)
          if ext == ".obsep":
              return ObsepImageReader.read_image(image_path, mask_path, self.callback_function, self.default_spacing)
          return TiffImageReader.read_image(image_path, mask_path, self.callback_function, self.default_spacing)
  
  
  class OifImagReader(BaseImageReader):
      def read(self, image_path: typing.Union[str, BytesIO, Path], mask_path=None, ext=None) -> Image:
          image_file = OifFile(image_path)
          tiffs = image_file.tiffs
          tif_file = TiffFile(image_file.open_file(tiffs.files[0]), name=tiffs.files[0])
          axes = tiffs.axes + tif_file.series[0].axes
          image_data = image_file.asarray()
          image_data = self.update_array_shape(image_data, axes)
          with suppress(KeyError):
              flat_parm = image_file.mainfile["Reference Image Parameter"]
              x_scale = flat_parm["HeightConvertValue"] * name_to_scalar[flat_parm["HeightUnit"]]
              y_scale = flat_parm["WidthConvertValue"] * name_to_scalar[flat_parm["WidthUnit"]]
              i = 0
              while True:
                  name = f"Axis {i} Parameters Common"
                  if name not in image_file.mainfile:
                      z_scale = 1
                      break
                  axis_info = image_file.mainfile[name]
                  if axis_info["AxisCode"] == "Z":
                      z_scale = axis_info["Interval"] * name_to_scalar[axis_info["UnitName"]]
                      break
                  i += 1
  
              self.spacing = z_scale, x_scale, y_scale
          # TODO add mask reading
          return self.image_class(
              image_data, self.spacing, file_path=os.path.abspath(image_path), axes_order=self.return_order()
          )
  
  
  class CziImageReader(BaseImageReader):
      """
      This class is to read data from czi files. Masks will be treated as TIFF.
      """
  
      def read(self, image_path: typing.Union[str, BytesIO, Path], mask_path=None, ext=None) -> Image:
          image_file = CziFile(image_path)
          image_data = image_file.asarray()
          image_data = self.update_array_shape(image_data, image_file.axes)
          metadata = image_file.metadata(False)
          with suppress(KeyError):
              scaling = metadata["ImageDocument"]["Metadata"]["Scaling"]["Items"]["Distance"]
              scale_info = {el["Id"]: el["Value"] for el in scaling}
              self.spacing = (
                  scale_info.get("Z", self.default_spacing[0]),
                  scale_info.get("Y", self.default_spacing[1]),
                  scale_info.get("X", self.default_spacing[2]),
              )
          # TODO add mask reading
          return self.image_class(
              image_data, self.spacing, file_path=os.path.abspath(image_path), axes_order=self.return_order()
          )
  
      @classmethod
      def update_array_shape(cls, array: np.ndarray, axes: str):
          if "B" in axes:
              index = axes.index("B")
              if array.shape[index] != 1:
                  raise NotImplementedError(
                      "Czi file with B axes is not currently supported by PartSeg."
                      " Please contact with author for update code"
                  )
              array = array.take(0, axis=index)
              axes = axes[:index] + axes[index + 1 :]
          if axes[-1] == "0":
              array = array[..., 0]
              axes = axes[:-1]
          return super().update_array_shape(array, axes)
  
  
  class ObsepImageReader(BaseImageReader):
      def read(self, image_path: typing.Union[str, BytesIO, Path], mask_path=None, ext=None) -> Image:
          directory = Path(os.path.dirname(image_path))
          xml_doc = ElementTree.parse(image_path).getroot()
          channels = xml_doc.findall("net/node/node/attribute[@name='image type']")
          if not channels:
              raise ValueError("Information about channel images not found")
          possible_extensions = [".tiff", ".tif", ".TIFF", ".TIF"]
          channel_list = []
          for channel in channels:
              try:
                  name = next(iter(channel)).attrib["val"]
              except StopIteration:  # pragma: no cover
                  raise ValueError("Missed information about channel name in obsep file")
              for ex in possible_extensions:
                  if (directory / (name + ex)).exists():
                      name += ex
                      break
              else:  # pragma: no cover
                  raise ValueError(f"Not found file for key {name}")
              channel_list.append(TiffImageReader.read_image(directory / name, default_spacing=self.default_spacing))
          for channel in channels:
              try:
                  name = next(iter(channel)).attrib["val"] + "_deconv"
              except StopIteration:  # pragma: no cover
                  raise ValueError("Missed information about channel name in obsep file")
              for ex in possible_extensions:
                  if (directory / (name + ex)).exists():
                      name += ex
                      break
              if (directory / name).exists():
                  channel_list.append(TiffImageReader.read_image(directory / name, default_spacing=self.default_spacing))
  
          image = channel_list[0]
          for el in channel_list[1:]:
              image = image.merge(el, "C")
  
          z_spacing = (
              float(xml_doc.find("net/node/attribute[@name='step width']/double").attrib["val"]) * name_to_scalar["um"]
          )
  
          image.set_spacing((z_spacing,) + image.spacing[1:])
          image.file_path = str(image_path)
          return image
  
  
  class TiffImageReader(BaseImageReader):
      """
      TIFF/LSM files reader. Base reading with :py:meth:`BaseImageReader.read_image`
  
      image_file: TiffFile
      mask_file: TiffFile
      """
  
      def __init__(self, callback_function=None):
          super().__init__(callback_function)
          self.colors = None
          self.channel_names = None
          self.ranges = None
          self.shift = (0, 0, 0)
          self.name = ""
  
      def read(self, image_path: typing.Union[str, BytesIO, Path], mask_path=None, ext=None) -> Image:
          """
          Read tiff image from tiff_file
          """
          self.spacing, self.colors, self.channel_names, self.ranges = self.default_spacing, None, None, None
          with TiffFile(image_path) as image_file:
              total_pages_num = len(image_file.series[0])
  
              # shape = self.image_file.series[0].shape
              axes = image_file.series[0].axes
  
              if image_file.is_lsm:
                  self.read_lsm_metadata(image_file)
              elif image_file.is_imagej:
                  self.read_imagej_metadata(image_file)
              elif image_file.is_ome:
                  self.read_ome_metadata(image_file)
              else:
                  x_spac, y_spac = self.read_resolution_from_tags(image_file)
                  self.spacing = self.default_spacing[0], y_spac, x_spac
              mutex = Lock()
              count_pages = [0]
  
              def report_func():
                  mutex.acquire()
                  count_pages[0] += 1
                  self.callback_function("step", count_pages[0])
                  mutex.release()
  
              if mask_path is not None:
                  with TiffFile(mask_path) as mask_file:
                      self.callback_function("max", total_pages_num + len(mask_file.series[0]))
                      self.verify_mask(mask_file, image_file)
                      mask_file.report_func = report_func
                      mask_data = mask_file.asarray()
                      mask_data = self.update_array_shape(mask_data, mask_file.series[0].axes)[..., 0]
              else:
                  mask_data = None
                  self.callback_function("max", total_pages_num)
  
              image_file.report_func = report_func
              try:
                  image_data = image_file.asarray()
              except ValueError as e:  # pragma: no cover
                  raise TiffFileException(*e.args)
              image_data = self.update_array_shape(image_data, axes)
  
          if not isinstance(image_path, (str, Path)):
              image_path = ""
          return self.image_class(
              image_data,
              self.spacing,
              mask=mask_data,
              default_coloring=self.colors,
              channel_names=self.channel_names,
              ranges=self.ranges,
              file_path=os.path.abspath(image_path),
              axes_order=self.return_order(),
              shift=self.shift,
              name=self.name,
          )
  
      @staticmethod
      def verify_mask(mask_file, image_file):
          """
          verify if mask fit to image. Raise ValueError exception on error
          :return:
          """
          if mask_file is None:  # pragma: no cover
              return
          image_series = image_file.pages[0]
          mask_series = mask_file.pages[0]
          for i, pos in enumerate(mask_series.axes):
              if mask_series.shape[i] == 1:  # pragma: no cover
                  continue
              try:
                  j = image_series.axes.index(pos)
              except ValueError:  # pragma: no cover
                  raise ValueError("Incompatible shape of mask and image (axes)")
                  # TODO add verification if problem with T/Z/I
              if image_series.shape[j] != mask_series.shape[i]:  # pragma: no cover
                  raise ValueError("Incompatible shape of mask and image")
              # TODO Add verification if mask have to few dimensions
  
      @staticmethod
      def decode_int(val: int):
          """
          This function split 32 bits int on 4 8-bits ints
  
          :param val: value to decode
          :return: list of four numbers with values from [0, 255]
          """
          return [(val >> x) & 255 for x in [24, 16, 8, 0]]
  
      def read_resolution_from_tags(self, image_file):
          tags = image_file.pages[0].tags
          try:
  
              if image_file.is_imagej:
                  scalar = name_to_scalar[image_file.imagej_metadata["unit"]]
              else:
                  unit = tags["ResolutionUnit"].value
                  if unit == 3:
                      scalar = name_to_scalar["centimeter"]
                  elif unit == 2:
                      scalar = name_to_scalar["cal"]
                  else:  # pragma: no cover
                      raise KeyError(f"wrong scalar {tags['ResolutionUnit']}, {tags['ResolutionUnit'].value}")
  
              x_spacing = tags["XResolution"].value[1] / tags["XResolution"].value[0] * scalar
              y_spacing = tags["YResolution"].value[1] / tags["YResolution"].value[0] * scalar
          except (KeyError, ZeroDivisionError):
              x_spacing, y_spacing = self.default_spacing[2], self.default_spacing[1]
          return x_spacing, y_spacing
  
      def read_imagej_metadata(self, image_file):
          try:
              z_spacing = image_file.imagej_metadata["spacing"] * name_to_scalar[image_file.imagej_metadata["unit"]]
          except KeyError:
              z_spacing = self.default_spacing[0]
          x_spacing, y_spacing = self.read_resolution_from_tags(image_file)
          self.spacing = z_spacing, y_spacing, x_spacing
          self.colors = image_file.imagej_metadata.get("LUTs")
          self.channel_names = image_file.imagej_metadata.get("Labels")
          if "Ranges" in image_file.imagej_metadata:
              ranges = image_file.imagej_metadata["Ranges"]
              self.ranges = list(zip(ranges[::2], ranges[1::2]))
  
      def _read_ome_channel_information(self, meta_data):
          if "Channel" not in meta_data["Pixels"]:
              return
          if isinstance(meta_data["Pixels"]["Channel"], (list, tuple)):
              with suppress(KeyError):
                  self.channel_names = [ch["Name"] for ch in meta_data["Pixels"]["Channel"]]
              with suppress(KeyError):
                  self.colors = [self.decode_int(ch["Color"])[:-1] for ch in meta_data["Pixels"]["Channel"]]
              return
          if "Name" in meta_data["Pixels"]["Channel"]:
              self.channel_names = [meta_data["Pixels"]["Channel"]["Name"]]
          if "Color" in meta_data["Pixels"]["Channel"]:
              self.channel_names = [meta_data["Pixels"]["Channel"]["Color"]]
  
      def read_ome_metadata(self, image_file):
          meta_data = tifffile.xml2dict(image_file.ome_metadata)["OME"]["Image"]
          with suppress(KeyError):
              self.spacing = [
                  meta_data["Pixels"][f"PhysicalSize{x}"] * name_to_scalar[meta_data["Pixels"][f"PhysicalSize{x}Unit"]]
                  for x in ["Z", "Y", "X"]
              ]
          with suppress(KeyError):
              self.shift = [
                  meta_data["Pixels"]["Plane"][0][f"Position{x}"]
                  * name_to_scalar[meta_data["Pixels"]["Plane"][0][f"Position{x}Unit"]]
                  for x in ["Z", "Y", "X"]
              ]
          self.name = meta_data.get("Name", "")
          self._read_ome_channel_information(meta_data)
  
      def read_lsm_metadata(self, image_file):
          self.spacing = [image_file.lsm_metadata[f"VoxelSize{x}"] for x in ["Z", "Y", "X"]]
          if "ChannelColors" in image_file.lsm_metadata:
              if "Colors" in image_file.lsm_metadata["ChannelColors"]:
                  self.colors = [x[:3] for x in image_file.lsm_metadata["ChannelColors"]["Colors"]]
              if "ColorNames" in image_file.lsm_metadata["ChannelColors"]:
                  self.channel_names = image_file.lsm_metadata["ChannelColors"]["ColorNames"]
  
  
  name_to_scalar = {
      "micron": 10 ** -6,
      "µm": 10 ** -6,
      "um": 10 ** -6,
      "nm": 10 ** -9,
      "mm": 10 ** -3,
      "millimeter": 10 ** -3,
      "pm": 10 ** -12,
      "picometer": 100 ** -12,
      "nanometer": 10 ** -9,
      "\\u00B5m": 10 ** -6,
      "centimeter": 10 ** -2,
      "cm": 10 ** -2,
      "cal": 2.54 * 10 ** -2,
  }  #: dict with known names of scalar to scalar value. May be some missed
  