code: # encoding: utf-8
  
  # ------------------------------------------------------------------------
  # Copyright 2020 All Histolab Contributors
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ------------------------------------------------------------------------
  
  import math
  import operator
  from functools import reduce
  from typing import Any, Callable
  
  import numpy as np
  import PIL
  import PIL.ImageOps
  import skimage.color as sk_color
  import skimage.exposure as sk_exposure
  import skimage.feature as sk_feature
  import skimage.filters as sk_filters
  import skimage.future as sk_future
  import skimage.morphology as sk_morphology
  import skimage.segmentation as sk_segmentation
  
  from ..util import apply_mask_image, np_to_pil, threshold_to_mask, warn
  from .util import mask_percent
  
  
  def adaptive_equalization(
      img: PIL.Image.Image, nbins: int = 256, clip_limit: float = 0.01
  ) -> PIL.Image.Image:
      """Increase image contrast using adaptive equalization.
  
      Contrast in local region of input image (gray or RGB) is increased using
      adaptive equalization
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image (gray or RGB)
      nbins : int
          Number of histogram bins. Default is 256.
      clip_limit : float, optional
          Clipping limit where higher value increases contrast. Default is 0.01
  
      Returns
      -------
      PIL.Image.Image
          image with contrast enhanced by adaptive equalization.
      """
      if not (isinstance(nbins, int) and nbins > 0):
          raise ValueError("Number of histogram bins must be a positive integer")
      img_arr = np.array(img)
      adapt_equ = sk_exposure.equalize_adapthist(img_arr, nbins, clip_limit)
      adapt_equ = np_to_pil(adapt_equ)
      return adapt_equ
  
  
  def blue_pen_filter(img: PIL.Image.Image) -> PIL.Image.Image:
      """Filter out blue pen marks from a diagnostic slide.
  
      The resulting mask is a composition of green filters with different thresholds
      for the RGB channels.
  
      Parameters
      ---------
      img : PIL.Image.Image
          Input RGB image
  
      Returns
      -------
      PIL.Image.Image
          Input image with the blue pen marks filtered out.
      """
      parameters = [
          {"red_thresh": 60, "green_thresh": 120, "blue_thresh": 190},
          {"red_thresh": 120, "green_thresh": 170, "blue_thresh": 200},
          {"red_thresh": 175, "green_thresh": 210, "blue_thresh": 230},
          {"red_thresh": 145, "green_thresh": 180, "blue_thresh": 210},
          {"red_thresh": 37, "green_thresh": 95, "blue_thresh": 160},
          {"red_thresh": 30, "green_thresh": 65, "blue_thresh": 130},
          {"red_thresh": 130, "green_thresh": 155, "blue_thresh": 180},
          {"red_thresh": 40, "green_thresh": 35, "blue_thresh": 85},
          {"red_thresh": 30, "green_thresh": 20, "blue_thresh": 65},
          {"red_thresh": 90, "green_thresh": 90, "blue_thresh": 140},
          {"red_thresh": 60, "green_thresh": 60, "blue_thresh": 120},
          {"red_thresh": 110, "green_thresh": 110, "blue_thresh": 175},
      ]
  
      blue_pen_filter_img = reduce(
          (lambda x, y: x & y), [blue_filter(img, **param) for param in parameters]
      )
      return apply_mask_image(img, blue_pen_filter_img)
  
  
  def dab_channel(img: PIL.Image.Image) -> PIL.Image.Image:
      """Obtain DAB channel from RGB image.
  
      Input image is first converted into HED space and the DAB channel is
      rescaled for increased contrast.
  
      Parameters
      ----------
      img : Image.Image
          Input RGB image
  
      Returns
      -------
      Image.Image
          Grayscale image corresponding to input image with DAB channel enhanced.
      """
      if img.mode not in ["RGB", "RGBA"]:
          raise ValueError("Input image must be RGB/RGBA.")
      dab = np.array(rgb_to_hed(img))[:, :, 2]
      dab = sk_exposure.rescale_intensity(dab)
      return np_to_pil(dab)
  
  
  def eosin_channel(img: PIL.Image.Image) -> PIL.Image.Image:
      """Obtain Eosin channel from RGB image.
  
      Input image is first converted into HED space and the Eosin channel is
      rescaled for increased contrast.
  
      Parameters
      ----------
      img : Image.Image
          Input RGB image
  
      Returns
      -------
      Image.Image
          Grayscale image corresponding to input image with Eosin channel enhanced.
      """
      if img.mode not in ["RGB", "RGBA"]:
          raise ValueError("Input image must be RGB/RGBA.")
      eosin = np.array(rgb_to_hed(img))[:, :, 1]
      eosin = sk_exposure.rescale_intensity(eosin)
      return np_to_pil(eosin)
  
  
  def green_pen_filter(img: PIL.Image.Image) -> PIL.Image.Image:
      """Filter out green pen marks from a diagnostic slide.
  
      The resulting mask is a composition of green filters with different thresholds
      for the RGB channels.
  
      Parameters
      ---------
      img : PIL.Image.Image
          Input RGB image
  
      Returns
      -------
      PIL.Image.Image
          Input image with the green pen marks filtered out.
      """
      parameters = [
          {"red_thresh": 150, "green_thresh": 160, "blue_thresh": 140},
          {"red_thresh": 70, "green_thresh": 110, "blue_thresh": 110},
          {"red_thresh": 45, "green_thresh": 115, "blue_thresh": 100},
          {"red_thresh": 30, "green_thresh": 75, "blue_thresh": 60},
          {"red_thresh": 195, "green_thresh": 220, "blue_thresh": 210},
          {"red_thresh": 225, "green_thresh": 230, "blue_thresh": 225},
          {"red_thresh": 170, "green_thresh": 210, "blue_thresh": 200},
          {"red_thresh": 20, "green_thresh": 30, "blue_thresh": 20},
          {"red_thresh": 50, "green_thresh": 60, "blue_thresh": 40},
          {"red_thresh": 30, "green_thresh": 50, "blue_thresh": 35},
          {"red_thresh": 65, "green_thresh": 70, "blue_thresh": 60},
          {"red_thresh": 100, "green_thresh": 110, "blue_thresh": 105},
          {"red_thresh": 165, "green_thresh": 180, "blue_thresh": 180},
          {"red_thresh": 140, "green_thresh": 140, "blue_thresh": 150},
          {"red_thresh": 185, "green_thresh": 195, "blue_thresh": 195},
      ]
  
      green_pen_filter_img = reduce(
          (lambda x, y: x & y), [green_filter(img, **param) for param in parameters]
      )
      return apply_mask_image(img, green_pen_filter_img)
  
  
  def hematoxylin_channel(img: PIL.Image.Image) -> PIL.Image.Image:
      """Obtain Hematoxylin channel from RGB image.
  
      Input image is first converted into HED space and the hematoxylin channel is
      rescaled for increased contrast.
  
      Parameters
      ----------
      img : Image.Image
          Input RGB image
  
      Returns
      -------
      Image.Image
          Grayscale image corresponding to input image with Hematoxylin channel enhanced.
      """
      if img.mode not in ["RGB", "RGBA"]:
          raise ValueError("Input image must be RGB/RGBA.")
      hematoxylin = np.array(rgb_to_hed(img))[:, :, 0]
      hematoxylin = sk_exposure.rescale_intensity(hematoxylin)
      return np_to_pil(hematoxylin)
  
  
  def histogram_equalization(img: PIL.Image.Image, nbins: int = 256) -> PIL.Image.Image:
      """Increase image contrast using histogram equalization.
  
      The input image (gray or RGB) is filterd using histogram equalization to increase
      contrast.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image.
      nbins : int. optional
          Number of histogram bins. Default is 256.
  
      Returns
      -------
      PIL.Image.Image
          Image with contrast enhanced by histogram equalization.
      """
      img_arr = np.array(img)
      hist_equ = sk_exposure.equalize_hist(img_arr.flatten(), nbins=nbins)
      hist_equ = hist_equ.reshape(img_arr.shape)
      return np_to_pil(hist_equ)
  
  
  def hysteresis_threshold(
      img: PIL.Image.Image, low: int = 50, high: int = 100
  ) -> PIL.Image.Image:
      """Apply two-level (hysteresis) threshold to an image.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image
      low : int, optional
          low threshold. Default is 50.
      high : int, optional
          high threshold. Default is 100.
  
      Returns
      -------
      PIL.Image.Image
          Image with the hysteresis threshold applied
      """
      if low is None or high is None:
          raise ValueError("thresholds cannot be None")
      hyst = sk_filters.apply_hysteresis_threshold(np.array(img), low, high)
      img_out = apply_mask_image(img, hyst)
      return img_out
  
  
  def invert(img: PIL.Image.Image) -> PIL.Image.Image:
      """Invert an image, i.e. take the complement of the correspondent array.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image
  
      Returns
      -------
      PIL.Image.Image
          Inverted image
      """
      if img.mode == "RGBA":
          red, green, blue, alpha = img.split()
          rgb_img = PIL.Image.merge("RGB", (red, green, blue))
          inverted_img_rgb = PIL.ImageOps.invert(rgb_img)
          red, green, blue = inverted_img_rgb.split()
          inverted_img = PIL.Image.merge("RGBA", (red, green, blue, alpha))
      else:
          inverted_img = PIL.ImageOps.invert(img)
  
      return inverted_img
  
  
  def kmeans_segmentation(
      img: PIL.Image.Image, n_segments: int = 800, compactness: float = 10.0
  ) -> PIL.Image.Image:
      """Segment an image with K-means segmentation
  
      By using K-means segmentation (color/space proximity) each segment is
      colored based on the average color for that segment.
  
      Parameters
      ---------
      img : PIL.Image.Image
          Input image
      n_segments : int, optional
          The number of segments. Default is 800.
      compactness : float, optional
          Color proximity versus space proximity factor. Default is 10.0.
  
      Returns
      -------
      PIL.Image.Image
          RGB image where each segment has been colored based on the average
          color for that segment.
  
      Raises
      ------
      ValueError
          If ``img`` mode is RGBA.
      """
      if img.mode == "RGBA":
          raise ValueError("Input image cannot be RGBA")
      img_arr = np.array(img)
      labels = sk_segmentation.slic(img_arr, n_segments, compactness, start_label=0)
      return np_to_pil(sk_color.label2rgb(labels, img_arr, kind="avg", bg_label=-1))
  
  
  def local_equalization(img: PIL.Image.Image, disk_size: int = 50) -> PIL.Image.Image:
      """Filter gray image using local equalization.
  
      Local equalization method uses local histograms based on a disk structuring element.
  
      Parameters
      ---------
      img: PIL.Image.Image
          Input image. Notice that it must be 2D
      disk_size: int, optional
          Radius of the disk structuring element used for the local histograms. Default is
          50.
  
      Returns
      -------
      PIL.Image.Image
          2D image with contrast enhanced using local equalization.
      """
  
      if len(np.array(img).shape) != 2:
          raise ValueError("Input must be 2D.")
      local_equ = sk_filters.rank.equalize(
          np.array(img), selem=sk_morphology.disk(disk_size)
      )
      return np_to_pil(local_equ)
  
  
  def local_otsu_threshold(
      img: PIL.Image.Image, disk_size: float = 3.0
  ) -> PIL.Image.Image:
      """Mask image based on local Otsu threshold.
  
      Compute local Otsu threshold for each pixel and return boolean mask
      based on pixels being less than the local Otsu threshold.
  
      Note that the input image must be 2D.
  
      Parameters
      ----------
      img: PIL.Image.Image
          Input 2-dimensional image
      disk_size : float, optional
          Radius of the disk structuring element used to compute
          the Otsu threshold for each pixel. Default is 3.0.
  
      Returns
      -------
      PIL.Image.Image
          Resulting image where local Otsu threshold values have been
          applied to original image.
      """
      if np.array(img).ndim != 2:
          raise ValueError("Input must be 2D.")
      if disk_size is None or disk_size < 0 or disk_size == np.inf:
          raise ValueError("Disk size must be a positive number.")
      img_arr = np.array(img)
      local_otsu = sk_filters.rank.otsu(img_arr, sk_morphology.disk(disk_size))
      return np_to_pil(local_otsu)
  
  
  def rag_threshold(
      img: PIL.Image.Image,
      n_segments: int = 800,
      compactness: float = 10.0,
      threshold: int = 9,
  ) -> PIL.Image.Image:
      """Combine similar K-means segmented regions based on threshold value.
  
      Segment an image with K-means, build region adjacency graph based on
      the segments, combine similar regions based on threshold value,
      and then output these resulting region segments.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image
      n_segments :  int, optional
          The number of segments. Default is 800.
      compactness : float, optional
          Color proximity versus space proximity factor. Default is 10.0.
      threshold : int, optional
          Threshold value for combining regions. Default is 9.
  
      Returns
      -------
      PIL.Image.Image
          Each segment has been colored based on the average
          color for that segment (and similar segments have been combined).
  
      Raises
      ------
      ValueError
          If ``img`` mode is RGBA.
      """
      if img.mode == "RGBA":
          raise ValueError("Input image cannot be RGBA")
      img_arr = np.array(img)
      labels = sk_segmentation.slic(img_arr, n_segments, compactness, start_label=0)
      green = sk_future.graph.rag_mean_color(img_arr, labels)
      labels2 = sk_future.graph.cut_threshold(labels, green, threshold)
      rag = sk_color.label2rgb(labels2, img_arr, kind="avg", bg_label=-1)
      return np_to_pil(rag)
  
  
  def red_pen_filter(img: PIL.Image.Image) -> PIL.Image.Image:
      """Filter out red pen marks on diagnostic slides.
  
      The resulting mask is a composition of red filters with different thresholds
      for the RGB channels.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input RGB image.
  
      Returns
      -------
      PIL.Image.Image
          Input image with the pen marks filtered out.
      """
      parameters = [
          {"red_thresh": 150, "green_thresh": 80, "blue_thresh": 90},
          {"red_thresh": 110, "green_thresh": 20, "blue_thresh": 30},
          {"red_thresh": 185, "green_thresh": 65, "blue_thresh": 105},
          {"red_thresh": 195, "green_thresh": 85, "blue_thresh": 125},
          {"red_thresh": 220, "green_thresh": 115, "blue_thresh": 145},
          {"red_thresh": 125, "green_thresh": 40, "blue_thresh": 70},
          {"red_thresh": 100, "green_thresh": 50, "blue_thresh": 65},
          {"red_thresh": 85, "green_thresh": 25, "blue_thresh": 45},
      ]
      red_pen_filter_img = reduce(
          (lambda x, y: x & y), [red_filter(img, **param) for param in parameters]
      )
      return apply_mask_image(img, red_pen_filter_img)
  
  
  def rgb_to_hed(img: PIL.Image.Image) -> PIL.Image.Image:
      """Convert RGB channels to HED channels.
  
      image color space (RGB) is converted to Hematoxylin-Eosin-Diaminobenzidine space.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image
  
      Returns
      -------
      PIL.Image.Image
          Image in HED space
      """
      if img.mode not in ["RGB", "RGBA"]:
          raise Exception("Input image must be RGB.")
      if img.mode == "RGBA":
          red, green, blue, _ = img.split()
          img = PIL.Image.merge("RGB", (red, green, blue))
          warn(
              "Input image must be RGB. "
              "NOTE: the image will be converted to RGB before HED conversion."
          )
  
      img_arr = np.array(img)
      hed_arr = sk_color.rgb2hed(img_arr)
      hed = np_to_pil(hed_arr)
  
      return hed
  
  
  def rgb_to_hsv(img: PIL.Image.Image) -> PIL.Image.Image:
      """Convert RGB channels to HSV channels.
  
      image color space (RGB) is converted to Hue - Saturation - Value (HSV) space.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image
  
      Returns
      -------
      PIL.Image.Image
          Image in HED space
  
      Raises
      ------
      Exception
          If the image mode is not RGB
      """
      if img.mode != "RGB":
          raise Exception("Input image must be RGB")
      img_arr = np.array(img)
      hsv_arr = sk_color.rgb2hsv(img_arr)
      hsv = np_to_pil(hsv_arr)
      return hsv
  
  
  def rgb_to_lab(
      img: PIL.Image.Image, illuminant: str = "D65", observer: int = "2"
  ) -> PIL.Image.Image:
      """Convert from the sRGB color space to the CIE Lab colorspace.
  
      sRGB color space reference: IEC 61966-2-1:1999
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image
      illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
          The name of the illuminant (the function is NOT case sensitive).
      observer : {“2”, “10”}, optional
          The aperture angle of the observer.
  
      Returns
      -------
      PIL.Image.Image
          Image in LAB space
  
      Raises
      ------
      Exception
          If the image mode is not RGB
      """
      if img.mode != "RGB":
          raise Exception("Input image must be RGB")
      img_arr = np.array(img)
      lab_arr = sk_color.rgb2lab(img_arr, illuminant=illuminant, observer=observer)
      lab = np_to_pil(lab_arr)
      return lab
  
  
  def rgb_to_od(img: PIL.Image.Image) -> np.ndarray:
      """Convert from RGB to optical density (OD_RGB) space.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image
  
      Returns
      -------
      np.ndarray
          Array representation of the image in OD space
      """
      if img.mode == "RGBA":
          red, green, blue, _ = img.split()
          img = PIL.Image.merge("RGB", (red, green, blue))
  
          warn(
              "Input image must be RGB. "
              "NOTE: the image will be converted to RGB before OD conversion."
          )
  
      img_arr = np.array(img)
  
      od_arr = -np.log((img_arr.astype(float) + 1) / 240)
      return od_arr
  
  
  def stretch_contrast(
      img: PIL.Image.Image, low: int = 40, high: int = 60
  ) -> PIL.Image.Image:
      """Increase image contrast.
  
      Th contrast in image is increased based on intensities in a specified range
  
      Parameters
      ----------
      img: PIL.Image.Image
          Input image
      low: int
          Range low value (0 to 255). Default is 40.
      high: int
          Range high value (0 to 255). Default is 60.
  
      Returns
      -------
      PIL.Image.Image
              Image with contrast enhanced.
      """
      if low not in range(256) or high not in range(256):
          raise Exception("low and high values must be in range [0, 255]")
      img_arr = np.array(img)
      low_p, high_p = np.percentile(img_arr, (low * 100 / 255, high * 100 / 255))
      return np_to_pil(sk_exposure.rescale_intensity(img_arr, in_range=(low_p, high_p)))
  
  
  # -------- Branching function --------
  
  
  def blue_filter(
      img: PIL.Image.Image, red_thresh: int, green_thresh: int, blue_thresh: int
  ) -> np.ndarray:
      """Filter out blueish colors in an RGB image.
  
      Create a mask to filter out blueish colors, where the mask is based on a pixel
      being above a red channel threshold value, above a green channel threshold value,
      and below a blue channel threshold value.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input RGB image
      red_thresh : int
          Red channel lower threshold value.
      green_thresh : int
          Green channel lower threshold value.
      blue_thresh : int
          Blue channel upper threshold value.
  
      Returns
      -------
      np.ndarray
          Boolean NumPy array representing the mask.
      """
      if np.array(img).ndim != 3:
          raise ValueError("Input must be 3D.")
      if not (
          0 <= red_thresh <= 255 and 0 <= green_thresh <= 255 and 0 <= blue_thresh <= 255
      ):
          raise ValueError("RGB Thresholds must be in range [0, 255]")
      img_arr = np.array(img)
      red = img_arr[:, :, 0] > red_thresh
      green = img_arr[:, :, 1] > green_thresh
      blue = img_arr[:, :, 2] < blue_thresh
      return red | green | blue
  
  
  def canny_edges(
      img: PIL.Image.Image,
      sigma: float = 1.0,
      low_threshold: float = 0.0,
      high_threshold: float = 25.0,
  ) -> np.ndarray:
      """Filter image based on Canny edge algorithm.
  
      Note that input image must be 2D grayscale image
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input 2-dimensional image
      sigma : float, optional
          Width (std dev) of Gaussian. Default is 1.0.
      low_threshold : float, optional
          Low hysteresis threshold value. Default is 0.0.
      high_threshold : float, optional
          High hysteresis threshold value. Default is 25.0.
  
      Returns
      -------
      np.ndarray
          Boolean NumPy array representing Canny edge map.
      """
      if np.array(img).ndim != 2:
          raise ValueError("Input must be 2D.")
      img_arr = np.array(img)
      return sk_feature.canny(img_arr, sigma, low_threshold, high_threshold)
  
  
  def filter_entropy(
      img: PIL.Image.Image,
      neighborhood: int = 9,
      threshold: float = 5.0,
      relate: Callable[..., Any] = operator.gt,
  ) -> np.ndarray:
      """Filter image based on entropy (complexity).
  
      The area of the image included in the local neighborhood is defined by a square
      neighborhood x neighborhood
  
      Note that input must be 2D.
  
      Parameters
      ----------
      img : PIL.Image.Image
          input 2-dimensional image
      neighborhood : int, optional
          Neighborhood size (defines height and width of 2D array of 1's). Default is 9.
      threshold : float, optional
          Threshold value. Default is 5.0
      relate : callable operator, optional
          Operator to be used to compute the mask from the threshold. Default is
          operator.lt
  
      Returns
      -------
      np.ndarray
          NumPy boolean array where True represent a measure of complexity.
      """
      if np.array(img).ndim != 2:
          raise ValueError("Input must be 2D.")
      img_arr = np.array(img)
      entropy = sk_filters.rank.entropy(img_arr, np.ones((neighborhood, neighborhood)))
      return threshold_to_mask(entropy, threshold, relate)
  
  
  def grays(img: PIL.Image.Image, tolerance: int = 15) -> np.ndarray:
      """Filter out gray pixels in RGB image.
  
      Gray pixels are those pixels where the red, green, and blue channel values
      are similar, i.e. under a specified tolerance.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image
      tolerance : int, optional
          if difference between values is below this threshold,
          values are considered similar and thus filtered out. Default is 15.
  
      Returns
      -------
      PIL.Image.Image
          Mask image where the grays values are masked out
      """
  
      if np.array(img).ndim != 3:
          raise ValueError("Input must be 3D.")
      # TODO: class image mode exception: raise exception if not RGB(A)
      img_arr = np.array(img).astype(np.int64)
      rg_diff = abs(img_arr[:, :, 0] - img_arr[:, :, 1]) > tolerance
      rb_diff = abs(img_arr[:, :, 0] - img_arr[:, :, 2]) > tolerance
      gb_diff = abs(img_arr[:, :, 1] - img_arr[:, :, 2]) > tolerance
      filter_grays = rg_diff | rb_diff | gb_diff
      return filter_grays
  
  
  def green_channel_filter(
      img: PIL.Image.Image,
      green_thresh: int = 200,
      avoid_overmask: bool = True,
      overmask_thresh: float = 90.0,
  ) -> np.ndarray:
      """Mask pixels in an RGB image with G-channel greater than a specified threshold.
  
      Create a mask to filter out pixels with a green channel value greater than
      a particular threshold, since hematoxylin and eosin are purplish and pinkish,
      which do not have much green to them.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input RGB image
      green_thresh : int, optional
          Green channel threshold value (0 to 255). Default is 200.
          If value is greater than green_thresh, mask out pixel.
      avoid_overmask : bool, optional
          If True, avoid masking above the overmask_thresh percentage. Default is True.
      overmask_thresh : float, optional
          If avoid_overmask is True, avoid masking above this percentage value. Default is
          90.0
  
      Returns
      -------
      np.ndarray
          Boolean mask where pixels above a particular green channel
          threshold have been masked out.
      """
      if green_thresh > 255.0 or green_thresh < 0.0:
          raise ValueError("threshold must be in range [0, 255]")
      green = np.array(img)[:, :, 1]
      g_mask = green <= green_thresh
      mask_percentage = mask_percent(g_mask)
      if avoid_overmask and (mask_percentage >= overmask_thresh) and (green_thresh < 255):
          new_green_thresh = math.ceil((255 + green_thresh) / 2)
          g_mask = green_channel_filter(
              np.array(img), new_green_thresh, avoid_overmask, overmask_thresh
          )
      return g_mask
  
  
  def green_filter(
      img: PIL.Image.Image, red_thresh: int, green_thresh: int, blue_thresh: int
  ) -> np.ndarray:
      """Filter out greenish colors in an RGB image.
      The mask is based on a pixel being above a red channel threshold value, below a
      green channel threshold value, and below a blue channel threshold value.
  
      Note that for the green ink, the green and blue channels tend to track together, so
      for blue channel we use a lower threshold rather than an upper threshold value.
  
      Parameters
      ----------
      img : PIL.Image.Image
          RGB input image.
      red_thresh : int
          Red channel upper threshold value.
      green_thresh : int
          Green channel lower threshold value.
      blue_thresh : int
          Blue channel lower threshold value.
  
      Returns
      -------
      np.ndarray
          Boolean  NumPy array representing the mask.
      """
      if np.array(img).ndim != 3:
          raise ValueError("Input must be 3D.")
      if not (
          0 <= red_thresh <= 255 and 0 <= green_thresh <= 255 and 0 <= blue_thresh <= 255
      ):
          raise ValueError("RGB Thresholds must be in range [0, 255]")
  
      img_arr = np.array(img)
      red = img_arr[:, :, 0] > red_thresh
      green = img_arr[:, :, 1] < green_thresh
      blue = img_arr[:, :, 2] < blue_thresh
      return red | green | blue
  
  
  def hysteresis_threshold_mask(
      img: PIL.Image.Image, low: int = 50, high: int = 100
  ) -> np.ndarray:
      """Mask an image using hysteresis threshold
  
      Compute the Hysteresis threshold on the complement of a grayscale image,
      and return boolean mask based on pixels above this threshold.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image.
      low : int, optional
          low threshold. Default is 50.
      high : int, optional
          high threshold. Default is 100.
  
      Returns
      -------
      np.ndarray
          Boolean NumPy array where True represents a pixel above Otsu threshold.
      """
      if low is None or high is None:
          raise ValueError("thresholds cannot be None")
      grey_scale = PIL.ImageOps.grayscale(img)
      comp = invert(grey_scale)
      hyst_mask = sk_filters.apply_hysteresis_threshold(np.array(comp), low, high)
      return hyst_mask
  
  
  def otsu_threshold(
      img: PIL.Image.Image, relate: Callable[..., Any] = operator.lt
  ) -> np.ndarray:
      """Mask image based on pixel above Otsu threshold.
  
      Compute Otsu threshold on image and return boolean mask based on pixels above this
      threshold.
  
      Note that Otsu threshold is expected to work correctly only for grayscale images.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image.
      relate : operator, optional
          Operator to be used to compute the mask from the threshold. Default is
          operator.lt
  
      Returns
      -------
      np.ndarray
          Boolean NumPy array where True represents a pixel above Otsu threshold.
      """
      if img.mode in ["RGB", "RGBA"]:
          image = PIL.ImageOps.grayscale(img)
          warn(
              "otsu_threshold is expected to work correctly only for grayscale images."
              "NOTE: the image will be converted to grayscale before applying Otsu"
              "threshold"
          )
      else:
          image = img
  
      otsu_thresh = sk_filters.threshold_otsu(np.array(image))
      return threshold_to_mask(image, otsu_thresh, relate)
  
  
  def red_filter(
      img: PIL.Image.Image, red_thresh: int, green_thresh: int, blue_thresh: int
  ) -> np.ndarray:
      """Mask reddish colors in an RGB image.
  
      Create a mask to filter out reddish colors, where the mask is based on a pixel
      being above a red channel threshold value, below a green channel threshold value,
      and below a blue channel threshold value.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input RGB image
      red_thresh : int
          Red channel lower threshold value.
      green_thresh : int
          Green channel upper threshold value.
      blue_thresh : int
          Blue channel upper threshold value.
  
      Returns
      -------
      np.ndarray
          Boolean NumPy array representing the mask.
      """
      if np.array(img).ndim != 3:
          raise ValueError("Input must be 3D.")
      if not (
          0 <= red_thresh <= 255 and 0 <= green_thresh <= 255 and 0 <= blue_thresh <= 255
      ):
          raise ValueError("RGB Thresholds must be in range [0, 255]")
  
      img_arr = np.array(img)
      red = img_arr[:, :, 0] < red_thresh
      green = img_arr[:, :, 1] > green_thresh
      blue = img_arr[:, :, 2] > blue_thresh
      return red | green | blue
  
  
  def yen_threshold(
      img: PIL.Image.Image, relate: Callable[..., Any] = operator.lt
  ) -> np.ndarray:
      """Mask image based on pixel below Yen's threshold.
  
      Compute Yen threshold on image and return boolean mask based on pixels below this
      threshold.
  
      Parameters
      ----------
      img : PIL.Image.Image
          Input image.
      relate : operator, optional
          Operator to be used to compute the mask from the threshold. Default is
          operator.lt
  
      Returns
      -------
      np.ndarray
          Boolean NumPy array where True represents a pixel below Yen's threshold.
      """
  
      yen_thresh = sk_filters.threshold_yen(np.array(img))
      return threshold_to_mask(img, yen_thresh, relate)
  
apis: numpy.array numpy.ones numpy.percentile