# encoding: utf-8
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# Copyright 2022 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
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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import math
import operator
from functools import reduce
from typing import Any, Callable, Union
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
[docs]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
[docs]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)
[docs]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
extracted via color deconvolution.
Parameters
----------
img : PIL.Image.Image
Input RGB image
Returns
-------
PIL.Image.Image
RGB image with DAB staining separated.
"""
if img.mode not in ["RGB", "RGBA"]:
raise ValueError("Input image must be RGB/RGBA.")
hed = rgb_to_hed(img)
null = np.zeros_like(hed[:, :, 0])
return hed_to_rgb(np.stack((null, null, hed[:, :, 2]), axis=-1))
[docs]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
extracted via color deconvolution.
Parameters
----------
img : PIL.Image.Image
Input RGB image
Returns
-------
PIL.Image.Image
RGB image with Eosin staining separated.
"""
if img.mode not in ["RGB", "RGBA"]:
raise ValueError("Input image must be RGB/RGBA.")
hed = rgb_to_hed(img)
null = np.zeros_like(hed[:, :, 0])
return hed_to_rgb(np.stack((null, hed[:, :, 1], null), axis=-1))
[docs]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)
[docs]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
extracted via color deconvolution.
Parameters
----------
img : Image.Image
Input RGB image
Returns
-------
PIL.Image.Image
RGB image with Hematoxylin staining separated.
"""
if img.mode not in ["RGB", "RGBA"]:
raise ValueError("Input image must be RGB/RGBA.")
hed = rgb_to_hed(img)
null = np.zeros_like(hed[:, :, 0])
return hed_to_rgb(np.stack((hed[:, :, 0], null, null), axis=-1))
[docs]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)
[docs]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
[docs]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
[docs]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))
[docs]def lab_to_rgb(
img: PIL.Image.Image, illuminant: str = "D65", observer: int = "2"
) -> PIL.Image.Image:
"""Lab to RGB color space conversion.
Parameters
----------
img : PIL.Image.Image
Input image in Lab space.
illuminant : {"A", "B", "C", "D50", "D55", "D65", "D75", "E"}, optional
The name of the illuminant (the function is NOT case sensitive). Default is
"D65".
observer : {"2", "10", "R"}, optional
The aperture angle of the observer. Default is "2".
Returns
-------
PIL.Image.Image
Image in RGB space.
"""
img_arr = np.array(img)
rgb_arr = sk_color.lab2rgb(img_arr, illuminant=illuminant, observer=observer)
rgb = np_to_pil(rgb_arr)
return rgb
[docs]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), footprint=sk_morphology.disk(disk_size)
)
return np_to_pil(local_equ)
[docs]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)
[docs]def rag_threshold(
img: PIL.Image.Image,
n_segments: int = 800,
compactness: float = 10.0,
threshold: int = 9,
mask: np.ndarray = None,
return_labels: bool = False,
) -> Union[PIL.Image.Image, np.ndarray]:
"""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.
mask : np.ndarray, optional
If provided, superpixels are computed only where mask is True,
and seed points are homogeneously distributed over the mask
using a K-means clustering strategy (See skimage).
Must be the same size as ``img``.
return_labels : bool, optional
If True, returns a labeled array where the value denotes segment
membership. Otherwise, returns a PIL image where each segment is colored
by the average color in it. Default is False.
Returns
-------
PIL.Image.Image, if not ``return_labels``
Each segment has been colored based on the average
color for that segment (and similar segments have been combined).
np.ndarray, if ``return_labels``
Value denotes segment membership.
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,
mask=mask,
start_label=0 if mask is None else 1,
)
green = sk_future.graph.rag_mean_color(img_arr, labels)
labels2 = sk_future.graph.cut_threshold(labels, green, threshold)
if return_labels:
return labels2
rag = sk_color.label2rgb(labels2, img_arr, kind="avg", bg_label=-1)
return np_to_pil(rag)
[docs]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)
[docs]def rgb_to_hed(img: PIL.Image.Image) -> np.ndarray:
"""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
-------
np.ndarray
Array representation of the 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)
return hed_arr
[docs]def rgb_to_hsv(img: PIL.Image.Image) -> np.ndarray:
"""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
-------
np.ndarray
Array representation of the image in HSV 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)
return sk_color.rgb2hsv(img_arr)
[docs]def rgb_to_lab(
img: PIL.Image.Image, illuminant: str = "D65", observer: int = "2"
) -> np.ndarray:
"""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", "B", "C", "D50", "D55", "D65", "D75", "E"}, optional
The name of the illuminant (the function is NOT case sensitive). Default is
"D65".
observer : {"2", "10", "R"}, optional
The aperture angle of the observer. Default is "2".
Returns
-------
np.ndarray
Array representation of the 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)
return lab_arr
[docs]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
[docs]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 --------
[docs]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
[docs]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)
[docs]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)
[docs]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
[docs]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
[docs]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
[docs]def hed_to_rgb(img_arr: np.ndarray) -> PIL.Image.Image:
"""Convert HED channels to RGB channels.
Parameters
----------
img_arr : np.ndarray
Array representation of the image in HED color space
Returns
-------
PIL.Image.Image
Image in RGB space
"""
rgb = sk_color.hed2rgb(img_arr)
return np_to_pil(rgb)
[docs]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
[docs]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)
[docs]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
[docs]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)