Grey -> Gray

histolab · Aug 5, 2020 · e42538a · e42538a
1 parent d5945e1
commit e42538a
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Showing 5 changed files with 28 additions and 28 deletions.
diff --git a/src/histolab/filters/image_filters.py b/src/histolab/filters/image_filters.py
@@ -170,7 +170,7 @@ class HematoxylinChannel(object):
     Returns
     -------
     Image.Image
-        Greyscale image corresponding to input image with Hematoxylin channel enhanced.
+        Grayscale image corresponding to input image with Hematoxylin channel enhanced.
     """
 
     def __call__(self, img):
@@ -195,7 +195,7 @@ class EosinChannel(object):
     Returns
     -------
     Image.Image
-        Greyscale image corresponding to input image with Eosin channel enhanced.
+        Grayscale image corresponding to input image with Eosin channel enhanced.
     """
 
     def __call__(self, img):
@@ -468,7 +468,7 @@ def __repr__(self) -> str:
 class HysteresisThresholdMask(object):
     """Mask an image using hysteresis threshold
 
-    Compute the Hysteresis threshold on the complement of a greyscale image,
+    Compute the Hysteresis threshold on the complement of a grayscale image,
     and return boolean mask based on pixels above this threshold.
 
     Parameters
@@ -634,7 +634,7 @@ def __repr__(self) -> str:
 class Grays(object):
     """Filter out gray pixels in RGB image.
 
-    Grey pixels are those pixels where the red, green, and blue channel values
+    Gray pixels are those pixels where the red, green, and blue channel values
     are similar, i.e. under a specified tolerance.
 
     Parameters

diff --git a/src/histolab/filters/image_filters_functional.py b/src/histolab/filters/image_filters_functional.py
@@ -109,7 +109,7 @@ def hematoxylin_channel(img: PIL.Image.Image) -> PIL.Image.Image:
     Returns
     -------
     Image.Image
-        Greyscale image corresponding to input image with Hematoxylin channel enhanced.
+        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.")
@@ -132,7 +132,7 @@ def eosin_channel(img: PIL.Image.Image) -> PIL.Image.Image:
     Returns
     -------
     Image.Image
-        Greyscale image corresponding to input image with Eosin channel enhanced.
+        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.")
@@ -164,7 +164,7 @@ def rgb_to_hsv(img: PIL.Image.Image) -> PIL.Image.Image:
     return hsv
 
 
-# TODO setup logger warning + greyscale --> invert ---> contrast stretch
+# TODO setup logger warning + grayscale --> invert ---> contrast stretch
 
 
 def stretch_contrast(
@@ -523,7 +523,7 @@ def hysteresis_threshold_mask(
 ) -> np.ndarray:
     """Mask an image using hysteresis threshold
 
-    Compute the Hysteresis threshold on the complement of a greyscale image,
+    Compute the Hysteresis threshold on the complement of a grayscale image,
     and return boolean mask based on pixels above this threshold.
 
     Parameters
@@ -575,7 +575,7 @@ def otsu_threshold(
         image = PIL.ImageOps.grayscale(img)
         warn(
             "otsu_threshold is expected to work correctly only for grayscale images."
-            "NOTE: the image will be converted to greyscale before applying Otsu"
+            "NOTE: the image will be converted to grayscale before applying Otsu"
             "threshold"
         )
     else:
@@ -622,7 +622,7 @@ def filter_entropy(
     return threshold_to_mask(entropy, threshold, relate)
 
 
-# input of canny filter is a greyscale
+# input of canny filter is a grayscale
 def canny_edges(
     img: PIL.Image.Image,
     sigma: float = 1.0,
@@ -659,7 +659,7 @@ def canny_edges(
 def grays(img: PIL.Image.Image, tolerance: int = 15) -> np.ndarray:
     """Filter out gray pixels in RGB image.
 
-    Grey pixels are those pixels where the red, green, and blue channel values
+    Gray pixels are those pixels where the red, green, and blue channel values
     are similar, i.e. under a specified tolerance.
 
     Parameters

diff --git a/src/histolab/tile.py b/src/histolab/tile.py
@@ -145,8 +145,8 @@ def _is_almost_white(self) -> bool:
         bool
             True if the image is almost white, False otherwise
         """
-        rgb2grey = imf.RgbToGrayscale()
-        image_gray = rgb2grey(self._image)
+        rgb2gray = imf.RgbToGrayscale()
+        image_gray = rgb2gray(self._image)
         image_gray_arr = np.array(image_gray)
         image_gray_arr = image_gray_arr / 255
 

diff --git a/tests/base.py b/tests/base.py
@@ -2,7 +2,7 @@
 import sparse
 
 # ============== IMAGES ==============
-IMAGE1_GREY = np.array(
+IMAGE1_GRAY = np.array(
     [
         [152, 100, 162, 160, 23],
         [160, 113, 31, 126, 36],
@@ -12,7 +12,7 @@
     ]
 )
 
-IMAGE2_GREY = np.array(
+IMAGE2_GRAY = np.array(
     [
         [89.04472016, 70.10608259, 233.5658837, 165.17043321, 2.37055166],
         [73.56836436, 222.25117531, 73.54528918, 246.26874863, 201.15243113],
@@ -22,7 +22,7 @@
     ]
 ).astype("float64")
 
-IMAGE3_GREY_BLACK = np.array(
+IMAGE3_GRAY_BLACK = np.array(
     [
         [0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
@@ -32,7 +32,7 @@
     ]
 )
 
-IMAGE4_GREY_WHITE = np.array(
+IMAGE4_GRAY_WHITE = np.array(
     [
         [1, 1, 1, 1, 1],
         [1, 1, 1, 1, 1],

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
@@ -7,16 +7,16 @@
 import numpy as np
 import pytest
 from tests.base import (
-    IMAGE1_GREY,
+    IMAGE1_GRAY,
     IMAGE1_RGB,
     IMAGE1_RGBA,
-    IMAGE2_GREY,
+    IMAGE2_GRAY,
     IMAGE2_RGB,
     IMAGE2_RGBA,
-    IMAGE3_GREY_BLACK,
+    IMAGE3_GRAY_BLACK,
     IMAGE3_RGB_BLACK,
     IMAGE3_RGBA_BLACK,
-    IMAGE4_GREY_WHITE,
+    IMAGE4_GRAY_WHITE,
     IMAGE4_RGB_WHITE,
     IMAGE4_RGBA_WHITE,
     SPARSE_BASE_MASK,
@@ -237,7 +237,7 @@ def test_region_coordinates():
 @pytest.fixture(
     params=[
         (
-            IMAGE1_GREY,
+            IMAGE1_GRAY,
             160,
             operator.gt,
             np.array(
@@ -251,7 +251,7 @@ def test_region_coordinates():
             ),
         ),
         (
-            IMAGE2_GREY,
+            IMAGE2_GRAY,
             140,
             operator.lt,
             np.array(
@@ -264,11 +264,11 @@ def test_region_coordinates():
                 ]
             ),
         ),
-        (IMAGE3_GREY_BLACK, 0, operator.gt, np.zeros((5, 5), dtype=bool)),
-        (IMAGE3_GREY_BLACK, 0, operator.lt, np.zeros((5, 5), dtype=bool)),
-        (IMAGE3_GREY_BLACK, 1, operator.lt, np.ones((5, 5), dtype=bool)),
-        (IMAGE4_GREY_WHITE, 0, operator.gt, np.ones((5, 5), dtype=bool)),
-        (IMAGE4_GREY_WHITE, 1, operator.lt, np.zeros((5, 5), dtype=bool)),
+        (IMAGE3_GRAY_BLACK, 0, operator.gt, np.zeros((5, 5), dtype=bool)),
+        (IMAGE3_GRAY_BLACK, 0, operator.lt, np.zeros((5, 5), dtype=bool)),
+        (IMAGE3_GRAY_BLACK, 1, operator.lt, np.ones((5, 5), dtype=bool)),
+        (IMAGE4_GRAY_WHITE, 0, operator.gt, np.ones((5, 5), dtype=bool)),
+        (IMAGE4_GRAY_WHITE, 1, operator.lt, np.zeros((5, 5), dtype=bool)),
         (
             IMAGE1_RGB,
             200,