Merge pull request #5350 from elejke/master

Add preserve_tone option to autocontrast

Tests/test_imageops.py

@@ -29,6 +29,7 @@ def test_sanity():
     ImageOps.autocontrast(hopper("L"), cutoff=(2, 10))
     ImageOps.autocontrast(hopper("L"), ignore=[0, 255])
     ImageOps.autocontrast(hopper("L"), mask=hopper("L"))
+    ImageOps.autocontrast(hopper("L"), preserve_tone=True)
 
     ImageOps.colorize(hopper("L"), (0, 0, 0), (255, 255, 255))
     ImageOps.colorize(hopper("L"), "black", "white")
@@ -336,7 +337,7 @@ def test_autocontrast_mask_toy_input():
         assert ImageStat.Stat(result_nomask).median == [128]
 
 
-def test_auto_contrast_mask_real_input():
+def test_autocontrast_mask_real_input():
     # Test the autocontrast with a rectangular mask
     with Image.open("Tests/images/iptc.jpg") as img:
 
@@ -362,3 +363,52 @@ def test_auto_contrast_mask_real_input():
             threshold=2,
             msg="autocontrast without mask pixel incorrect",
         )
+
+
+def test_autocontrast_preserve_tone():
+    def autocontrast(mode, preserve_tone):
+        im = hopper(mode)
+        return ImageOps.autocontrast(im, preserve_tone=preserve_tone).histogram()
+
+    assert autocontrast("RGB", True) != autocontrast("RGB", False)
+    assert autocontrast("L", True) == autocontrast("L", False)
+
+
+def test_autocontrast_preserve_gradient():
+    gradient = Image.linear_gradient("L")
+
+    # test with a grayscale gradient that extends to 0,255.
+    # Should be a noop.
+    out = ImageOps.autocontrast(gradient, cutoff=0, preserve_tone=True)
+
+    assert_image_equal(gradient, out)
+
+    # cutoff the top and bottom
+    # autocontrast should make the first and last histogram entries equal
+    # and, with rounding, should be 10% of the image pixels
+    out = ImageOps.autocontrast(gradient, cutoff=10, preserve_tone=True)
+    hist = out.histogram()
+    assert hist[0] == hist[-1]
+    assert hist[-1] == 256 * round(256 * 0.10)
+
+    # in rgb
+    img = gradient.convert("RGB")
+    out = ImageOps.autocontrast(img, cutoff=0, preserve_tone=True)
+    assert_image_equal(img, out)
+
+
+@pytest.mark.parametrize(
+    "color", ((255, 255, 255), (127, 255, 0), (127, 127, 127), (0, 0, 0))
+)
+def test_autocontrast_preserve_one_color(color):
+    img = Image.new("RGB", (10, 10), color)
+
+    # single color images shouldn't change
+    out = ImageOps.autocontrast(img, cutoff=0, preserve_tone=True)
+    assert_image_equal(img, out)  # single color, no cutoff
+
+    # even if there is a cutoff
+    out = ImageOps.autocontrast(
+        img, cutoff=10, preserve_tone=True
+    )  # single color 10 cutoff
+    assert_image_equal(img, out)

docs/releasenotes/8.2.0.rst

@@ -83,6 +83,15 @@ be specified through a keyword argument::
 
     im.save("out.tif", icc_profile=...)
 
+
+ImageOps.autocontrast: preserve_tone
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The default behaviour of :py:meth:`~PIL.ImageOps.autocontrast` is to normalize
+separate histograms for each color channel, changing the tone of the image. The new
+``preserve_tone`` argument keeps the tone unchanged by using one luminance histogram
+for all channels.
+
 Security
 ========
 

src/PIL/ImageOps.py

@@ -61,7 +61,7 @@ def _lut(image, lut):
 # actions
 
 
-def autocontrast(image, cutoff=0, ignore=None, mask=None):
+def autocontrast(image, cutoff=0, ignore=None, mask=None, preserve_tone=False):
     """
     Maximize (normalize) image contrast. This function calculates a
     histogram of the input image (or mask region), removes ``cutoff`` percent of the
@@ -77,9 +77,17 @@ def autocontrast(image, cutoff=0, ignore=None, mask=None):
     :param mask: Histogram used in contrast operation is computed using pixels
                  within the mask. If no mask is given the entire image is used
                  for histogram computation.
+    :param preserve_tone: Preserve image tone in Photoshop-like style autocontrast.
+
+                          .. versionadded:: 8.2.0
+
     :return: An image.
     """
-    histogram = image.histogram(mask)
+    if preserve_tone:
+        histogram = image.convert("L").histogram(mask)
+    else:
+        histogram = image.histogram(mask)
+
     lut = []
     for layer in range(0, len(histogram), 256):
         h = histogram[layer : layer + 256]