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Co-authored-by: Andrew Murray <radarhere@users.noreply.github.com> Co-authored-by: Hugo van Kemenade <1324225+hugovk@users.noreply.github.com>
54 lines
1.6 KiB
ReStructuredText
54 lines
1.6 KiB
ReStructuredText
.. py:module:: PIL.ImageMorph
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.. py:currentmodule:: PIL.ImageMorph
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:py:mod:`~PIL.ImageMorph` module
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================================
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The :py:mod:`~PIL.ImageMorph` module allows `morphology`_ operators ("MorphOp") to be
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applied to L mode images::
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from PIL import Image, ImageMorph
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img = Image.open("Tests/images/hopper.bw")
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mop = ImageMorph.MorphOp(op_name="erosion4")
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count, imgOut = mop.apply(img)
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imgOut.show()
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.. _morphology: https://en.wikipedia.org/wiki/Mathematical_morphology
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In addition to applying operators, you can also analyse images.
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You can inspect an image in isolation to determine which pixels are non-empty::
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print(mop.get_on_pixels(img)) # [(0, 0), (1, 0), (2, 0), ...]
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Or you can retrieve a list of pixels that match the operator. This is the number of
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pixels that will be non-empty after the operator is applied::
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coords = mop.match(img)
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print(coords) # [(17, 1), (18, 1), (34, 1), ...]
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print(len(coords)) # 550
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imgOut = mop.apply(img)[1]
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print(len(mop.get_on_pixels(imgOut))) # 550
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If you would like more customized operators, you can pass patterns to the MorphOp
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class::
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mop = ImageMorph.MorphOp(patterns=["1:(... ... ...)->0", "4:(00. 01. ...)->1"])
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Or you can pass lookup table ("LUT") data directly. This LUT data can be constructed
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with the :py:class:`~PIL.ImageMorph.LutBuilder`::
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builder = ImageMorph.LutBuilder()
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mop = ImageMorph.MorphOp(lut=builder.build_lut())
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.. autoclass:: LutBuilder
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:members:
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:undoc-members:
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:show-inheritance:
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.. autoclass:: MorphOp
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:members:
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:undoc-members:
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:show-inheritance:
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