2021-04-09 12:10:36 +03:00
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#!/usr/bin/env python3
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2010-07-31 06:52:47 +04:00
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# minimal sanity check
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2023-12-21 14:13:31 +03:00
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from __future__ import annotations
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2013-04-09 09:42:49 +04:00
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2019-07-06 23:40:53 +03:00
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import sys
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2013-04-09 09:42:49 +04:00
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2019-07-06 23:40:53 +03:00
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from PIL import Image, features
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2010-07-31 06:52:47 +04:00
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try:
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Image.core.ping
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2012-10-11 07:52:53 +04:00
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except ImportError as v:
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2012-10-29 22:50:20 +04:00
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print("***", v)
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2010-07-31 06:52:47 +04:00
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sys.exit()
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except AttributeError:
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pass
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2013-06-30 06:13:55 +04:00
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2023-12-26 14:26:11 +03:00
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def testimage() -> None:
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2010-07-31 06:52:47 +04:00
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"""
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PIL lets you create in-memory images with various pixel types:
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2018-10-18 19:46:20 +03:00
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>>> from PIL import Image, ImageDraw, ImageFilter, ImageMath
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2010-07-31 06:52:47 +04:00
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>>> im = Image.new("1", (128, 128)) # monochrome
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2022-04-10 19:25:40 +03:00
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>>> def _info(im): return im.format, im.mode, im.size
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2010-07-31 06:52:47 +04:00
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>>> _info(im)
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(None, '1', (128, 128))
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>>> _info(Image.new("L", (128, 128))) # grayscale (luminance)
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(None, 'L', (128, 128))
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>>> _info(Image.new("P", (128, 128))) # palette
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(None, 'P', (128, 128))
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>>> _info(Image.new("RGB", (128, 128))) # truecolor
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(None, 'RGB', (128, 128))
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>>> _info(Image.new("I", (128, 128))) # 32-bit integer
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(None, 'I', (128, 128))
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>>> _info(Image.new("F", (128, 128))) # 32-bit floating point
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(None, 'F', (128, 128))
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Or open existing files:
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Improve handling of file resources
Follow Python's file object semantics. User code is responsible for
closing resources (usually through a context manager) in a deterministic
way.
To achieve this, remove __del__ functions. These functions used to
closed open file handlers in an attempt to silence Python
ResourceWarnings. However, using __del__ has the following drawbacks:
- __del__ isn't called until the object's reference count reaches 0.
Therefore, resource handlers remain open or in use longer than
necessary.
- The __del__ method isn't guaranteed to execute on system exit. See the
Python documentation:
https://docs.python.org/3/reference/datamodel.html#object.__del__
> It is not guaranteed that __del__() methods are called for objects
> that still exist when the interpreter exits.
- Exceptions that occur inside __del__ are ignored instead of raised.
This has the potential of hiding bugs. This is also in the Python
documentation:
> Warning: Due to the precarious circumstances under which __del__()
> methods are invoked, exceptions that occur during their execution
> are ignored, and a warning is printed to sys.stderr instead.
Instead, always close resource handlers when they are no longer in use.
This will close the file handler at a specified point in the user's code
and not wait until the interpreter chooses to. It is always guaranteed
to run. And, if an exception occurs while closing the file handler, the
bug will not be ignored.
Now, when code receives a ResourceWarning, it will highlight an area
that is mishandling resources. It should not simply be silenced, but
fixed by closing resources with a context manager.
All warnings that were emitted during tests have been cleaned up. To
enable warnings, I passed the `-Wa` CLI option to Python. This exposed
some mishandling of resources in ImageFile.__init__() and
SpiderImagePlugin.loadImageSeries(), they too were fixed.
2019-05-25 19:30:58 +03:00
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>>> with Image.open("Tests/images/hopper.gif") as im:
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... _info(im)
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2010-07-31 06:52:47 +04:00
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('GIF', 'P', (128, 128))
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2016-03-29 14:53:48 +03:00
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>>> _info(Image.open("Tests/images/hopper.ppm"))
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2010-07-31 06:52:47 +04:00
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('PPM', 'RGB', (128, 128))
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>>> try:
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2016-03-29 14:53:48 +03:00
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... _info(Image.open("Tests/images/hopper.jpg"))
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2020-04-07 09:58:21 +03:00
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... except OSError as v:
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2012-10-29 22:50:20 +04:00
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... print(v)
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2010-07-31 06:52:47 +04:00
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('JPEG', 'RGB', (128, 128))
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PIL doesn't actually load the image data until it's needed,
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or you call the "load" method:
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2016-03-29 14:53:48 +03:00
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>>> im = Image.open("Tests/images/hopper.ppm")
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2012-10-29 22:50:20 +04:00
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>>> print(im.im) # internal image attribute
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2010-07-31 06:52:47 +04:00
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None
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>>> a = im.load()
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2012-10-29 22:50:20 +04:00
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>>> type(im.im) # doctest: +ELLIPSIS
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<... '...ImagingCore'>
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2010-07-31 06:52:47 +04:00
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You can apply many different operations on images. Most
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operations return a new image:
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2016-03-29 14:53:48 +03:00
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>>> im = Image.open("Tests/images/hopper.ppm")
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2010-07-31 06:52:47 +04:00
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>>> _info(im.convert("L"))
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(None, 'L', (128, 128))
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>>> _info(im.copy())
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(None, 'RGB', (128, 128))
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>>> _info(im.crop((32, 32, 96, 96)))
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(None, 'RGB', (64, 64))
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>>> _info(im.filter(ImageFilter.BLUR))
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(None, 'RGB', (128, 128))
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>>> im.getbands()
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('R', 'G', 'B')
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>>> im.getbbox()
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(0, 0, 128, 128)
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>>> len(im.getdata())
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16384
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>>> im.getextrema()
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2014-09-04 10:50:47 +04:00
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((0, 255), (0, 255), (0, 255))
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2010-07-31 06:52:47 +04:00
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>>> im.getpixel((0, 0))
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2014-09-04 10:50:47 +04:00
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(20, 20, 70)
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2010-07-31 06:52:47 +04:00
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>>> len(im.getprojection())
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2
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>>> len(im.histogram())
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768
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Added an `image.entropy()` method
This calculates the entropy for the image, based on the histogram.
Because this uses image histogram data directly, the existing C
function underpinning the `image.histogram()` method was abstracted
into a static function to parse extrema tuple arguments, and a new
C function was added to calculate image entropy, making use of the
new static extrema function.
The extrema-parsing function was written by @homm, based on the
macro abstraction I wrote, during the discussion of my first
entropy-method pull request: https://git.io/fhodS
The new `image.entropy()` method is based on `image.histogram()`,
and will accept the same arguments to calculate the histogram data
it will use to assess the entropy of the image.
The algorithm and methodology is based on existing Python code:
* https://git.io/fhmIU
... A test case in the `Tests/` directory, and doctest lines in
`selftest.py`, have both been added and checked.
Changes proposed in this pull request:
* Added “math.h” include to _imaging.c
* The addition of an `image.entropy()` method to the `Image`
Python class,
* The abstraction of the extrema-parsing logic of of the C
function `_histogram` into a static function, and
* The use of that static function in both the `_histogram` and
`_entropy` C functions.
* Minor documentation addenda in the docstrings for both the
`image.entropy()` and `image.histogram()` methods were also
added.
* Removed outdated boilerplate from testing code
* Removed unused “unittest” import
2019-01-25 20:38:11 +03:00
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>>> '%.7f' % im.entropy()
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'8.8212866'
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2012-10-29 22:50:20 +04:00
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>>> _info(im.point(list(range(256))*3))
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2010-07-31 06:52:47 +04:00
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(None, 'RGB', (128, 128))
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>>> _info(im.resize((64, 64)))
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(None, 'RGB', (64, 64))
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>>> _info(im.rotate(45))
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(None, 'RGB', (128, 128))
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2012-10-29 22:50:20 +04:00
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>>> [_info(ch) for ch in im.split()]
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2010-07-31 06:52:47 +04:00
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[(None, 'L', (128, 128)), (None, 'L', (128, 128)), (None, 'L', (128, 128))]
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>>> len(im.convert("1").tobitmap())
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10456
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2012-10-29 22:50:20 +04:00
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>>> len(im.tobytes())
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2010-07-31 06:52:47 +04:00
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49152
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2022-01-15 01:02:31 +03:00
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>>> _info(im.transform((512, 512), Image.Transform.AFFINE, (1,0,0,0,1,0)))
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2010-07-31 06:52:47 +04:00
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(None, 'RGB', (512, 512))
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2022-01-15 01:02:31 +03:00
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>>> _info(im.transform((512, 512), Image.Transform.EXTENT, (32,32,96,96)))
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2010-07-31 06:52:47 +04:00
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(None, 'RGB', (512, 512))
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The ImageDraw module lets you draw stuff in raster images:
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>>> im = Image.new("L", (128, 128), 64)
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>>> d = ImageDraw.ImageDraw(im)
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>>> d.line((0, 0, 128, 128), fill=128)
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>>> d.line((0, 128, 128, 0), fill=128)
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>>> im.getextrema()
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(64, 128)
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In 1.1.4, you can specify colors in a number of ways:
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>>> xy = 0, 0, 128, 128
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>>> im = Image.new("RGB", (128, 128), 0)
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>>> d = ImageDraw.ImageDraw(im)
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>>> d.rectangle(xy, "#f00")
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>>> im.getpixel((0, 0))
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(255, 0, 0)
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>>> d.rectangle(xy, "#ff0000")
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>>> im.getpixel((0, 0))
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(255, 0, 0)
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>>> d.rectangle(xy, "rgb(255,0,0)")
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>>> im.getpixel((0, 0))
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(255, 0, 0)
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>>> d.rectangle(xy, "rgb(100%,0%,0%)")
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>>> im.getpixel((0, 0))
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(255, 0, 0)
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>>> d.rectangle(xy, "hsl(0, 100%, 50%)")
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>>> im.getpixel((0, 0))
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(255, 0, 0)
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>>> d.rectangle(xy, "red")
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>>> im.getpixel((0, 0))
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(255, 0, 0)
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In 1.1.6, you can use the ImageMath module to do image
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calculations.
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2024-04-09 00:34:52 +03:00
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>>> im = ImageMath.lambda_eval( \
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lambda args: args["float"](args["im"] + 20), im=im.convert("L") \
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)
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2010-07-31 06:52:47 +04:00
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>>> im.mode, im.size
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('F', (128, 128))
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PIL can do many other things, but I'll leave that for another
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2021-01-10 00:26:45 +03:00
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day.
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2010-07-31 06:52:47 +04:00
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Cheers /F
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"""
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if __name__ == "__main__":
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# check build sanity
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exit_status = 0
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2019-10-12 17:01:18 +03:00
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features.pilinfo(sys.stdout, False)
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2010-07-31 06:52:47 +04:00
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# use doctest to make sure the test program behaves as documented!
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2013-06-30 06:13:55 +04:00
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import doctest
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2019-06-13 18:54:57 +03:00
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2012-10-29 22:50:20 +04:00
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print("Running selftest:")
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2015-12-02 08:37:50 +03:00
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status = doctest.testmod(sys.modules[__name__])
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2010-07-31 06:52:47 +04:00
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if status[0]:
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2012-10-29 22:50:20 +04:00
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print("*** %s tests of %d failed." % status)
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2010-07-31 06:52:47 +04:00
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exit_status = 1
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else:
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2012-10-29 22:50:20 +04:00
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print("--- %s tests passed." % status[1])
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2010-07-31 06:52:47 +04:00
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sys.exit(exit_status)
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