mirror of
https://github.com/python-pillow/Pillow.git
synced 2024-12-26 18:06:18 +03:00
a7e3b2e47b
There are two main issues fixed with this commit: * bytes vs. str: All file, image, and palette data are now handled as bytes. A new _binary module consolidates the hacks needed to do this across Python versions. tostring/fromstring methods have been renamed to tobytes/frombytes, but the Python 2.6/2.7 versions alias them to the old names for compatibility. Users should move to tobytes/frombytes. One other potentially-breaking change is that text data in image files (such as tags, comments) are now explicitly handled with a specific character encoding in mind. This works well with the Unicode str in Python 3, but may trip up old code expecting a straight byte-for-byte translation to a Python string. This also required a change to Gohlke's tags tests (in Tests/test_file_png.py) to expect Unicode strings from the code. * True div vs. floor div: Many division operations used the "/" operator to do floor division, which is now the "//" operator in Python 3. These were fixed. As of this commit, on the first pass, I have one failing test (improper handling of a slice object in a C module, test_imagepath.py) in Python 3, and three that that I haven't tried running yet (test_imagegl, test_imagegrab, and test_imageqt). I also haven't tested anything on Windows. All but the three skipped tests run flawlessly against Pythons 2.6 and 2.7.
441 lines
13 KiB
Python
441 lines
13 KiB
Python
#
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# The Python Imaging Library.
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# $Id$
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#
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# standard image operations
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#
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# History:
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# 2001-10-20 fl Created
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# 2001-10-23 fl Added autocontrast operator
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# 2001-12-18 fl Added Kevin's fit operator
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# 2004-03-14 fl Fixed potential division by zero in equalize
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# 2005-05-05 fl Fixed equalize for low number of values
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#
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# Copyright (c) 2001-2004 by Secret Labs AB
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# Copyright (c) 2001-2004 by Fredrik Lundh
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#
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# See the README file for information on usage and redistribution.
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#
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from . import Image
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import operator
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from functools import reduce
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##
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# (New in 1.1.3) The <b>ImageOps</b> module contains a number of
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# 'ready-made' image processing operations. This module is somewhat
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# experimental, and most operators only work on L and RGB images.
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#
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# @since 1.1.3
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##
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#
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# helpers
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def _border(border):
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if isinstance(border, tuple):
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if len(border) == 2:
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left, top = right, bottom = border
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elif len(border) == 4:
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left, top, right, bottom = border
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else:
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left = top = right = bottom = border
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return left, top, right, bottom
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def _color(color, mode):
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if Image.isStringType(color):
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from . import ImageColor
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color = ImageColor.getcolor(color, mode)
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return color
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def _lut(image, lut):
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if image.mode == "P":
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# FIXME: apply to lookup table, not image data
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raise NotImplementedError("mode P support coming soon")
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elif image.mode in ("L", "RGB"):
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if image.mode == "RGB" and len(lut) == 256:
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lut = lut + lut + lut
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return image.point(lut)
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else:
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raise IOError("not supported for this image mode")
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#
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# actions
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##
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# Maximize (normalize) image contrast. This function calculates a
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# histogram of the input image, removes <i>cutoff</i> percent of the
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# lightest and darkest pixels from the histogram, and remaps the image
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# so that the darkest pixel becomes black (0), and the lightest
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# becomes white (255).
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#
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# @param image The image to process.
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# @param cutoff How many percent to cut off from the histogram.
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# @param ignore The background pixel value (use None for no background).
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# @return An image.
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def autocontrast(image, cutoff=0, ignore=None):
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"Maximize image contrast, based on histogram"
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histogram = image.histogram()
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lut = []
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for layer in range(0, len(histogram), 256):
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h = histogram[layer:layer+256]
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if ignore is not None:
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# get rid of outliers
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try:
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h[ignore] = 0
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except TypeError:
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# assume sequence
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for ix in ignore:
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h[ix] = 0
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if cutoff:
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# cut off pixels from both ends of the histogram
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# get number of pixels
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n = 0
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for ix in range(256):
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n = n + h[ix]
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# remove cutoff% pixels from the low end
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cut = n * cutoff // 100
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for lo in range(256):
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if cut > h[lo]:
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cut = cut - h[lo]
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h[lo] = 0
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else:
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h[lo] = h[lo] - cut
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cut = 0
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if cut <= 0:
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break
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# remove cutoff% samples from the hi end
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cut = n * cutoff // 100
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for hi in range(255, -1, -1):
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if cut > h[hi]:
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cut = cut - h[hi]
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h[hi] = 0
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else:
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h[hi] = h[hi] - cut
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cut = 0
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if cut <= 0:
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break
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# find lowest/highest samples after preprocessing
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for lo in range(256):
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if h[lo]:
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break
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for hi in range(255, -1, -1):
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if h[hi]:
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break
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if hi <= lo:
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# don't bother
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lut.extend(list(range(256)))
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else:
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scale = 255.0 / (hi - lo)
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offset = -lo * scale
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for ix in range(256):
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ix = int(ix * scale + offset)
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if ix < 0:
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ix = 0
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elif ix > 255:
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ix = 255
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lut.append(ix)
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return _lut(image, lut)
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##
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# Colorize grayscale image. The <i>black</i> and <i>white</i>
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# arguments should be RGB tuples; this function calculates a colour
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# wedge mapping all black pixels in the source image to the first
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# colour, and all white pixels to the second colour.
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#
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# @param image The image to colourize.
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# @param black The colour to use for black input pixels.
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# @param white The colour to use for white input pixels.
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# @return An image.
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def colorize(image, black, white):
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"Colorize a grayscale image"
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assert image.mode == "L"
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black = _color(black, "RGB")
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white = _color(white, "RGB")
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red = []; green = []; blue = []
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for i in range(256):
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red.append(black[0]+i*(white[0]-black[0])//255)
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green.append(black[1]+i*(white[1]-black[1])//255)
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blue.append(black[2]+i*(white[2]-black[2])//255)
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image = image.convert("RGB")
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return _lut(image, red + green + blue)
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##
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# Remove border from image. The same amount of pixels are removed
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# from all four sides. This function works on all image modes.
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#
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# @param image The image to crop.
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# @param border The number of pixels to remove.
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# @return An image.
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# @see Image#Image.crop
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def crop(image, border=0):
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"Crop border off image"
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left, top, right, bottom = _border(border)
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return image.crop(
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(left, top, image.size[0]-right, image.size[1]-bottom)
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)
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##
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# Deform the image.
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#
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# @param image The image to deform.
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# @param deformer A deformer object. Any object that implements a
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# <b>getmesh</b> method can be used.
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# @param resample What resampling filter to use.
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# @return An image.
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def deform(image, deformer, resample=Image.BILINEAR):
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"Deform image using the given deformer"
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return image.transform(
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image.size, Image.MESH, deformer.getmesh(image), resample
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)
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##
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# Equalize the image histogram. This function applies a non-linear
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# mapping to the input image, in order to create a uniform
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# distribution of grayscale values in the output image.
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#
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# @param image The image to equalize.
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# @param mask An optional mask. If given, only the pixels selected by
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# the mask are included in the analysis.
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# @return An image.
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def equalize(image, mask=None):
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"Equalize image histogram"
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if image.mode == "P":
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image = image.convert("RGB")
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h = image.histogram(mask)
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lut = []
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for b in range(0, len(h), 256):
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histo = [_f for _f in h[b:b+256] if _f]
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if len(histo) <= 1:
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lut.extend(list(range(256)))
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else:
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step = (reduce(operator.add, histo) - histo[-1]) // 255
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if not step:
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lut.extend(list(range(256)))
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else:
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n = step // 2
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for i in range(256):
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lut.append(n // step)
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n = n + h[i+b]
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return _lut(image, lut)
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##
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# Add border to the image
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#
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# @param image The image to expand.
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# @param border Border width, in pixels.
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# @param fill Pixel fill value (a colour value). Default is 0 (black).
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# @return An image.
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def expand(image, border=0, fill=0):
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"Add border to image"
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left, top, right, bottom = _border(border)
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width = left + image.size[0] + right
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height = top + image.size[1] + bottom
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out = Image.new(image.mode, (width, height), _color(fill, image.mode))
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out.paste(image, (left, top))
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return out
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##
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# Returns a sized and cropped version of the image, cropped to the
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# requested aspect ratio and size.
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# <p>
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# The <b>fit</b> function was contributed by Kevin Cazabon.
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#
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# @param size The requested output size in pixels, given as a
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# (width, height) tuple.
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# @param method What resampling method to use. Default is Image.NEAREST.
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# @param bleed Remove a border around the outside of the image (from all
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# four edges. The value is a decimal percentage (use 0.01 for one
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# percent). The default value is 0 (no border).
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# @param centering Control the cropping position. Use (0.5, 0.5) for
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# center cropping (e.g. if cropping the width, take 50% off of the
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# left side, and therefore 50% off the right side). (0.0, 0.0)
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# will crop from the top left corner (i.e. if cropping the width,
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# take all of the crop off of the right side, and if cropping the
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# height, take all of it off the bottom). (1.0, 0.0) will crop
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# from the bottom left corner, etc. (i.e. if cropping the width,
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# take all of the crop off the left side, and if cropping the height
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# take none from the top, and therefore all off the bottom).
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# @return An image.
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def fit(image, size, method=Image.NEAREST, bleed=0.0, centering=(0.5, 0.5)):
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"""
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This method returns a sized and cropped version of the image,
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cropped to the aspect ratio and size that you request.
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"""
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# by Kevin Cazabon, Feb 17/2000
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# kevin@cazabon.com
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# http://www.cazabon.com
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# ensure inputs are valid
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if not isinstance(centering, list):
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centering = [centering[0], centering[1]]
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if centering[0] > 1.0 or centering[0] < 0.0:
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centering [0] = 0.50
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if centering[1] > 1.0 or centering[1] < 0.0:
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centering[1] = 0.50
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if bleed > 0.49999 or bleed < 0.0:
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bleed = 0.0
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# calculate the area to use for resizing and cropping, subtracting
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# the 'bleed' around the edges
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# number of pixels to trim off on Top and Bottom, Left and Right
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bleedPixels = (
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int((float(bleed) * float(image.size[0])) + 0.5),
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int((float(bleed) * float(image.size[1])) + 0.5)
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)
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liveArea = (
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bleedPixels[0], bleedPixels[1], image.size[0] - bleedPixels[0] - 1,
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image.size[1] - bleedPixels[1] - 1
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)
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liveSize = (liveArea[2] - liveArea[0], liveArea[3] - liveArea[1])
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# calculate the aspect ratio of the liveArea
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liveAreaAspectRatio = float(liveSize[0])/float(liveSize[1])
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# calculate the aspect ratio of the output image
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aspectRatio = float(size[0]) / float(size[1])
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# figure out if the sides or top/bottom will be cropped off
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if liveAreaAspectRatio >= aspectRatio:
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# liveArea is wider than what's needed, crop the sides
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cropWidth = int((aspectRatio * float(liveSize[1])) + 0.5)
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cropHeight = liveSize[1]
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else:
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# liveArea is taller than what's needed, crop the top and bottom
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cropWidth = liveSize[0]
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cropHeight = int((float(liveSize[0])/aspectRatio) + 0.5)
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# make the crop
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leftSide = int(liveArea[0] + (float(liveSize[0]-cropWidth) * centering[0]))
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if leftSide < 0:
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leftSide = 0
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topSide = int(liveArea[1] + (float(liveSize[1]-cropHeight) * centering[1]))
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if topSide < 0:
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topSide = 0
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out = image.crop(
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(leftSide, topSide, leftSide + cropWidth, topSide + cropHeight)
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)
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# resize the image and return it
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return out.resize(size, method)
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##
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# Flip the image vertically (top to bottom).
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#
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# @param image The image to flip.
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# @return An image.
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def flip(image):
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"Flip image vertically"
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return image.transpose(Image.FLIP_TOP_BOTTOM)
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##
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# Convert the image to grayscale.
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#
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# @param image The image to convert.
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# @return An image.
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def grayscale(image):
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"Convert to grayscale"
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return image.convert("L")
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##
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# Invert (negate) the image.
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#
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# @param image The image to invert.
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# @return An image.
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def invert(image):
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"Invert image (negate)"
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lut = []
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for i in range(256):
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lut.append(255-i)
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return _lut(image, lut)
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##
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# Flip image horizontally (left to right).
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#
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# @param image The image to mirror.
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# @return An image.
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def mirror(image):
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"Flip image horizontally"
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return image.transpose(Image.FLIP_LEFT_RIGHT)
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##
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# Reduce the number of bits for each colour channel.
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#
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# @param image The image to posterize.
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# @param bits The number of bits to keep for each channel (1-8).
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# @return An image.
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def posterize(image, bits):
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"Reduce the number of bits per color channel"
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lut = []
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mask = ~(2**(8-bits)-1)
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for i in range(256):
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lut.append(i & mask)
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return _lut(image, lut)
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##
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# Invert all pixel values above a threshold.
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#
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# @param image The image to posterize.
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# @param threshold All pixels above this greyscale level are inverted.
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# @return An image.
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def solarize(image, threshold=128):
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"Invert all values above threshold"
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lut = []
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for i in range(256):
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if i < threshold:
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lut.append(i)
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else:
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lut.append(255-i)
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return _lut(image, lut)
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# --------------------------------------------------------------------
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# PIL USM components, from Kevin Cazabon.
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def gaussian_blur(im, radius=None):
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""" PIL_usm.gblur(im, [radius])"""
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if radius is None:
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radius = 5.0
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im.load()
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return im.im.gaussian_blur(radius)
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gblur = gaussian_blur
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def unsharp_mask(im, radius=None, percent=None, threshold=None):
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""" PIL_usm.usm(im, [radius, percent, threshold])"""
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if radius is None:
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radius = 5.0
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if percent is None:
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percent = 150
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if threshold is None:
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threshold = 3
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im.load()
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return im.im.unsharp_mask(radius, percent, threshold)
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usm = unsharp_mask
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