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jklymakjklymak
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ENH: anti-alias down-sampled images
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‎doc/api/next_api_changes/2019-07-17-JMK.rst

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Default interpolation for `image` is new "antialiased" option
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-------------------------------------------------------------
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Images displayed in Matplotlib previously used nearest-neighbor
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interpolation, leading to aliasing effects for non-integer upscaling, and
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single dots being lost.
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New default for :rc:`image.interpolation` is the new option "antialiased".
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`imshow(A, interpolation='antialiased')` will apply a Hanning filter when
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resampling the data in A for display (or saving to file) *if* the upsample
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rate is less than a factor of three, and not an integer; downsampled data is
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always smoothed at resampling.
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To get the old behavior, set :rc:`interpolation` to the old default "nearest"
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(or speciful the ``interpolation`` kwarg of `.Axes.imshow`)
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To always get the anti-aliasing behavior, no matter what the up/down sample
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rate, set :rc:`interpolation` to "hanning" (or one of the other filters
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available.
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Note that the "hanning" filter was chosen because it has only a modest
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performance penalty. Anti-aliasing can be improved with other filters.

‎lib/matplotlib/axes/_axes.py

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@@ -5482,17 +5482,25 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
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The interpolation method used. If *None*
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:rc:`image.interpolation` is used, which defaults to 'nearest'.
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Supported values are 'none', 'nearest', 'bilinear', 'bicubic',
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'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser',
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'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc',
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'lanczos'.
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Supported values are 'none', 'antialiased', 'nearest', 'bilinear',
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'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite',
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'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell',
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'sinc', 'lanczos'.
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If *interpolation* is 'none', then no interpolation is performed
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on the Agg, ps, pdf and svg backends. Other backends will fall back
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to 'nearest'. Note that most SVG renders perform interpolation at
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rendering and that the default interpolation method they implement
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may differ.
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If *interpolation* is the default 'antialiased', then 'nearest'
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interpolation is used if the image is upsampled by more than a
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factor of three (i.e. the number of display pixels is at least three
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times the size of the data array). If the upsampling rate is
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smaller than 3, or the image is downsampled, then 'hanning'
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interpolation is used to act as an anti-aliasing filter, unless the
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image happens to be upsampled by exactly a factor of two or one.
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See
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:doc:`/gallery/images_contours_and_fields/interpolation_methods`
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for an overview of the supported interpolation methods.

‎lib/matplotlib/image.py

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# map interpolation strings to module constants
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_interpd_ = {
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'antialiased': None, # this will use nearest or Hanning if downsampled
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'none': _image.NEAREST, # fall back to nearest when not supported
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'nearest': _image.NEAREST,
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'bilinear': _image.BILINEAR,
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allocating the output array and fetching the relevant properties from the
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Image object *image_obj*.
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"""
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# decide if we need to apply anti-aliasing if the data is upsampled:
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# compare the number of displayed pixels to the number of
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# the data pixels.
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interpolation = image_obj.get_interpolation()
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if interpolation == 'antialiased':
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# don't antialias if upsampling by an integer number or
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# if zooming in more than a factor of 3
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# do antialiasing....
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# compare the number of displayed pixels of the image to the number of
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# the data pixels.
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disppixelsX = int(transform.transform([data.shape[1], 0])[0])
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disppixelsY = int(transform.transform([data.shape[0], 0])[0])
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if ((disppixelsX < 3 * data.shape[1] and
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disppixelsX != data.shape[1] and
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disppixelsX != 2 * data.shape[1]) or
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(disppixelsY < 3 * data.shape[0] and
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disppixelsY != data.shape[0] and
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disppixelsY != 2 * data.shape[0])):
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interpolation = 'hanning'
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else:
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interpolation = 'nearest'
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out = np.zeros(out_shape + data.shape[2:], data.dtype) # 2D->2D, 3D->3D.
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if resample is None:
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resample = image_obj.get_resample()
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_image.resample(data, out, transform,
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_interpd_[image_obj.get_interpolation()],
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_interpd_[interpolation],
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resample,
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alpha,
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image_obj.get_filternorm(),
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A_scaled += 0.1
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# resample the input data to the correct resolution and shape
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A_resampled = _resample(self, A_scaled, out_shape, t)
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# done with A_scaled now, remove from namespace to be sure!
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del A_scaled
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# un-scale the resampled data to approximately the
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Parameters
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----------
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s : {'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', \
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'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', \
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'bessel', 'mitchell', 'sinc', 'lanczos', 'none'}
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s : {'antialiased', 'nearest', 'bilinear', 'bicubic', 'spline16',
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'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', \
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'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', 'none'}
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"""
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if s is None:

‎lib/matplotlib/rcsetup.py

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'mathtext.fallback_to_cm': [True, validate_bool],
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'image.aspect': ['equal', validate_aspect], # equal, auto, a number
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'image.interpolation': ['nearest', validate_string],
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'image.interpolation': ['antialiased', validate_string],
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'image.cmap': ['viridis', validate_string], # gray, jet, etc.
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'image.lut': [256, validate_int], # lookup table
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'image.origin': ['upper',

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