8000 Clip RGB data to vaild range in Axes.imshow · matplotlib/matplotlib@1d45a59 · GitHub
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Clip RGB data to vaild range in Axes.imshow
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‎doc/users/credits.rst

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@@ -386,6 +386,7 @@ Yu Feng,
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Yunfei Yang,
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Yuri D'Elia,
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Yuval Langer,
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Zac Hatfield-Dodds,
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Zach Pincus,
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Zair Mubashar,
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alex,

‎doc/users/next_whats_new/2018_01_11_imshow-rgb-scaling-clipping.rst

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`Axes.imshow` clips RGB values to the valid range
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-------------------------------------------------
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When `Axes.imshow` is passed an RGB or RGBA value with out-of-range
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values, it now issues a warning and clips them to the valid range.
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The old behaviour, wrapping back in to the range, often hid outliers
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and made interpreting RGB images unreliable.

‎lib/matplotlib/axes/_axes.py

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@@ -5239,10 +5239,14 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
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- MxNx3 -- RGB (float or uint8)
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- MxNx4 -- RGBA (float or uint8)
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The value for each component of MxNx3 and MxNx4 float arrays
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should be in the range 0.0 to 1.0. MxN arrays are mapped
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to colors based on the `norm` (mapping scalar to scalar)
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and the `cmap` (mapping the normed scalar to a color).
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MxN arrays are mapped to colors based on the `norm` (mapping
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scalar to scalar) and the `cmap` (mapping the normed scalar to
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a color).
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Elements of RGB and RGBA arrays represent pixels of an MxN image.
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All values should be in the range [0 .. 1] for floats or
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[0 .. 255] for integers. Out-of-range values will be clipped to
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these bounds.
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cmap : `~matplotlib.colors.Colormap`, optional, default: None
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If None, default to rc `image.cmap` value. `cmap` is ignored
@@ -5284,7 +5288,8 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
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settings for `vmin` and `vmax` will be ignored.
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alpha : scalar, optional, default: None
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The alpha blending value, between 0 (transparent) and 1 (opaque)
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The alpha blending value, between 0 (transparent) and 1 (opaque).
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The ``alpha`` argument is ignored for RGBA input data.
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origin : ['upper' | 'lower'], optional, default: None
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Place the [0,0] index of the array in the upper left or lower left

‎lib/matplotlib/cm.py

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@@ -259,7 +259,7 @@ def to_rgba(self, x, alpha=None, bytes=False, norm=True):
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xx = (xx * 255).astype(np.uint8)
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elif xx.dtype == np.uint8:
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if not bytes:
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xx = xx.astype(float) / 255
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xx = xx.astype(np.float32) / 255
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else:
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raise ValueError("Image RGB array must be uint8 or "
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"floating point; found %s" % xx.dtype)

‎lib/matplotlib/image.py

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@@ -13,6 +13,7 @@
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from math import ceil
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import os
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import logging
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import numpy as np
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@@ -34,6 +35,8 @@
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from matplotlib.transforms import (Affine2D, BboxBase, Bbox, BboxTransform,
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IdentityTransform, TransformedBbox)
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_log = logging.getLogger(__name__)
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# map interpolation strings to module constants
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_interpd_ = {
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'none': _image.NEAREST, # fall back to nearest when not supported
@@ -610,6 +613,23 @@ def set_data(self, A):
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or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):
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raise TypeError("Invalid dimensions for image data")
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if self._A.ndim == 3:
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# If the input data has values outside the valid range (after
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# normalisation), we issue a warning and then clip X to the bounds
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# - otherwise casting wraps extreme values, hiding outliers and
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# making reliable interpretation impossible.
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high = 255 if np.issubdtype(self._A.dtype, np.integer) else 1
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if self._A.min() < 0 or high < self._A.max():
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_log.warning(
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'Clipping input data to the valid range for imshow with '
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'RGB data ([0..1] for floats or [0..255] for integers).'
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)
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self._A = np.clip(self._A, 0, high)
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# Cast unsupported integer types to uint8
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if self._A.dtype != np.uint8 and np.issubdtype(self._A.dtype,
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np.integer):
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self._A = self._A.astype(np.uint8)
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self._imcache = None
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self._rgbacache = None
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self.stale = True

‎lib/matplotlib/tests/test_image.py

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@@ -602,7 +602,7 @@ def test_minimized_rasterized():
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def test_load_from_url():
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req = six.moves.urllib.request.urlopen(
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"http://matplotlib.org/_static/logo_sidebar_horiz.png")
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Z = plt.imread(req)
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plt.imread(req)
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@image_comparison(baseline_images=['log_scale_image'],
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assert len(warns) == 0
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@pytest.mark.parametrize('as_float', [True, False])
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def test_imshow_logs_when_clipping_invalid_rgb(as_float, caplog):
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arr = np.arange(300).reshape((10, 10, 3))
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if as_float:
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arr = arr / 255
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.imshow(arr)
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assert 'Clipping input data to the valid range for imshow' in caplog.text
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@image_comparison(baseline_images=['imshow_flatfield'],
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remove_text=True, style='mpl20',
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extensions=['png'])

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