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Merge pull request #1032 from tonysyu/better-rescale-intensity

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Tweak range definition in `rescale_intensity`
This commit is contained in:
Stefan van der Walt
2014-07-12 00:56:06 +02:00
parent f587b5318b 4c000c4d89
commit 2c9d22c5e1
3 changed files with 112 additions and 30 deletions
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TODO.txt
+5 -1
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@@ -1,5 +1,9 @@
Remember to list any API changes below in `doc/source/api_changes.txt`.
Version 0.13
------------
* Remove deprecated `None` defaults for `skimage.exposure.rescale_intensity`
Version 0.12
------------
* Change `label` to mark background as 0, not -1, which is consistent with
@@ -7,7 +11,7 @@ Version 0.12
* Remove `skimage.morphology.label` from `skimage.morphology.__init__`--it now
lives in `skimage.measure.label`.
* Remove deprecated `reverse_map` parameter of `skimage.transform.warp`
* Change depecrated `enforce_connectivity=False` on skimage.segmentation.slic
* Change deprecated `enforce_connectivity=False` on skimage.segmentation.slic
and set it to True as default
* Remove deprecated `skimage.measure.fit.BaseModel._params` attribute
* Remove deprecated `skimage.measure.fit.BaseModel._params`,
skimage/exposure/exposure.py
+73 -27
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@@ -3,7 +3,6 @@ import numpy as np
from skimage import img_as_float
from skimage.util.dtype import dtype_range, dtype_limits
from skimage._shared.utils import deprecated
__all__ = ['histogram', 'cumulative_distribution', 'equalize',
@@ -136,25 +135,73 @@ def equalize_hist(image, nbins=256):
return out.reshape(image.shape)
def rescale_intensity(image, in_range=None, out_range=None):
def intensity_range(image, range_values='image', clip_negative=False):
"""Return image intensity range (min, max) based on desired value type.
Parameters
----------
image : array
Input image.
range_values : str or 2-tuple
The image intensity range is configured by this parameter.
The possible values for this parameter are enumerated below.
'image'
Return image min/max as the range.
'dtype'
Return min/max of the image's dtype as the range.
dtype-name
Return intensity range based on desired `dtype`. Must be valid key
in `DTYPE_RANGE`. Note: `image` is ignored for this range type.
2-tuple
Return `range_values` as min/max intensities. Note that there's no
reason to use this function if you just want to specify the
intensity range explicitly. This option is included for functions
that use `intensity_range` to support all desired range types.
clip_negative : bool
If True, clip the negative range (i.e. return 0 for min intensity)
even if the image dtype allows negative values.
"""
if range_values == 'dtype':
range_values = image.dtype.type
if range_values == 'image':
i_min = np.min(image)
i_max = np.max(image)
elif range_values in DTYPE_RANGE:
i_min, i_max = DTYPE_RANGE[range_values]
if clip_negative:
i_min = 0
else:
i_min, i_max = range_values
return i_min, i_max
def rescale_intensity(image, in_range='image', out_range='dtype'):
"""Return image after stretching or shrinking its intensity levels.
The image intensities are uniformly rescaled such that the minimum and
maximum values given by `in_range` match those given by `out_range`.
The desired intensity range of the input and output, `in_range` and
`out_range` respectively, are used to stretch or shrink the intensity range
of the input image. See examples below.
Parameters
----------
image : array
Image array.
in_range : 2-tuple (float, float) or str
Min and max *allowed* intensity values of input image. If None, the
*allowed* min/max values are set to the *actual* min/max values in the
input image. Intensity values outside this range are clipped.
If string, use data limits of dtype specified by the string.
out_range : 2-tuple (float, float) or str
Min and max intensity values of output image. If None, use the min/max
intensities of the image data type. See `skimage.util.dtype` for
details. If string, use data limits of dtype specified by the string.
in_range, out_range : str or 2-tuple
Min and max intensity values of input and output image.
The possible values for this parameter are enumerated below.
'image'
Use image min/max as the intensity range.
'dtype'
Use min/max of the image's dtype as the intensity range.
dtype-name
Use intensity range based on desired `dtype`. Must be valid key
in `DTYPE_RANGE`.
2-tuple
Use `range_values` as explicit min/max intensities.
Returns
-------
@@ -164,7 +211,9 @@ def rescale_intensity(image, in_range=None, out_range=None):
Examples
--------
By default, intensities are stretched to the limits allowed by the dtype:
By default, the min/max intensities of the input image are stretched to
the limits allowed by the image's dtype, since `in_range` defaults to
'image' and `out_range` defaults to 'dtype':
>>> image = np.array([51, 102, 153], dtype=np.uint8)
>>> rescale_intensity(image)
@@ -203,20 +252,17 @@ def rescale_intensity(image, in_range=None, out_range=None):
dtype = image.dtype.type
if in_range is None:
imin = np.min(image)
imax = np.max(image)
elif in_range in DTYPE_RANGE:
imin, imax = DTYPE_RANGE[in_range]
else:
imin, imax = in_range
in_range = 'image'
msg = "`in_range` should not be set to None. Use {!r} instead."
warnings.warn(msg.format(in_range))
if out_range is None or out_range in DTYPE_RANGE:
out_range = dtype if out_range is None else out_range
omin, omax = DTYPE_RANGE[out_range]
if imin >= 0:
omin = 0
else:
omin, omax = out_range
if out_range is None:
out_range = 'dtype'
msg = "`out_range` should not be set to None. Use {!r} instead."
warnings.warn(msg.format(out_range))
imin, imax = intensity_range(image, in_range)
omin, omax = intensity_range(image, out_range, clip_negative=(imin >= 0))
image = np.clip(image, imin, imax)
skimage/exposure/tests/test_exposure.py
+34 -2
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@@ -3,9 +3,11 @@ import warnings
import numpy as np
from numpy.testing import assert_array_almost_equal as assert_close
from numpy.testing import assert_array_equal, assert_raises
import skimage
from skimage import data
from skimage import exposure
from skimage.exposure.exposure import intensity_range
from skimage.color import rgb2gray
from skimage.util.dtype import dtype_range
@@ -41,6 +43,36 @@ def check_cdf_slope(cdf):
assert 0.9 < slope < 1.1
# Test intensity range
# ====================
def test_intensity_range_uint8():
image = np.array([0, 1], dtype=np.uint8)
input_and_expected = [('image', [0, 1]),
('dtype', [0, 255]),
((10, 20), [10, 20])]
for range_values, expected_values in input_and_expected:
out = intensity_range(image, range_values=range_values)
yield assert_array_equal, out, expected_values
def test_intensity_range_float():
image = np.array([0.1, 0.2], dtype=np.float64)
input_and_expected = [('image', [0.1, 0.2]),
('dtype', [-1, 1]),
((0.3, 0.4), [0.3, 0.4])]
for range_values, expected_values in input_and_expected:
out = intensity_range(image, range_values=range_values)
yield assert_array_equal, out, expected_values
def test_intensity_range_clipped_float():
image = np.array([0.1, 0.2], dtype=np.float64)
out = intensity_range(image, range_values='dtype', clip_negative=True)
assert_array_equal(out, (0, 1))
# Test rescale intensity
# ======================
@@ -134,7 +166,7 @@ def test_adapthist_grayscale():
img = rgb2gray(img)
img = np.dstack((img, img, img))
adapted = exposure.equalize_adapthist(img, 10, 9, clip_limit=0.01,
nbins=128)
nbins=128)
assert_almost_equal = np.testing.assert_almost_equal
assert img.shape == adapted.shape
assert_almost_equal(peak_snr(img, adapted), 97.531, 3)
@@ -374,6 +406,6 @@ def test_adjust_inv_sigmoid_cutoff_half():
assert_array_equal(result, expected)
def test_neggative():
def test_negative():
image = np.arange(-10, 245, 4).reshape(8, 8).astype(np.double)
assert_raises(ValueError, exposure.adjust_gamma, image)