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scikit-image/skimage/exposure/exposure.py
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Egor Panfilov d2d3588183 Removed None defaults in rescale_intensity
2016-06-20 22:28:10 +03:00

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from __future__ import division
import numpy as np
from ..color import rgb2gray
from ..util.dtype import dtype_range, dtype_limits
from .._shared.utils import warn
__all__ = ['histogram', 'cumulative_distribution', 'equalize_hist',
'rescale_intensity', 'adjust_gamma', 'adjust_log', 'adjust_sigmoid']
DTYPE_RANGE = dtype_range.copy()
DTYPE_RANGE.update((d.__name__, limits) for d, limits in dtype_range.items())
DTYPE_RANGE.update({'uint10': (0, 2 ** 10 - 1),
'uint12': (0, 2 ** 12 - 1),
'uint14': (0, 2 ** 14 - 1),
'bool': dtype_range[np.bool_],
'float': dtype_range[np.float64]})
def histogram(image, nbins=256):
"""Return histogram of image.
Unlike `numpy.histogram`, this function returns the centers of bins and
does not rebin integer arrays. For integer arrays, each integer value has
its own bin, which improves speed and intensity-resolution.
The histogram is computed on the flattened image: for color images, the
function should be used separately on each channel to obtain a histogram
for each color channel.
Parameters
----------
image : array
Input image.
nbins : int
Number of bins used to calculate histogram. This value is ignored for
integer arrays.
Returns
-------
hist : array
The values of the histogram.
bin_centers : array
The values at the center of the bins.
See Also
--------
cumulative_distribution
Examples
--------
>>> from skimage import data, exposure, img_as_float
>>> image = img_as_float(data.camera())
>>> np.histogram(image, bins=2)
(array([107432, 154712]), array([ 0. , 0.5, 1. ]))
>>> exposure.histogram(image, nbins=2)
(array([107432, 154712]), array([ 0.25, 0.75]))
"""
sh = image.shape
if len(sh) == 3 and sh[-1] < 4:
warn("This might be a color image. The histogram will be "
"computed on the flattened image. You can instead "
"apply this function to each color channel.")
# For integer types, histogramming with bincount is more efficient.
if np.issubdtype(image.dtype, np.integer):
offset = 0
image_min = np.min(image)
if image_min < 0:
offset = image_min
image_range = np.max(image).astype(np.int64) - image_min
# get smallest dtype that can hold both minimum and offset maximum
offset_dtype = np.promote_types(np.min_scalar_type(image_range),
np.min_scalar_type(image_min))
if image.dtype != offset_dtype:
# prevent overflow errors when offsetting
image = image.astype(offset_dtype)
image = image - offset
hist = np.bincount(image.ravel())
bin_centers = np.arange(len(hist)) + offset
# clip histogram to start with a non-zero bin
idx = np.nonzero(hist)[0][0]
return hist[idx:], bin_centers[idx:]
else:
hist, bin_edges = np.histogram(image.flat, bins=nbins)
bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.
return hist, bin_centers
def cumulative_distribution(image, nbins=256):
"""Return cumulative distribution function (cdf) for the given image.
Parameters
----------
image : array
Image array.
nbins : int
Number of bins for image histogram.
Returns
-------
img_cdf : array
Values of cumulative distribution function.
bin_centers : array
Centers of bins.
See Also
--------
histogram
References
----------
.. [1] http://en.wikipedia.org/wiki/Cumulative_distribution_function
Examples
--------
>>> from skimage import data, exposure, img_as_float
>>> image = img_as_float(data.camera())
>>> hi = exposure.histogram(image)
>>> cdf = exposure.cumulative_distribution(image)
>>> np.alltrue(cdf[0] == np.cumsum(hi[0])/float(image.size))
True
"""
hist, bin_centers = histogram(image, nbins)
img_cdf = hist.cumsum()
img_cdf = img_cdf / float(img_cdf[-1])
return img_cdf, bin_centers
def equalize_hist(image, nbins=256, mask=None):
"""Return image after histogram equalization.
Parameters
----------
image : array
Image array.
nbins : int, optional
Number of bins for image histogram. Note: this argument is
ignored for integer images, for which each integer is its own
bin.
mask: ndarray of bools or 0s and 1s, optional
Array of same shape as `image`. Only points at which mask == True
are used for the equalization, which is applied to the whole image.
Returns
-------
out : float array
Image array after histogram equalization.
Notes
-----
This function is adapted from [1]_ with the author's permission.
References
----------
.. [1] http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html
.. [2] http://en.wikipedia.org/wiki/Histogram_equalization
"""
if mask is not None:
mask = np.array(mask, dtype=bool)
cdf, bin_centers = cumulative_distribution(image[mask], nbins)
else:
cdf, bin_centers = cumulative_distribution(image, nbins)
out = np.interp(image.flat, bin_centers, cdf)
return out.reshape(image.shape)
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 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, 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
-------
out : array
Image array after rescaling its intensity. This image is the same dtype
as the input image.
See Also
--------
equalize_hist
Examples
--------
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)
array([ 0, 127, 255], dtype=uint8)
It's easy to accidentally convert an image dtype from uint8 to float:
>>> 1.0 * image
array([ 51., 102., 153.])
Use `rescale_intensity` to rescale to the proper range for float dtypes:
>>> image_float = 1.0 * image
>>> rescale_intensity(image_float)
array([ 0. , 0.5, 1. ])
To maintain the low contrast of the original, use the `in_range` parameter:
>>> rescale_intensity(image_float, in_range=(0, 255))
array([ 0.2, 0.4, 0.6])
If the min/max value of `in_range` is more/less than the min/max image
intensity, then the intensity levels are clipped:
>>> rescale_intensity(image_float, in_range=(0, 102))
array([ 0.5, 1. , 1. ])
If you have an image with signed integers but want to rescale the image to
just the positive range, use the `out_range` parameter:
>>> image = np.array([-10, 0, 10], dtype=np.int8)
>>> rescale_intensity(image, out_range=(0, 127))
array([ 0, 63, 127], dtype=int8)
"""
dtype = image.dtype.type
imin, imax = intensity_range(image, in_range)
omin, omax = intensity_range(image, out_range, clip_negative=(imin >= 0))
image = np.clip(image, imin, imax)
image = (image - imin) / float(imax - imin)
return dtype(image * (omax - omin) + omin)
def _assert_non_negative(image):
if np.any(image < 0):
raise ValueError('Image Correction methods work correctly only on '
'images with non-negative values. Use '
'skimage.exposure.rescale_intensity.')
def adjust_gamma(image, gamma=1, gain=1):
"""Performs Gamma Correction on the input image.
Also known as Power Law Transform.
This function transforms the input image pixelwise according to the
equation ``O = I**gamma`` after scaling each pixel to the range 0 to 1.
Parameters
----------
image : ndarray
Input image.
gamma : float
Non negative real number. Default value is 1.
gain : float
The constant multiplier. Default value is 1.
Returns
-------
out : ndarray
Gamma corrected output image.
See Also
--------
adjust_log
Notes
-----
For gamma greater than 1, the histogram will shift towards left and
the output image will be darker than the input image.
For gamma less than 1, the histogram will shift towards right and
the output image will be brighter than the input image.
References
----------
.. [1] http://en.wikipedia.org/wiki/Gamma_correction
Examples
--------
>>> from skimage import data, exposure, img_as_float
>>> image = img_as_float(data.moon())
>>> gamma_corrected = exposure.adjust_gamma(image, 2)
>>> # Output is darker for gamma > 1
>>> image.mean() > gamma_corrected.mean()
True
"""
_assert_non_negative(image)
dtype = image.dtype.type
if gamma < 0:
raise ValueError("Gamma should be a non-negative real number.")
scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
out = ((image / scale) ** gamma) * scale * gain
return dtype(out)
def adjust_log(image, gain=1, inv=False):
"""Performs Logarithmic correction on the input image.
This function transforms the input image pixelwise according to the
equation ``O = gain*log(1 + I)`` after scaling each pixel to the range 0 to 1.
For inverse logarithmic correction, the equation is ``O = gain*(2**I - 1)``.
Parameters
----------
image : ndarray
Input image.
gain : float
The constant multiplier. Default value is 1.
inv : float
If True, it performs inverse logarithmic correction,
else correction will be logarithmic. Defaults to False.
Returns
-------
out : ndarray
Logarithm corrected output image.
See Also
--------
adjust_gamma
References
----------
.. [1] http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf
"""
_assert_non_negative(image)
dtype = image.dtype.type
scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
if inv:
out = (2 ** (image / scale) - 1) * scale * gain
return dtype(out)
out = np.log2(1 + image / scale) * scale * gain
return dtype(out)
def adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False):
"""Performs Sigmoid Correction on the input image.
Also known as Contrast Adjustment.
This function transforms the input image pixelwise according to the
equation ``O = 1/(1 + exp*(gain*(cutoff - I)))`` after scaling each pixel
to the range 0 to 1.
Parameters
----------
image : ndarray
Input image.
cutoff : float
Cutoff of the sigmoid function that shifts the characteristic curve
in horizontal direction. Default value is 0.5.
gain : float
The constant multiplier in exponential's power of sigmoid function.
Default value is 10.
inv : bool
If True, returns the negative sigmoid correction. Defaults to False.
Returns
-------
out : ndarray
Sigmoid corrected output image.
See Also
--------
adjust_gamma
References
----------
.. [1] Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast
Enhancement Functions",
http://www.cis.rit.edu/fairchild/PDFs/PAP07.pdf
"""
_assert_non_negative(image)
dtype = image.dtype.type
scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
if inv:
out = (1 - 1 / (1 + np.exp(gain * (cutoff - image / scale)))) * scale
return dtype(out)
out = (1 / (1 + np.exp(gain * (cutoff - image / scale)))) * scale
return dtype(out)
def is_low_contrast(image, fraction_threshold=0.05, lower_percentile=1,
upper_percentile=99, method='linear'):
"""Detemine if an image is low contrast.
Parameters
----------
image : array-like
The image under test.
fraction_threshold : float, optional
The low contrast fraction threshold. An image is considered low-
contrast when its range of brightness spans less than this
fraction of its data type's full range. [1]_
lower_bound : float, optional
Disregard values below this percentile when computing image contrast.
upper_bound : float, optional
Disregard values above this percentile when computing image contrast.
method : str, optional
The contrast determination method. Right now the only available
option is "linear".
Returns
-------
out : bool
True when the image is determined to be low contrast.
References
----------
.. [1] http://scikit-image.org/docs/dev/user_guide/data_types.html
Examples
--------
>>> image = np.linspace(0, 0.04, 100)
>>> is_low_contrast(image)
True
>>> image[-1] = 1
>>> is_low_contrast(image)
True
>>> is_low_contrast(image, upper_percentile=100)
False
"""
image = np.asanyarray(image)
if image.ndim == 3 and image.shape[2] in [3, 4]:
image = rgb2gray(image)
dlimits = dtype_limits(image)
limits = np.percentile(image, [lower_percentile, upper_percentile])
ratio = (limits[1] - limits[0]) / (dlimits[1] - dlimits[0])
return ratio < fraction_threshold
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