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import numpy as np
from ..util.dtype import dtype_range, dtype_limits
from .._shared import utils
__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[bool],
'float': dtype_range[np.float64],
}
)
def _offset_array(arr, low_boundary, high_boundary):
"""Offset the array to get the lowest value at 0 if negative."""
if low_boundary < 0:
offset = low_boundary
dyn_range = high_boundary - low_boundary
# get smallest dtype that can hold both minimum and offset maximum
offset_dtype = np.promote_types(
np.min_scalar_type(dyn_range), np.min_scalar_type(low_boundary)
)
if arr.dtype != offset_dtype:
# prevent overflow errors when offsetting
arr = arr.astype(offset_dtype)
arr = arr - offset
return arr
def _bincount_histogram_centers(image, source_range):
"""Compute bin centers for bincount-based histogram."""
if source_range not in ['image', 'dtype']:
raise ValueError(f'Incorrect value for `source_range` argument: {source_range}')
if source_range == 'image':
image_min = int(image.min().astype(np.int64))
image_max = int(image.max().astype(np.int64))
elif source_range == 'dtype':
image_min, image_max = dtype_limits(image, clip_negative=False)
bin_centers = np.arange(image_min, image_max + 1)
return bin_centers
def _bincount_histogram(image, source_range, bin_centers=None):
"""
Efficient histogram calculation for an image of integers.
This function is significantly more efficient than np.histogram but
works only on images of integers. It is based on np.bincount.
Parameters
----------
image : array
Input image.
source_range : string
'image' determines the range from the input image.
'dtype' determines the range from the expected range of the images
of that data type.
Returns
-------
hist : array
The values of the histogram.
bin_centers : array
The values at the center of the bins.
"""
if bin_centers is None:
bin_centers = _bincount_histogram_centers(image, source_range)
image_min, image_max = bin_centers[0], bin_centers[-1]
image = _offset_array(image, image_min, image_max)
hist = np.bincount(image.ravel(), minlength=image_max - min(image_min, 0) + 1)
if source_range == 'image':
idx = max(image_min, 0)
hist = hist[idx:]
return hist, bin_centers
def _get_outer_edges(image, hist_range):
"""Determine the outer bin edges to use for `numpy.histogram`.
These are obtained from either the image or hist_range.
Parameters
----------
image : ndarray
Image for which the histogram is to be computed.
hist_range: 2-tuple of int or None
Range of values covered by the histogram bins. If None, the minimum
and maximum values of `image` are used.
Returns
-------
first_edge, last_edge : int
The range spanned by the histogram bins.
Notes
-----
This function is adapted from ``np.lib.histograms._get_outer_edges``.
"""
if hist_range is not None:
first_edge, last_edge = hist_range
if first_edge > last_edge:
raise ValueError("max must be larger than min in hist_range parameter.")
if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
raise ValueError(
f'supplied hist_range of [{first_edge}, {last_edge}] is ' f'not finite'
)
elif image.size == 0:
# handle empty arrays. Can't determine hist_range, so use 0-1.
first_edge, last_edge = 0, 1
else:
first_edge, last_edge = image.min(), image.max()
if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
raise ValueError(
f'autodetected hist_range of [{first_edge}, {last_edge}] is '
f'not finite'
)
# expand empty hist_range to avoid divide by zero
if first_edge == last_edge:
first_edge = first_edge - 0.5
last_edge = last_edge + 0.5
return first_edge, last_edge
def _get_bin_edges(image, nbins, hist_range):
"""Computes histogram bins for use with `numpy.histogram`.
Parameters
----------
image : ndarray
Image for which the histogram is to be computed.
nbins : int
The number of bins.
hist_range: 2-tuple of int
Range of values covered by the histogram bins.
Returns
-------
bin_edges : ndarray
The histogram bin edges.
Notes
-----
This function is a simplified version of
``np.lib.histograms._get_bin_edges`` that only supports uniform bins.
"""
first_edge, last_edge = _get_outer_edges(image, hist_range)
# numpy/gh-10322 means that type resolution rules are dependent on array
# shapes. To avoid this causing problems, we pick a type now and stick
# with it throughout.
bin_type = np.result_type(first_edge, last_edge, image)
if np.issubdtype(bin_type, np.integer):
bin_type = np.result_type(bin_type, float)
# compute bin edges
bin_edges = np.linspace(
first_edge, last_edge, nbins + 1, endpoint=True, dtype=bin_type
)
return bin_edges
def _get_numpy_hist_range(image, source_range):
if source_range == 'image':
hist_range = None
elif source_range == 'dtype':
hist_range = dtype_limits(image, clip_negative=False)
else:
raise ValueError(f'Incorrect value for `source_range` argument: {source_range}')
return hist_range
@utils.channel_as_last_axis(multichannel_output=False)
def histogram(
image, nbins=256, source_range='image', normalize=False, *, channel_axis=None
):
"""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.
If `channel_axis` is not set, the histogram is computed on the flattened
image. For color or multichannel images, set ``channel_axis`` to use a
common binning for all channels. Alternatively, one may apply the function
separately on each channel to obtain a histogram for each color channel
with separate binning.
Parameters
----------
image : array
Input image.
nbins : int, optional
Number of bins used to calculate histogram. This value is ignored for
integer arrays.
source_range : string, optional
'image' (default) determines the range from the input image.
'dtype' determines the range from the expected range of the images
of that data type.
normalize : bool, optional
If True, normalize the histogram by the sum of its values.
channel_axis : int or None, optional
If None, the image is assumed to be a grayscale (single channel) image.
Otherwise, this parameter indicates which axis of the array corresponds
to channels.
Returns
-------
hist : array
The values of the histogram. When ``channel_axis`` is not None, hist
will be a 2D array where the first axis corresponds to channels.
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([ 93585, 168559]), array([0. , 0.5, 1. ]))
>>> exposure.histogram(image, nbins=2)
(array([ 93585, 168559]), array([0.25, 0.75]))
"""
sh = image.shape
if len(sh) == 3 and sh[-1] < 4 and channel_axis is None:
utils.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, or set '
'channel_axis.'
)
if channel_axis is not None:
channels = sh[-1]
hist = []
# compute bins based on the raveled array
if np.issubdtype(image.dtype, np.integer):
# here bins corresponds to the bin centers
bins = _bincount_histogram_centers(image, source_range)
else:
# determine the bin edges for np.histogram
hist_range = _get_numpy_hist_range(image, source_range)
bins = _get_bin_edges(image, nbins, hist_range)
for chan in range(channels):
h, bc = _histogram(image[..., chan], bins, source_range, normalize)
hist.append(h)
# Convert to numpy arrays
bin_centers = np.asarray(bc)
hist = np.stack(hist, axis=0)
else:
hist, bin_centers = _histogram(image, nbins, source_range, normalize)
return hist, bin_centers
def _histogram(image, bins, source_range, normalize):
"""
Parameters
----------
image : ndarray
Image for which the histogram is to be computed.
bins : int or ndarray
The number of histogram bins. For images with integer dtype, an array
containing the bin centers can also be provided. For images with
floating point dtype, this can be an array of bin_edges for use by
``np.histogram``.
source_range : string, optional
'image' (default) determines the range from the input image.
'dtype' determines the range from the expected range of the images
of that data type.
normalize : bool, optional
If True, normalize the histogram by the sum of its values.
"""
image = image.flatten()
# For integer types, histogramming with bincount is more efficient.
if np.issubdtype(image.dtype, np.integer):
bin_centers = bins if isinstance(bins, np.ndarray) else None
hist, bin_centers = _bincount_histogram(image, source_range, bin_centers)
else:
hist_range = _get_numpy_hist_range(image, source_range)
hist, bin_edges = np.histogram(image, bins=bins, range=hist_range)
bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.0
if normalize:
hist = hist / np.sum(hist)
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, optional
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] https://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)
>>> all(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])
# cast img_cdf to single precision for float32 or float16 inputs
cdf_dtype = utils._supported_float_type(image.dtype)
img_cdf = img_cdf.astype(cdf_dtype, copy=False)
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/histogram-equalization-with-python-and.html
.. [2] https://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)
out = out.reshape(image.shape)
# Unfortunately, np.interp currently always promotes to float64, so we
# have to cast back to single precision when float32 output is desired
return out.astype(utils._supported_float_type(image.dtype), copy=False)
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, optional
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, optional
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 _output_dtype(dtype_or_range, image_dtype):
"""Determine the output dtype for rescale_intensity.
The dtype is determined according to the following rules:
- if ``dtype_or_range`` is a dtype, that is the output dtype.
- if ``dtype_or_range`` is a dtype string, that is the dtype used, unless
it is not a NumPy data type (e.g. 'uint12' for 12-bit unsigned integers),
in which case the data type that can contain it will be used
(e.g. uint16 in this case).
- if ``dtype_or_range`` is a pair of values, the output data type will be
``_supported_float_type(image_dtype)``. This preserves float32 output for
float32 inputs.
Parameters
----------
dtype_or_range : type, string, or 2-tuple of int/float
The desired range for the output, expressed as either a NumPy dtype or
as a (min, max) pair of numbers.
image_dtype : np.dtype
The input image dtype.
Returns
-------
out_dtype : type
The data type appropriate for the desired output.
"""
if type(dtype_or_range) in [list, tuple, np.ndarray]:
# pair of values: always return float.
return utils._supported_float_type(image_dtype)
if type(dtype_or_range) == type:
# already a type: return it
return dtype_or_range
if dtype_or_range in DTYPE_RANGE:
# string key in DTYPE_RANGE dictionary
try:
# if it's a canonical numpy dtype, convert
return np.dtype(dtype_or_range).type
except TypeError: # uint10, uint12, uint14
# otherwise, return uint16
return np.uint16
else:
raise ValueError(
'Incorrect value for out_range, should be a valid image data '
f'type or a pair of values, got {dtype_or_range}.'
)
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, optional
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.
Notes
-----
.. versionchanged:: 0.17
The dtype of the output array has changed to match the input dtype, or
float if the output range is specified by a pair of values.
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. In that case, the
output dtype will be float:
>>> image = np.array([-10, 0, 10], dtype=np.int8)
>>> rescale_intensity(image, out_range=(0, 127))
array([ 0. , 63.5, 127. ])
To get the desired range with a specific dtype, use ``.astype()``:
>>> rescale_intensity(image, out_range=(0, 127)).astype(np.int8)
array([ 0, 63, 127], dtype=int8)
If the input image is constant, the output will be clipped directly to the
output range:
>>> image = np.array([130, 130, 130], dtype=np.int32)
>>> rescale_intensity(image, out_range=(0, 127)).astype(np.int32)
array([127, 127, 127], dtype=int32)
"""
if out_range in ['dtype', 'image']:
out_dtype = _output_dtype(image.dtype.type, image.dtype)
else:
out_dtype = _output_dtype(out_range, image.dtype)
imin, imax = map(float, intensity_range(image, in_range))
omin, omax = map(
float, intensity_range(image, out_range, clip_negative=(imin >= 0))
)
if np.any(np.isnan([imin, imax, omin, omax])):
utils.warn(
"One or more intensity levels are NaN. Rescaling will broadcast "
"NaN to the full image. Provide intensity levels yourself to "
"avoid this. E.g. with np.nanmin(image), np.nanmax(image).",
stacklevel=2,
)
image = np.clip(image, imin, imax)
if imin != imax:
image = (image - imin) / (imax - imin)
return (image * (omax - omin) + omin).astype(out_dtype)
else:
return np.clip(image, omin, omax).astype(out_dtype)
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_u8(image, gamma, gain):
"""LUT based implementation of gamma adjustment."""
lut = 255 * gain * (np.linspace(0, 1, 256) ** gamma)
lut = np.minimum(np.rint(lut), 255).astype('uint8')
return lut[image]
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, optional
Non negative real number. Default value is 1.
gain : float, optional
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] https://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
"""
if gamma < 0:
raise ValueError("Gamma should be a non-negative real number.")
dtype = image.dtype.type
if dtype is np.uint8:
out = _adjust_gamma_u8(image, gamma, gain)
else:
_assert_non_negative(image)
scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
out = (((image / scale) ** gamma) * scale * gain).astype(dtype)
return 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, optional
The constant multiplier. Default value is 1.
inv : float, optional
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 out.astype(dtype)
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, optional
Cutoff of the sigmoid function that shifts the characteristic curve
in horizontal direction. Default value is 0.5.
gain : float, optional
The constant multiplier in exponential's power of sigmoid function.
Default value is 10.
inv : bool, optional
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://markfairchild.org/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 out.astype(dtype)
def is_low_contrast(
image,
fraction_threshold=0.05,
lower_percentile=1,
upper_percentile=99,
method='linear',
):
"""Determine 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_percentile : float, optional
Disregard values below this percentile when computing image contrast.
upper_percentile : 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.
Notes
-----
For boolean images, this function returns False only if all values are
the same (the method, threshold, and percentile arguments are ignored).
References
----------
.. [1] https://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.dtype == bool:
return not ((image.max() == 1) and (image.min() == 0))
if image.ndim == 3:
from ..color import rgb2gray, rgba2rgb # avoid circular import
if image.shape[2] == 4:
image = rgba2rgb(image)
if image.shape[2] == 3:
image = rgb2gray(image)
dlimits = dtype_limits(image, clip_negative=False)
limits = np.percentile(image, [lower_percentile, upper_percentile])
ratio = (limits[1] - limits[0]) / (dlimits[1] - dlimits[0])
return ratio < fraction_threshold