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_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_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 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 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.
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
"""
_assert_non_negative(image)
dtype = image.dtype.type
if gamma < 0:
return "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_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.
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
"""
_assert_non_negative(image)
dtype = image.dtype.type
if gamma < 0:
return "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_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 check_contrast(image,
fraction_threshold=0.20,
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_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.
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, clip_negative=False)
limits = np.percentile(image, [lower_percentile, upper_percentile])
ratio = (limits[1] - limits[0]) / (dlimits[1] - dlimits[0])
return ratio, ratio < fraction_threshold
def imshow_all(*images, **kwargs):
""" Plot a series of images side-by-side.
Convert all images to float so that images have a common intensity range.
Parameters
----------
limits : str
Control the intensity limits. By default, 'image' is used set the
min/max intensities to the min/max of all images. Setting `limits` to
'dtype' can also be used if you want to preserve the image exposure.
titles : list of str
Titles for subplots. If the length of titles is less than the number
of images, empty strings are appended.
kwargs : dict
Additional keyword-arguments passed to `imshow`.
"""
images = [img_as_float(img) for img in images]
hide_ticks = kwargs.pop('titles', [])
titles = kwargs.pop('titles', [])
if len(titles) != len(images):
titles = list(titles) + [''] * (len(images) - len(titles))
limits = kwargs.pop('limits', 'image')
if limits == 'image':
kwargs.setdefault('vmin', min(img.min() for img in images))
kwargs.setdefault('vmax', max(img.max() for img in images))
elif limits == 'dtype':
vmin, vmax = dtype_limits(images[0])
kwargs.setdefault('vmin', vmin)
kwargs.setdefault('vmax', vmax)
nrows, ncols = kwargs.get('shape', (1, len(images)))
size = nrows * kwargs.pop('size', 5)
width = size * len(images)
if nrows > 1:
width /= nrows * 1.33
fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(width, size))
for ax, img, label in zip(axes.ravel(), images, titles):
ax.imshow(img, **kwargs)
# bare = kwargs.pop('bare', [])
# if bare == True:
# ax.xaxis.set_visible(False)
# ax.yaxis.set_visible(False)
ax.set_title(label)
def imshow_all(*images, **kwargs):
""" Plot a series of images side-by-side.
Convert all images to float so that images have a common intensity range.
Parameters
----------
limits : str
Control the intensity limits. By default, 'image' is used set the
min/max intensities to the min/max of all images. Setting `limits` to
'dtype' can also be used if you want to preserve the image exposure.
titles : list of str
Titles for subplots. If the length of titles is less than the number
of images, empty strings are appended.
kwargs : dict
Additional keyword-arguments passed to `imshow`.
"""
images = [img_as_float(img) for img in images]
titles = kwargs.pop('titles', [])
if len(titles) != len(images):
titles = list(titles) + [''] * (len(images) - len(titles))
limits = kwargs.pop('limits', 'image')
if limits == 'image':
kwargs.setdefault('vmin', min(img.min() for img in images))
kwargs.setdefault('vmax', max(img.max() for img in images))
elif limits == 'dtype':
vmin, vmax = dtype_limits(images[0])
kwargs.setdefault('vmin', vmin)
kwargs.setdefault('vmax', vmax)
nrows, ncols = kwargs.get('shape', (1, len(images)))
size = nrows * kwargs.pop('size', 5)
width = size * len(images)
if nrows > 1:
width /= nrows * 1.33
fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(width, size))
for ax, img, label in zip(axes.ravel(), images, titles):
ax.imshow(img, **kwargs)
ax.set_title(label)
ax.set_axis_off()