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Merge pull request #505 from ankit-maverick/gammaCorrect

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Implementation of Pixelwise Image correction methods
This commit is contained in:
Johannes Schönberger
2013-06-11 10:21:09 -07:00
parent 8c2c69a2f4 04625ebabf
commit f07467b176
3 changed files with 307 additions and 6 deletions
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skimage/exposure/__init__.py
+7 -2
View File
@@ -1,5 +1,7 @@
from .exposure import histogram, equalize, equalize_hist, \
rescale_intensity, cumulative_distribution
rescale_intensity, cumulative_distribution, \
adjust_gamma, adjust_sigmoid, adjust_log
from ._adapthist import equalize_adapthist
__all__ = ['histogram',
@@ -7,4 +9,7 @@ __all__ = ['histogram',
'equalize_hist',
'equalize_adapthist',
'rescale_intensity',
'cumulative_distribution']
'cumulative_distribution',
'adjust_gamma',
'adjust_sigmoid',
'adjust_log']
skimage/exposure/exposure.py
+139 -4
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@@ -2,14 +2,13 @@ import warnings
import numpy as np
from skimage import img_as_float
from skimage.util.dtype import dtype_range
import skimage.color as color
from skimage.util.dtype import convert
from skimage.util.dtype import dtype_range, dtype_limits
from skimage._shared.utils import deprecated
__all__ = ['histogram', 'cumulative_distribution', 'equalize',
'rescale_intensity']
'rescale_intensity', 'adjust_gamma',
'adjust_log', 'adjust_sigmoid']
def histogram(image, nbins=256):
@@ -216,3 +215,139 @@ def rescale_intensity(image, in_range=None, out_range=None):
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.
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.
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.
References
----------
.. [1] http://bme.med.upatras.gr/improc/matalb_code_toc.htm#12. Adjust Contrast :
"""
_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)
skimage/exposure/tests/test_exposure.py
+161
View File
@@ -2,6 +2,7 @@ import warnings
import numpy as np
from numpy.testing import assert_array_almost_equal as assert_close
from numpy.testing import assert_array_equal
import skimage
from skimage import data
from skimage import exposure
@@ -175,3 +176,163 @@ def norm_brightness_err(img1, img2):
if __name__ == '__main__':
from numpy import testing
testing.run_module_suite()
# Test Gamma Correction
# =====================
def test_adjust_gamma_one():
"""Same image should be returned for gamma equal to one"""
image = np.random.uniform(0, 255, (8, 8))
result = exposure.adjust_gamma(image, 1)
assert_array_equal(result, image)
def test_adjust_gamma_zero():
"""White image should be returned for gamma equal to zero"""
image = np.random.uniform(0, 255, (8, 8))
result = exposure.adjust_gamma(image, 0)
dtype = image.dtype.type
assert_array_equal(result, dtype_range[dtype][1])
def test_adjust_gamma_less_one():
"""Verifying the output with expected results for gamma
correction with gamma equal to half"""
image = np.arange(0, 255, 4, np.uint8).reshape(8,8)
expected = np.array([[ 0, 31, 45, 55, 63, 71, 78, 84],
[ 90, 95, 100, 105, 110, 115, 119, 123],
[127, 131, 135, 139, 142, 146, 149, 153],
[156, 159, 162, 165, 168, 171, 174, 177],
[180, 183, 186, 188, 191, 194, 196, 199],
[201, 204, 206, 209, 211, 214, 216, 218],
[221, 223, 225, 228, 230, 232, 234, 236],
[238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.uint8)
result = exposure.adjust_gamma(image, 0.5)
assert_array_equal(result, expected)
def test_adjust_gamma_greater_one():
"""Verifying the output with expected results for gamma
correction with gamma equal to two"""
image = np.arange(0, 255, 4, np.uint8).reshape(8,8)
expected = np.array([[ 0, 0, 0, 0, 1, 1, 2, 3],
[ 4, 5, 6, 7, 9, 10, 12, 14],
[ 16, 18, 20, 22, 25, 27, 30, 33],
[ 36, 39, 42, 45, 49, 52, 56, 60],
[ 64, 68, 72, 76, 81, 85, 90, 95],
[100, 105, 110, 116, 121, 127, 132, 138],
[144, 150, 156, 163, 169, 176, 182, 189],
[196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.uint8)
result = exposure.adjust_gamma(image, 2)
assert_array_equal(result, expected)
# Test Logarithmic Correction
# ===========================
def test_adjust_log():
"""Verifying the output with expected results for logarithmic
correction with multiplier constant multiplier equal to unity"""
image = np.arange(0, 255, 4, np.uint8).reshape(8,8)
expected = np.array([[ 0, 5, 11, 16, 22, 27, 33, 38],
[ 43, 48, 53, 58, 63, 68, 73, 77],
[ 82, 86, 91, 95, 100, 104, 109, 113],
[117, 121, 125, 129, 133, 137, 141, 145],
[149, 153, 157, 160, 164, 168, 172, 175],
[179, 182, 186, 189, 193, 196, 199, 203],
[206, 209, 213, 216, 219, 222, 225, 228],
[231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8)
result = exposure.adjust_log(image, 1)
assert_array_equal(result, expected)
def test_adjust_inv_log():
"""Verifying the output with expected results for inverse logarithmic
correction with multiplier constant multiplier equal to unity"""
image = np.arange(0, 255, 4, np.uint8).reshape(8,8)
expected = np.array([[ 0, 2, 5, 8, 11, 14, 17, 20],
[ 23, 26, 29, 32, 35, 38, 41, 45],
[ 48, 51, 55, 58, 61, 65, 68, 72],
[ 76, 79, 83, 87, 90, 94, 98, 102],
[106, 110, 114, 118, 122, 126, 130, 134],
[138, 143, 147, 151, 156, 160, 165, 170],
[174, 179, 184, 188, 193, 198, 203, 208],
[213, 218, 224, 229, 234, 239, 245, 250]], dtype=np.uint8)
result = exposure.adjust_log(image, 1, True)
assert_array_equal(result, expected)
# Test Sigmoid Correction
# =======================
def test_adjust_sigmoid_cutoff_one():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to one and gain of 5"""
image = np.arange(0, 255, 4, np.uint8).reshape(8,8)
expected = np.array([[ 1, 1, 1, 2, 2, 2, 2, 2],
[ 3, 3, 3, 4, 4, 4, 5, 5],
[ 5, 6, 6, 7, 7, 8, 9, 10],
[ 10, 11, 12, 13, 14, 15, 16, 18],
[ 19, 20, 22, 24, 25, 27, 29, 32],
[ 34, 36, 39, 41, 44, 47, 50, 54],
[ 57, 61, 64, 68, 72, 76, 80, 85],
[ 89, 94, 99, 104, 108, 113, 118, 123]], dtype=np.uint8)
result = exposure.adjust_sigmoid(image, 1, 5)
assert_array_equal(result, expected)
def test_adjust_sigmoid_cutoff_zero():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to zero and gain of 10"""
image = np.arange(0, 255, 4, np.uint8).reshape(8,8)
expected = np.array([[127, 137, 147, 156, 166, 175, 183, 191],
[198, 205, 211, 216, 221, 225, 229, 232],
[235, 238, 240, 242, 244, 245, 247, 248],
[249, 250, 250, 251, 251, 252, 252, 253],
[253, 253, 253, 253, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254],
[254, 254, 254, 254, 254, 254, 254, 254]], dtype=np.uint8)
result = exposure.adjust_sigmoid(image, 0, 10)
assert_array_equal(result, expected)
def test_adjust_sigmoid_cutoff_half():
"""Verifying the output with expected results for sigmoid correction
with cutoff equal to half and gain of 10"""
image = np.arange(0, 255, 4, np.uint8).reshape(8,8)
expected = np.array([[ 1, 1, 2, 2, 3, 3, 4, 5],
[ 5, 6, 7, 9, 10, 12, 14, 16],
[ 19, 22, 25, 29, 34, 39, 44, 50],
[ 57, 64, 72, 80, 89, 99, 108, 118],
[128, 138, 148, 158, 167, 176, 184, 192],
[199, 205, 211, 217, 221, 226, 229, 233],
[236, 238, 240, 242, 244, 246, 247, 248],
[249, 250, 250, 251, 251, 252, 252, 253]], dtype=np.uint8)
result = exposure.adjust_sigmoid(image, 0.5, 10)
assert_array_equal(result, expected)
def test_adjust_inv_sigmoid_cutoff_half():
"""Verifying the output with expected results for inverse sigmoid
correction with cutoff equal to half and gain of 10"""
image = np.arange(0, 255, 4, np.uint8).reshape(8,8)
expected = np.array([[253, 253, 252, 252, 251, 251, 250, 249],
[249, 248, 247, 245, 244, 242, 240, 238],
[235, 232, 229, 225, 220, 215, 210, 204],
[197, 190, 182, 174, 165, 155, 146, 136],
[126, 116, 106, 96, 87, 78, 70, 62],
[ 55, 49, 43, 37, 33, 28, 25, 21],
[ 18, 16, 14, 12, 10, 8, 7, 6],
[ 5, 4, 4, 3, 3, 2, 2, 1]], dtype=np.uint8)
result = exposure.adjust_sigmoid(image, 0.5, 10, True)
assert_array_equal(result, expected)