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https://github.com/wassname/scikit-image.git
synced 2026-08-15 12:54:54 +08:00
Separating correction methods into different functions
This commit is contained in:
Ankit Agrawal
@@ -218,8 +218,9 @@ def rescale_intensity(image, in_range=None, out_range=None):
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return dtype(image * (omax - omin) + omin)
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def rescale_intensity_gamma(image, gamma = 1):
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"""Performs Gamma Correction also known as Power Law Transform.
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def rescale_intensity_gamma(image, gamma = 1, gain = 1):
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"""Performs Gamma Correction on the input image.
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Also known as Power Law Transform.
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Parameters
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----------
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@@ -227,59 +228,117 @@ def rescale_intensity_gamma(image, gamma = 1):
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Input image.
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gamma : float
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Non negative real number
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Non negative real number. Default value is 1.
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param1 : float
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For type 'gamma', gamma varying from zero to infinity. Default value 1.
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For type 'logarithmic', param1 should be -1 for inverse logarithmic,
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else correction will be logarithmic. Default to logarithmic.
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For type 'sigmoid', gain. Default value 10.
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param2 : float
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For type 'gamma', positive constatnt multiplier. Default value 1.
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For type 'logarithmic', positive constatnt multiplier. Default value 1.
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For type 'sigmoid', cutoff between 0 and 1. Default value 0.5.
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gain : float
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The constant multiplier. Default value is 1.
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Returns
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-------
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out : ndarray
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Corrected input image according to the type used.
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Gamma corrected output image.
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Notes
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-----
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This function transforms the input image pixelwise according to the
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equation O = I**gamma after scaling each pixel to the range 0 to 1.
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For gamma greater than 1, the histogram will shift towards left and
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the output image will be darker than the input image.
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For gamma less than 1, the histogram will shift towards right and
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the output image will be brighter than the input image.
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References
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----------
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..[1] http://en.wikipedia.org/wiki/Gamma_correction
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"""
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"""
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dtype = image.dtype.type
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if gamma < 0:
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return "Gamma should be a non-negative real number"
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scale = float(dtype_range[dtype][1] - dtype_range[dtype][0])
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out = ((image / scale)**gamma) * scale * param2
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out = ((image / scale) ** gamma) * scale * gain
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return dtype(out)
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def rescale_intensity_logarithmic(image, gain = 1, inv = 1):
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"""Performs Logarithmic correction on the input image.
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Parameters
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----------
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image : ndarray
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Input image.
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gain : float
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The constant multiplier. Default value is 1.
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inv : float
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Value passed should be -1 for inverse logarithmic correction,
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else correction will be logarithmic. Default to logarithmic.
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Returns
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-------
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out : ndarray
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Logarithm corrected output image.
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Notes
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-----
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This function transforms the input image pixelwise according to the
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equation O = gain*log(1 + I) after scaling each pixel to the range 0 to 1.
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For inverse logarithmic correction, the equation is O = gain*(2**I - 1)
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References
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----------
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..[1] http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf
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"""
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dtype = image.dtype.type
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scale = float(dtype_range[dtype][1] - dtype_range[dtype][0])
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if inv == -1:
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out = (2**(image / scale) - 1) * scale * param2
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out = (2 ** (image / scale) - 1) * scale * gain
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return dtype(out)
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out = np.log2(1 + image / scale) * scale * param2
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out = np.log2(1 + image / scale) * scale * gain
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return dtype(out)
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def rescale_intensity_sigmoid(image, cutoff = 0.5, gain = 1):
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def rescale_intensity_sigmoid(image, cutoff = 0.5, gain = 10):
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"""Performs Sigmoid Correction on input image also known
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as Contrast Adjustment.
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Parameters
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----------
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image : ndarray
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Input image.
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cutoff : float
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Cutoff of the sigmoid function. Default value is 0.5.
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gain : float
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The constant multiplier in exponential's power of sigmoid function.
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Default value is 10.
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Returns
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-------
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out : ndarray
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Sigmoid corrected output image.
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Notes
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-----
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This function transforms the input image pixelwise according to the
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equation O = 1/(1 + exp*(gain*(cutoff - I))) after scaling each pixel to
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the range 0 to 1.
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References
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----------
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..[1] http://bme.med.upatras.gr/improc/matalb_code_toc.htm#12. Adjust Contrast :
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"""
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dtype = image.dtype.type
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scale = float(dtype_range[dtype][1] - dtype_range[dtype][0])
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out = (1 / (1 + np.exp(gain * (cutoff - image/scale)))) * scale
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return dtype(out)
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..[2] http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf
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..[3] http://bme.med.upatras.gr/improc/matalb_code_toc.htm#12. Adjust Contrast :
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@@ -180,72 +180,72 @@ if __name__ == '__main__':
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# Test Gamma Correction
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# =====================
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def test_gamma_correct_one():
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def test_rescale_intensity_gamma_one():
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"""Same image should be returned for gamma equal to one"""
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image = data.camera()
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result = exposure.correct(image, 'gamma', 1)
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result = exposure.rescale_intensity_gamma(image, 1)
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assert result.mean() == image.mean()
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assert result.std() == image.std()
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def test_gamma_correct_zero():
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def test_rescale_intensity_gamma_zero():
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"""White image should be returned for gamma equal to zero"""
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image = data.camera()
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result = exposure.correct(image, 'gamma', 0)
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result = exposure.rescale_intensity_gamma(image, 0)
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dtype = image.dtype.type
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assert result.mean() == dtype_range[dtype][1]
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assert result.std() == 0
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def test_gamma_correct_less_one():
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def test_rescale_intensity_gamma_less_one():
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"""Output's mean should be greater than input's mean for gamma less than
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one"""
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image = data.camera()
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result = exposure.correct(image, 'gamma', 0.5)
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result = exposure.rescale_intensity_gamma(image, 0.5)
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assert result.mean() > image.mean()
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def test_gamma_correct_greater_one():
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def test_rescale_intensity_gamma_greater_one():
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"""Output's mean should be less than input's mean for gamma greater than
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one"""
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image = data.camera()
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result = exposure.correct(image,'gamma', 2)
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result = exposure.rescale_intensity_gamma(image, 2)
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assert result.mean() < image.mean()
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# Test Logarithmic Correction
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# ===========================
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def test_logarithmic_correct():
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def test_rescale_intensity_logarithmic():
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"""Output's mean should be greater than input's mean for logarithmic
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correction with multiplier constant equal to unity"""
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image = data.camera()
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result = exposure.correct(image, 'logarithmic')
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result = exposure.rescale_intensity_logarithmic(image, 1)
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assert result.mean() > image.mean()
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def test_inv_logarithmic_correct():
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def test_rescale_intensity_inv_logarithmic():
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"""Output's mean should be less than input's mean for inverse logarithmic
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correction with multiplier constant equal to unity"""
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image = data.camera()
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result = exposure.correct(image, 'logarithmic', -1)
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result = exposure.rescale_intensity_logarithmic(image, 1, -1)
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assert result.mean() < image.mean()
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# Test Sigmoid Correction
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# =======================
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def test_sigmoid_correct_cutoff_one():
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def test_rescale_intensity_sigmoid_cutoff_one():
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"""Output's mean should be less than input's mean for sigmoid
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correction with cutoff equal to one and gain of 10"""
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image = data.camera()
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result = exposure.correct(image, 'sigmoid', 10, 1)
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result = exposure.rescale_intensity_sigmoid(image, 1, 10)
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assert result.mean() < image.mean()
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def test_sigmoid_correct_cutoff_zero():
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def test_rescale_intensity_sigmoid_cutoff_zero():
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"""Output's mean should be greater than input's mean for sigmoid
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correction with cutoff equal to zero and gain of 10"""
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image = data.camera()
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result = exposure.correct(image, 'sigmoid', 10, 0)
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result = exposure.rescale_intensity_sigmoid(image, 0, 10)
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assert result.mean() > image.mean()
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