mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-21 12:50:27 +08:00
Corrections and Improvements
This commit is contained in:
Ankit Agrawal
@@ -263,7 +263,7 @@ def rescale_intensity_gamma(image, gamma=1, gain=1):
|
||||
return dtype(out)
|
||||
|
||||
|
||||
def rescale_intensity_logarithmic(image, gain=1, inv=1):
|
||||
def rescale_intensity_log(image, gain=1, inv=False):
|
||||
"""Performs Logarithmic correction on the input image.
|
||||
|
||||
Parameters
|
||||
@@ -273,8 +273,8 @@ def rescale_intensity_logarithmic(image, gain=1, inv=1):
|
||||
gain : float
|
||||
The constant multiplier. Default value is 1.
|
||||
inv : float
|
||||
Value passed should be -1 for inverse logarithmic correction,
|
||||
else correction will be logarithmic. Default to logarithmic.
|
||||
If True, it performs inverse logarithmic correction,
|
||||
else correction will be logarithmic. Defaults to False.
|
||||
|
||||
Returns
|
||||
-------
|
||||
@@ -295,7 +295,7 @@ def rescale_intensity_logarithmic(image, gain=1, inv=1):
|
||||
dtype = image.dtype.type
|
||||
scale = float(dtype_range[dtype][1] - dtype_range[dtype][0])
|
||||
|
||||
if inv == -1:
|
||||
if inv == True:
|
||||
out = (2 ** (image / scale) - 1) * scale * gain
|
||||
return dtype(out)
|
||||
|
||||
@@ -303,7 +303,7 @@ def rescale_intensity_logarithmic(image, gain=1, inv=1):
|
||||
return dtype(out)
|
||||
|
||||
|
||||
def rescale_intensity_sigmoid(image, cutoff=0.5, gain=10):
|
||||
def rescale_intensity_sigmoid(image, cutoff=0.5, gain=10, inv=False):
|
||||
"""Performs Sigmoid Correction on input image.
|
||||
|
||||
Also known as Contrast Adjustment.
|
||||
@@ -313,11 +313,13 @@ def rescale_intensity_sigmoid(image, cutoff=0.5, gain=10):
|
||||
image : ndarray
|
||||
Input image.
|
||||
cutoff : float
|
||||
Cutoff of the sigmoid function. Default value is 0.5.
|
||||
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 : If True, returns the negative sigmoid correction. Defaults to
|
||||
False.
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
@@ -336,5 +338,8 @@ def rescale_intensity_sigmoid(image, cutoff=0.5, gain=10):
|
||||
"""
|
||||
dtype = image.dtype.type
|
||||
scale = float(dtype_range[dtype][1] - dtype_range[dtype][0])
|
||||
if inv == True:
|
||||
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)
|
||||
|
||||
@@ -183,24 +183,23 @@ if __name__ == '__main__':
|
||||
|
||||
def test_rescale_intensity_gamma_one():
|
||||
"""Same image should be returned for gamma equal to one"""
|
||||
image = data.camera()
|
||||
image = np.random.random((8, 8))
|
||||
result = exposure.rescale_intensity_gamma(image, 1)
|
||||
assert_array_equal(result, image)
|
||||
|
||||
|
||||
def test_rescale_intensity_gamma_zero():
|
||||
"""White image should be returned for gamma equal to zero"""
|
||||
image = data.camera()
|
||||
image = np.random.random((8, 8))
|
||||
result = exposure.rescale_intensity_gamma(image, 0)
|
||||
dtype = image.dtype.type
|
||||
assert result.mean() == dtype_range[dtype][1]
|
||||
assert result.std() == 0
|
||||
assert_array_equal(result, dtype_range[dtype][1])
|
||||
|
||||
|
||||
def test_rescale_intensity_gamma_less_one():
|
||||
"""Verifying the output with expected results for gamma
|
||||
correction with gamma equal to half"""
|
||||
image = np.uint8(4 * np.arange(64).reshape(8,8))
|
||||
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],
|
||||
@@ -217,7 +216,7 @@ def test_rescale_intensity_gamma_less_one():
|
||||
def test_rescale_intensity_gamma_greater_one():
|
||||
"""Verifying the output with expected results for gamma
|
||||
correction with gamma equal to two"""
|
||||
image = np.uint8(4 * np.arange(64).reshape(8,8))
|
||||
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],
|
||||
@@ -237,7 +236,7 @@ def test_rescale_intensity_gamma_greater_one():
|
||||
def test_rescale_intensity_logarithmic():
|
||||
"""Verifying the output with expected results for logarithmic
|
||||
correction with multiplier constant multiplier equal to unity"""
|
||||
image = np.uint8(4 * np.arange(64).reshape(8,8))
|
||||
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],
|
||||
@@ -247,14 +246,14 @@ def test_rescale_intensity_logarithmic():
|
||||
[206, 209, 213, 216, 219, 222, 225, 228],
|
||||
[231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8)
|
||||
|
||||
result = exposure.rescale_intensity_logarithmic(image, 1)
|
||||
result = exposure.rescale_intensity_log(image, 1)
|
||||
assert_array_equal(result, expected)
|
||||
|
||||
|
||||
def test_rescale_intensity_inv_logarithmic():
|
||||
"""Verifying the output with expected results for inverse logarithmic
|
||||
correction with multiplier constant multiplier equal to unity"""
|
||||
image = np.uint8(4 * np.arange(64).reshape(8,8))
|
||||
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],
|
||||
@@ -264,7 +263,7 @@ def test_rescale_intensity_inv_logarithmic():
|
||||
[174, 179, 184, 188, 193, 198, 203, 208],
|
||||
[213, 218, 224, 229, 234, 239, 245, 250]], dtype=np.uint8)
|
||||
|
||||
result = exposure.rescale_intensity_logarithmic(image, 1, -1)
|
||||
result = exposure.rescale_intensity_log(image, 1, True)
|
||||
assert_array_equal(result, expected)
|
||||
|
||||
|
||||
@@ -274,7 +273,7 @@ def test_rescale_intensity_inv_logarithmic():
|
||||
def test_rescale_intensity_sigmoid_cutoff_one():
|
||||
"""Verifying the output with expected results for sigmoid correction
|
||||
with cutoff equal to one and gain of 5"""
|
||||
image = np.uint8(4 * np.arange(64).reshape(8,8))
|
||||
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],
|
||||
@@ -291,7 +290,7 @@ def test_rescale_intensity_sigmoid_cutoff_one():
|
||||
def test_rescale_intensity_sigmoid_cutoff_zero():
|
||||
"""Verifying the output with expected results for sigmoid correction
|
||||
with cutoff equal to zero and gain of 10"""
|
||||
image = np.uint8(4 * np.arange(64).reshape(8,8))
|
||||
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],
|
||||
@@ -308,7 +307,7 @@ def test_rescale_intensity_sigmoid_cutoff_zero():
|
||||
def test_rescale_intensity_sigmoid_cutoff_half():
|
||||
"""Verifying the output with expected results for sigmoid correction
|
||||
with cutoff equal to half and gain of 10"""
|
||||
image = np.uint8(4 * np.arange(64).reshape(8,8))
|
||||
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],
|
||||
|
||||
Reference in New Issue
Block a user