def test_out_dtype(self):
"""
Test that the output array is of type uint8.
"""
byt = bytscl(self.array1)
dtype = byt.dtype
self.assertEqual(dtype, 'uint8')
def test_out_dtype(self):
"""
Test that the output array is of type uint8.
"""
byt = bytscl(self.array1)
dtype = byt.dtype
self.assertEqual(dtype, 'uint8')
def test_Top_keyword(self):
"""
Test that the Top keyword works as expected.
"""
# Set Top to 200
byt = bytscl(self.array1, Top=200)
mx = numpy.max(byt)
self.assertEqual(mx, 200)
def test_output_range(self):
"""
Test that the output array is [0,255].
"""
byt = bytscl(self.array1)
outside = (byt < 0) | (byt > 255)
total = numpy.sum(outside)
self.assertEqual(total, 0)
def test_Top_keyword(self):
"""
Test that the Top keyword works as expected.
"""
# Set Top to 200
byt = bytscl(self.array1, Top=200)
mx = numpy.max(byt)
self.assertEqual(mx, 200)
def test_output_range(self):
"""
Test that the output array is [0,255].
"""
byt = bytscl(self.array1)
outside = (byt < 0) | (byt > 255)
total = numpy.sum(outside)
self.assertEqual(total, 0)
def test_Min_keyword(self):
"""
Test that the Min keyword works as expected.
"""
# Set Min to 200
byt = bytscl(self.array2, Min=200)
control = numpy.sum(self.array2 <= 200)
total = numpy.sum(byt == 0)
self.assertEqual(total, control)
def test_Max_keyword(self):
"""
Test that the Max keyword works as expected.
"""
# Set Max to 200
byt = bytscl(self.array2, Max=200)
control = numpy.sum(self.array2 >= 200)
total = numpy.sum(byt == 255)
self.assertEqual(total, control)
def test_Min_keyword(self):
"""
Test that the Min keyword works as expected.
"""
# Set Min to 200
byt = bytscl(self.array2, Min=200)
control = numpy.sum(self.array2 <= 200)
total = numpy.sum(byt == 0)
self.assertEqual(total, control)
def test_Max_keyword(self):
"""
Test that the Max keyword works as expected.
"""
# Set Max to 200
byt = bytscl(self.array2, Max=200)
control = numpy.sum(self.array2 >= 200)
total = numpy.sum(byt == 255)
self.assertEqual(total, control)
def test_NaN_keyword(self):
"""
Test that the NaN keyword works as expected.
"""
# If array has any NaN's then the output will return all zeros
array = self.array1.copy()
array[0,0] = numpy.nan
byt = bytscl(array, NaN=True)
total = numpy.sum(byt)
self.assertTrue(total != 0)
def test_NaN_keyword(self):
"""
Test that the NaN keyword works as expected.
"""
# If array has any NaN's then the output will return all zeros
array = self.array1.copy()
array[0, 0] = numpy.nan
byt = bytscl(array, NaN=True)
total = numpy.sum(byt)
self.assertTrue(total != 0)
def hist_equal(array, BinSIZE=None, MaxV=None, MinV=None, Omax=None, Omin=None, Percent=None, Top=None, Histogram_Only=False):
"""
Image contrast enhancement.
Replicates the hist_equal function available within IDL (Interactive Data Language, EXELISvis).
Converts an array to a histogram equalised byte array.
:param array:
A numpy array of any type.
:param BinSIZE:
The binsize to be used in constructing the histogram. The default is 1 for arrays with a datatype of byte (uint8). Arrays of other datatypes the binsize is computed as (MaxV - MinV) / 5000. (floating point).
:param MaxV:
The maximum data value to be considered in the contrast stretch. The default is 255 for arrays with a datatype of byte (uint8). Otherwise the maximum data value of array is used.
:param MinV:
The minimum data value to be considered in the contrast stretch. The default is 0 for arrays with a datatype of byte (uint8). Otherwise the minimum data value of array is used.
:param Omax:
(Optional) A string name used to refer to the dictionary key that will contain the maximum value used in generating the histogram.
:param Omin:
(Optional) A string name used to refer to the dictionary key that will contain the minimum value used in generating the histogram.
:param Percent:
A scalar between the values 0 and 100 that will be used to stretch the array histogram.
:param Top:
The maximum value of the scaled result. Default is 255. The mimimum value of the scaled result is always 0.
:param Histogram_Only:
Type Bool. Default is false. If set to True, then a numpy array of type int32 will be returned that contains the cumulative sum of the histogram.
:return:
Varies. If Histogram_Only is set to True, then the cumulative sum of the histogram will be returned. Additional optional returns Omax and and Omin. Otherwise a byte scaled version of array is returned. Additional optional returns Omax and and Omin.
Example:
>>> # 100x100 array of samples from N(3, 6.25)
>>> a = 2.5 * numpy.random.randn(100,100) + 3
>>> scl_a = hist_equal(a)
>>> scl_pct_a = hist_equal(a, Percent=2)
:author:
Josh Sixsmith; [email protected]; [email protected]
:history:
* 2013/10/24: Created
:copyright:
Copyright (c) 2013, Josh Sixsmith
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those
of the authors and should not be interpreted as representing official policies,
either expressed or implied, of the FreeBSD Project.
"""
def linear_percent(cumulative_histogram, percent, min_, Binsize):
"""
Image contrast enhancement.
Given a cumulative histogram, upper and lower DN values are computed and returned.
:param cumulative_histogram:
A 1D numpy array. Must the cumulative sum of a histogram.
:param perecent:
A value in the range of 0-100.
:param min_:
The minumum value to be used in the determining the stretch.
:param Binsize:
The binsize used in constructing the histogram of which the cumulative histogram was then derived.
:return:
Two scalars, MaxDN and MinDN, corresponding to the maximum and minimum values of the original array to be used in the contrast stretch.
:author:
Josh Sixsmith; [email protected]; [email protected]
:history:
* 2013/10/24: Created
"""
ch = cumulative_histogram
if len(ch.shape) != 1:
raise Exception('Only 1D arrays are supported.')
# Calculate upper and lower values
low = (percent/100.)
high = (1 - (percent/100.))
# number of elements
n = ch[-1]
x1 = numpy.searchsorted(ch, n * low)
while ch[x1] == ch[x1 + 1]:
x1 = x1 + 1
x2 = numpy.searchsorted(ch, n * high)
while ch[x2] == ch[x2 - 1]:
x2 = x2 - 1
minDN = x1 * Binsize + min_
maxDN = x2 * Binsize + min_
return maxDN, minDN
if (array.dtype == 'uint8'):
MaxV = 255
MinV = 0
if (MaxV == None):
MaxV = numpy.amax(array)
if (MinV == None):
MinV = numpy.amin(array)
if (Top == None):
Top = 255
if (BinSIZE == None):
if (array.dtype == 'uint8'):
BinSIZE = 1
else:
BinSIZE = (MaxV - MinV) / 5000.
# Retrieve the dimensions of the array
dims = array.shape
h = histogram(array.flatten(), binsize=BinSIZE, max=MaxV, min=MinV, omax='omax', omin='omin')
# Need to check for omin and omax so they can be returned
return_extra = False
if ((type(Omin) == str) | (type(Omax) == str)):
return_extra = True
d = {}
if (type(Omin) == str):
d[Omin] = h['omin']
if (type(Omax) == str):
d[Omax] = h['omax']
# Zeroing the first element of the histogram
hist = h['histogram']
hist[0] = 0
cumu_hist = numpy.cumsum(hist, dtype='float')
if (Histogram_Only):
cumu_hist = cumu_hist.astype('int32')
# Need to check for omin and omax so they can be returned
if return_extra:
return cumu_hist, d
else:
return cumu_hist
# Evaluate a linear percent stretch
if (Percent != None):
if (Percent <= 0) or (Percent >= 100):
raise Exception('Percent must be between 0 and 100')
maxDN, MinDN = linear_percent(cumu_hist, percent=Percent, min_=MinV, Binsize=BinSIZE)
scl = bytscl(array, Max=maxDN, Min=MinDN, Top=Top)
if return_extra:
return scl, d
else:
return scl
scl_lookup = bytscl(cumu_hist, Top=Top)
# apply the scl_lookup in order to retrieve the new scaled value
if (type(array) == 'uint8'):
# We know the binsize for byte data, i.e. 1
# Clip the lower bounds
arr = array.clip(min=MinV)
scl = (scl_lookup[arr.flatten() - MinV]).reshape(dims)
else:
# We need to divide by the binsize in order to the bin position
# Clip the lower bounds
arr = array.clip(min=MinV)
arr = numpy.floor((arr - MinV) / BinSIZE).astype('int')
scl = (scl_lookup[arr.flatten()]).reshape(dims)
if return_extra:
return scl, d
else:
return scl
def hist_equal(array,
BinSIZE=None,
MaxV=None,
MinV=None,
Omax=None,
Omin=None,
Percent=None,
Top=None,
Histogram_Only=False):
"""
Image contrast enhancement.
Replicates the hist_equal function available within IDL (Interactive Data Language, EXELISvis).
Converts an array to a histogram equalised byte array.
:param array:
A numpy array of any type.
:param BinSIZE:
The binsize to be used in constructing the histogram. The default is 1 for arrays with a datatype of byte (uint8). Arrays of other datatypes the binsize is computed as (MaxV - MinV) / 5000. (floating point).
:param MaxV:
The maximum data value to be considered in the contrast stretch. The default is 255 for arrays with a datatype of byte (uint8). Otherwise the maximum data value of array is used.
:param MinV:
The minimum data value to be considered in the contrast stretch. The default is 0 for arrays with a datatype of byte (uint8). Otherwise the minimum data value of array is used.
:param Omax:
(Optional) A string name used to refer to the dictionary key that will contain the maximum value used in generating the histogram.
:param Omin:
(Optional) A string name used to refer to the dictionary key that will contain the minimum value used in generating the histogram.
:param Percent:
A scalar between the values 0 and 100 that will be used to stretch the array histogram.
:param Top:
The maximum value of the scaled result. Default is 255. The mimimum value of the scaled result is always 0.
:param Histogram_Only:
Type Bool. Default is false. If set to True, then a numpy array of type int32 will be returned that contains the cumulative sum of the histogram.
:return:
Varies. If Histogram_Only is set to True, then the cumulative sum of the histogram will be returned. Additional optional returns Omax and and Omin. Otherwise a byte scaled version of array is returned. Additional optional returns Omax and and Omin.
Example:
>>> # 100x100 array of samples from N(3, 6.25)
>>> a = 2.5 * numpy.random.randn(100,100) + 3
>>> scl_a = hist_equal(a)
>>> scl_pct_a = hist_equal(a, Percent=2)
:author:
Josh Sixsmith; [email protected]; [email protected]
:history:
* 2013/10/24: Created
:copyright:
Copyright (c) 2013, Josh Sixsmith
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those
of the authors and should not be interpreted as representing official policies,
either expressed or implied, of the FreeBSD Project.
"""
def linear_percent(cumulative_histogram, percent, min_, Binsize):
"""
Image contrast enhancement.
Given a cumulative histogram, upper and lower DN values are computed and returned.
:param cumulative_histogram:
A 1D numpy array. Must the cumulative sum of a histogram.
:param perecent:
A value in the range of 0-100.
:param min_:
The minumum value to be used in the determining the stretch.
:param Binsize:
The binsize used in constructing the histogram of which the cumulative histogram was then derived.
:return:
Two scalars, MaxDN and MinDN, corresponding to the maximum and minimum values of the original array to be used in the contrast stretch.
:author:
Josh Sixsmith; [email protected]; [email protected]
:history:
* 2013/10/24: Created
"""
ch = cumulative_histogram
if len(ch.shape) != 1:
raise Exception('Only 1D arrays are supported.')
# Calculate upper and lower values
low = (percent / 100.)
high = (1 - (percent / 100.))
# number of elements
n = ch[-1]
x1 = numpy.searchsorted(ch, n * low)
while ch[x1] == ch[x1 + 1]:
x1 = x1 + 1
x2 = numpy.searchsorted(ch, n * high)
while ch[x2] == ch[x2 - 1]:
x2 = x2 - 1
minDN = x1 * Binsize + min_
maxDN = x2 * Binsize + min_
return maxDN, minDN
if (array.dtype == 'uint8'):
MaxV = 255
MinV = 0
if (MaxV == None):
MaxV = numpy.amax(array)
if (MinV == None):
MinV = numpy.amin(array)
if (Top == None):
Top = 255
if (BinSIZE == None):
if (array.dtype == 'uint8'):
BinSIZE = 1
else:
BinSIZE = (MaxV - MinV) / 5000.
# Retrieve the dimensions of the array
dims = array.shape
h = histogram(array.flatten(),
binsize=BinSIZE,
max=MaxV,
min=MinV,
omax='omax',
omin='omin')
# Need to check for omin and omax so they can be returned
return_extra = False
if ((type(Omin) == str) | (type(Omax) == str)):
return_extra = True
d = {}
if (type(Omin) == str):
d[Omin] = h['omin']
if (type(Omax) == str):
d[Omax] = h['omax']
# Zeroing the first element of the histogram
hist = h['histogram']
hist[0] = 0
cumu_hist = numpy.cumsum(hist, dtype='float')
if (Histogram_Only):
cumu_hist = cumu_hist.astype('int32')
# Need to check for omin and omax so they can be returned
if return_extra:
return cumu_hist, d
else:
return cumu_hist
# Evaluate a linear percent stretch
if (Percent != None):
if (Percent <= 0) or (Percent >= 100):
raise Exception('Percent must be between 0 and 100')
maxDN, MinDN = linear_percent(cumu_hist,
percent=Percent,
min_=MinV,
Binsize=BinSIZE)
scl = bytscl(array, Max=maxDN, Min=MinDN, Top=Top)
if return_extra:
return scl, d
else:
return scl
scl_lookup = bytscl(cumu_hist, Top=Top)
# apply the scl_lookup in order to retrieve the new scaled value
if (type(array) == 'uint8'):
# We know the binsize for byte data, i.e. 1
# Clip the lower bounds
arr = array.clip(min=MinV)
scl = (scl_lookup[arr.flatten() - MinV]).reshape(dims)
else:
# We need to divide by the binsize in order to the bin position
# Clip the lower bounds
arr = array.clip(min=MinV)
arr = numpy.floor((arr - MinV) / BinSIZE).astype('int')
scl = (scl_lookup[arr.flatten()]).reshape(dims)
if return_extra:
return scl, d
else:
return scl