def hist_normalization(picture):
histogram = Image.fromarray(picture).convert("L").histogram()
lut = []
for b in range(0, len(histogram), 256):
step = reduce(operator.add, histogram[b:b + 256]) / 255
n = 0
for i in range(256):
lut.append(n / step)
n = n + histogram[i + b]
return np.asarray(Image.fromarray(picture).point(lut * 1))
def matdims(arr, oned_as='column'):
"""
Determine equivalent MATLAB dimensions for given array
Parameters
----------
arr : ndarray
Input array
oned_as : {'column', 'row'}, optional
Whether 1-D arrays are returned as MATLAB row or column matrices.
Default is 'column'.
Returns
-------
dims : tuple
Shape tuple, in the form MATLAB expects it.
Notes
-----
We had to decide what shape a 1 dimensional array would be by
default. ``np.atleast_2d`` thinks it is a row vector. The
default for a vector in MATLAB (e.g. ``>> 1:12``) is a row vector.
Versions of scipy up to and including 0.11 resulted (accidentally)
in 1-D arrays being read as column vectors. For the moment, we
maintain the same tradition here.
Examples
--------
>>> matdims(np.array(1)) # numpy scalar
(1, 1)
>>> matdims(np.array([1])) # 1d array, 1 element
(1, 1)
>>> matdims(np.array([1,2])) # 1d array, 2 elements
(2, 1)
>>> matdims(np.array([[2],[3]])) # 2d array, column vector
(2, 1)
>>> matdims(np.array([[2,3]])) # 2d array, row vector
(1, 2)
>>> matdims(np.array([[[2,3]]])) # 3d array, rowish vector
(1, 1, 2)
>>> matdims(np.array([])) # empty 1d array
(0, 0)
>>> matdims(np.array([[]])) # empty 2d
(0, 0)
>>> matdims(np.array([[[]]])) # empty 3d
(0, 0, 0)
Optional argument flips 1-D shape behavior.
>>> matdims(np.array([1,2]), 'row') # 1d array, 2 elements
(1, 2)
The argument has to make sense though
>>> matdims(np.array([1,2]), 'bizarre')
Traceback (most recent call last):
...
ValueError: 1D option "bizarre" is strange
"""
shape = arr.shape
if shape == (): # scalar
return (1,1)
if reduce(operator.mul, shape) == 0: # zero elememts
return (0,) * np.max([arr.ndim, 2])
if len(shape) == 1: # 1D
if oned_as == 'column':
return shape + (1,)
elif oned_as == 'row':
return (1,) + shape
else:
raise ValueError('1D option "%s" is strange'
% oned_as)
return shape
def matdims(arr, oned_as='column'):
"""
Determine equivalent MATLAB dimensions for given array
Parameters
----------
arr : ndarray
Input array
oned_as : {'column', 'row'}, optional
Whether 1-D arrays are returned as MATLAB row or column matrices.
Default is 'column'.
Returns
-------
dims : tuple
Shape tuple, in the form MATLAB expects it.
Notes
-----
We had to decide what shape a 1 dimensional array would be by
default. ``np.atleast_2d`` thinks it is a row vector. The
default for a vector in MATLAB (e.g. ``>> 1:12``) is a row vector.
Versions of scipy up to and including 0.11 resulted (accidentally)
in 1-D arrays being read as column vectors. For the moment, we
maintain the same tradition here.
Examples
--------
>>> matdims(np.array(1)) # numpy scalar
(1, 1)
>>> matdims(np.array([1])) # 1d array, 1 element
(1, 1)
>>> matdims(np.array([1,2])) # 1d array, 2 elements
(2, 1)
>>> matdims(np.array([[2],[3]])) # 2d array, column vector
(2, 1)
>>> matdims(np.array([[2,3]])) # 2d array, row vector
(1, 2)
>>> matdims(np.array([[[2,3]]])) # 3d array, rowish vector
(1, 1, 2)
>>> matdims(np.array([])) # empty 1d array
(0, 0)
>>> matdims(np.array([[]])) # empty 2d
(0, 0)
>>> matdims(np.array([[[]]])) # empty 3d
(0, 0, 0)
Optional argument flips 1-D shape behavior.
>>> matdims(np.array([1,2]), 'row') # 1d array, 2 elements
(1, 2)
The argument has to make sense though
>>> matdims(np.array([1,2]), 'bizarre')
Traceback (most recent call last):
...
ValueError: 1D option "bizarre" is strange
"""
shape = arr.shape
if shape == (): # scalar
return (1, 1)
if reduce(operator.mul, shape) == 0: # zero elememts
return (0, ) * np.max([arr.ndim, 2])
if len(shape) == 1: # 1D
if oned_as == 'column':
return shape + (1, )
elif oned_as == 'row':
return (1, ) + shape
else:
raise ValueError('1D option "%s" is strange' % oned_as)
return shape
def _basis(j):
p = [(xq - x[i]) / (x[j] - x[i]) for i in range(k) if i != j]
return reduce((lambda q, w: q * w), p)