def _as_col_tensor(h):
if isinstance(h, tf.Tensor):
h = tf.reshape(h, [-1, 1])
else:
h = as_column_vector(h)
h = tf.constant(h, tf.float32)
return h
def _as_col_tensor(h):
if isinstance(h, tf.Tensor):
h = tf.reshape(h, [-1, 1])
else:
h = as_column_vector(h)
h = tf.constant(h, tf.float32)
return h
def colfilter(X, h):
"""Filter the columns of image *X* using filter vector *h*, without decimation.
If len(h) is odd, each output sample is aligned with each input sample
and *Y* is the same size as *X*. If len(h) is even, each output sample is
aligned with the mid point of each pair of input samples, and Y.shape =
X.shape + [1 0].
:param X: an image whose columns are to be filtered
:param h: the filter coefficients.
:returns Y: the filtered image.
.. codeauthor:: Rich Wareham <*****@*****.**>, August 2013
.. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
.. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
"""
# Interpret all inputs as arrays
X = asfarray(X)
h = as_column_vector(h)
r, c = X.shape
m = h.shape[0]
m2 = np.fix(m*0.5)
# Symmetrically extend with repeat of end samples.
# Use 'reflect' so r < m2 works OK.
xe = reflect(np.arange(-m2, r+m2, dtype=np.int), -0.5, r-0.5)
# Perform filtering on the columns of the extended matrix X(xe,:), keeping
# only the 'valid' output samples, so Y is the same size as X if m is odd.
Y = _column_convolve(X[xe,:], h)
return Y
def colfilter(X, h):
"""Filter the columns of image *X* using filter vector *h*, without decimation.
If len(h) is odd, each output sample is aligned with each input sample
and *Y* is the same size as *X*. If len(h) is even, each output sample is
aligned with the mid point of each pair of input samples, and Y.shape =
X.shape + [1 0].
:param X: an image whose columns are to be filtered
:param h: the filter coefficients.
:returns Y: the filtered image.
.. codeauthor:: Rich Wareham <*****@*****.**>, August 2013
.. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
.. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
"""
# Interpret all inputs as arrays
X = asfarray(X)
h = as_column_vector(h)
r, c = X.shape
m = h.shape[0]
m2 = np.fix(m * 0.5)
# Symmetrically extend with repeat of end samples.
# Use 'reflect' so r < m2 works OK.
xe = reflect(np.arange(-m2, r + m2, dtype=np.int), -0.5, r - 0.5)
# Perform filtering on the columns of the extended matrix X(xe,:), keeping
# only the 'valid' output samples, so Y is the same size as X if m is odd.
Y = _column_convolve(X[xe, :], h)
return Y
def colfilter(X, h):
"""Filter the columns of image *X* using filter vector *h*, without decimation.
If len(h) is odd, each output sample is aligned with each input sample
and *Y* is the same size as *X*. If len(h) is even, each output sample is
aligned with the mid point of each pair of input samples, and Y.shape =
X.shape + [1 0].
The filtering will be accelerated via OpenCL.
:param X: an image whose columns are to be filtered
:param h: the filter coefficients.
:returns Y: the filtered image.
.. codeauthor:: Rich Wareham <*****@*****.**>, August 2013
.. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
.. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
"""
# Interpret all inputs as arrays
X = asfarray(X)
h = as_column_vector(h)
return to_array(axis_convolve(X, h))
def colfilter(X, h):
"""Filter the columns of image *X* using filter vector *h*, without decimation.
If len(h) is odd, each output sample is aligned with each input sample
and *Y* is the same size as *X*. If len(h) is even, each output sample is
aligned with the mid point of each pair of input samples, and Y.shape =
X.shape + [1 0].
The filtering will be accelerated via OpenCL.
:param X: an image whose columns are to be filtered
:param h: the filter coefficients.
:returns Y: the filtered image.
.. codeauthor:: Rich Wareham <*****@*****.**>, August 2013
.. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
.. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
"""
# Interpret all inputs as arrays
X = asfarray(X)
h = as_column_vector(h)
return to_array(axis_convolve(X, h))
def coldfilt(X, ha, hb):
"""Filter the columns of image X using the two filters ha and hb =
reverse(ha). ha operates on the odd samples of X and hb on the even
samples. Both filters should be even length, and h should be approx linear
phase with a quarter sample advance from its mid pt (i.e. :math:`|h(m/2)| >
|h(m/2 + 1)|`).
.. code-block:: text
ext top edge bottom edge ext
Level 1: ! | ! | !
odd filt on . b b b b a a a a a a a a b b b b
odd filt on . a a a a b b b b b b b b a a a a
Level 2: ! | ! | !
+q filt on x b b a a a a b b
-q filt on o a a b b b b a a
The output is decimated by two from the input sample rate and the results
from the two filters, Ya and Yb, are interleaved to give Y. Symmetric
extension with repeated end samples is used on the composite X columns
before each filter is applied.
Raises ValueError if the number of rows in X is not a multiple of 4, the
length of ha does not match hb or the lengths of ha or hb are non-even.
.. codeauthor:: Rich Wareham <*****@*****.**>, August 2013
.. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
.. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
"""
# Make sure all inputs are arrays
X = asfarray(X)
ha = asfarray(ha)
hb = asfarray(hb)
r, c = X.shape
if r % 4 != 0:
raise ValueError('No. of rows in X must be a multiple of 4')
if ha.shape != hb.shape:
raise ValueError('Shapes of ha and hb must be the same')
if ha.shape[0] % 2 != 0:
raise ValueError('Lengths of ha and hb must be even')
m = ha.shape[0]
m2 = np.fix(m*0.5)
# Set up vector for symmetric extension of X with repeated end samples.
xe = reflect(np.arange(-m, r+m), -0.5, r-0.5)
# Select odd and even samples from ha and hb. Note that due to 0-indexing
# 'odd' and 'even' are not perhaps what you might expect them to be.
hao = as_column_vector(ha[0:m:2])
hae = as_column_vector(ha[1:m:2])
hbo = as_column_vector(hb[0:m:2])
hbe = as_column_vector(hb[1:m:2])
t = np.arange(5, r+2*m-2, 4)
r2 = r/2;
Y = np.zeros((r2,c), dtype=X.dtype)
if np.sum(ha*hb) > 0:
s1 = slice(0, r2, 2)
s2 = slice(1, r2, 2)
else:
s2 = slice(0, r2, 2)
s1 = slice(1, r2, 2)
# Perform filtering on columns of extended matrix X(xe,:) in 4 ways.
Y[s1,:] = _column_convolve(X[xe[t-1],:],hao) + _column_convolve(X[xe[t-3],:],hae)
Y[s2,:] = _column_convolve(X[xe[t],:],hbo) + _column_convolve(X[xe[t-2],:],hbe)
return Y
def colifilt(X, ha, hb):
""" Filter the columns of image X using the two filters ha and hb =
reverse(ha). ha operates on the odd samples of X and hb on the even
samples. Both filters should be even length, and h should be approx linear
phase with a quarter sample advance from its mid pt (i.e `:math:`|h(m/2)| >
|h(m/2 + 1)|`).
.. code-block:: text
ext left edge right edge ext
Level 2: ! | ! | !
+q filt on x b b a a a a b b
-q filt on o a a b b b b a a
Level 1: ! | ! | !
odd filt on . b b b b a a a a a a a a b b b b
odd filt on . a a a a b b b b b b b b a a a a
The output is interpolated by two from the input sample rate and the
results from the two filters, Ya and Yb, are interleaved to give Y.
Symmetric extension with repeated end samples is used on the composite X
columns before each filter is applied.
.. codeauthor:: Rich Wareham <*****@*****.**>, August 2013
.. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
.. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
"""
# Make sure all inputs are arrays
X = asfarray(X)
ha = asfarray(ha)
hb = asfarray(hb)
r, c = X.shape
if r % 2 != 0:
raise ValueError('No. of rows in X must be a multiple of 2')
if ha.shape != hb.shape:
raise ValueError('Shapes of ha and hb must be the same')
if ha.shape[0] % 2 != 0:
raise ValueError('Lengths of ha and hb must be even')
m = ha.shape[0]
m2 = np.fix(m*0.5)
Y = np.zeros((r*2,c), dtype=X.dtype)
if not np.any(np.nonzero(X[:])[0]):
return Y
if m2 % 2 == 0:
# m/2 is even, so set up t to start on d samples.
# Set up vector for symmetric extension of X with repeated end samples.
# Use 'reflect' so r < m2 works OK.
xe = reflect(np.arange(-m2, r+m2, dtype=np.int), -0.5, r-0.5)
t = np.arange(3, r+m, 2)
if np.sum(ha*hb) > 0:
ta = t
tb = t - 1
else:
ta = t - 1
tb = t
# Select odd and even samples from ha and hb. Note that due to 0-indexing
# 'odd' and 'even' are not perhaps what you might expect them to be.
hao = as_column_vector(ha[0:m:2])
hae = as_column_vector(ha[1:m:2])
hbo = as_column_vector(hb[0:m:2])
hbe = as_column_vector(hb[1:m:2])
s = np.arange(0,r*2,4)
Y[s,:] = _column_convolve(X[xe[tb-2],:],hae)
Y[s+1,:] = _column_convolve(X[xe[ta-2],:],hbe)
Y[s+2,:] = _column_convolve(X[xe[tb ],:],hao)
Y[s+3,:] = _column_convolve(X[xe[ta ],:],hbo)
else:
# m/2 is odd, so set up t to start on b samples.
# Set up vector for symmetric extension of X with repeated end samples.
# Use 'reflect' so r < m2 works OK.
xe = reflect(np.arange(-m2, r+m2, dtype=np.int), -0.5, r-0.5)
t = np.arange(2, r+m-1, 2)
if np.sum(ha*hb) > 0:
ta = t
tb = t - 1
else:
ta = t - 1
tb = t
# Select odd and even samples from ha and hb. Note that due to 0-indexing
# 'odd' and 'even' are not perhaps what you might expect them to be.
hao = as_column_vector(ha[0:m:2])
hae = as_column_vector(ha[1:m:2])
hbo = as_column_vector(hb[0:m:2])
hbe = as_column_vector(hb[1:m:2])
s = np.arange(0,r*2,4)
Y[s,:] = _column_convolve(X[xe[tb],:],hao)
Y[s+1,:] = _column_convolve(X[xe[ta],:],hbo)
Y[s+2,:] = _column_convolve(X[xe[tb],:],hae)
Y[s+3,:] = _column_convolve(X[xe[ta],:],hbe)
return Y
def coldfilt(X, ha, hb):
"""Filter the columns of image X using the two filters ha and hb =
reverse(ha). ha operates on the odd samples of X and hb on the even
samples. Both filters should be even length, and h should be approx linear
phase with a quarter sample advance from its mid pt (i.e. :math:`|h(m/2)| >
|h(m/2 + 1)|`).
.. code-block:: text
ext top edge bottom edge ext
Level 1: ! | ! | !
odd filt on . b b b b a a a a a a a a b b b b
odd filt on . a a a a b b b b b b b b a a a a
Level 2: ! | ! | !
+q filt on x b b a a a a b b
-q filt on o a a b b b b a a
The output is decimated by two from the input sample rate and the results
from the two filters, Ya and Yb, are interleaved to give Y. Symmetric
extension with repeated end samples is used on the composite X columns
before each filter is applied.
Raises ValueError if the number of rows in X is not a multiple of 4, the
length of ha does not match hb or the lengths of ha or hb are non-even.
.. codeauthor:: Rich Wareham <*****@*****.**>, August 2013
.. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
.. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
"""
# Make sure all inputs are arrays
X = asfarray(X)
ha = asfarray(ha)
hb = asfarray(hb)
r, c = X.shape
if r % 4 != 0:
raise ValueError('No. of rows in X must be a multiple of 4')
if ha.shape != hb.shape:
raise ValueError('Shapes of ha and hb must be the same')
if ha.shape[0] % 2 != 0:
raise ValueError('Lengths of ha and hb must be even')
m = ha.shape[0]
m2 = np.fix(m * 0.5)
# Set up vector for symmetric extension of X with repeated end samples.
xe = reflect(np.arange(-m, r + m), -0.5, r - 0.5)
# Select odd and even samples from ha and hb. Note that due to 0-indexing
# 'odd' and 'even' are not perhaps what you might expect them to be.
hao = as_column_vector(ha[0:m:2])
hae = as_column_vector(ha[1:m:2])
hbo = as_column_vector(hb[0:m:2])
hbe = as_column_vector(hb[1:m:2])
t = np.arange(5, r + 2 * m - 2, 4)
r2 = r // 2
Y = np.zeros((r2, c), dtype=X.dtype)
if np.sum(ha * hb) > 0:
s1 = slice(0, r2, 2)
s2 = slice(1, r2, 2)
else:
s2 = slice(0, r2, 2)
s1 = slice(1, r2, 2)
# Perform filtering on columns of extended matrix X(xe,:) in 4 ways.
Y[s1, :] = _column_convolve(X[xe[t - 1], :], hao) + _column_convolve(
X[xe[t - 3], :], hae)
Y[s2, :] = _column_convolve(X[xe[t], :], hbo) + _column_convolve(
X[xe[t - 2], :], hbe)
return Y
def colifilt(X, ha, hb):
""" Filter the columns of image X using the two filters ha and hb =
reverse(ha). ha operates on the odd samples of X and hb on the even
samples. Both filters should be even length, and h should be approx linear
phase with a quarter sample advance from its mid pt (i.e `:math:`|h(m/2)| >
|h(m/2 + 1)|`).
.. code-block:: text
ext left edge right edge ext
Level 2: ! | ! | !
+q filt on x b b a a a a b b
-q filt on o a a b b b b a a
Level 1: ! | ! | !
odd filt on . b b b b a a a a a a a a b b b b
odd filt on . a a a a b b b b b b b b a a a a
The output is interpolated by two from the input sample rate and the
results from the two filters, Ya and Yb, are interleaved to give Y.
Symmetric extension with repeated end samples is used on the composite X
columns before each filter is applied.
.. codeauthor:: Rich Wareham <*****@*****.**>, August 2013
.. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
.. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
"""
# Make sure all inputs are arrays
X = asfarray(X)
ha = asfarray(ha)
hb = asfarray(hb)
r, c = X.shape
if r % 2 != 0:
raise ValueError('No. of rows in X must be a multiple of 2')
if ha.shape != hb.shape:
raise ValueError('Shapes of ha and hb must be the same')
if ha.shape[0] % 2 != 0:
raise ValueError('Lengths of ha and hb must be even')
m = ha.shape[0]
m2 = np.fix(m * 0.5)
Y = np.zeros((r * 2, c), dtype=X.dtype)
if not np.any(np.nonzero(X[:])[0]):
return Y
if m2 % 2 == 0:
# m/2 is even, so set up t to start on d samples.
# Set up vector for symmetric extension of X with repeated end samples.
# Use 'reflect' so r < m2 works OK.
xe = reflect(np.arange(-m2, r + m2, dtype=np.int), -0.5, r - 0.5)
t = np.arange(3, r + m, 2)
if np.sum(ha * hb) > 0:
ta = t
tb = t - 1
else:
ta = t - 1
tb = t
# Select odd and even samples from ha and hb. Note that due to 0-indexing
# 'odd' and 'even' are not perhaps what you might expect them to be.
hao = as_column_vector(ha[0:m:2])
hae = as_column_vector(ha[1:m:2])
hbo = as_column_vector(hb[0:m:2])
hbe = as_column_vector(hb[1:m:2])
s = np.arange(0, r * 2, 4)
Y[s, :] = _column_convolve(X[xe[tb - 2], :], hae)
Y[s + 1, :] = _column_convolve(X[xe[ta - 2], :], hbe)
Y[s + 2, :] = _column_convolve(X[xe[tb], :], hao)
Y[s + 3, :] = _column_convolve(X[xe[ta], :], hbo)
else:
# m/2 is odd, so set up t to start on b samples.
# Set up vector for symmetric extension of X with repeated end samples.
# Use 'reflect' so r < m2 works OK.
xe = reflect(np.arange(-m2, r + m2, dtype=np.int), -0.5, r - 0.5)
t = np.arange(2, r + m - 1, 2)
if np.sum(ha * hb) > 0:
ta = t
tb = t - 1
else:
ta = t - 1
tb = t
# Select odd and even samples from ha and hb. Note that due to 0-indexing
# 'odd' and 'even' are not perhaps what you might expect them to be.
hao = as_column_vector(ha[0:m:2])
hae = as_column_vector(ha[1:m:2])
hbo = as_column_vector(hb[0:m:2])
hbe = as_column_vector(hb[1:m:2])
s = np.arange(0, r * 2, 4)
Y[s, :] = _column_convolve(X[xe[tb], :], hao)
Y[s + 1, :] = _column_convolve(X[xe[ta], :], hbo)
Y[s + 2, :] = _column_convolve(X[xe[tb], :], hae)
Y[s + 3, :] = _column_convolve(X[xe[ta], :], hbe)
return Y