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Savitzky Golay filter from http://www.scipy.org/Cookbook/SavitzkyGolay
import numpy as np
import scipy, scipy.signal
def savitzky_golay( y, window_size, order, deriv = 0 ):
r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
The Savitzky-Golay filter removes high frequency noise from data.
It has the advantage of preserving the original shape and
features of the signal better than other types of filtering
approaches, such as moving averages techhniques.
Parameters
----------
y : array_like, shape (N,)
the values of the time history of the signal.
window_size : int
the length of the window. Must be an odd integer number.
order : int
the order of the polynomial used in the filtering.
Must be less then `window_size` - 1.
deriv: int
the order of the derivative to compute (default = 0 means only smoothing)
Returns
-------
ys : ndarray, shape (N)
the smoothed signal (or it's n-th derivative).
Notes
-----
The Savitzky-Golay is a type of low-pass filter, particularly
suited for smoothing noisy data. The main idea behind this
approach is to make for each point a least-square fit with a
polynomial of high order over a odd-sized window centered at
the point.
Examples
--------
t = np.linspace(-4, 4, 500)
y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
ysg = savitzky_golay(y, window_size=31, order=4)
import matplotlib.pyplot as plt
plt.plot(t, y, label='Noisy signal')
plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
plt.plot(t, ysg, 'r', label='Filtered signal')
plt.legend()
plt.show()
References
----------
.. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
Data by Simplified Least Squares Procedures. Analytical
Chemistry, 1964, 36 (8), pp 1627-1639.
.. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
Cambridge University Press ISBN-13: 9780521880688
"""
try:
window_size = np.abs( np.int( window_size ) )
order = np.abs( np.int( order ) )
except ValueError, msg:
raise ValueError( "window_size and order have to be of type int" )
if window_size % 2 != 1 or window_size < 1:
raise TypeError( "window_size size must be a positive odd number" )
if window_size < order + 2:
raise TypeError( "window_size is too small for the polynomials order" )
order_range = range( order + 1 )
half_window = ( window_size - 1 ) // 2
# precompute coefficients
b = np.mat( [[k ** i for i in order_range] for k in range( -half_window, half_window + 1 )] )
m = np.linalg.pinv( b ).A[deriv]
# pad the signal at the extremes with
# values taken from the signal itself
firstvals = y[0] - np.abs( y[1:half_window + 1][::-1] - y[0] )
lastvals = y[-1] + np.abs( y[-half_window - 1:-1][::-1] - y[-1] )
y = np.concatenate( ( firstvals, y, lastvals ) )
return np.convolve( m, y, mode = 'valid' )
def savitzky_golay_piecewise( xvals, data, kernel = 11, order = 4 ):
turnpoint = 0
last = len( xvals )
if xvals[1] > xvals[0] : #x is increasing?
for i in range( 1, last ) : #yes
if xvals[i] < xvals[i - 1] : #search where x starts to fall
turnpoint = i
break
else: #no, x is decreasing
for i in range( 1, last ) : #search where it starts to rise
if xvals[i] > xvals[i - 1] :
turnpoint = i
break
if turnpoint == 0 : #no change in direction of x
return savitzky_golay( data, kernel, order )
else:
#smooth the first piece
firstpart = savitzky_golay( data[0:turnpoint], kernel, order )
#recursively smooth the rest
rest = savitzky_golay_piecewise( xvals[turnpoint:], data[turnpoint:], kernel, order )
return numpy.concatenate( ( firstpart, rest ) )
def sgolay2d ( z, window_size, order, derivative = None ):
"""
"""
# number of terms in the polynomial expression
n_terms = ( order + 1 ) * ( order + 2 ) / 2.0
if window_size % 2 == 0:
raise ValueError( 'window_size must be odd' )
if window_size ** 2 < n_terms:
raise ValueError( 'order is too high for the window size' )
half_size = window_size // 2
# exponents of the polynomial.
# p(x,y) = a0 + a1*x + a2*y + a3*x^2 + a4*y^2 + a5*x*y + ...
# this line gives a list of two item tuple. Each tuple contains
# the exponents of the k-th term. First element of tuple is for x
# second element for y.
# Ex. exps = [(0,0), (1,0), (0,1), (2,0), (1,1), (0,2), ...]
exps = [ ( k - n, n ) for k in range( order + 1 ) for n in range( k + 1 ) ]
# coordinates of points
ind = np.arange( -half_size, half_size + 1, dtype = np.float64 )
dx = np.repeat( ind, window_size )
dy = np.tile( ind, [window_size, 1] ).reshape( window_size ** 2, )
# build matrix of system of equation
A = np.empty( ( window_size ** 2, len( exps ) ) )
for i, exp in enumerate( exps ):
A[:, i] = ( dx ** exp[0] ) * ( dy ** exp[1] )
# pad input array with appropriate values at the four borders
new_shape = z.shape[0] + 2 * half_size, z.shape[1] + 2 * half_size
Z = np.zeros( ( new_shape ) )
# top band
band = z[0, :]
Z[:half_size, half_size:-half_size] = band - np.abs( np.flipud( z[1:half_size + 1, :] ) - band )
# bottom band
band = z[-1, :]
Z[-half_size:, half_size:-half_size] = band + np.abs( np.flipud( z[-half_size - 1:-1, :] ) - band )
# left band
band = np.tile( z[:, 0].reshape( -1, 1 ), [1, half_size] )
Z[half_size:-half_size, :half_size] = band - np.abs( np.fliplr( z[:, 1:half_size + 1] ) - band )
# right band
band = np.tile( z[:, -1].reshape( -1, 1 ), [1, half_size] )
Z[half_size:-half_size, -half_size:] = band + np.abs( np.fliplr( z[:, -half_size - 1:-1] ) - band )
# central band
Z[half_size:-half_size, half_size:-half_size] = z
# top left corner
band = z[0, 0]
Z[:half_size, :half_size] = band - np.abs( np.flipud( np.fliplr( z[1:half_size + 1, 1:half_size + 1] ) ) - band )
# bottom right corner
band = z[-1, -1]
Z[-half_size:, -half_size:] = band + np.abs( np.flipud( np.fliplr( z[-half_size - 1:-1, -half_size - 1:-1] ) ) - band )
# top right corner
band = Z[half_size, -half_size:]
Z[:half_size, -half_size:] = band - np.abs( np.flipud( Z[half_size + 1:2 * half_size + 1, -half_size:] ) - band )
# bottom left corner
band = Z[-half_size:, half_size].reshape( -1, 1 )
Z[-half_size:, :half_size] = band - np.abs( np.fliplr( Z[-half_size:, half_size + 1:2 * half_size + 1] ) - band )
# solve system and convolve
if derivative == None:
m = np.linalg.pinv( A )[0].reshape( ( window_size, -1 ) )
return scipy.signal.fftconvolve( Z, m, mode = 'valid' )
elif derivative == 'col':
c = np.linalg.pinv( A )[1].reshape( ( window_size, -1 ) )
return scipy.signal.fftconvolve( Z, -c, mode = 'valid' )
elif derivative == 'row':
r = np.linalg.pinv( A )[2].reshape( ( window_size, -1 ) )
return scipy.signal.fftconvolve( Z, -r, mode = 'valid' )
elif derivative == 'both':
c = np.linalg.pinv( A )[1].reshape( ( window_size, -1 ) )
r = np.linalg.pinv( A )[2].reshape( ( window_size, -1 ) )
return scipy.signal.fftconvolve( Z, -r, mode = 'valid' ), scipy.signal.fftconvolve( Z, -c, mode = 'valid' )
@Eyshika

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Eyshika commented Aug 28, 2017

Can this be implemented using OpenCV too ?

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