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add filters (moving avg + 1euro filter)

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Brian Delhaisse
2019-10-23 08:52:59 +02:00
parent 605b2c8194
commit 7f52217fb6
2 changed files with 233 additions and 2 deletions
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pyrobolearn/utils/__init__.py
+2 -2
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@@ -33,8 +33,8 @@ from . import plotting
# import parsers
from . import parsers
# import parsers
# from . import parsers
# import filters
from . import filters
# Built-in functions
pyrobolearn/utils/filters.py
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@@ -0,0 +1,231 @@
# -*- coding: utf-8 -*-
#!/usr/bin/env python
"""Define several filters such as moving average and 1euro filters.
"""
import numpy as np
__author__ = "Brian Delhaisse"
__copyright__ = "Copyright 2019, PyRoboLearn"
__credits__ = ["Brian Delhaisse"]
__license__ = "GNU GPLv3"
__version__ = "1.0.0"
__maintainer__ = "Brian Delhaisse"
__email__ = "briandelhaisse@gmail.com"
__status__ = "Development"
class Filter(object):
"""Abstract Filter class"""
pass
class MovingAverageFilter(Filter):
r"""Moving Average Filter
The moving average filter computes the moving mean given by:
.. math:: \mu_{N+1} = \frac{N}{N+1} \mu_N + \frac{1}{N+1} x_{N+1}
where :math:`\mu_0 = 1`.
"""
def __init__(self):
"""Initialize the moving average filter"""
self.mu = None
self.N = 0
def __call__(self, x):
"""
Filter the given noisy sample input value.
Args:
x (float): noisy sample input value.
Returns:
float: moving average filtered value.
"""
if self.mu is None:
self.mu, self.N = x, 1
return self.mu
self.N += 1
self.mu = (self.N-1.)/self.N * self.mu + 1./self.N * x
return self.mu
class MovingMedianFilter(Filter):
"""Moving Median Filter
Compared to the moving average filter, it is a bit less sensitive to outliers especially for short spikes.
"""
pass
class OneEuroFilter(Filter):
"""1 Euro Filter
The code given here is taken from [4] which is distributed under the BSD-3 license:
"OneEuroFilter.py -
Author: Nicolas Roussel (nicolas.roussel@inria.fr)
Copyright 2019 Inria
BSD License https://opensource.org/licenses/BSD-3-Clause
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.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or
promote products derived from this software without specific prior written permission.
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 HOLDER 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."
From the documentation presented in [1] and reproduced here for completeness purpose:
"Tuning the filter
To minimize jitter and lag when tracking human motion, the two parameters (fcmin and beta) can be set using a
simple two-step procedure. First beta is set to 0 and fcmin (mincutoff) to a reasonable middle-ground value such
as 1 Hz. Then the body part is held steady or moved at a very low speed while fcmin is adjusted to remove jitter
and preserve an acceptable lag during these slow movements (decreasing fcmin reduces jitter but increases lag,
fcmin must be > 0). Next, the body part is moved quickly in different directions while beta is increased with a
focus on minimizing lag. First find the right order of magnitude to tune beta, which depends on the kind of data
you manipulate and their units: do not hesitate to start with values like 0.001 or 0.0001. You can first multiply
and divide beta by factor 10 until you notice an effect on latency when moving quickly. Note that parameters
fcmin and beta have clear conceptual relationships: if high speed lag is a problem, increase beta; if slow speed
jitter is a problem, decrease fcmin."
References:
- [1] Main webpage: http://cristal.univ-lille.fr/~casiez/1euro/
- [2] Interactive demo: http://cristal.univ-lille.fr/~casiez/1euro/InteractiveDemo/
- [3] Blog: https://jaantollander.com/2018-12-29-noise-filtering-using-one-euro-filter.html
- [4] Python version: http://cristal.univ-lille.fr/~casiez/1euro/OneEuroFilter.py
"""
class LowPassFilter(object):
"""Low Pass Filter
This low pass filter is used in the One euro filter.
"""
def __init__(self, alpha):
"""
Initialize the Low-pass filter.
Args:
alpha (float): alpha value.
"""
self.alpha = alpha
self.__y = self.__s = None
@property
def alpha(self):
return self.__alpha
@alpha.setter
def alpha(self, alpha):
alpha = float(alpha)
if alpha <= 0 or alpha > 1.0:
raise ValueError("alpha (%s) should be in (0.0, 1.0]" % alpha)
self.__alpha = alpha
@property
def last_value(self):
return self.__y
def __call__(self, value, timestamp=None, alpha=None):
"""
Filter method of Low-pass filter.
Args:
value (float): noisy sample value.
timestamp (float): time stamp value.
alpha (float): alpha value.
Returns:
float: filtered value.
"""
if alpha is not None:
self.alpha = alpha
if self.__y is None:
s = value
else:
s = self.__alpha * value + (1.0 - self.__alpha) * self.__s
self.__y = value
self.__s = s
return s
def __init__(self, freq, mincutoff=1.0, beta=0.0, dcutoff=1.0):
"""
Initialize the One euro filter.
Args:
freq (float): data update rate.
mincutoff (float): minimum cutoff frequency.
beta (float): cutoff slope.
dcutoff (float): cutoff frequency for derivate.
"""
if freq <= 0:
raise ValueError("freq should be >0")
if mincutoff <= 0:
raise ValueError("mincutoff should be >0")
if dcutoff <= 0:
raise ValueError("dcutoff should be >0")
self.__freq = float(freq)
self.__mincutoff = float(mincutoff)
self.__beta = float(beta)
self.__dcutoff = float(dcutoff)
self.__x = self.LowPassFilter(self.__alpha(self.__mincutoff))
self.__dx = self.LowPassFilter(self.__alpha(self.__dcutoff))
self.__last_time = None
def __alpha(self, cutoff):
"""
Alpha computation.
Args:
cutoff (float): cutoff frequency in Hz.
Returns:
float: alpha value for low-pass filter.
"""
te = 1.0 / self.__freq
tau = 1.0 / (2 * np.pi * cutoff)
return 1.0 / (1.0 + tau / te)
def __call__(self, x, timestamp=None):
"""
Filter the noisy sample input value(s).
Args:
x (float): noisy sample input value.
timestamp (float): time stamp value.
Returns:
float: filtered sample value.
"""
# update the sampling frequency based on timestamps
if self.__last_time and timestamp:
self.__freq = 1.0 / (timestamp - self.__last_time)
self.__last_time = timestamp
# estimate the current variation per second
prev_x = self.__x.last_value
dx = 0.0 if prev_x is None else (x - prev_x) * self.__freq # FIXME: 0.0 or value?
edx = self.__dx(dx, timestamp, alpha=self.__alpha(self.__dcutoff))
# use it to update the cutoff frequency
cutoff = self.__mincutoff + self.__beta * np.fabs(edx)
# filter the given value
return self.__x(x, timestamp, alpha=self.__alpha(cutoff))