From d0e34e4791dd020374f7009a1184ecbc121066bd Mon Sep 17 00:00:00 2001
From: Manuel Guenther <manuel.guenther@idiap.ch>
Date: Thu, 27 Nov 2014 19:36:13 +0100
Subject: [PATCH] Moved WienerMachine to bob.ip.base.Wiener
Conflicts:
setup.py
---
bob/learn/misc/cpp/WienerMachine.cpp | 214 ------------------
bob/learn/misc/cpp/WienerTrainer.cpp | 101 ---------
.../include/bob.learn.misc/WienerMachine.h | 212 -----------------
.../include/bob.learn.misc/WienerTrainer.h | 76 -------
bob/learn/misc/old/main.cc | 4 -
bob/learn/misc/old/wiener.cc | 105 ---------
bob/learn/misc/old/wiener_trainer.cc | 44 ----
bob/learn/misc/test_wiener.py | 107 ---------
bob/learn/misc/test_wiener_trainer.py | 73 ------
setup.py | 2 -
10 files changed, 938 deletions(-)
delete mode 100644 bob/learn/misc/cpp/WienerMachine.cpp
delete mode 100644 bob/learn/misc/cpp/WienerTrainer.cpp
delete mode 100644 bob/learn/misc/include/bob.learn.misc/WienerMachine.h
delete mode 100644 bob/learn/misc/include/bob.learn.misc/WienerTrainer.h
delete mode 100644 bob/learn/misc/old/wiener.cc
delete mode 100644 bob/learn/misc/old/wiener_trainer.cc
delete mode 100644 bob/learn/misc/test_wiener.py
delete mode 100644 bob/learn/misc/test_wiener_trainer.py
diff --git a/bob/learn/misc/cpp/WienerMachine.cpp b/bob/learn/misc/cpp/WienerMachine.cpp
deleted file mode 100644
index d7540d8..0000000
--- a/bob/learn/misc/cpp/WienerMachine.cpp
+++ /dev/null
@@ -1,214 +0,0 @@
-/**
- * @date Fri Sep 30 16:56:06 2011 +0200
- * @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
- *
- * @brief Implements a WienerMachine
- *
- * Copyright (C) Idiap Research Institute, Martigny, Switzerland
- */
-
-#include <bob.core/array_copy.h>
-#include <bob.core/cast.h>
-#include <bob.learn.misc/WienerMachine.h>
-#include <bob.sp/FFT2D.h>
-#include <complex>
-
-bob::learn::misc::WienerMachine::WienerMachine():
- m_Ps(0,0),
- m_variance_threshold(1e-8),
- m_Pn(0),
- m_W(0,0),
- m_fft(),
- m_ifft(),
- m_buffer1(0,0), m_buffer2(0,0)
-{
-}
-
-bob::learn::misc::WienerMachine::WienerMachine(const blitz::Array<double,2>& Ps,
- const double Pn, const double variance_threshold):
- m_Ps(bob::core::array::ccopy(Ps)),
- m_variance_threshold(variance_threshold),
- m_Pn(Pn),
- m_W(m_Ps.extent(0),m_Ps.extent(1)),
- m_fft(m_Ps.extent(0),m_Ps.extent(1)),
- m_ifft(m_Ps.extent(0),m_Ps.extent(1)),
- m_buffer1(m_Ps.extent(0),m_Ps.extent(1)),
- m_buffer2(m_Ps.extent(0),m_Ps.extent(1))
-{
- computeW();
-}
-
-bob::learn::misc::WienerMachine::WienerMachine(const size_t height,
- const size_t width, const double Pn, const double variance_threshold):
- m_Ps(height,width),
- m_variance_threshold(variance_threshold),
- m_Pn(Pn),
- m_W(height,width),
- m_fft(height,width),
- m_ifft(height,width),
- m_buffer1(0,0), m_buffer2(0,0)
-{
- m_Ps = 1.;
- computeW();
-}
-
-bob::learn::misc::WienerMachine::WienerMachine(const bob::learn::misc::WienerMachine& other):
- m_Ps(bob::core::array::ccopy(other.m_Ps)),
- m_variance_threshold(other.m_variance_threshold),
- m_Pn(other.m_Pn),
- m_W(bob::core::array::ccopy(other.m_W)),
- m_fft(other.m_fft),
- m_ifft(other.m_ifft),
- m_buffer1(m_Ps.extent(0),m_Ps.extent(1)),
- m_buffer2(m_Ps.extent(0),m_Ps.extent(1))
-{
-}
-
-bob::learn::misc::WienerMachine::WienerMachine(bob::io::base::HDF5File& config)
-{
- load(config);
-}
-
-bob::learn::misc::WienerMachine::~WienerMachine() {}
-
-bob::learn::misc::WienerMachine& bob::learn::misc::WienerMachine::operator=
-(const bob::learn::misc::WienerMachine& other)
-{
- if (this != &other)
- {
- m_Ps.reference(bob::core::array::ccopy(other.m_Ps));
- m_Pn = other.m_Pn;
- m_variance_threshold = other.m_variance_threshold;
- m_W.reference(bob::core::array::ccopy(other.m_W));
- m_fft.setShape(m_Ps.extent(0),m_Ps.extent(1));
- m_ifft.setShape(m_Ps.extent(0),m_Ps.extent(1));
- m_buffer1.resize(m_Ps.extent(0),m_Ps.extent(1));
- m_buffer2.resize(m_Ps.extent(0),m_Ps.extent(1));
- }
- return *this;
-}
-
-bool bob::learn::misc::WienerMachine::operator==(const bob::learn::misc::WienerMachine& b) const
-{
- return bob::core::array::isEqual(m_Ps, b.m_Ps) &&
- m_variance_threshold == b.m_variance_threshold &&
- m_Pn == b.m_Pn &&
- bob::core::array::isEqual(m_W, b.m_W);
-}
-
-bool bob::learn::misc::WienerMachine::operator!=(const bob::learn::misc::WienerMachine& b) const
-{
- return !(this->operator==(b));
-}
-
-bool bob::learn::misc::WienerMachine::is_similar_to(const bob::learn::misc::WienerMachine& b,
- const double r_epsilon, const double a_epsilon) const
-{
- return bob::core::array::isClose(m_Ps, b.m_Ps, r_epsilon, a_epsilon) &&
- bob::core::isClose(m_variance_threshold, b.m_variance_threshold, r_epsilon, a_epsilon) &&
- bob::core::isClose(m_Pn, b.m_Pn, r_epsilon, a_epsilon) &&
- bob::core::array::isClose(m_W, b.m_W, r_epsilon, a_epsilon);
-}
-
-void bob::learn::misc::WienerMachine::load(bob::io::base::HDF5File& config)
-{
- //reads all data directly into the member variables
- m_Ps.reference(config.readArray<double,2>("Ps"));
- m_Pn = config.read<double>("Pn");
- m_variance_threshold = config.read<double>("variance_threshold");
- m_W.reference(config.readArray<double,2>("W"));
- m_fft.setShape(m_Ps.extent(0),m_Ps.extent(1));
- m_ifft.setShape(m_Ps.extent(0),m_Ps.extent(1));
- m_buffer1.resize(m_Ps.extent(0),m_Ps.extent(1));
- m_buffer2.resize(m_Ps.extent(0),m_Ps.extent(1));
-}
-
-void bob::learn::misc::WienerMachine::resize(const size_t height,
- const size_t width)
-{
- m_Ps.resizeAndPreserve(height,width);
- m_W.resizeAndPreserve(height,width);
- m_fft.setShape(height,width);
- m_ifft.setShape(height,width);
- m_buffer1.resizeAndPreserve(height,width);
- m_buffer2.resizeAndPreserve(height,width);
-}
-
-void bob::learn::misc::WienerMachine::save(bob::io::base::HDF5File& config) const
-{
- config.setArray("Ps", m_Ps);
- config.set("Pn", m_Pn);
- config.set("variance_threshold", m_variance_threshold);
- config.setArray("W", m_W);
-}
-
-void bob::learn::misc::WienerMachine::computeW()
-{
- // W = 1 / (1 + Pn / Ps_thresholded)
- m_W = 1. / (1. + m_Pn / m_Ps);
-}
-
-
-void bob::learn::misc::WienerMachine::forward_(const blitz::Array<double,2>& input,
- blitz::Array<double,2>& output) const
-{
- m_fft(bob::core::array::cast<std::complex<double> >(input), m_buffer1);
- m_buffer1 *= m_W;
- m_ifft(m_buffer1, m_buffer2);
- output = blitz::abs(m_buffer2);
-}
-
-void bob::learn::misc::WienerMachine::forward(const blitz::Array<double,2>& input,
- blitz::Array<double,2>& output) const
-{
- if (m_W.extent(0) != input.extent(0)) { //checks input
- boost::format m("number of input rows (%d) is not compatible with internal weight matrix (%d)");
- m % input.extent(0) % m_W.extent(0);
- throw std::runtime_error(m.str());
- }
- if (m_W.extent(1) != input.extent(1)) { //checks input
- boost::format m("number of input columns (%d) is not compatible with internal weight matrix (%d)");
- m % input.extent(1) % m_W.extent(1);
- throw std::runtime_error(m.str());
- }
- if (m_W.extent(0) != output.extent(0)) { //checks output
- boost::format m("number of output rows (%d) is not compatible with internal weight matrix (%d)");
- m % output.extent(0) % m_W.extent(0);
- throw std::runtime_error(m.str());
- }
- if (m_W.extent(1) != output.extent(1)) { //checks output
- boost::format m("number of output columns (%d) is not compatible with internal weight matrix (%d)");
- m % output.extent(1) % m_W.extent(1);
- throw std::runtime_error(m.str());
- }
- forward_(input, output);
-}
-
-void bob::learn::misc::WienerMachine::setVarianceThreshold(
- const double variance_threshold)
-{
- m_variance_threshold = variance_threshold;
- applyVarianceThreshold();
- computeW();
-}
-
-void bob::learn::misc::WienerMachine::setPs(const blitz::Array<double,2>& Ps)
-{
- if (m_Ps.extent(0) != Ps.extent(0)) {
- boost::format m("number of rows (%d) for input `Ps' does not match the expected (internal) size (%d)");
- m % Ps.extent(0) % m_Ps.extent(0);
- throw std::runtime_error(m.str());
- }
- if (m_Ps.extent(1) != Ps.extent(1)) {
- boost::format m("number of columns (%d) for input `Ps' does not match the expected (internal) size (%d)");
- m % Ps.extent(1) % m_Ps.extent(1);
- throw std::runtime_error(m.str());
- }
- m_Ps = bob::core::array::ccopy(Ps);
- computeW();
-}
-
-void bob::learn::misc::WienerMachine::applyVarianceThreshold()
-{
- m_Ps = blitz::where(m_Ps < m_variance_threshold, m_variance_threshold, m_Ps);
-}
diff --git a/bob/learn/misc/cpp/WienerTrainer.cpp b/bob/learn/misc/cpp/WienerTrainer.cpp
deleted file mode 100644
index 709e25f..0000000
--- a/bob/learn/misc/cpp/WienerTrainer.cpp
+++ /dev/null
@@ -1,101 +0,0 @@
-/**
- * @date Fri Sep 30 16:58:42 2011 +0200
- * @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
- *
- * Copyright (C) Idiap Research Institute, Martigny, Switzerland
- */
-
-#include <bob.learn.misc/WienerTrainer.h>
-#include <bob.core/cast.h>
-#include <bob.sp/FFT2D.h>
-#include <complex>
-
-bob::learn::misc::WienerTrainer::WienerTrainer()
-{
-}
-
-bob::learn::misc::WienerTrainer::WienerTrainer(const bob::learn::misc::WienerTrainer& other)
-{
-}
-
-bob::learn::misc::WienerTrainer::~WienerTrainer()
-{
-}
-
-bob::learn::misc::WienerTrainer& bob::learn::misc::WienerTrainer::operator=
-(const bob::learn::misc::WienerTrainer& other)
-{
- return *this;
-}
-
-bool bob::learn::misc::WienerTrainer::operator==
- (const bob::learn::misc::WienerTrainer& other) const
-{
- return true;
-}
-
-bool bob::learn::misc::WienerTrainer::operator!=
- (const bob::learn::misc::WienerTrainer& other) const
-{
- return !(this->operator==(other));
-}
-
-bool bob::learn::misc::WienerTrainer::is_similar_to
- (const bob::learn::misc::WienerTrainer& other, const double r_epsilon,
- const double a_epsilon) const
-{
- return true;
-}
-
-void bob::learn::misc::WienerTrainer::train(bob::learn::misc::WienerMachine& machine,
- const blitz::Array<double,3>& ar)
-{
- // Data is checked now and conforms, just proceed w/o any further checks.
- const size_t n_samples = ar.extent(0);
- const size_t height = ar.extent(1);
- const size_t width = ar.extent(2);
- // machine dimensions
- const size_t height_m = machine.getHeight();
- const size_t width_m = machine.getWidth();
-
- // Checks that the dimensions are matching
- if (height != height_m) {
- boost::format m("number of inputs (height) for machine (%u) does not match number of columns at input parameter (%u)");
- m % height_m % height;
- throw std::runtime_error(m.str());
- }
- if (width != width_m) {
- boost::format m("number of inputs (width) for machine (%u) does not match number of depths at input parameter (%u)");
- m % width_m % width;
- throw std::runtime_error(m.str());
- }
-
- // FFT2D
- bob::sp::FFT2D fft2d(height, width);
-
- // Loads the data
- blitz::Array<double,3> data(height, width, n_samples);
- blitz::Array<std::complex<double>,2> sample_fft(height, width);
- blitz::Range all = blitz::Range::all();
- for (size_t i=0; i<n_samples; ++i) {
- blitz::Array<double,2> sample = ar(i,all,all);
- blitz::Array<std::complex<double>,2> sample_c = bob::core::array::cast<std::complex<double> >(sample);
- fft2d(sample_c, sample_fft);
- data(all,all,i) = blitz::abs(sample_fft);
- }
- // Computes the mean of the training data
- blitz::Array<double,2> tmp(height,width);
- blitz::thirdIndex k;
- tmp = blitz::mean(data,k);
- // Removes the mean from the data
- for (size_t i=0; i<n_samples; ++i) {
- data(all,all,i) -= tmp;
- }
- // Computes power of 2 values
- data *= data;
- // Sums to get the variance
- tmp = blitz::sum(data,k) / n_samples;
-
- // sets the Wiener machine with the results:
- machine.setPs(tmp);
-}
diff --git a/bob/learn/misc/include/bob.learn.misc/WienerMachine.h b/bob/learn/misc/include/bob.learn.misc/WienerMachine.h
deleted file mode 100644
index 6e6ca3e..0000000
--- a/bob/learn/misc/include/bob.learn.misc/WienerMachine.h
+++ /dev/null
@@ -1,212 +0,0 @@
-/**
- * @date Fri Sep 30 16:56:06 2011 +0200
- * @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
- *
- * Copyright (C) Idiap Research Institute, Martigny, Switzerland
- */
-
-#ifndef BOB_LEARN_MISC_WIENERMACHINE_H
-#define BOB_LEARN_MISC_WIENERMACHINE_H
-
-#include <bob.learn.misc/Machine.h>
-#include <blitz/array.h>
-#include <complex>
-#include <bob.io.base/HDF5File.h>
-#include <bob.sp/FFT2D.h>
-
-namespace bob { namespace learn { namespace misc {
-
-/**
- * @brief A Wiener machine, which can be used to denoise a signal,
- * by comparing with a statistical model of the noiseless signal.\n
- *
- * Reference:\n
- * Computer Vision: Algorithms and Applications, Richard Szeliski
- * (Part 3.4.3)
- */
-class WienerMachine: Machine<blitz::Array<double,2>, blitz::Array<double,2> >
-{
- public: //api
- /**
- * @brief Default constructor. Builds an otherwise invalid 0 x 0 Wiener
- * machine. This is equivalent to construct a WienerMachine with two
- * size_t parameters set to 0, as in WienerMachine(0, 0, 0).
- */
- WienerMachine();
-
- /**
- * @brief Constructor, builds a new Wiener machine. Wiener filter is
- * initialized with the given size, Ps being sets to the variance
- * threshold.
- */
- WienerMachine(const size_t height, const size_t width, const double Pn,
- const double variance_threshold=1e-8);
-
- /**
- * @brief Builds a new machine with the given variance estimate Ps and
- * noise level Pn.
- */
- WienerMachine(const blitz::Array<double,2>& Ps, const double Pn,
- const double variance_threshold=1e-8);
-
- /**
- * @brief Builds a new machine with the given Wiener filter.
- */
- WienerMachine(const blitz::Array<double,2>& W);
-
- /**
- * @brief Copy constructor
- */
- WienerMachine(const WienerMachine& other);
-
- /**
- * @brief Starts a new WienerMachine from an existing Configuration
- * object.
- */
- WienerMachine(bob::io::base::HDF5File& config);
-
- /**
- * @brief Destructor
- */
- virtual ~WienerMachine();
-
- /**
- * @brief Assignment operator
- */
- WienerMachine& operator=(const WienerMachine& other);
-
- /**
- * @brief Equal to
- */
- bool operator==(const WienerMachine& other) const;
-
- /**
- * @brief Not equal to
- */
- bool operator!=(const WienerMachine& other) const;
-
- /**
- * @brief Is similar to
- */
- bool is_similar_to(const WienerMachine& b, const double r_epsilon=1e-5,
- const double a_epsilon=1e-8) const;
-
- /**
- * @brief Loads data from an existing configuration object. Resets the
- * current state.
- */
- void load(bob::io::base::HDF5File& config);
-
- /**
- * @brief Saves an existing machine to a Configuration object.
- */
- void save(bob::io::base::HDF5File& config) const;
-
- /**
- * @brief Forwards data through the machine.
- *
- * The input and output are NOT checked for compatibility each time. It
- * is your responsibility to do it.
- */
- void forward_(const blitz::Array<double,2>& input,
- blitz::Array<double,2>& output) const;
-
- /**
- * @brief Forwards data through the machine.
- *
- * The input and output are checked for compatibility each time the
- * forward method is applied.
- */
- void forward(const blitz::Array<double,2>& input,
- blitz::Array<double,2>& output) const;
-
- /**
- * @brief Resizes the machine.
- */
- void resize(const size_t height, const size_t width);
-
- /**
- * @brief Returns the height of the filter/input
- */
- size_t getHeight() const { return m_W.extent(0); }
-
- /**
- * @brief Returns the width of the filter/input
- */
- size_t getWidth() const { return m_W.extent(1); }
-
- /**
- * @brief Returns the current variance Ps estimated at each frequency
- */
- const blitz::Array<double, 2>& getPs() const
- { return m_Ps; }
-
- /**
- * @brief Returns the current variance threshold applied to Ps
- */
- double getVarianceThreshold() const
- { return m_variance_threshold; }
-
- /**
- * @brief Returns the current noise level Pn
- */
- double getPn() const
- { return m_Pn; }
-
- /**
- * @brief Returns the current Wiener filter (in the frequency domain).
- */
- const blitz::Array<double, 2>& getW() const
- { return m_W; }
-
- /**
- * @brief Sets the height of the filter/input
- */
- void setHeight(const size_t height)
- { resize(height, m_W.extent(1)); }
-
- /**
- * @brief Returns the width of the filter/input
- */
- void setWidth(const size_t width)
- { resize(m_W.extent(0), width); }
-
- /**
- * @brief Sets the current variance Ps estimated at each frequency.
- * This will also update the Wiener filter, using thresholded values.
- */
- void setPs(const blitz::Array<double,2>& Ps);
-
- /**
- * @brief Sets the current variance threshold to be used.
- * This will also update the Wiener filter
- */
- void setVarianceThreshold(const double variance_threshold);
-
- /**
- * @brief Sets the current noise level Pn to be considered.
- * This will update the Wiener filter
- */
- void setPn(const double Pn)
- { m_Pn = Pn; computeW(); }
-
-
- private: //representation
- void computeW(); /// Compute the Wiener filter using Pn, Ps, etc.
- void applyVarianceThreshold(); /// Apply variance flooring threshold
-
- blitz::Array<double, 2> m_Ps; ///< variance at each frequency estimated empirically
- double m_variance_threshold; ///< Threshold on Ps values when computing the Wiener filter
- /// (to avoid division by zero)
- double m_Pn; ///< variance of the noise
- blitz::Array<double, 2> m_W; ///< Wiener filter in the frequency domain (W=1/(1+Pn/Ps))
- bob::sp::FFT2D m_fft;
- bob::sp::IFFT2D m_ifft;
-
- mutable blitz::Array<std::complex<double>, 2> m_buffer1; ///< a buffer for speed
- mutable blitz::Array<std::complex<double>, 2> m_buffer2; ///< a buffer for speed
-};
-
-} } } // namespaces
-
-#endif /* BOB_LEARN_MISC_WIENERMACHINE_H */
diff --git a/bob/learn/misc/include/bob.learn.misc/WienerTrainer.h b/bob/learn/misc/include/bob.learn.misc/WienerTrainer.h
deleted file mode 100644
index 3e8f379..0000000
--- a/bob/learn/misc/include/bob.learn.misc/WienerTrainer.h
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * @date Fri Sep 30 16:58:42 2011 +0200
- * @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
- *
- * @brief Trainer for the Wiener Machine
- *
- * Copyright (C) Idiap Research Institute, Martigny, Switzerland
- */
-
-#ifndef BOB_LEARN_MISC_WIENER_TRAINER_H
-#define BOB_LEARN_MISC_WIENER_TRAINER_H
-
-#include <bob.learn.misc/Trainer.h>
-#include <bob.learn.misc/WienerMachine.h>
-#include <blitz/array.h>
-
-namespace bob { namespace learn { namespace misc {
-
-/**
- * @brief Sets a Wiener machine to perform a Wiener filtering, using the
- * Fourier statistics of a given dataset.\n
- *
- * Reference:\n
- * "Computer Vision: Algorithms and Applications", Richard Szeliski
- * (Part 3.4.3)
- */
-class WienerTrainer: Trainer<bob::learn::misc::WienerMachine, blitz::Array<double,3> >
-{
- public: //api
- /**
- * @brief Default constructor.
- * Initializes a new Wiener trainer.
- */
- WienerTrainer();
-
- /**
- * @brief Copy constructor
- */
- WienerTrainer(const WienerTrainer& other);
-
- /**
- * @brief Destructor
- */
- virtual ~WienerTrainer();
-
- /**
- * @brief Assignment operator
- */
- WienerTrainer& operator=(const WienerTrainer& other);
-
- /**
- * @brief Equal to
- */
- bool operator==(const WienerTrainer& other) const;
- /**
- * @brief Not equal to
- */
- bool operator!=(const WienerTrainer& other) const;
- /**
- * @brief Similar to
- */
- bool is_similar_to(const WienerTrainer& other, const double r_epsilon=1e-5,
- const double a_epsilon=1e-8) const;
-
- /**
- * @brief Trains the WienerMachine to perform the filtering.
- */
- virtual void train(bob::learn::misc::WienerMachine& machine,
- const blitz::Array<double,3>& data);
-
- private: //representation
-};
-
-} } } // namespaces
-
-#endif /* BOB_LEARN_MISC_WIENER_TRAINER_H */
diff --git a/bob/learn/misc/old/main.cc b/bob/learn/misc/old/main.cc
index f09c85a..f73f4fe 100644
--- a/bob/learn/misc/old/main.cc
+++ b/bob/learn/misc/old/main.cc
@@ -33,7 +33,6 @@ void bind_machine_ztnorm();
void bind_machine_jfa();
void bind_machine_ivector();
void bind_machine_plda();
-void bind_machine_wiener();
/** trainer bindings **/
void bind_trainer_gmm();
@@ -41,7 +40,6 @@ void bind_trainer_kmeans();
void bind_trainer_jfa();
void bind_trainer_ivector();
void bind_trainer_plda();
-void bind_trainer_wiener();
void bind_trainer_empca();
BOOST_PYTHON_MODULE(_old_library) {
@@ -83,7 +81,6 @@ BOOST_PYTHON_MODULE(_old_library) {
bind_machine_jfa();
bind_machine_ivector();
bind_machine_plda();
- bind_machine_wiener();
/** trainer bindings **/
bind_trainer_gmm();
@@ -91,7 +88,6 @@ BOOST_PYTHON_MODULE(_old_library) {
bind_trainer_jfa();
bind_trainer_ivector();
bind_trainer_plda();
- bind_trainer_wiener();
bind_trainer_empca();
}
diff --git a/bob/learn/misc/old/wiener.cc b/bob/learn/misc/old/wiener.cc
deleted file mode 100644
index fee9aee..0000000
--- a/bob/learn/misc/old/wiener.cc
+++ /dev/null
@@ -1,105 +0,0 @@
-/**
- * @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
- * @date Fri Sep 30 16:58:42 2011 +0200
- *
- * @brief Bindings for a WienerMachine
- *
- * Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
- */
-
-#include <bob.blitz/capi.h>
-#include <bob.blitz/cleanup.h>
-#include <bob.io.base/api.h>
-
-#include "ndarray.h"
-#include <bob.learn.misc/WienerMachine.h>
-
-using namespace boost::python;
-
-static void py_forward1_(const bob::learn::misc::WienerMachine& m,
- bob::python::const_ndarray input, bob::python::ndarray output)
-{
- blitz::Array<double,2> output_ = output.bz<double,2>();
- m.forward_(input.bz<double,2>(), output_);
-}
-
-static void py_forward1(const bob::learn::misc::WienerMachine& m,
- bob::python::const_ndarray input, bob::python::ndarray output)
-{
- blitz::Array<double,2> output_ = output.bz<double,2>();
- m.forward(input.bz<double,2>(), output_);
-}
-
-static object py_forward2(const bob::learn::misc::WienerMachine& m,
- bob::python::const_ndarray input)
-{
- const bob::io::base::array::typeinfo& info = input.type();
- bob::python::ndarray output(bob::io::base::array::t_float64, info.shape[0], info.shape[1]);
- blitz::Array<double,2> output_ = output.bz<double,2>();
- m.forward(input.bz<double,2>(), output_);
- return output.self();
-}
-
-static tuple get_shape(const bob::learn::misc::WienerMachine& m)
-{
- return make_tuple(m.getHeight(), m.getWidth());
-}
-
-static void set_shape(bob::learn::misc::WienerMachine& m,
- const blitz::TinyVector<int,2>& s)
-{
- m.resize(s(0), s(1));
-}
-
-static void py_set_ps(bob::learn::misc::WienerMachine& m,
- bob::python::const_ndarray ps)
-{
- m.setPs(ps.bz<double,2>());
-}
-
-
-static boost::shared_ptr<bob::learn::misc::WienerMachine> _init(boost::python::object file){
- if (!PyBobIoHDF5File_Check(file.ptr())) PYTHON_ERROR(TypeError, "Would have expected a bob.io.base.HDF5File");
- PyBobIoHDF5FileObject* hdf5 = (PyBobIoHDF5FileObject*) file.ptr();
- return boost::shared_ptr<bob::learn::misc::WienerMachine>(new bob::learn::misc::WienerMachine(*hdf5->f));
-}
-
-static void _load(bob::learn::misc::WienerMachine& self, boost::python::object file){
- if (!PyBobIoHDF5File_Check(file.ptr())) PYTHON_ERROR(TypeError, "Would have expected a bob.io.base.HDF5File");
- PyBobIoHDF5FileObject* hdf5 = (PyBobIoHDF5FileObject*) file.ptr();
- self.load(*hdf5->f);
-}
-
-static void _save(const bob::learn::misc::WienerMachine& self, boost::python::object file){
- if (!PyBobIoHDF5File_Check(file.ptr())) PYTHON_ERROR(TypeError, "Would have expected a bob.io.base.HDF5File");
- PyBobIoHDF5FileObject* hdf5 = (PyBobIoHDF5FileObject*) file.ptr();
- self.save(*hdf5->f);
-}
-
-
-void bind_machine_wiener()
-{
- class_<bob::learn::misc::WienerMachine, boost::shared_ptr<bob::learn::misc::WienerMachine> >("WienerMachine", "A Wiener filter.\nReference:\n'Computer Vision: Algorithms and Applications', Richard Szeliski, (Part 3.4.3)", init<const size_t, const size_t, const double, optional<const double> >((arg("self"), arg("height"), arg("width"), arg("pn"), arg("variance_threshold")=1e-8), "Constructs a new Wiener filter dedicated to images of the given dimensions. The filter is initialized with zero values."))
- .def(init<const blitz::Array<double,2>&, const double> ((arg("self"), arg("ps"), arg("pn")), "Constructs a new WienerMachine from a set of variance estimates ps, a noise level pn."))
- .def(init<>((arg("self")), "Default constructor, builds a machine as with 'WienerMachine(0,0,0)'."))
- .def("__init__", boost::python::make_constructor(&_init), "Constructs a new WienerMachine from a configuration file.")
- .def(init<const bob::learn::misc::WienerMachine&>((arg("self"), arg("machine")), "Copy constructs an WienerMachine"))
- .def(self == self)
- .def(self != self)
- .def("is_similar_to", &bob::learn::misc::WienerMachine::is_similar_to, (arg("self"), arg("other"), arg("r_epsilon")=1e-5, arg("a_epsilon")=1e-8), "Compares this WienerMachine with the 'other' one to be approximately the same.")
- .def("load", &_load, (arg("self"), arg("config")), "Loads the filter from a configuration file.")
- .def("save", &_save, (arg("self"), arg("config")), "Saves the filter to a configuration file.")
- .add_property("pn", &bob::learn::misc::WienerMachine::getPn, &bob::learn::misc::WienerMachine::setPn, "Noise level Pn")
- .add_property("variance_threshold", &bob::learn::misc::WienerMachine::getVarianceThreshold, &bob::learn::misc::WienerMachine::setVarianceThreshold, "Variance flooring threshold (min variance value)")
- .add_property("ps",make_function(&bob::learn::misc::WienerMachine::getPs, return_value_policy<copy_const_reference>()), &py_set_ps, "Variance Ps estimated at each frequency")
- .add_property("w", make_function(&bob::learn::misc::WienerMachine::getW, return_value_policy<copy_const_reference>()), "The Wiener filter W (W=1/(1+Pn/Ps)) (read-only)")
- .add_property("height", &bob::learn::misc::WienerMachine::getHeight, &bob::learn::misc::WienerMachine::setHeight, "Height of the filter/image to process")
- .add_property("width", &bob::learn::misc::WienerMachine::getWidth, &bob::learn::misc::WienerMachine::setWidth, "Width of the filter/image to process")
- .add_property("shape", &get_shape, &set_shape)
- .def("__call__", &py_forward1, (arg("self"), arg("input"), arg("output")), "Filters the input and saves results on the output.")
- .def("forward", &py_forward1, (arg("self"), arg("input"), arg("output")), "Filters the input and saves results on the output.")
- .def("forward_", &py_forward1_, (arg("self"), arg("input"), arg("output")), "Filters the input and saves results on the output. Input is not checked.")
- .def("__call__", &py_forward2, (arg("self"), arg("input")), "Filters the input and returns the output. This method implies in copying out the output data and is, therefore, less efficient as its counterpart that sets the output given as parameter. If you have to do a tight loop, consider using that variant instead of this one.")
- .def("forward", &py_forward2, (arg("self"), arg("input")), "Filter the input and returns the output. This method implies in copying out the output data and is, therefore, less efficient as its counterpart that sets the output given as parameter. If you have to do a tight loop, consider using that variant instead of this one.")
- ;
-}
diff --git a/bob/learn/misc/old/wiener_trainer.cc b/bob/learn/misc/old/wiener_trainer.cc
deleted file mode 100644
index 234f9b4..0000000
--- a/bob/learn/misc/old/wiener_trainer.cc
+++ /dev/null
@@ -1,44 +0,0 @@
-/**
- * @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
- * @date Fri Sep 30 16:58:42 2011 +0200
- *
- * @brief Python bindings to WienerTrainer
- *
- * Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
- */
-
-#include "ndarray.h"
-#include <bob.learn.misc/WienerTrainer.h>
-#include <bob.learn.misc/WienerMachine.h>
-
-using namespace boost::python;
-
-void py_train1(bob::learn::misc::WienerTrainer& t,
- bob::learn::misc::WienerMachine& m, bob::python::const_ndarray data)
-{
- t.train(m, data.bz<double,3>());
-}
-
-object py_train2(bob::learn::misc::WienerTrainer& t,
- bob::python::const_ndarray data)
-{
- const blitz::Array<double,3> data_ = data.bz<double,3>();
- const int height = data_.extent(1);
- const int width = data_.extent(2);
- bob::learn::misc::WienerMachine m(height, width, 0.);
- t.train(m, data_);
- return object(m);
-}
-
-void bind_trainer_wiener() {
-
- class_<bob::learn::misc::WienerTrainer, boost::shared_ptr<bob::learn::misc::WienerTrainer> >("WienerTrainer", "Trains a WienerMachine on a given dataset.\nReference:\n'Computer Vision: Algorithms and Applications', Richard Szeliski\n(Part 3.4.3)", init<>((arg("self")), "Initializes a new WienerTrainer."))
- .def(init<const bob::learn::misc::WienerTrainer&>((arg("self"), arg("other")), "Copy constructs a WienerTrainer"))
- .def(self == self)
- .def(self != self)
- .def("is_similar_to", &bob::learn::misc::WienerTrainer::is_similar_to, (arg("self"), arg("other"), arg("r_epsilon")=1e-5, arg("a_epsilon")=1e-8), "Compares this WienerTrainer with the 'other' one to be approximately the same.")
- .def("train", &py_train1, (arg("self"), arg("machine"), arg("data")), "Trains the provided WienerMachine with the given dataset.")
- .def("train", &py_train2, (arg("self"), arg("data")), "Trains a WienerMachine using the given dataset to perform the filtering. This method returns the trained WienerMachine.")
- ;
-
-}
diff --git a/bob/learn/misc/test_wiener.py b/bob/learn/misc/test_wiener.py
deleted file mode 100644
index 39cd0d6..0000000
--- a/bob/learn/misc/test_wiener.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
-#
-# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
-
-"""Tests the WienerMachine
-"""
-
-import os
-import numpy
-import tempfile
-import nose.tools
-
-import bob.sp
-import bob.io.base
-
-from . import WienerMachine
-
-def test_initialization():
-
- # Getters/Setters
- m = WienerMachine(5,4,0.5)
- nose.tools.eq_(m.height, 5)
- nose.tools.eq_(m.width, 4)
- nose.tools.eq_(m.shape, (5,4))
- m.height = 8
- m.width = 7
- nose.tools.eq_(m.height, 8)
- nose.tools.eq_(m.width, 7)
- nose.tools.eq_(m.shape, (8,7))
- m.shape = (5,6)
- nose.tools.eq_(m.height, 5)
- nose.tools.eq_(m.width, 6)
- nose.tools.eq_(m.shape, (5,6))
- ps1 = 0.2 + numpy.fabs(numpy.random.randn(5,6))
- ps2 = 0.2 + numpy.fabs(numpy.random.randn(5,6))
- m.ps = ps1
- assert numpy.allclose(m.ps, ps1)
- m.ps = ps2
- assert numpy.allclose(m.ps, ps2)
- pn1 = 0.5
- m.pn = pn1
- assert abs(m.pn - pn1) < 1e-5
- var_thd = 1e-5
- m.variance_threshold = var_thd
- assert abs(m.variance_threshold - var_thd) < 1e-5
-
- # Comparison operators
- m2 = WienerMachine(m)
- assert m == m2
- assert (m != m2 ) is False
- m3 = WienerMachine(ps2, pn1)
- m3.variance_threshold = var_thd
- assert m == m3
- assert (m != m3 ) is False
-
- # Computation of the Wiener filter W
- w_py = 1 / (1. + m.pn / m.ps)
- assert numpy.allclose(m.w, w_py)
-
-def test_load_save():
-
- m = WienerMachine(5,4,0.5)
-
- # Save and read from file
- filename = str(tempfile.mkstemp(".hdf5")[1])
- m.save(bob.io.base.HDF5File(filename, 'w'))
- m_loaded = WienerMachine(bob.io.base.HDF5File(filename))
- assert m == m_loaded
- assert (m != m_loaded ) is False
- assert m.is_similar_to(m_loaded)
- # Make them different
- m_loaded.variance_threshold = 0.001
- assert (m == m_loaded ) is False
- assert m != m_loaded
-
- # Clean-up
- os.unlink(filename)
-
-def test_forward():
-
- ps = 0.2 + numpy.fabs(numpy.random.randn(5,6))
- pn = 0.5
- m = WienerMachine(ps,pn)
-
- # Python way
- sample = numpy.random.randn(5,6)
- sample_fft = bob.sp.fft(sample.astype(numpy.complex128))
- w = m.w
- sample_fft_filtered = sample_fft * m.w
- sample_filtered_py = numpy.absolute(bob.sp.ifft(sample_fft_filtered))
-
- # Bob c++ way
- sample_filtered0 = m.forward(sample)
- sample_filtered1 = m(sample)
- sample_filtered2 = numpy.zeros((5,6),numpy.float64)
- m.forward_(sample, sample_filtered2)
- sample_filtered3 = numpy.zeros((5,6),numpy.float64)
- m.forward(sample, sample_filtered3)
- sample_filtered4 = numpy.zeros((5,6),numpy.float64)
- m(sample, sample_filtered4)
- assert numpy.allclose(sample_filtered0, sample_filtered_py)
- assert numpy.allclose(sample_filtered1, sample_filtered_py)
- assert numpy.allclose(sample_filtered2, sample_filtered_py)
- assert numpy.allclose(sample_filtered3, sample_filtered_py)
- assert numpy.allclose(sample_filtered4, sample_filtered_py)
diff --git a/bob/learn/misc/test_wiener_trainer.py b/bob/learn/misc/test_wiener_trainer.py
deleted file mode 100644
index 53d619e..0000000
--- a/bob/learn/misc/test_wiener_trainer.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
-#
-# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
-
-"""Tests the WienerTrainer
-"""
-
-import numpy
-import bob.sp
-
-from . import WienerMachine, WienerTrainer
-
-def train_wiener_ps(training_set):
-
- # Python implementation
- n_samples = training_set.shape[0]
- height = training_set.shape[1]
- width = training_set.shape[2]
- training_fftabs = numpy.zeros((n_samples, height, width), dtype=numpy.float64)
-
- for n in range(n_samples):
- sample = (training_set[n,:,:]).astype(numpy.complex128)
- training_fftabs[n,:,:] = numpy.absolute(bob.sp.fft(sample))
-
- mean = numpy.mean(training_fftabs, axis=0)
-
- for n in range(n_samples):
- training_fftabs[n,:,:] -= mean
-
- training_fftabs = training_fftabs * training_fftabs
- var_ps = numpy.mean(training_fftabs, axis=0)
-
- return var_ps
-
-
-def test_initialization():
-
- # Constructors and comparison operators
- t1 = WienerTrainer()
- t2 = WienerTrainer()
- t3 = WienerTrainer(t2)
- t4 = t3
- assert t1 == t2
- assert (t1 != t2) is False
- assert t1.is_similar_to(t2)
- assert t1 == t3
- assert (t1 != t3) is False
- assert t1.is_similar_to(t3)
- assert t1 == t4
- assert (t1 != t4) is False
- assert t1.is_similar_to(t4)
-
-
-def test_train():
-
- n_samples = 20
- height = 5
- width = 6
- training_set = 0.2 + numpy.fabs(numpy.random.randn(n_samples, height, width))
-
- # Python implementation
- var_ps = train_wiener_ps(training_set)
- # Bob C++ implementation (variant 1) + comparison against python one
- t = WienerTrainer()
- m1 = t.train(training_set)
- assert numpy.allclose(var_ps, m1.ps)
- # Bob C++ implementation (variant 2) + comparison against python one
- m2 = WienerMachine(height, width, 0.)
- t.train(m2, training_set)
- assert numpy.allclose(var_ps, m2.ps)
-
diff --git a/setup.py b/setup.py
index c07c543..feabf72 100644
--- a/setup.py
+++ b/setup.py
@@ -62,7 +62,6 @@ setup(
#"bob/learn/misc/cpp/KMeansMachine.cpp",
#"bob/learn/misc/cpp/LinearScoring.cpp",
#"bob/learn/misc/cpp/PLDAMachine.cpp",
- #"bob/learn/misc/cpp/WienerMachine.cpp",
#"bob/learn/misc/cpp/ZTNorm.cpp",
#"bob/learn/misc/cpp/EMPCATrainer.cpp",
@@ -73,7 +72,6 @@ setup(
#"bob/learn/misc/cpp/MAP_GMMTrainer.cpp",
#"bob/learn/misc/cpp/ML_GMMTrainer.cpp",
#"bob/learn/misc/cpp/PLDATrainer.cpp",
- #"bob/learn/misc/cpp/WienerTrainer.cpp",
],
bob_packages = bob_packages,
packages = packages,
--
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