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/////////////////////////////////////////////////////////////////////////////
//
// GEM - Graphics Environment for Multimedia
//
// pix_linNN~
// Calculates an audio signal out of a video frame
// with a linear neural network, which can be trained
//
// the network has one neuron per audio sample: this neuron has
// three inputs (a RGB-signal), a weight vector for each of the inputs,
// a bias value and a linear output function
// (see LinNeuralNet.h for more info)
//
// header file
//
// Copyright (c) 2004 Georg Holzmann <grh@gmx.at>
// (and of course lot's of other developers for PD and GEM)
//
// For information on usage and redistribution, and for a DISCLAIMER OF ALL
// WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
//
/////////////////////////////////////////////////////////////////////////////
#ifndef _INCLUDE_PIX_LINNN_H__
#define _INCLUDE_PIX_LINNN_H__
#include <string>
#include <sstream>
#include <fstream>
#include "Base/GemPixObj.h"
#include "LinNeuralNet.h"
using std::string;
using std::endl;
using std::ifstream;
using std::ofstream;
using std::istringstream;
/*-----------------------------------------------------------------
* CLASS
* pix_linNN~
*
* calculates an audio signal out of a video frame with
* a linear neural network
*
* KEYWORDS
* pix audio
*
* DESCRIPTION
* 1 signal-outlet
*/
class GEM_EXTERN pix_linNN : public GemPixObj
{
CPPEXTERN_HEADER(pix_linNN, GemPixObj)
public:
/* Constructor
*/
pix_linNN(t_floatarg arg0, t_floatarg arg1);
protected:
/* Destructor
*/
virtual ~pix_linNN();
//-----------------------------------
/* Image STUFF:
*/
/* The pixBlock with the current image
* pixBlock m_pixBlock;
*/
unsigned char *m_data_;
int m_xsize_;
int m_ysize_;
int m_csize_;
int m_format_;
/* precision of the image:
* 1 means every pixel is taken for the calculation,
* 2 every second pixel, 3 every third, ...
*/
int precision_;
/* processImage
*/
virtual void processImage(imageStruct &image);
//-----------------------------------
/* Neural Network STUFF:
*/
/* the linear neural nets
* (size: buffsize)
*/
LinNeuralNet *net_;
/* training modus on
* (will only be on for one audio buffer)
*/
bool train_on_;
/* the number of neurons, which should be
* (= size of the array nets_)
* THE SAME as the audio buffer size
*/
int neuron_nr_;
//-----------------------------------
/* Audio STUFF:
*/
/* the outlet
*/
t_outlet *out0_;
/* DSP perform
*/
static t_int* perform(t_int* w);
/* DSP-Message
*/
virtual void dspMess(void *data, t_signal** sp);
//-----------------------------------
/* File IO:
*/
/* saves the contents of the current net to file
* (it saves the neuron_nr_, learning rate
* IW-matrix and b1-vector of the net)
*/
virtual void saveNet(string filename);
/* loads the parameters of the net from file
* (it loads the neuron_nr_, learning rate
* IW-matrix and b1-vector of the net)
*/
virtual void loadNet(string filename);
private:
//-----------------------------------
/* static members
* (interface to the PD world)
*/
/* set/get the precision of the image calculation
*/
static void setPrecision(void *data, t_floatarg precision);
static void getPrecision(void *data);
/* method to train the network
*/
static void setTrainOn(void *data);
/* changes the number of neurons
* (which should be the same as the audio buffer)
* ATTENTION: a new IW-matrix and b1-vector will be initialized
*/
static void setNeurons(void *data, t_floatarg neurons);
static void getNeurons(void *data);
/* sets the learnrate of the net
*/
static void setLearnrate(void *data, t_floatarg learn_rate);
static void getLearnrate(void *data);
/* DSP callback
*/
static void dspMessCallback(void* data, t_signal** sp);
/* File IO:
*/
static void saveToFile(void *data, t_symbol *filename);
static void loadFromFile(void *data, t_symbol *filename);
};
#endif // for header file
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