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eye_locator.h File Reference

#include "eye_locator_defines.h"
#include "frame.h"
#include "annie/annie.h"

Go to the source code of this file.

Compounds
class	eye_locator_filter
class	eye_locator_network
Defines
#define	VERBOSE   false
#define	EYE_NODES   (EYE_WIDTH * EYE_HEIGHT)
#define	ITERATIONS   500
#define	LEARNING_RATE   0.25
#define	MOMENTUM   0.15
#define	OK_ERROR   0.015
#define	TRUE_OUTPUT   0.95
#define	FALSE_OUTPUT   0.05
#define	CUT_OFF   ((TRUE_OUTPUT + FALSE_OUTPUT)/2.0)
#define	MAX_COUNTER_PER_CYCLE   100
#define	MAX_EYES_PER_CYCLE   100
Functions
annie::TrainingSet *	make_eye_training_set (string EyeDir, string fileName)
void	trainEyeNN (string NonEyeDir, annie::TrainingSet &ts, string fileName, int hidden)

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Define Documentation

#define CUT_OFF   ((TRUE_OUTPUT + FALSE_OUTPUT)/2.0)

this is the cutoff value that says where an input is no longer recognised to be a non eye and starts to be a eye. anything above this value is considered to be a eye

#define EYE_NODES   (EYE_WIDTH * EYE_HEIGHT)

the number of input nodes in the nural network this value does not equal INPUT_HEIGHT * INPUT_WIDTH because we only look at an oval in the center of the image, not at the entire image.

#define FALSE_OUTPUT   0.05

this is the output that the network will be trained to produce when a non eye is placed on the inputs

#define ITERATIONS   500

the number of training itterations that the network will go through before the error between the network and the desired results is computed if the error is still to high it will train it this many times again,

#define LEARNING_RATE   0.25

this is the learning rate that is passed to the neural network library

#define MAX_COUNTER_PER_CYCLE   100

this is the maximum number of false positives that will be added into the training set each time through a training cycle, this is so that after the first run, adding in only eyes, that all possible images in the counter set wount be added, it makes it simpler to get a representitive set

#define MAX_EYES_PER_CYCLE   100

this is the maximum number of new eyes add in each time through the training cycle. see MAX_COUNTER_PER_CYCLE to se why

#define MOMENTUM   0.15

this is the momentum that is passed to the nural network program

#define OK_ERROR   0.015

this is the error rate that is ok to have. When the computed error rate is lower then this training for the current step will stop. note that you don't want it to be 0, because it takes for ever to achive this and the network will not be as good at recognising data that is not in the training set

#define TRUE_OUTPUT   0.95

this is the value that the network will be trained to output when a eye is placed on the inputs

#define VERBOSE   false

if annie should or shouldnot print during training

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Function Documentation

annie::TrainingSet* make_eye_training_set (  string    EyeDir, string    fileName )

the first step in training the network is to compile all of the eyes into a training set to train the network with Parameters: EyeDir  a directory containing image files of eyes. the eyes should be EYE_WIDTH by EYE_HEIGHT fileName  the name of the file to save the training set data to Returns: true if no errors were encountered else false

void trainEyeNN (  string    NonEyeDir, annie::TrainingSet &    ts, string    fileName, int    hidden )

train the network in an interesting way

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