// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void XtArrayInitialization::initialize(EMMPM_Data::Pointer data)
{
size_t total;
total = data->rows * data->columns;
const float rangeMin = 0.0f;
const float rangeMax = 1.0f;
typedef boost::uniform_real<> NumberDistribution;
typedef boost::mt19937 RandomNumberGenerator;
typedef boost::variate_generator<RandomNumberGenerator&,
NumberDistribution> Generator;
NumberDistribution distribution(rangeMin, rangeMax);
RandomNumberGenerator generator;
Generator numberGenerator(generator, distribution);
generator.seed(EMMPM_getMilliSeconds()); // seed with the current time
/* Initialize classification of each pixel randomly with a uniform disribution */
for (size_t i = 0; i < total; i++)
{
data->xt[i] = numberGenerator() * data->classes;
}
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void acvmpm(EMMPM_Data* data, EMMPM_CallbackFunctions* callbacks)
{
double* yk;
double sqrt2pi, current, con[EMMPM_MAX_CLASSES];
double x, post[EMMPM_MAX_CLASSES], sum, edge;
int k, l, prior;
int i, j, d;
size_t ld, ijd, ij, lij, i1j1;
int dims = data->dims;
int rows = data->rows;
int cols = data->columns;
int classes = data->classes;
unsigned char* y = data->y;
unsigned char* xt = data->xt;
double* probs = data->probs;
double* m = data->m;
double* v = data->v;
double* ccost = data->ccost;
double* ns = data->ns;
double* ew = data->ew;
double* sw = data->sw;
double* nw = data->nw;
char msgbuff[256];
float totalLoops;
float currentLoopCount = 0.0;
// double local_beta;
size_t nsCols = data->columns - 1;
// size_t nsRows = data->rows;
size_t ewCols = data->columns;
// size_t ewRows = data->rows - 1;
size_t swCols = data->columns - 1;
// size_t swRows = data->rows - 1;
size_t nwCols = data->columns-1;
// size_t nwRows = data->rows-1;
memset(post, 0, EMMPM_MAX_CLASSES * sizeof(double));
memset(con, 0, EMMPM_MAX_CLASSES * sizeof(double));
totalLoops = (float)(data->emIterations * data->mpmIterations + data->mpmIterations);
memset(msgbuff, 0, 256);
data->progress++;
yk = (double*)malloc(cols * rows * classes * sizeof(double));
sqrt2pi = sqrt(2.0 * M_PI);
unsigned long long int millis = EMMPM_getMilliSeconds();
for (l = 0; l < classes; l++)
{
con[l] = 0;
for (d = 0; d < dims; d++)
{
ld = dims * l + d;
con[l] += -log(sqrt2pi * sqrt(v[ld]));
}
}
for (i = 0; i < rows; i++)
{
for (j = 0; j < cols; j++)
{
for (l = 0; l < classes; l++)
{
lij = (cols * rows * l) + (cols * i) + j;
probs[lij] = 0;
yk[lij] = con[l];
for (d = 0; d < dims; d++)
{
ld = dims * l + d;
ijd = (dims * cols * i) + (dims * j) + d;
yk[lij] += ((y[ijd] - m[ld]) * (y[ijd] - m[ld]) / (-2.0 * v[ld]));
}
}
}
}
printf("Serial Millis to complete initialization: %llu \n", EMMPM_getMilliSeconds() - millis);
/* Perform the MPM loops */
for (k = 0; k < data->mpmIterations; k++)
{
data->currentMPMLoop = k;
if (data->cancel) { data->progress = 100.0; break; }
data->inside_mpm_loop = 1;
millis = EMMPM_getMilliSeconds();
for (i = 0; i < rows; i++)
{
for (j = 0; j < cols; j++)
{
ij = (cols * i) + j;
sum = 0;
for (l = 0; l < classes; l++)
{
/* edge penalties (in both x and y) */
prior = 0;
edge = 0;
if (i - 1 >= 0)
{
if (j - 1 >= 0)
{
i1j1 = (cols*(i-1))+j-1;
if (xt[i1j1] != l)
{
prior++;
i1j1 = (swCols*(i-1))+j-1;
edge += sw[i1j1];
}
}
//Mark1
i1j1 = (cols*(i-1))+j;
if (xt[i1j1] != l)
{
prior++;
i1j1 = (ewCols*(i-1))+j;
edge += ew[i1j1];
}
//mark2
if (j + 1 < cols)
{
i1j1 = (cols*(i-1))+j+1;
if (xt[i1j1] != l)
{
prior++;
i1j1 = (nwCols*(i-1))+j;
edge += nw[i1j1];
}
}
}
//mark3
if (i + 1 < rows)
{
if (j - 1 >= 0)
{
i1j1 = (cols*(i+1))+j-1;
if (xt[i1j1] != l)
{
prior++;
i1j1 = (nwCols*(i))+j-1;
edge += nw[i1j1];
}
}
//mark4
i1j1 = (cols*(i+1))+j;
if (xt[i1j1] != l)
{
prior++;
i1j1 = (ewCols*(i))+j;
edge += ew[i1j1];
}
//mark5
if (j + 1 < cols)
{
i1j1 = (cols*(i+1))+j+1;
if (xt[i1j1] != l)
{
prior++;
i1j1 = (swCols*(i))+j;
edge += sw[i1j1];
}
}
}
//mark6
if (j - 1 >= 0)
{
i1j1 = (cols*(i))+j-1;
if (xt[i1j1] != l)
{
prior++;
i1j1 = (nsCols*(i))+j-1;
edge += ns[i1j1];
}
}
//mark7
if (j + 1 < cols)
{
i1j1 = (cols*(i))+j+1;
if (xt[i1j1] != l)
{
prior++;
i1j1 = (nsCols*(i))+j;
edge += ns[i1j1];
}
}
lij = (cols * rows * l) + (cols * i) + j;
post[l] = exp(yk[lij] - (data->workingBeta * (double)prior) - edge - (data->beta_c * ccost[lij]) - data->w_gamma[l]);
sum += post[l];
}
x = genrand_real2(data->rngVars);
current = 0;
for (l = 0; l < classes; l++)
{
lij = (cols * rows * l) + (cols * i) + j;
ij = (cols*i)+j;
if ((x >= current) && (x <= (current + post[l] / sum)))
{
xt[ij] = l;
probs[lij] += 1.0;
}
current += post[l] / sum;
}
}
}
printf("Millis to complete loop: %llu \n", EMMPM_getMilliSeconds() - millis);
EMMPM_ConvertXtToOutputImage(data, callbacks);
if (callbacks->EMMPM_ProgressFunc != NULL)
{
data->currentMPMLoop = k;
snprintf(msgbuff, 256, "MPM Loop %d", data->currentMPMLoop);
callbacks->EMMPM_ProgressFunc(msgbuff, data->progress);
}
if (NULL != callbacks->EMMPM_ProgressStatsFunc)
{
currentLoopCount = data->mpmIterations * data->currentEMLoop + data->currentMPMLoop;
data->progress = currentLoopCount / totalLoops * 100.0;
callbacks->EMMPM_ProgressStatsFunc(data);
}
}
data->inside_mpm_loop = 0;
if (!data->cancel)
{
/* Normalize probabilities */
for (i = 0; i < data->rows; i++)
{
for (j = 0; j < data->columns; j++)
{
for (l = 0; l < classes; l++)
{
lij = (cols * rows * l) + (cols * i) + j;
data->probs[lij] = data->probs[lij] / (double)data->mpmIterations;
}
}
}
}
/* Clean Up Memory */
free(yk);
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
int main(int argc,char *argv[])
{
// unsigned long long int millis = EMMPM_getMilliSeconds();
#if defined (EMMPMLib_USE_PARALLEL_ALGORITHMS)
tbb::task_scheduler_init init;
std::cout << "Default Number of Threads: " << init.default_num_threads() << std::endl;
#endif
int err = 0;
EMMPM_Data::Pointer data = EMMPM_Data::New();
// EMMPM_CallbackFunctions* callbacks = EMMPM_AllocateCallbackFunctionStructure();
#if 1
/* Parse the command line arguments */
EMMPMInputParser parser;
err = parser.parseCLIArguments(argc, argv, data.get());
if (err < 0)
{
printf("Error trying to parse the arguments.\n");
return 0;
}
#else
char* infile = (char*)(malloc(1024));
memset(infile, 0, 1024);
snprintf(infile, 1024, "/Users/mjackson/Desktop/EMMPM.tif");
char* outfile = (char*)(malloc(1024));
memset(outfile, 0, 1024);
snprintf(outfile, 1024, "/tmp/out.tif");
data->input_file_name = infile;
data->output_file_name = outfile;
data->emIterations = 1;
data->mpmIterations = 1;
data->in_beta = 1.0;
data->classes = 2;
data->dims = 1;
data->initType = EMMPM_Basic;
data->simulatedAnnealing = 0;
data->grayTable[0] = 0;
data->grayTable[1] = 255;
data->verbose = 1;
data->w_gamma[0] = 1.0;
data->w_gamma[1] = 1.0;
data->useCurvaturePenalty = 0;
data->ccostLoopDelay = 0;
data->beta_e = 1.0;
data->beta_c = 0.0;
data->r_max = 1.0;
#endif
/* Set the Callback functions to provide feedback */
Observer obs;
CLIStatsDelegate::Pointer statsDelegate = CLIStatsDelegate::New();
// Get our input image from the Image IO functions
TiffUtilities tifUtil;
err = tifUtil.readInputImage(data);
if (err < 0)
{
printf("Error Reading the input image.\n");
return 0;
}
InitializationFunction::Pointer initFunction = BasicInitialization::New();
// Set the initialization function based on the command line arguments
switch(data->initType)
{
case EMMPM_ManualInit:
initFunction = InitializationFunction::New();
break;
case EMMPM_UserInitArea:
initFunction = UserDefinedAreasInitialization::New();
break;
default:
break;
}
obs.updateProgressAndMessage("EM/MPM Starting.... ", 0);
// Allocate all the memory here
data->allocateDataStructureMemory();
if (err)
{
printf("Error allocating memory for the EMMPM Data Structure.\n %s(%d)\n", __FILE__, __LINE__);
return 1;
}
EMMPM::Pointer emmpm = EMMPM::New();
emmpm->addObserver(&obs);
emmpm->setData(data);
emmpm->setStatsDelegate(statsDelegate.get());
emmpm->setInitializationFunction(initFunction);
emmpm->execute();
err = tifUtil.writeOutputImage(data);
if (err < 0)
{
return 0;
}
#if 0
#if defined (EMMPMLib_USE_PARALLEL_ALGORITHMS)
std::cout << "Parrallel Time to Complete:";
#else
std::cout << "Serial Time To Complete: ";
#endif
std::cout << (EMMPM_getMilliSeconds() - millis) << std::endl;
#endif
EMMPMUtilities::MonitorMeansAndVariances(data);
// Write the stats file
std::stringstream ss;
ss << data->output_file_name << "_Stats.txt";
std::cout << "StatsFile: " << ss.str() << std::endl;
FILE* f = fopen(ss.str().c_str(), "wb");
fprintf(f, "InputFile:%s\n", data->input_file_name);
fprintf(f, "SegmentedFile:%s\n" , data->output_file_name);
fprintf(f, "NumClasses:%d\n", data->classes);
fprintf(f, "Class,Mu,Sigma\n");
// Remember the Sigma is the Square Root of the variance
for(int i = 0; i < data->classes; ++i)
{
fprintf(f, "%d,%f,%f\n", i, data->mean[i] , sqrtf(data->variance[i]) );
}
fclose(f);
std::cout << "EM/MPM Ending" << std::endl;
return 1;
}
