CameraSettings::CameraSettings(const std::vector<float> parameters)
{
SetDefaults();
p_brightness.set(parameters[0], 0, 255, "an attribute of visual perception in which a source appears to be radiating or reflecting light");
p_contrast.set(parameters[1], 0, 127, "the difference in color and light between parts of an image");
p_saturation.set(parameters[2], 0, 255, "the difference between a color against gray");
p_gain.set(parameters[3], 0, 255, "ISO sensitivity");
p_exposure.set(parameters[4], 0, 510, "total amount of light allowed to fall on the photographic medium");
p_autoWhiteBalance.set(parameters[5], 0, 0, "permanently off");
p_hue.set(parameters[6], -180, 180, "the degree to which a stimulus can be described as similar to or different from stimuli that are described as red, green, blue, and yellow");
p_redChroma.set(parameters[7], 0, 255, "the perceived intensity of a specific color (red)");
p_blueChroma.set(parameters[8], 0, 255, "the perceived intensity of a specific color (blue)");
p_autoExposure.set(parameters[9], 0, 0, "permanently off");
p_autoGain.set(parameters[10], 0, 0, "permanently off");
p_powerLineFrequency.set(parameters[11], 0, 2, "off, 50Hz and 60Hz");
p_whiteBalanceTemperature.set(parameters[12], 0, 10000, "");
p_sharpness.set(parameters[13], 0, 255, "");
p_exposureAuto.set(parameters[14], 1, 1, "permanently off");
p_exposureAbsolute.set(parameters[15], 0, 10000, "total amount of light allowed to fall on the photographic medium");
p_exposureAutoPriority.set(parameters[16], 0, 0, "permanently off");
p_valid = true;
copyParams();
return;
}
void CameraSettings::SetDefaults()
{
p_valid = false;
p_brightness.set(0, 0, 255, "an attribute of visual perception in which a source appears to be radiating or reflecting light");
p_contrast.set(0, 0, 127, "the difference in color and light between parts of an image");
p_saturation.set(0, 0, 255, "the difference between a color against gray");
p_gain.set(0, 0, 255, "ISO sensitivity");
p_exposure.set(0, 0, 510, "total amount of light allowed to fall on the photographic medium");
p_autoWhiteBalance.set(0, 1, 1, "permanently off");
p_hue.set(0, -180, 180, "the degree to which a stimulus can be described as similar to or different from stimuli that are described as red, green, blue, and yellow");
p_redChroma.set(0, 0, 255, "the perceived intensity of a specific color (red)");
p_blueChroma.set(0, 0, 255, "the perceived intensity of a specific color (blue)");
p_autoExposure.set(0, 0, 0, "permanently off");
p_autoGain.set(0, 0, 0, "permanently off");
p_powerLineFrequency.set(1, 0, 2, "off, 50Hz and 60Hz");
p_whiteBalanceTemperature.set(0, 0, 10000, "");
p_sharpness.set(0, 0, 255, "");
p_exposureAuto.set(1, 1, 1, "permanently off");
p_exposureAbsolute.set(0, 0, 10000, "total amount of light allowed to fall on the photographic medium");
p_exposureAutoPriority.set(0, 0, 0, "permanently off");
copyParams();
// Extra
activeCamera = BOTTOM_CAMERA;
return;
}
CameraSettings::CameraSettings(const CameraSettings& source)
{
// Image settings
p_brightness = source.p_brightness;
p_contrast = source.p_contrast;
p_saturation = source.p_saturation;
p_gain = source.p_gain;
p_exposure = source.p_exposure;
p_autoWhiteBalance = source.p_autoWhiteBalance;
p_hue = source.p_hue;
p_redChroma = source.p_redChroma;
p_blueChroma = source.p_blueChroma;
p_autoExposure = source.p_autoExposure;
p_autoGain = source.p_autoGain;
p_powerLineFrequency = source.p_powerLineFrequency;
p_whiteBalanceTemperature = source.p_whiteBalanceTemperature;
p_sharpness = source.p_sharpness;
p_exposureAuto = source.p_exposureAuto;
p_exposureAbsolute = source.p_exposureAbsolute;
p_exposureAutoPriority = source.p_exposureAutoPriority;
p_valid = true;
copyParams();
// Extra
activeCamera = source.activeCamera;
return;
}
CameraSettings::CameraSettings(const std::vector<Parameter> parameters)
{
SetDefaults();
for(int i=0; i<parameters.size(); i++) {
if(!SetByName(parameters[i]))
debug << "CameraSettings::LoadFromFile(): \"" << parameters[i] << "\" not found." << endl;
}
p_valid = true;
copyParams();
return;
}
bool VstoneCameraUndistortion::undistortion(const cv::Mat &src, cv::Mat &dst)
{
if (src.empty()) {
printf("error : VstoneCameraUndistortion::undistortion() src image is null...\n");
return false;
}
if (dst.empty()) {
printf("error : VstoneCameraUndistortion::undistortion() dst image is null...\n");
return false;
}
VstoneCameraUndistortionPLB body(src, dst);
copyParams(body);
cv::parallel_for_(cv::Range(0, dst.rows), body);
return true;
}
irr::scene::ISceneNode *CIrrOdeGeomCylinder::clone(irr::scene::ISceneNode* newParent, irr::scene::ISceneManager* newManager) {
CIrrOdeGeomCylinder *pRet=new CIrrOdeGeomCylinder(newParent?newParent:getParent(),newManager?newManager:m_pSceneManager);
copyParams(pRet);
CIrrOdeSceneNode::cloneChildren(pRet,newManager);
return pRet;
}
void doAllInOne(tree *tr, analdef *adef)
{
int i, n, bestIndex, bootstrapsPerformed;
#ifdef _WAYNE_MPI
int
bootStopTests = 1,
j,
bootStrapsPerProcess = 0;
#endif
double loopTime;
int *originalRateCategories;
int *originalInvariant;
#ifdef _WAYNE_MPI
int slowSearches, fastEvery;
#else
int slowSearches, fastEvery = 5;
#endif
int treeVectorLength = -1;
topolRELL_LIST *rl;
double bestLH, mlTime, overallTime;
long radiusSeed = adef->rapidBoot;
FILE *f;
char bestTreeFileName[1024];
hashtable *h = (hashtable*)NULL;
unsigned int **bitVectors = (unsigned int**)NULL;
boolean bootStopIt = FALSE;
double pearsonAverage = 0.0;
pInfo *catParams = allocParams(tr);
pInfo *gammaParams = allocParams(tr);
unsigned int vLength;
n = adef->multipleRuns;
#ifdef _WAYNE_MPI
if(n % processes != 0)
n = processes * ((n / processes) + 1);
#endif
if(adef->bootStopping)
{
h = initHashTable(tr->mxtips * 100);
treeVectorLength = adef->multipleRuns;
bitVectors = initBitVector(tr, &vLength);
}
rl = (topolRELL_LIST *)rax_malloc(sizeof(topolRELL_LIST));
initTL(rl, tr, n);
originalRateCategories = (int*)rax_malloc(tr->cdta->endsite * sizeof(int));
originalInvariant = (int*)rax_malloc(tr->cdta->endsite * sizeof(int));
initModel(tr, tr->rdta, tr->cdta, adef);
if(adef->grouping)
printBothOpen("\n\nThe topologies of all Bootstrap and ML trees will adhere to the constraint tree specified in %s\n", tree_file);
if(adef->constraint)
printBothOpen("\n\nThe topologies of all Bootstrap and ML trees will adhere to the bifurcating backbone constraint tree specified in %s\n", tree_file);
#ifdef _WAYNE_MPI
long parsimonySeed0 = adef->parsimonySeed;
long replicateSeed0 = adef->rapidBoot;
n = n / processes;
#endif
for(i = 0; i < n && !bootStopIt; i++)
{
#ifdef _WAYNE_MPI
j = i + n * processID;
tr->treeID = j;
#else
tr->treeID = i;
#endif
tr->checkPointCounter = 0;
loopTime = gettime();
#ifdef _WAYNE_MPI
if(i == 0)
{
if(parsimonySeed0 != 0)
adef->parsimonySeed = parsimonySeed0 + 10000 * processID;
adef->rapidBoot = replicateSeed0 + 10000 * processID;
radiusSeed = adef->rapidBoot;
}
#endif
if(i % 10 == 0)
{
if(i > 0)
reductionCleanup(tr, originalRateCategories, originalInvariant);
if(adef->grouping || adef->constraint)
{
FILE *f = myfopen(tree_file, "rb");
assert(adef->restart);
if (! treeReadLenMULT(f, tr, adef))
exit(-1);
fclose(f);
}
else
makeParsimonyTree(tr, adef);
tr->likelihood = unlikely;
if(i == 0)
{
double t;
onlyInitrav(tr, tr->start);
treeEvaluate(tr, 1);
t = gettime();
modOpt(tr, adef, FALSE, 5.0);
#ifdef _WAYNE_MPI
printBothOpen("\nTime for BS model parameter optimization on Process %d: %f seconds\n", processID, gettime() - t);
#else
printBothOpen("\nTime for BS model parameter optimization %f\n", gettime() - t);
#endif
memcpy(originalRateCategories, tr->cdta->rateCategory, sizeof(int) * tr->cdta->endsite);
memcpy(originalInvariant, tr->invariant, sizeof(int) * tr->cdta->endsite);
if(adef->bootstrapBranchLengths)
{
if(tr->rateHetModel == CAT)
{
copyParams(tr->NumberOfModels, catParams, tr->partitionData, tr);
assert(tr->cdta->endsite == tr->originalCrunchedLength);
catToGamma(tr, adef);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
copyParams(tr->NumberOfModels, gammaParams, tr->partitionData, tr);
gammaToCat(tr);
copyParams(tr->NumberOfModels, tr->partitionData, catParams, tr);
}
else
{
assert(tr->cdta->endsite == tr->originalCrunchedLength);
}
}
}
}
computeNextReplicate(tr, &adef->rapidBoot, originalRateCategories, originalInvariant, TRUE, TRUE);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1);
computeBOOTRAPID(tr, adef, &radiusSeed);
#ifdef _WAYNE_MPI
saveTL(rl, tr, j);
#else
saveTL(rl, tr, i);
#endif
if(adef->bootstrapBranchLengths)
{
double
lh = tr->likelihood;
if(tr->rateHetModel == CAT)
{
copyParams(tr->NumberOfModels, tr->partitionData, gammaParams, tr);
catToGamma(tr, adef);
resetBranches(tr);
onlyInitrav(tr, tr->start);
treeEvaluate(tr, 2.0);
gammaToCat(tr);
copyParams(tr->NumberOfModels, tr->partitionData, catParams, tr);
tr->likelihood = lh;
}
else
{
treeEvaluate(tr, 2.0);
tr->likelihood = lh;
}
}
printBootstrapResult(tr, adef, TRUE);
loopTime = gettime() - loopTime;
writeInfoFile(adef, tr, loopTime);
if(adef->bootStopping)
#ifdef _WAYNE_MPI
{
int
nn = (i + 1) * processes;
if((nn > START_BSTOP_TEST) &&
(i * processes < FC_SPACING * bootStopTests) &&
((i + 1) * processes >= FC_SPACING * bootStopTests)
)
{
MPI_Barrier(MPI_COMM_WORLD);
concatenateBSFiles(processes, bootstrapFileName);
MPI_Barrier(MPI_COMM_WORLD);
bootStopIt = computeBootStopMPI(tr, bootstrapFileName, adef, &pearsonAverage);
bootStopTests++;
}
}
#else
bootStopIt = bootStop(tr, h, i, &pearsonAverage, bitVectors, treeVectorLength, vLength, adef);
#endif
}
#ifdef _WAYNE_MPI
MPI_Barrier(MPI_COMM_WORLD);
bootstrapsPerformed = i * processes;
bootStrapsPerProcess = i;
concatenateBSFiles(processes, bootstrapFileName);
removeBSFiles(processes, bootstrapFileName);
MPI_Barrier(MPI_COMM_WORLD);
#else
bootstrapsPerformed = i;
#endif
rax_freeParams(tr->NumberOfModels, catParams);
rax_free(catParams);
rax_freeParams(tr->NumberOfModels, gammaParams);
rax_free(gammaParams);
if(adef->bootStopping)
{
freeBitVectors(bitVectors, 2 * tr->mxtips);
rax_free(bitVectors);
freeHashTable(h);
rax_free(h);
}
{
double t;
printBothOpenMPI("\n\n");
if(adef->bootStopping)
{
if(bootStopIt)
{
switch(tr->bootStopCriterion)
{
case FREQUENCY_STOP:
printBothOpenMPI("Stopped Rapid BS search after %d replicates with FC Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("Pearson Average of %d random splits: %f\n",BOOTSTOP_PERMUTATIONS , pearsonAverage);
break;
case MR_STOP:
printBothOpenMPI("Stopped Rapid BS search after %d replicates with MR-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_STOP:
printBothOpenMPI("Stopped Rapid BS search after %d replicates with MRE-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_IGN_STOP:
printBothOpenMPI("Stopped Rapid BS search after %d replicates with MRE_IGN-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
default:
assert(0);
}
}
else
{
switch(tr->bootStopCriterion)
{
case FREQUENCY_STOP:
printBothOpenMPI("Rapid BS search did not converge after %d replicates with FC Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("Pearson Average of %d random splits: %f\n",BOOTSTOP_PERMUTATIONS , pearsonAverage);
break;
case MR_STOP:
printBothOpenMPI("Rapid BS search did not converge after %d replicates with MR-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_STOP:
printBothOpenMPI("Rapid BS search did not converge after %d replicates with MRE-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
case MRE_IGN_STOP:
printBothOpenMPI("Rapid BS search did not converge after %d replicates with MR_IGN-based Bootstopping criterion\n", bootstrapsPerformed);
printBothOpenMPI("WRF Average of %d random splits: %f\n", BOOTSTOP_PERMUTATIONS, pearsonAverage);
break;
default:
assert(0);
}
}
}
t = gettime() - masterTime;
printBothOpenMPI("Overall Time for %d Rapid Bootstraps %f seconds\n", bootstrapsPerformed, t);
printBothOpenMPI("Average Time per Rapid Bootstrap %f seconds\n", (double)(t/((double)bootstrapsPerformed)));
if(!adef->allInOne)
{
printBothOpenMPI("All %d bootstrapped trees written to: %s\n", bootstrapsPerformed, bootstrapFileName);
#ifdef _WAYNE_MPI
MPI_Finalize();
#endif
exit(0);
}
}
/* ML-search */
mlTime = gettime();
double t = mlTime;
printBothOpenMPI("\nStarting ML Search ...\n\n");
/***CLEAN UP reduction stuff */
reductionCleanup(tr, originalRateCategories, originalInvariant);
/****/
#ifdef _WAYNE_MPI
restoreTL(rl, tr, n * processID);
#else
restoreTL(rl, tr, 0);
#endif
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
#ifdef _WAYNE_MPI
if(bootstrapsPerformed <= 100)
fastEvery = 5;
else
fastEvery = bootstrapsPerformed / 20;
for(i = 0; i < bootstrapsPerformed; i++)
rl->t[i]->likelihood = unlikely;
for(i = 0; i < bootStrapsPerProcess; i++)
{
j = i + n * processID;
if(i % fastEvery == 0)
{
restoreTL(rl, tr, j);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1);
optimizeRAPID(tr, adef);
saveTL(rl, tr, j);
}
}
#else
for(i = 0; i < bootstrapsPerformed; i++)
{
rl->t[i]->likelihood = unlikely;
if(i % fastEvery == 0)
{
restoreTL(rl, tr, i);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1);
optimizeRAPID(tr, adef);
saveTL(rl, tr, i);
}
}
#endif
printBothOpenMPI("Fast ML optimization finished\n\n");
t = gettime() - t;
#ifdef _WAYNE_MPI
printBothOpen("Fast ML search on Process %d: Time %f seconds\n\n", processID, t);
j = n * processID;
qsort(&(rl->t[j]), n, sizeof(topolRELL*), compareTopolRell);
restoreTL(rl, tr, j);
#else
printBothOpen("Fast ML search Time: %f seconds\n\n", t);
qsort(&(rl->t[0]), bootstrapsPerformed, sizeof(topolRELL*), compareTopolRell);
restoreTL(rl, tr, 0);
#endif
t = gettime();
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
slowSearches = bootstrapsPerformed / 5;
if(bootstrapsPerformed % 5 != 0)
slowSearches++;
slowSearches = MIN(slowSearches, 10);
#ifdef _WAYNE_MPI
if(processes > 1)
{
if(slowSearches % processes == 0)
slowSearches = slowSearches / processes;
else
slowSearches = (slowSearches / processes) + 1;
}
for(i = 0; i < slowSearches; i++)
{
j = i + n * processID;
restoreTL(rl, tr, j);
rl->t[j]->likelihood = unlikely;
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1.0);
thoroughOptimization(tr, adef, rl, j);
}
#else
for(i = 0; i < slowSearches; i++)
{
restoreTL(rl, tr, i);
rl->t[i]->likelihood = unlikely;
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 1.0);
thoroughOptimization(tr, adef, rl, i);
}
#endif
/*************************************************************************************************************/
if(tr->rateHetModel == CAT)
{
catToGamma(tr, adef);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
}
bestIndex = -1;
bestLH = unlikely;
#ifdef _WAYNE_MPI
for(i = 0; i < slowSearches; i++)
{
j = i + n * processID;
restoreTL(rl, tr, j);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 2);
printBothOpen("Slow ML Search %d Likelihood: %f\n", j, tr->likelihood);
if(tr->likelihood > bestLH)
{
bestLH = tr->likelihood;
bestIndex = j;
}
}
/*printf("processID = %d, bestIndex = %d; bestLH = %f\n", processID, bestIndex, bestLH);*/
#else
for(i = 0; i < slowSearches; i++)
{
restoreTL(rl, tr, i);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 2);
printBothOpen("Slow ML Search %d Likelihood: %f\n", i, tr->likelihood);
if(tr->likelihood > bestLH)
{
bestLH = tr->likelihood;
bestIndex = i;
}
}
#endif
printBothOpenMPI("Slow ML optimization finished\n\n");
t = gettime() - t;
#ifdef _WAYNE_MPI
printBothOpen("Slow ML search on Process %d: Time %f seconds\n", processID, t);
#else
printBothOpen("Slow ML search Time: %f seconds\n", t);
#endif
t = gettime();
restoreTL(rl, tr, bestIndex);
resetBranches(tr);
evaluateGenericInitrav(tr, tr->start);
treeEvaluate(tr, 2);
Thorough = 1;
tr->doCutoff = FALSE;
treeOptimizeThorough(tr, 1, 10);
evaluateGenericInitrav(tr, tr->start);
modOpt(tr, adef, TRUE, adef->likelihoodEpsilon);
t = gettime() - t;
#ifdef _WAYNE_MPI
printBothOpen("Thorough ML search on Process %d: Time %f seconds\n", processID, t);
#else
printBothOpen("Thorough ML search Time: %f seconds\n", t);
#endif
#ifdef _WAYNE_MPI
bestLH = tr->likelihood;
printf("\nprocessID = %d, bestLH = %f\n", processID, bestLH);
if(processes > 1)
{
double *buffer;
int bestProcess;
buffer = (double *)rax_malloc(sizeof(double) * processes);
for(i = 0; i < processes; i++)
buffer[i] = unlikely;
buffer[processID] = bestLH;
for(i = 0; i < processes; i++)
MPI_Bcast(&buffer[i], 1, MPI_DOUBLE, i, MPI_COMM_WORLD);
bestLH = buffer[0];
bestProcess = 0;
for(i = 1; i < processes; i++)
if(buffer[i] > bestLH)
{
bestLH = buffer[i];
bestProcess = i;
}
rax_free(buffer);
if(processID != bestProcess)
{
MPI_Finalize();
exit(0);
}
}
#endif
printBothOpen("\nFinal ML Optimization Likelihood: %f\n", tr->likelihood);
printBothOpen("\nModel Information:\n\n");
printModelParams(tr, adef);
strcpy(bestTreeFileName, workdir);
strcat(bestTreeFileName, "RAxML_bestTree.");
strcat(bestTreeFileName, run_id);
Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, TRUE, adef, SUMMARIZE_LH, FALSE, FALSE, FALSE, FALSE);
f = myfopen(bestTreeFileName, "wb");
fprintf(f, "%s", tr->tree_string);
fclose(f);
if(adef->perGeneBranchLengths)
printTreePerGene(tr, adef, bestTreeFileName, "w");
overallTime = gettime() - masterTime;
mlTime = gettime() - mlTime;
printBothOpen("\nML search took %f secs or %f hours\n", mlTime, mlTime / 3600.0);
printBothOpen("\nCombined Bootstrap and ML search took %f secs or %f hours\n", overallTime, overallTime / 3600.0);
printBothOpen("\nDrawing Bootstrap Support Values on best-scoring ML tree ...\n\n");
freeTL(rl);
rax_free(rl);
calcBipartitions(tr, adef, bestTreeFileName, bootstrapFileName);
overallTime = gettime() - masterTime;
printBothOpen("Program execution info written to %s\n", infoFileName);
printBothOpen("All %d bootstrapped trees written to: %s\n\n", bootstrapsPerformed, bootstrapFileName);
printBothOpen("Best-scoring ML tree written to: %s\n\n", bestTreeFileName);
if(adef->perGeneBranchLengths && tr->NumberOfModels > 1)
printBothOpen("Per-Partition branch lengths of best-scoring ML tree written to %s.PARTITION.0 to %s.PARTITION.%d\n\n", bestTreeFileName, bestTreeFileName,
tr->NumberOfModels - 1);
printBothOpen("Best-scoring ML tree with support values written to: %s\n\n", bipartitionsFileName);
printBothOpen("Best-scoring ML tree with support values as branch labels written to: %s\n\n", bipartitionsFileNameBranchLabels);
printBothOpen("Overall execution time for full ML analysis: %f secs or %f hours or %f days\n\n", overallTime, overallTime/3600.0, overallTime/86400.0);
#ifdef _WAYNE_MPI
MPI_Finalize();
#endif
exit(0);
}
/*
Start the CGI command program. This commences the CGI gateway program. This will be called after content for
form and upload requests (or if "RunHandler" before specified), otherwise it runs before receiving content data.
*/
static void startCgi(HttpQueue *q)
{
HttpRx *rx;
HttpTx *tx;
HttpRoute *route;
HttpConn *conn;
MprCmd *cmd;
Cgi *cgi;
cchar *baseName, **argv, *fileName, **envv;
ssize varCount;
int argc, count;
argv = 0;
argc = 0;
cgi = q->queueData;
conn = q->conn;
rx = conn->rx;
route = rx->route;
tx = conn->tx;
/*
The command uses the conn dispatcher. This serializes all I/O for both the connection and the CGI gateway.
*/
if ((cmd = mprCreateCmd(conn->dispatcher)) == 0) {
return;
}
cgi->cmd = cmd;
if (conn->http->forkCallback) {
cmd->forkCallback = conn->http->forkCallback;
cmd->forkData = conn->http->forkData;
}
argc = 1; /* argv[0] == programName */
buildArgs(conn, cmd, &argc, &argv);
fileName = argv[0];
baseName = mprGetPathBase(fileName);
/*
nph prefix means non-parsed-header. Don't parse the CGI output for a CGI header
*/
if (strncmp(baseName, "nph-", 4) == 0 ||
(strlen(baseName) > 4 && strcmp(&baseName[strlen(baseName) - 4], "-nph") == 0)) {
/* Pretend we've seen the header for Non-parsed Header CGI programs */
cgi->seenHeader = 1;
tx->flags |= HTTP_TX_USE_OWN_HEADERS;
}
/*
Build environment variables
*/
varCount = mprGetHashLength(rx->headers) + mprGetHashLength(rx->svars) + mprGetJsonLength(rx->params);
if ((envv = mprAlloc((varCount + 1) * sizeof(char*))) != 0) {
count = copyParams(conn, envv, 0, rx->params, route->envPrefix);
count = copyVars(conn, envv, count, rx->svars, "");
count = copyVars(conn, envv, count, rx->headers, "HTTP_");
assert(count <= varCount);
}
#if !VXWORKS
/*
This will be ignored on VxWorks because there is only one global current directory for all tasks
*/
mprSetCmdDir(cmd, mprGetPathDir(fileName));
#endif
mprSetCmdCallback(cmd, cgiCallback, cgi);
if (mprStartCmd(cmd, argc, argv, envv, MPR_CMD_IN | MPR_CMD_OUT | MPR_CMD_ERR) < 0) {
httpError(conn, HTTP_CODE_NOT_FOUND, "Cannot run CGI process: %s, URI %s", fileName, rx->uri);
return;
}
#if ME_WIN_LIKE
mprCreateEvent(conn->dispatcher, "cgi-win", 10, waitForCgi, cgi, MPR_EVENT_CONTINUOUS);
#endif
}
irr::scene::ISceneNode *CIrrOdeWorld::clone(irr::scene::ISceneNode* newParent, irr::scene::ISceneManager* newManager) {
CIrrOdeWorld *pWorld=new CIrrOdeWorld(newParent?newParent:getParent(),newManager?newManager:m_pSceneManager);
copyParams(pWorld);
CIrrOdeSceneNode::cloneChildren(pWorld,newManager);
return pWorld;
}