int main(const int argc, const char **argv)
{
MYLOGVERB = LOG_INFO;
mgr = new ModelManager("Test ObjRec");
nub::soft_ref<SimEventQueueConfigurator>
seqc(new SimEventQueueConfigurator(*mgr));
mgr->addSubComponent(seqc);
//our brain
nub::ref<StdBrain> brain(new StdBrain(*mgr));
mgr->addSubComponent(brain);
mgr->exportOptions(MC_RECURSE);
mgr->setOptionValString(&OPT_VisualCortexType, "IOC");
//mgr.setOptionValString(&OPT_VisualCortexType, "I");
//mgr->setOptionValString(&OPT_VisualCortexType, "GNO");
//mgr.setOptionValString(&OPT_VisualCortexType, "N");
//manager.setOptionValString(&OPT_UseOlderVersion, "false");
// set the FOA and fovea radii
mgr->setOptionValString(&OPT_SaliencyMapType, "Fast");
mgr->setOptionValString(&OPT_SMfastInputCoeff, "1");
mgr->setOptionValString(&OPT_WinnerTakeAllType, "Fast");
mgr->setOptionValString(&OPT_SimulationTimeStep, "0.2");
mgr->setModelParamVal("FOAradius", 50, MC_RECURSE);
mgr->setModelParamVal("FoveaRadius", 50, MC_RECURSE);
mgr->setOptionValString(&OPT_IORtype, "Disc");
if (mgr->parseCommandLine(
(const int)argc, (const char**)argv, "<Network file> <server ip>", 2, 2) == false)
return 1;
// catch signals and redirect them to terminate for clean exit:
signal(SIGHUP, terminateProc); signal(SIGINT, terminateProc);
signal(SIGQUIT, terminateProc); signal(SIGTERM, terminateProc);
signal(SIGALRM, terminateProc);
mgr->start();
ComplexChannel *cc =
&*dynCastWeak<ComplexChannel>(brain->getVC());
//Get a new descriptor vector
DescriptorVec descVec(*mgr, "Descriptor Vector", "DecscriptorVec", cc);
//Get new classifier
Bayes bayesNet(descVec.getFVSize(), 0);
//get command line options
const char *bayesNetFile = mgr->getExtraArg(0).c_str();
const char *server_ip = mgr->getExtraArg(1).c_str();
bool train = false;
int foveaRadius = mgr->getModelParamVal<int>("FoveaRadius", MC_RECURSE);
printf("Setting fovea to %i, train = %i\n", foveaRadius, train);
//load the network if testing
//if (!train)
bayesNet.load(bayesNetFile);
descVec.setFoveaSize(foveaRadius);
xwin = new XWinManaged(Dims(256,256),
-1, -1, "ILab Robot Head Demo");
server = nv2_label_server_create(9930,
server_ip,
9931);
nv2_label_server_set_verbosity(server,1); //allow warnings
int send_interval = 1;
while(!terminate)
{
double prob = 0, statSig = 0;
Point2D clickLoc = xwin->getLastMouseClick();
if (clickLoc.isValid())
train = !train;
struct nv2_image_patch p;
const enum nv2_image_patch_result res =
nv2_label_server_get_current_patch(server, &p);
std::string objName = "nomatch";
if (res == NV2_IMAGE_PATCH_END)
{
fprintf(stdout, "ok, quitting\n");
break;
}
else if (res == NV2_IMAGE_PATCH_NONE)
{
usleep(10000);
continue;
}
else if (res == NV2_IMAGE_PATCH_VALID &&
p.type == NV2_PIXEL_TYPE_RGB24)
{
printf("Valid patch %s %ix%i\n", p.training_label,
p.width, p.height);
//showimg
Image<PixRGB<byte> > img(p.width, p.height, NO_INIT);
// unsigned char *imgPtr = const_cast<unsigned char*>
// (reinterpret_cast<const unsigned char*>(img.getArrayPtr()));
memcpy(img.getArrayPtr(), p.data, p.width*p.height*3);
Image<PixRGB<byte> > objImg = rescale(img, 256, 256);
int cls = classifyImage(objImg, descVec, bayesNet, &prob, &statSig);
if (cls != -1 && prob > -150)
objName = bayesNet.getClassName(cls);
else
objName = "nomatch";
printf("This is %s: Class %i prob %f\n",
objName.c_str(), cls, prob);
// if (strcmp(p.training_label, "none") != 0 &&
// false) { //training
if (cls == -1)
{
printf("Can you tell me what this is?\n");
std::getline(std::cin, objName);
learnImage(objImg, 0, descVec, bayesNet, objName.c_str());
bayesNet.save(bayesNetFile);
} else {
printf("Is this a %s?\n", objName.c_str());
if (train)
{
std::string tmp;
std::getline(std::cin, tmp);
if (tmp != "")
objName = tmp;
LINFO("Learning %s\n", objName.c_str());
fflush(stdout);
learnImage(objImg, 0, descVec, bayesNet, objName.c_str());
bayesNet.save(bayesNetFile);
}
}
}
if (objName != "nomatch")
{
printf("Object is %s\n", objName.c_str());
struct nv2_patch_label l;
l.protocol_version = NV2_LABEL_PROTOCOL_VERSION;
l.patch_id = p.id;
snprintf(l.source, sizeof(l.source), "%s",
"ObjRec");
snprintf(l.name, sizeof(l.name), "%s", // (%ux%u #%u)",
objName.c_str());
//(unsigned int) p.width,
//(unsigned int) p.height,
//(unsigned int) p.id);
snprintf(l.extra_info, sizeof(l.extra_info),
"%i", (int)statSig);
if (l.patch_id % send_interval == 0)
{
nv2_label_server_send_label(server, &l);
fprintf(stdout, "sent label '%s (%s)'\n",
l.name, l.extra_info);
}
else
{
fprintf(stdout, "DROPPED label '%s (%s)'\n",
l.name, l.extra_info);
}
}
nv2_image_patch_destroy(&p);
}
nv2_label_server_destroy(server);
}
int runFilter(int argc, char *argv[])
{
char * filenameInput=argv[1];
char * filenameOutput=argv[2];
unsigned int inputType = guessFilenameTypeStupid(filenameInput);
struct Image * inputImage = readImage(filenameInput,inputType,0);
struct Image * outputImage = 0; //This will get allocated when and if needed
if (inputImage!=0)
{
unsigned int outputType = guessFilenameTypeStupid(filenameOutput);
unsigned int i=0;
for (i=0; i<argc; i++)
{
if ( strcmp(argv[i],"--learn")==0 )
{
destroyImage(inputImage);
learnImage(filenameInput,atoi(argv[i+1]),atoi(argv[i+2]));
exit(0);
} else
if ( strcmp(argv[i],"--rgbcube")==0 )
{
unsigned int dim=32;
unsigned char R = (char) atoi(argv[i]+1);
unsigned char G = (char) atoi(argv[i]+2);
unsigned char B = (char) atoi(argv[i]+3);
outputImage = createImage( dim , dim , 3 , 8 );
bitbltColorRGB(outputImage->pixels , 0 , 0 , dim , dim , R , G , B , dim-1 , dim-1);
writeImageFile(outputImage,PPM_CODEC ,"new_mX.pnm");
writeImageFile(outputImage,PPM_CODEC ,"new_pX.pnm");
writeImageFile(outputImage,PPM_CODEC ,"new_mY.pnm");
writeImageFile(outputImage,PPM_CODEC ,"new_pY.pnm");
writeImageFile(outputImage,PPM_CODEC ,"new_mZ.pnm");
writeImageFile(outputImage,PPM_CODEC ,"new_pZ.pnm");
destroyImage(outputImage);
} else
if ( strcmp(argv[i],"--envcube")==0 )
{
fprintf(stdout,"Converting Environment Cube \n");
unsigned int outputType = guessFilenameTypeStupid(filenameOutput);
//outputImage = createSameDimensionsImage(inputImage);
unsigned int outputWidth = inputImage->width;
unsigned int outputHeight = (unsigned int ) (3*inputImage->width)/4;
outputImage = createImage( outputWidth , outputHeight , 3 , 8 );
createCubeMapFace( outputImage->pixels , outputImage->width , outputImage->height , outputImage->channels , outputImage->bitsperpixel ,
inputImage->pixels , inputImage->width , inputImage->height , inputImage->channels , inputImage->bitsperpixel
);
struct Image * partImg=0;
unsigned int outX=0 , outY=0 , outWidth=0 , outHeight=0;
getCubeMap2DCoords(outputWidth,outputHeight, /*x*/ -1 , /*y*/ 0 , /*z*/ 0 , &outX , &outY , &outWidth , &outHeight );
partImg=createImageBitBlt( outputImage , outX , outY , outWidth , outHeight );
writeImageFile(partImg,PPM_CODEC ,"new_mX.pnm"); destroyImage(partImg);
getCubeMap2DCoords(outputWidth,outputHeight, /*x*/ 1 , /*y*/ 0 , /*z*/ 0 , &outX , &outY , &outWidth , &outHeight );
partImg=createImageBitBlt( outputImage , outX , outY , outWidth , outHeight );
writeImageFile(partImg,PPM_CODEC ,"new_pX.pnm"); destroyImage(partImg);
getCubeMap2DCoords(outputWidth,outputHeight, /*x*/ 0 , /*y*/ -1 , /*z*/ 0 , &outX , &outY , &outWidth , &outHeight );
partImg=createImageBitBlt( outputImage , outX , outY , outWidth , outHeight );
writeImageFile(partImg,PPM_CODEC ,"new_mY.pnm"); destroyImage(partImg);
getCubeMap2DCoords(outputWidth,outputHeight, /*x*/ 0 , /*y*/ 1 , /*z*/ 0 , &outX , &outY , &outWidth , &outHeight );
partImg=createImageBitBlt( outputImage , outX , outY , outWidth , outHeight );
writeImageFile(partImg,PPM_CODEC ,"new_pY.pnm"); destroyImage(partImg);
getCubeMap2DCoords(outputWidth,outputHeight, /*x*/ 0 , /*y*/ 0 , /*z*/ -1 , &outX , &outY , &outWidth , &outHeight );
partImg=createImageBitBlt( outputImage , outX , outY , outWidth , outHeight );
writeImageFile(partImg,PPM_CODEC ,"new_mZ.pnm"); destroyImage(partImg);
getCubeMap2DCoords(outputWidth,outputHeight, /*x*/ 0 , /*y*/ 0 , /*z*/ 1 , &outX , &outY , &outWidth , &outHeight );
partImg=createImageBitBlt( outputImage , outX , outY , outWidth , outHeight );
writeImageFile(partImg,PPM_CODEC ,"new_pZ.pnm"); destroyImage(partImg);
} else
if ( strcmp(argv[i],"--compare")==0 )
{
unsigned int outputType = guessFilenameTypeStupid(filenameOutput);
outputImage = readImage(filenameOutput,outputType ,0);
float noise = calculatePSNR( outputImage->pixels , outputImage->width , outputImage->height , outputImage->channels ,
inputImage->pixels , inputImage->width , inputImage->height , inputImage->channels );
fprintf(stdout,"Compared Detected Noise is %0.4f dB \n",noise);
exit(0);
} else
if ( strcmp(argv[i],"--gaussian")==0 )
{
monochrome(inputImage);
outputImage = createSameDimensionsImage(inputImage);
unsigned int normalizeGaussianKernel=1;
unsigned int kernelWidth=5;
unsigned int kernelHeight=5;
float * convolutionMatrix=allocateGaussianKernel(kernelWidth,kernelHeight,normalizeGaussianKernel);
float divisor=1.0;
float * inF = copyUCharImage2Float(inputImage->pixels , inputImage->width , inputImage->height , inputImage->channels );
float * outF = (float*) malloc(sizeof(float) * outputImage->width * outputImage->height * outputImage->channels );
convolutionFilter1ChF(
outF , outputImage->width , outputImage->height ,
inF, inputImage->width , inputImage->height ,
convolutionMatrix , kernelWidth , kernelHeight , &divisor
);
free(convolutionMatrix);
castFloatImage2UChar(outputImage->pixels, outF, outputImage->width , outputImage->height , outputImage->channels );
free(inF);
free(outF);
} else
if ( strcmp(argv[i],"--ctbilateral")==0 )
{
monochrome(inputImage);
outputImage = createSameDimensionsImage(inputImage);
float sigma = atof(argv[i+1]);
constantTimeBilateralFilter(
inputImage->pixels , inputImage->width , inputImage->height , inputImage->channels ,
outputImage->pixels , outputImage->width , outputImage->height
,&sigma //sigma
,atoi(argv[i+2]) //bins
,atoi(argv[i+3]) //useDeriche
);
} else
if ( strcmp(argv[i],"--deriche")==0 )
{
monochrome(inputImage);
outputImage = createSameDimensionsImage(inputImage);
float sigma = atof(argv[i+1]);
dericheRecursiveGaussianGray( outputImage->pixels , outputImage->width , outputImage->height , inputImage->channels ,
inputImage->pixels , inputImage->width , inputImage->height ,
&sigma , atoi(argv[i+2])
);
} else
if ( strcmp(argv[i],"--dericheF")==0 )
{
fprintf(stderr,"This is a test call for casting code , this shouldnt be normally used..\n");
monochrome(inputImage);
outputImage = createSameDimensionsImage(inputImage);
float sigma = atof(argv[i+1]);
//outputImage = copyImage(inputImage);
float * inF = copyUCharImage2Float(inputImage->pixels , inputImage->width , inputImage->height , inputImage->channels );
float * outF = (float*) malloc(sizeof(float) * outputImage->width * outputImage->height * outputImage->channels );
dericheRecursiveGaussianGrayF( outF , outputImage->width , outputImage->height , inputImage->channels ,
inF , inputImage->width , inputImage->height ,
&sigma , atoi(argv[i+2])
);
castFloatImage2UChar(outputImage->pixels, outF, outputImage->width , outputImage->height , outputImage->channels );
free(inF);
free(outF);
} else
if ( strcmp(argv[i],"--median")==0 )
{
outputImage = copyImage(inputImage);
medianFilter3ch(
outputImage->pixels , outputImage->width , outputImage->height ,
inputImage->pixels , inputImage->width , inputImage->height ,
atoi(argv[i+1]) , atoi(argv[i+2])
);
} else
if ( strcmp(argv[i],"--meansat")==0 )
{
outputImage = copyImage(inputImage);
meanFilterSAT(
outputImage->pixels , outputImage->width , outputImage->height , outputImage->channels ,
inputImage->pixels , inputImage->width , inputImage->height , inputImage->channels ,
atoi(argv[i+1]) , atoi(argv[i+2])
);
} else
if ( strcmp(argv[i],"--monochrome")==0 )
{
outputImage = copyImage(inputImage);
monochrome(outputImage);
} else
if ( strcmp(argv[i],"--bilateral")==0 )
{
outputImage = copyImage(inputImage);
bilateralFilter( outputImage->pixels , outputImage->width , outputImage->height ,
inputImage->pixels , inputImage->width , inputImage->height ,
atof(argv[i+1]) , atof(argv[i+2]) , atoi(argv[i+3])
);
} else
if ( strcmp(argv[i],"--contrast")==0 )
{
outputImage = copyImage(inputImage);
contrast(outputImage,atof(argv[i+1]));
} else
if ( strcmp(argv[i],"--sattest")==0 )
{
float * tmp = allocateGaussianKernel(3,5.0,1);
if (tmp!=0) { free(tmp); }
tmp = allocateGaussianKernel(9,5.0,1);
if (tmp!=0) { free(tmp); }
tmp = allocateGaussianKernel(15,5.0,1);
if (tmp!=0) { free(tmp); }
summedAreaTableTest();
unsigned int * integralImageOutput = 0;
integralImageOutput = generateSummedAreaTableRGB(inputImage->pixels , inputImage->width , inputImage->height);
if (integralImageOutput!=0)
{
free(integralImageOutput);
fprintf(stderr,"integralImage test was successful\n");
}
}
}
writeImageFile(outputImage,outputType ,filenameOutput);
destroyImage(outputImage);
destroyImage(inputImage);
return 1;
}
return 0;
}
