/*
// Getting feature pyramid
//
// API
// int getFeaturePyramid(IplImage * image, const filterObject **all_F,
const int n_f,
const int lambda, const int k,
const int startX, const int startY,
const int W, const int H, featurePyramid **maps);
// INPUT
// image - image
// OUTPUT
// maps - feature maps for all levels
// RESULT
// Error status
*/
int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps)
{
IplImage *imgResize;
float step;
int numStep;
int maxNumCells;
int W, H;
if(image->depth == IPL_DEPTH_32F)
{
imgResize = image;
}
else
{
imgResize = cvCreateImage(cvSize(image->width , image->height) ,
IPL_DEPTH_32F , 3);
cvConvert(image, imgResize);
}
W = imgResize->width;
H = imgResize->height;
step = powf(2.0f, 1.0f / ((float)LAMBDA));
maxNumCells = W / SIDE_LENGTH;
if( maxNumCells > H / SIDE_LENGTH )
{
maxNumCells = H / SIDE_LENGTH;
}
numStep = (int)(logf((float) maxNumCells / (5.0f)) / logf( step )) + 1;
allocFeaturePyramidObject(maps, numStep + LAMBDA);
getPathOfFeaturePyramid(imgResize, step , LAMBDA, 0,
SIDE_LENGTH / 2, maps);
getPathOfFeaturePyramid(imgResize, step, numStep, LAMBDA,
SIDE_LENGTH , maps);
if(image->depth != IPL_DEPTH_32F)
{
cvReleaseImage(&imgResize);
}
return LATENT_SVM_OK;
}
int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps,
const int bx, const int by)
{
IplImage *imgResize;
float step;
unsigned int numStep;
unsigned int maxNumCells;
unsigned int W, H;
if (image->depth == IPL_DEPTH_32F)
{
imgResize = image;
}
else
{
imgResize = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
cvConvert(image, imgResize);
}
W = imgResize->width;
H = imgResize->height;
step = powf(2.0f, 1.0f / ((float) Lambda));
maxNumCells = W / Side_Length;
if (maxNumCells > H / Side_Length)
{
maxNumCells = H / Side_Length;
}
numStep = (int) (logf((float) maxNumCells / (5.0f)) / logf(step)) + 1;
allocFeaturePyramidObject(maps, numStep + Lambda);
#ifdef PROFILE
TickMeter tm;
tm.start();
cout << "(featurepyramid.cpp)getPathOfFeaturePyramid START " << endl;
#endif
uploadImageToGPU1D(imgResize);
getPathOfFeaturePyramidGPUStream(imgResize, step , Lambda, 0,
Side_Length / 2, bx, by, maps);
getPathOfFeaturePyramidGPUStream(imgResize, step, numStep, Lambda,
Side_Length , bx, by, maps);
cleanImageFromGPU1D();
#ifdef PROFILE
tm.stop();
cout << "(featurepyramid.cpp)getPathOfFeaturePyramid END time = "
<< tm.getTimeSec() << " sec" << endl;
#endif
if (image->depth != IPL_DEPTH_32F)
{
cvReleaseImage(&imgResize);
}
return LATENT_SVM_OK;
}
/*
// Getting feature pyramid
//
// API
// int getFeaturePyramid(IplImage * image, const filterObject **all_F,
const int n_f,
const int lambda, const int k,
const int startX, const int startY,
const int W, const int H, featurePyramid **maps);
// INPUT
// image - image
// lambda - resize scale
// k - size of cells
// startX - X coordinate of the image rectangle to search
// startY - Y coordinate of the image rectangle to search
// W - width of the image rectangle to search
// H - height of the image rectangle to search
// OUTPUT
// maps - feature maps for all levels
// RESULT
// Error status
*/
int getFeaturePyramid(IplImage * image,
const int lambda, const int k,
const int startX, const int startY,
const int W, const int H, CvLSVMFeaturePyramid **maps)
{
IplImage *img2, *imgTmp, *imgResize;
float step, tmp;
int cntStep;
int maxcall;
int i;
int err;
CvLSVMFeatureMap *map;
//geting subimage
cvSetImageROI(image, cvRect(startX, startY, W, H));
img2 = cvCreateImage(cvGetSize(image), image->depth, image->nChannels);
cvCopy(image, img2, NULL);
cvResetImageROI(image);
if(img2->depth != IPL_DEPTH_32F)
{
imgResize = cvCreateImage(cvSize(img2->width , img2->height) , IPL_DEPTH_32F , 3);
cvConvert(img2, imgResize);
}
else
{
imgResize = img2;
}
step = powf(2.0f, 1.0f/ ((float)lambda));
maxcall = W/k;
if( maxcall > H/k )
{
maxcall = H/k;
}
cntStep = (int)(logf((float)maxcall/(5.0f))/logf(step)) + 1;
//printf("Count step: %f %d\n", step, cntStep);
allocFeaturePyramidObject(maps, lambda, cntStep + lambda);
for(i = 0; i < lambda; i++)
{
tmp = 1.0f / powf(step, (float)i);
imgTmp = resize_opencv (imgResize, tmp);
//imgTmp = resize_article_dp(img2, tmp, 4);
err = getFeatureMaps_dp(imgTmp, 4, &map);
err = normalizationAndTruncationFeatureMaps(map, 0.2f);
err = PCAFeatureMaps(map);
(*maps)->pyramid[i] = map;
//printf("%d, %d\n", map->sizeY, map->sizeX);
cvReleaseImage(&imgTmp);
}
/**********************************one**************/
for(i = 0; i < cntStep; i++)
{
tmp = 1.0f / powf(step, (float)i);
imgTmp = resize_opencv (imgResize, tmp);
//imgTmp = resize_article_dp(imgResize, tmp, 8);
err = getFeatureMaps_dp(imgTmp, 8, &map);
err = normalizationAndTruncationFeatureMaps(map, 0.2f);
err = PCAFeatureMaps(map);
(*maps)->pyramid[i + lambda] = map;
//printf("%d, %d\n", map->sizeY, map->sizeX);
cvReleaseImage(&imgTmp);
}/*for(i = 0; i < cntStep; i++)*/
if(img2->depth != IPL_DEPTH_32F)
{
cvReleaseImage(&imgResize);
}
cvReleaseImage(&img2);
return LATENT_SVM_OK;
}
