static int getPathOfFeaturePyramid(IplImage * image,
float step, int numStep, int startIndex,
int sideLength, CvLSVMFeaturePyramid **maps)
{
CvLSVMFeatureMap *map;
IplImage *scaleTmp;
float scale;
int i;
for(i = 0; i < numStep; i++)
{
scale = 1.0f / powf(step, (float)i);
scaleTmp = resize_opencv (image, scale);
getFeatureMaps(scaleTmp, sideLength, &map);
normalizeAndTruncate(map, VAL_OF_TRUNCATE);
PCAFeatureMaps(map);
(*maps)->pyramid[startIndex + i] = map;
cvReleaseImage(&scaleTmp);
}/*for(i = 0; i < numStep; i++)*/
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;
}
