CvLSVMFeatureMap* featureMapBorderPartFilter(CvLSVMFeatureMap *map,
int maxXBorder, int maxYBorder)
{
int bx, by;
int sizeX, sizeY, i, j, k;
CvLSVMFeatureMap *new_map;
computeBorderSize(maxXBorder, maxYBorder, &bx, &by);
sizeX = map->sizeX + 2 * bx;
sizeY = map->sizeY + 2 * by;
allocFeatureMapObject(&new_map, sizeX, sizeY, map->p, map->xp);
for (i = 0; i < sizeX * sizeY * map->p; i++)
{
new_map->Map[i] = 0.0;
}
for (i = by; i < map->sizeY + by; i++)
{
for (j = bx; j < map->sizeX + bx; j++)
{
for (k = 0; k < map->p; k++)
{
new_map->Map[(i * sizeX + j) * map->p + k] =
map->Map[((i - by) * map->sizeX + j - bx) * map->p + k];
}
}
}
return new_map;
}
/*
// Feature map reduction in GPU
// In each cell we reduce dimension of the feature vector
// according to original paper special procedure
//
// API
//int PCAFeatureMapsGPUStream(const int numStep, const int bx, const int by,
CvLSVMFeatureMapGPU **devs_map_in, CvLSVMFeatureMap **feature_maps,
CUstream *streams)
// INPUT
// numStep
// bx
// by
// devs_map_in
// streams
// OUTPUT
// feature_maps
// RESULT
// Error status
*/
int PCAFeatureMapsGPUStream(const int numStep, const int bx, const int by,
CvLSVMFeatureMapGPU **devs_map_in, CvLSVMFeatureMap **feature_maps,
CUstream *streams)
{
int sizeX, sizeY, pp;
int size_map_pca;
int i;
CUresult res;
CvLSVMFeatureMapGPU **devs_map_pca;
pp = NUM_SECTOR * 3 + 4;
devs_map_pca = (CvLSVMFeatureMapGPU **) malloc(
sizeof(CvLSVMFeatureMapGPU*) * (numStep));
// allocate memory
for (i = 0; i < numStep; i++)
{
sizeX = devs_map_in[i]->sizeX + 2 * bx;
sizeY = devs_map_in[i]->sizeY + 2 * by;
size_map_pca = sizeX * sizeY * pp;
allocFeatureMapObject(&feature_maps[i], sizeX, sizeY, pp);
allocFeatureMapObjectGPU<float>(&devs_map_pca[i], sizeX, sizeY, pp);
}
// exucute async
for (i = 0; i < numStep; i++)
{
sizeX = devs_map_pca[i]->sizeX;
sizeY = devs_map_pca[i]->sizeY;
size_map_pca = sizeX * sizeY * pp;
// initilize device memory value of 0
res = cuMemsetD32Async(devs_map_pca[i]->map, 0, size_map_pca,
streams[i]);
CUDA_CHECK(res, "cuMemset(dev_map_pca)");
// launch kernel
PCAFeatureMapsAddNullableBorderGPULaunch(devs_map_in[i],
devs_map_pca[i], bx, by, streams[i]);
}
for (i = 0; i < numStep; i++)
{
sizeX = devs_map_pca[i]->sizeX;
sizeY = devs_map_pca[i]->sizeY;
size_map_pca = sizeX * sizeY * pp;
// copy memory from device to host
res = cuMemcpyDtoHAsync(feature_maps[i]->map, devs_map_pca[i]->map,
sizeof(float) * size_map_pca, streams[i]);
CUDA_CHECK(res, "cuMemcpyDtoH(dev_map_pca)");
}
// free device memory
for (i = 0; i < numStep; i++)
{
freeFeatureMapObjectGPU(&devs_map_pca[i]);
}
free(devs_map_pca);
return LATENT_SVM_OK;
}
/*
// Getting feature map for the selected subimage
//
// API
// int getFeatureMaps(const IplImage * image, const int k, featureMap **map);
// INPUT
// image - selected subimage
// k - size of cells
// OUTPUT
// map - feature map
// RESULT
// Error status
*/
int getFeatureMaps_dp(const IplImage* image,const int k, CvLSVMFeatureMap **map)
{
int sizeX, sizeY;
int p, px, strsz;
int height, width, channels;
int i, j, kk, c, ii, jj, d;
float * datadx, * datady;
float tmp, x, y, tx, ty;
IplImage * dx, * dy;
int *nearest_x, *nearest_y;
float *w, a_x, b_x;
float kernel[3] = {-1.f, 0.f, 1.f};
CvMat kernel_dx = cvMat(1, 3, CV_32F, kernel);
CvMat kernel_dy = cvMat(3, 1, CV_32F, kernel);
float * r;
int * alfa;
float boundary_x[CNTPARTION+1];
float boundary_y[CNTPARTION+1];
float max, tmp_scal;
int maxi;
height = image->height;
width = image->width ;
channels = image->nChannels;
dx = cvCreateImage(cvSize(image->width , image->height) , IPL_DEPTH_32F , 3);
dy = cvCreateImage(cvSize(image->width , image->height) , IPL_DEPTH_32F , 3);
sizeX = width / k;
sizeY = height / k;
px = CNTPARTION + 2 * CNTPARTION; // контрастное и не контрастное изображение
p = px;
strsz = sizeX * p;
allocFeatureMapObject(map, sizeX, sizeY, p, px);
cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));
for(i = 0; i <= CNTPARTION; i++)
{
boundary_x[i] = cosf((((float)i) * (((float)PI) / (float) (CNTPARTION))));
boundary_y[i] = sinf((((float)i) * (((float)PI) / (float) (CNTPARTION))));
}/*for(i = 0; i <= CNTPARTION; i++) */
r = (float *)malloc( sizeof(float) * (width * height));
alfa = (int *)malloc( sizeof(int ) * (width * height * 2));
for(j = 1; j < height-1; j++)
{
datadx = (float*)(dx->imageData + dx->widthStep *j);
datady = (float*)(dy->imageData + dy->widthStep *j);
for(i = 1; i < width-1; i++)
{
c = 0;
x = (datadx[i*channels+c]);
y = (datady[i*channels+c]);
r[j * width + i] =sqrtf(x*x + y*y);
for(kk = 1; kk < channels; kk++)
{
tx = (datadx[i*channels+kk]);
ty = (datady[i*channels+kk]);
tmp =sqrtf(tx*tx + ty*ty);
if(tmp > r[j * width + i])
{
r[j * width + i] = tmp;
c = kk;
x = tx;
y = ty;
}
}/*for(kk = 1; kk < channels; kk++)*/
max = boundary_x[0]*x + boundary_y[0]*y;
maxi = 0;
for (kk = 0; kk < CNTPARTION; kk++) {
tmp_scal = boundary_x[kk]*x + boundary_y[kk]*y;
if (tmp_scal> max) {
max = tmp_scal;
maxi = kk;
}else if (-tmp_scal> max) {
max = -tmp_scal;
maxi = kk + CNTPARTION;
}
}
alfa[j * width * 2 + i * 2 ] = maxi % CNTPARTION;
alfa[j * width * 2 + i * 2 + 1] = maxi;
}/*for(i = 0; i < width; i++)*/
}/*for(j = 0; j < height; j++)*/
//подсчет весов и смещений
nearest_x = (int *)malloc(sizeof(int) * k);
nearest_y = (int *)malloc(sizeof(int) * k);
w = (float*)malloc(sizeof(float) * (k * 2));
for(i = 0; i < k / 2; i++)
{
nearest_x[i] = -1;
nearest_y[i] = -1;
}/*for(i = 0; i < k / 2; i++)*/
for(i = k / 2; i < k; i++)
{
nearest_x[i] = 1;
nearest_y[i] = 1;
}/*for(i = k / 2; i < k; i++)*/
for(j = 0; j < k / 2; j++)
{
b_x = k / 2 + j + 0.5f;
a_x = k / 2 - j - 0.5f;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = 0; j < k / 2; j++)*/
for(j = k / 2; j < k; j++)
{
a_x = j - k / 2 + 0.5f;
b_x =-j + k / 2 - 0.5f + k;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = k / 2; j < k; j++)*/
//интерпол¤ци¤
for(i = 0; i < sizeY; i++)
{
for(j = 0; j < sizeX; j++)
{
for(ii = 0; ii < k; ii++)
{
for(jj = 0; jj < k; jj++)
{
if ((i * k + ii > 0) && (i * k + ii < height - 1) && (j * k + jj > 0) && (j * k + jj < width - 1))
{
d = (k*i + ii)* width + (j*k + jj);
(*map)->Map[(i ) * strsz + (j ) * (*map)->p + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 ] * w[jj * 2 ];
(*map)->Map[(i ) * strsz + (j ) * (*map)->p + alfa[d * 2 + 1] + CNTPARTION] +=
r[d] * w[ii * 2 ] * w[jj * 2 ];
if ((i + nearest_y[ii] >= 0) && (i + nearest_y[ii] <= sizeY - 1))
{
(*map)->Map[(i + nearest_y[ii]) * strsz + (j ) * (*map)->p + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
(*map)->Map[(i + nearest_y[ii]) * strsz + (j ) * (*map)->p + alfa[d * 2 + 1] + CNTPARTION] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
}
if ((j + nearest_x[jj] >= 0) && (j + nearest_x[jj] <= sizeX - 1))
{
(*map)->Map[(i ) * strsz + (j + nearest_x[jj]) * (*map)->p + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 ] * w[jj * 2 + 1];
(*map)->Map[(i ) * strsz + (j + nearest_x[jj]) * (*map)->p + alfa[d * 2 + 1] + CNTPARTION] +=
r[d] * w[ii * 2 ] * w[jj * 2 + 1];
}
if ((i + nearest_y[ii] >= 0) && (i + nearest_y[ii] <= sizeY - 1) && (j + nearest_x[jj] >= 0) && (j + nearest_x[jj] <= sizeX - 1))
{
(*map)->Map[(i + nearest_y[ii]) * strsz + (j + nearest_x[jj]) * (*map)->p + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
(*map)->Map[(i + nearest_y[ii]) * strsz + (j + nearest_x[jj]) * (*map)->p + alfa[d * 2 + 1] + CNTPARTION] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
}
}
}/*for(jj = 0; jj < k; jj++)*/
}/*for(ii = 0; ii < k; ii++)*/
}/*for(j = 1; j < sizeX - 1; j++)*/
}/*for(i = 1; i < sizeY - 1; i++)*/
cvReleaseImage(&dx);
cvReleaseImage(&dy);
free(w);
free(nearest_x);
free(nearest_y);
free(r);
free(alfa);
return LATENT_SVM_OK;
}
/*
// Getting feature map for the selected subimage
//
// API
// int getFeatureMaps(const IplImage * image, const int k, featureMap **map);
// INPUT
// image - selected subimage
// k - size of cells
// OUTPUT
// map - feature map
// RESULT
// Error status
*/
int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
{
int sizeX, sizeY;
int p, px, stringSize;
int height, width, numChannels;
int i, j, kk, c, ii, jj, d;
float * datadx, * datady;
int ch;
float magnitude, x, y, tx, ty;
IplImage * dx, * dy;
int *nearest;
float *w, a_x, b_x;
float kernel[3] = {-1.f, 0.f, 1.f};
CvMat kernel_dx = cvMat(1, 3, CV_32F, kernel);
CvMat kernel_dy = cvMat(3, 1, CV_32F, kernel);
float * r;
int * alfa;
float boundary_x[NUM_SECTOR + 1];
float boundary_y[NUM_SECTOR + 1];
float max, dotProd;
int maxi;
height = image->height;
width = image->width ;
numChannels = image->nChannels;
dx = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
dy = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
sizeX = width / k;
sizeY = height / k;
px = 3 * NUM_SECTOR;
p = px;
stringSize = sizeX * p;
allocFeatureMapObject(map, sizeX, sizeY, p);
cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));
float arg_vector;
for(i = 0; i <= NUM_SECTOR; i++)
{
arg_vector = ( (float) i ) * ( (float)(PI) / (float)(NUM_SECTOR) );
boundary_x[i] = cosf(arg_vector);
boundary_y[i] = sinf(arg_vector);
}/*for(i = 0; i <= NUM_SECTOR; i++) */
r = (float *)malloc( sizeof(float) * (width * height));
alfa = (int *)malloc( sizeof(int ) * (width * height * 2));
for(j = 1; j < height - 1; j++)
{
datadx = (float*)(dx->imageData + dx->widthStep * j);
datady = (float*)(dy->imageData + dy->widthStep * j);
for(i = 1; i < width - 1; i++)
{
c = 0;
x = (datadx[i * numChannels + c]);
y = (datady[i * numChannels + c]);
r[j * width + i] =sqrtf(x * x + y * y);
for(ch = 1; ch < numChannels; ch++)
{
tx = (datadx[i * numChannels + ch]);
ty = (datady[i * numChannels + ch]);
magnitude = sqrtf(tx * tx + ty * ty);
if(magnitude > r[j * width + i])
{
r[j * width + i] = magnitude;
c = ch;
x = tx;
y = ty;
}
}/*for(ch = 1; ch < numChannels; ch++)*/
max = boundary_x[0] * x + boundary_y[0] * y;
maxi = 0;
for (kk = 0; kk < NUM_SECTOR; kk++)
{
dotProd = boundary_x[kk] * x + boundary_y[kk] * y;
if (dotProd > max)
{
max = dotProd;
maxi = kk;
}
else
{
if (-dotProd > max)
{
max = -dotProd;
maxi = kk + NUM_SECTOR;
}
}
}
alfa[j * width * 2 + i * 2 ] = maxi % NUM_SECTOR;
alfa[j * width * 2 + i * 2 + 1] = maxi;
}/*for(i = 0; i < width; i++)*/
}/*for(j = 0; j < height; j++)*/
nearest = (int *)malloc(sizeof(int ) * k);
w = (float*)malloc(sizeof(float) * (k * 2));
for(i = 0; i < k / 2; i++)
{
nearest[i] = -1;
}/*for(i = 0; i < k / 2; i++)*/
for(i = k / 2; i < k; i++)
{
nearest[i] = 1;
}/*for(i = k / 2; i < k; i++)*/
for(j = 0; j < k / 2; j++)
{
b_x = k / 2 + j + 0.5f;
a_x = k / 2 - j - 0.5f;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = 0; j < k / 2; j++)*/
for(j = k / 2; j < k; j++)
{
a_x = j - k / 2 + 0.5f;
b_x =-j + k / 2 - 0.5f + k;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = k / 2; j < k; j++)*/
for(i = 0; i < sizeY; i++)
{
for(j = 0; j < sizeX; j++)
{
for(ii = 0; ii < k; ii++)
{
for(jj = 0; jj < k; jj++)
{
if ((i * k + ii > 0) &&
(i * k + ii < height - 1) &&
(j * k + jj > 0) &&
(j * k + jj < width - 1))
{
d = (k * i + ii) * width + (j * k + jj);
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] +=
r[d] * w[ii * 2] * w[jj * 2];
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2] * w[jj * 2];
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1))
{
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
}
if ((j + nearest[jj] >= 0) &&
(j + nearest[jj] <= sizeX - 1))
{
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2] * w[jj * 2 + 1];
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2] * w[jj * 2 + 1];
}
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1) &&
(j + nearest[jj] >= 0) &&
(j + nearest[jj] <= sizeX - 1))
{
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
}
}
}/*for(jj = 0; jj < k; jj++)*/
}/*for(ii = 0; ii < k; ii++)*/
}/*for(j = 1; j < sizeX - 1; j++)*/
}/*for(i = 1; i < sizeY - 1; i++)*/
cvReleaseImage(&dx);
cvReleaseImage(&dy);
free(w);
free(nearest);
free(r);
free(alfa);
return LATENT_SVM_OK;
}
