void pkmSIFTFlow::computeFlow(Mat image1, Mat image2, int nchannels)
{
#ifdef _DEBUG
assert(image1.cols == image2.cols &&
image1.rows == image2.rows);
#endif
width = image1.cols;
height = image1.rows;
sift1_level1 = image1;
sift2_level1 = image2;
pyrDown(sift1_level1, sift1_level2, cv::Size(width/2, height/2));
pyrDown(sift2_level1, sift2_level2, cv::Size(width/2, height/2));
pyrDown(sift1_level2, sift1_level3, cv::Size(width/4, height/4));
pyrDown(sift2_level2, sift2_level3, cv::Size(width/4, height/4));
pyrDown(sift1_level3, sift1_level4, cv::Size(width/8, height/8));
pyrDown(sift2_level3, sift2_level4, cv::Size(width/8, height/8));
if (!sift1_level3.isContinuous()) {
printf("[ERROR] Matrix not continuous!!!\n");
}
if (!sift2_level3.isContinuous()) {
printf("[ERROR] Matrix not continuous!!!\n");
}
if (!sift1_level2.isContinuous()) {
printf("[ERROR] Matrix not continuous!!!\n");
}
if (!sift2_level2.isContinuous()) {
printf("[ERROR] Matrix not continuous!!!\n");
}
if (!sift1_level1.isContinuous()) {
printf("[ERROR] Matrix not continuous!!!\n");
}
if (!sift2_level1.isContinuous()) {
printf("[ERROR] Matrix not continuous!!!\n");
}
bpflow.LoadImages(width, height, nchannels, sift1_level4.ptr<unsigned char>(0), sift2_level4.ptr<unsigned char>(0));
bpflow.setPara(alpha, d);
// first assume homogeneous setup
bpflow.setHomogeneousMRF(wsize);
// level 4
vx_level4 = Mat::zeros(height/8, width/8, CV_32FC1);
vy_level4 = Mat::zeros(height/8, width/8, CV_32FC1);
bpflow.LoadOffset(vx_level4.ptr(0), vy_level4.ptr(0));
bpflow.LoadWinSize(winSizeX_level4.ptr(0), winSizeY_level4.ptr(0));
bpflow.ComputeDataTerm();
bpflow.ComputeRangeTerm(gamma);
bpflow.MessagePassing(nIterations, 2, pEnergyList);
bpflow.ComputeVelocity();
{
Mat flow(height/8, width/8, CV_32FC2, bpflow.flow().pData);
vector<Mat> flows;
split(flow, flows);
vx_level4 = flows[0];
vy_level4 = flows[1];
}
pyrUp(vx_level4, vx_level3, cv::Size(width/4, height/4));
pyrUp(vy_level4, vy_level3, cv::Size(width/4, height/4));
// level 3
bpflow.LoadOffset(vx_level3.ptr(0), vy_level3.ptr(0));
bpflow.LoadWinSize(winSizeX_level3.ptr(0), winSizeY_level3.ptr(0));
bpflow.ComputeDataTerm();
bpflow.ComputeRangeTerm(gamma);
bpflow.MessagePassing(nIterations, 2, pEnergyList);
bpflow.ComputeVelocity();
{
Mat flow(height/4, width/4, CV_32FC2, bpflow.flow().pData);
vector<Mat> flows;
split(flow, flows);
vx_level3 = flows[0];
vy_level3 = flows[1];
}
pyrUp(vx_level3, vx_level2, cv::Size(width/2, height/2));
pyrUp(vy_level3, vy_level2, cv::Size(width/2, height/2));
// level 2
bpflow.LoadOffset(vx_level2.ptr(0), vy_level2.ptr(0));
bpflow.LoadWinSize(winSizeX_level2.ptr(0), winSizeY_level2.ptr(0));
bpflow.ComputeDataTerm();
bpflow.ComputeRangeTerm(gamma);
bpflow.MessagePassing(nIterations, 2, pEnergyList);
bpflow.ComputeVelocity();
{
Mat flow(height/2, width/2, CV_32FC2, bpflow.flow().pData);
vector<Mat> flows;
split(flow, flows);
vx_level2 = flows[0];
vy_level2 = flows[1];
}
pyrUp(vx_level2, vx_level1, cv::Size(width, height));
pyrUp(vy_level2, vy_level1, cv::Size(width, height));
// level 1
bpflow.LoadOffset(vx_level1.ptr(0), vy_level1.ptr(0));
bpflow.LoadWinSize(winSizeX_level1.ptr(0), winSizeY_level1.ptr(0));
bpflow.ComputeDataTerm();
bpflow.ComputeRangeTerm(gamma);
bpflow.MessagePassing(nIterations, 2, pEnergyList);
bpflow.ComputeVelocity();
{
Mat flow(height, width, CV_32FC2, bpflow.flow().pData);
vector<Mat> flows;
split(flow, flows);
vx_level1 = flows[0];
vy_level1 = flows[1];
}
}
void initKernel(Mat& kernel, const Mat& blurredGray, const int width, const Mat& mask,
const int pyrLevel, const int iterations, float thresholdR, float thresholdS) {
assert(blurredGray.type() == CV_8U && "gray value image needed");
assert(mask.type() == CV_8U && "mask should be binary image");
// #ifndef NDEBUG
// imshow("blurred", blurredGray);
// #endif
// save min and maximum value of the original image to be able to restore
// the latent image with the correct brightness
double grayMin; double grayMax;
minMaxLoc(blurredGray, &grayMin, &grayMax);
// build an image pyramid with gray value images
vector<Mat> pyramid, masks;
pyramid.push_back(blurredGray);
masks.push_back(mask);
for (int i = 0; i < (pyrLevel - 1); i++) {
Mat downImage, downMask;
pyrDown(pyramid[i], downImage, Size(pyramid[i].cols/2, pyramid[i].rows/2));
pyrDown(masks[i], downMask, Size(masks[i].cols/2, masks[i].rows/2));
pyramid.push_back(downImage);
masks.push_back(downMask);
}
// init kernel but in the iterations the tmp-kernel is used
kernel = Mat::zeros(width, width, CV_32F);
Mat tmpKernel;
// go through image pyramid from small to large
for (int l = pyramid.size() - 1; l >= 0; l--) {
#ifdef IMWRITE
imshow("pyr Image", pyramid[l]);
double min; double max;
minMaxLoc(pyramid[l], &min, &max);
cout << "pyr: " << min << " " << max << endl;
waitKey();
#endif
// compute image gradient for x and y direction
//
// gaussian blur (in-place operation is supported)
GaussianBlur(pyramid[l], pyramid[l], Size(3,3), 0, 0, BORDER_DEFAULT);
// parameter for sobel filtering to obtain gradients
array<Mat,2> gradients, tmpGradients;
const int delta = 0;
const int ddepth = CV_32F;
const int ksize = 3;
const int scale = 1;
// gradient x and y
Sobel(pyramid[l], tmpGradients[0], ddepth, 1, 0, ksize, scale, delta, BORDER_DEFAULT);
Sobel(pyramid[l], tmpGradients[1], ddepth, 0, 1, ksize, scale, delta, BORDER_DEFAULT);
// cut off gradients outside the mask
tmpGradients[0].copyTo(gradients[0], masks[l]);
tmpGradients[1].copyTo(gradients[1], masks[l]);
// normalize gradients into range [-1,1]
normalizeOne(gradients);
// #ifdef IMWRITE
// showGradients("x gradient", gradients[0]);
// showGradients("y gradient", gradients[1]);
// #endif
// compute gradient confidence for al pixels
Mat gradientConfidence;
computeGradientConfidence(gradientConfidence, gradients, width, masks[l]);
// #ifdef IMWRITE
// showFloat("confidence", gradientConfidence);
// #endif
// each iterations works on an updated image
Mat currentImage;
pyramid[l].copyTo(currentImage);
// assert(iterations == 1 && "Implement multiple iterations");
for (int i = 0; i < iterations; i++) {
#ifdef IMWRITE
imshow("current Image", currentImage);
minMaxLoc(currentImage, &min, &max);
cout << "current: " << min << " " << max << endl;
waitKey();
#endif
// select edges for kernel estimation (normalized gradients [-1,1])
array<Mat,2> selectedEdges;
selectEdges(currentImage, gradientConfidence, thresholdR, thresholdS, selectedEdges);
#ifdef IMWRITE
showGradients("x gradient selection", selectedEdges[0]);
showGradients("y gradient selection", selectedEdges[1]);
minMaxLoc(selectedEdges[0], &min, &max);
cout << "x gradients: " << min << " " << max << endl;
waitKey();
#endif
// estimate kernel with gaussian prior
fastKernelEstimation(selectedEdges, gradients, kernel, 0.0);
#ifdef IMWRITE
showFloat("tmp-kernel", kernel, true);
minMaxLoc(kernel, &min, &max);
cout << "kernel: " << min << " " << max << " sum: " << sum(kernel)[0] << endl;
waitKey();
#endif
// coarse image estimation with a spatial prior
Mat latentImage;
// FIXME: it looks like there are some edges of the gradients in the latent image.
// with more iterations it becomes worse
// coarseImageEstimation(pyramid[l], kernel, selectedEdges, latentImage);
// use oother spatial deconv method for now
deconvolveIRLS(pyramid[l], latentImage, kernel);
#ifdef IMWRITE
string name = "two-phase-latent-" + to_string(i);
imshow(name, latentImage);
waitKey();
string filename = name + ".png";
imwrite(filename, latentImage);
#endif
// set current image to coarse latent image
latentImage.copyTo(currentImage);
// decrease thresholds τ_r and τ_s will to include more and more edges
thresholdR = thresholdR / 1.1;
thresholdS = thresholdS / 1.1;
}
// set next pyramid image to the upscaled latent image
if (l > 0) {
Mat upImage;
pyrUp(currentImage, upImage, Size(pyramid[l - 1].cols, pyramid[l - 1].rows));
pyramid[l - 1] = upImage;
}
}
// #ifdef IMWRITE
// imshow("kernel", kernel);
// waitKey();
// #endif
}
int main( int argc, char** argv )
{
printf( "Scale Space Cost Aggregation\n" );
if( argc != 11 ) {
printf( "Usage: [CC_METHOD] [CA_METHOD] [PP_METHOD] [C_ALPHA] [lImg] [rImg] [lDis] [rDis] [maxDis] [disSc]\n" );
printf( "\nPress any key to continue...\n" );
getchar();
return -1;
}
string ccName = argv[ 1 ];
string caName = argv[ 2 ];
string ppName = argv[ 3 ];
double costAlpha = atof( argv[ 4 ] );
string lFn = argv[ 5 ];
string rFn = argv[ 6 ];
string lDisFn = argv[ 7 ];
string rDisFn = argv[ 8 ];
int maxDis = atoi( argv[ 9 ] );
int disSc = atoi( argv[ 10 ] );
//
// Load left right image
//
printf( "\n--------------------------------------------------------\n" );
printf( "Load Image: (%s) (%s)\n", argv[ 5 ], argv[ 6 ] );
printf( "--------------------------------------------------------\n" );
Mat lImg = imread( lFn, CV_LOAD_IMAGE_COLOR );
Mat rImg = imread( rFn, CV_LOAD_IMAGE_COLOR );
if( !lImg.data || !rImg.data ) {
printf( "Error: can not open image\n" );
printf( "\nPress any key to continue...\n" );
getchar();
return -1;
}
// set image format
cvtColor( lImg, lImg, CV_BGR2RGB );
cvtColor( rImg, rImg, CV_BGR2RGB );
lImg.convertTo( lImg, CV_64F, 1 / 255.0f );
rImg.convertTo( rImg, CV_64F, 1 / 255.0f );
// time
double duration;
duration = static_cast<double>(getTickCount());
//
// Stereo Match at each pyramid
//
int PY_LVL = 5;
// build pyramid and cost volume
Mat lP = lImg.clone();
Mat rP = rImg.clone();
SSCA** smPyr = new SSCA*[ PY_LVL ];
CCMethod* ccMtd = getCCType( ccName );
CAMethod* caMtd = getCAType( caName );
PPMethod* ppMtd = getPPType( ppName );
for( int p = 0; p < PY_LVL; p ++ ) {
if( maxDis < 5 ) {
PY_LVL = p;
break;
}
printf( "\n\tPyramid: %d:", p );
smPyr[ p ] = new SSCA( lP, rP, maxDis, disSc );
smPyr[ p ]->CostCompute( ccMtd );
smPyr[ p ]->CostAggre( caMtd );
// pyramid downsample
maxDis = maxDis / 2 + 1;
disSc *= 2;
pyrDown( lP, lP );
pyrDown( rP, rP );
}
printf( "\n--------------------------------------------------------\n" );
printf( "\n Cost Aggregation in Scale Space\n" );
printf( "\n--------------------------------------------------------\n" );
// new method
SolveAll( smPyr, PY_LVL, costAlpha );
// old method
//for( int p = PY_LVL - 2 ; p >= 0; p -- ) {
// smPyr[ p ]->AddPyrCostVol( smPyr[ p + 1 ], costAlpha );
//}
//
// Match + Postprocess
//
smPyr[ 0 ]->Match();
smPyr[ 0 ]->PostProcess( ppMtd );
Mat lDis = smPyr[ 0 ]->getLDis();
Mat rDis = smPyr[ 0 ]->getRDis();
#ifdef _DEBUG
for( int s = 0; s < PY_LVL; s ++ ) {
smPyr[ s ]->Match();
Mat sDis = smPyr[ s ]->getLDis();
ostringstream sStr;
sStr << s;
string sFn = sStr.str( ) + "_ld.png";
imwrite( sFn, sDis );
}
saveOnePixCost( smPyr, PY_LVL );
#endif
#ifdef USE_MEDIAN_FILTER
//
// Median Filter Output
//
MeanFilter( lDis, lDis, 3 );
#endif
duration = static_cast<double>(getTickCount())-duration;
duration /= cv::getTickFrequency(); // the elapsed time in sec
printf( "\n--------------------------------------------------------\n" );
printf( "Total Time: %.2lf s\n", duration );
printf( "--------------------------------------------------------\n" );
//
// Save Output
//
imwrite( lDisFn, lDis );
imwrite( rDisFn, rDis );
delete [] smPyr;
delete ccMtd;
delete caMtd;
delete ppMtd;
return 0;
}
void MultiBandBlender::feed(const Mat &img, const Mat &mask, Point tl)
{
CV_Assert(img.type() == CV_16SC3 || img.type() == CV_8UC3);
CV_Assert(mask.type() == CV_8U);
// Keep source image in memory with small border
int gap = 3 * (1 << num_bands_);
Point tl_new(std::max(dst_roi_.x, tl.x - gap),
std::max(dst_roi_.y, tl.y - gap));
Point br_new(std::min(dst_roi_.br().x, tl.x + img.cols + gap),
std::min(dst_roi_.br().y, tl.y + img.rows + gap));
// Ensure coordinates of top-left, bottom-right corners are divided by (1 << num_bands_).
// After that scale between layers is exactly 2.
//
// We do it to avoid interpolation problems when keeping sub-images only. There is no such problem when
// image is bordered to have size equal to the final image size, but this is too memory hungry approach.
tl_new.x = dst_roi_.x + (((tl_new.x - dst_roi_.x) >> num_bands_) << num_bands_);
tl_new.y = dst_roi_.y + (((tl_new.y - dst_roi_.y) >> num_bands_) << num_bands_);
int width = br_new.x - tl_new.x;
int height = br_new.y - tl_new.y;
width += ((1 << num_bands_) - width % (1 << num_bands_)) % (1 << num_bands_);
height += ((1 << num_bands_) - height % (1 << num_bands_)) % (1 << num_bands_);
br_new.x = tl_new.x + width;
br_new.y = tl_new.y + height;
int dy = std::max(br_new.y - dst_roi_.br().y, 0);
int dx = std::max(br_new.x - dst_roi_.br().x, 0);
tl_new.x -= dx; br_new.x -= dx;
tl_new.y -= dy; br_new.y -= dy;
int top = tl.y - tl_new.y;
int left = tl.x - tl_new.x;
int bottom = br_new.y - tl.y - img.rows;
int right = br_new.x - tl.x - img.cols;
// Create the source image Laplacian pyramid
Mat img_with_border;
copyMakeBorder(img, img_with_border, top, bottom, left, right,
BORDER_REFLECT);
std::vector<Mat> src_pyr_laplace;
if (can_use_gpu_ && img_with_border.depth() == CV_16S)
createLaplacePyrGpu(img_with_border, num_bands_, src_pyr_laplace);
else
createLaplacePyr(img_with_border, num_bands_, src_pyr_laplace);
// Create the weight map Gaussian pyramid
Mat weight_map;
std::vector<Mat> weight_pyr_gauss(num_bands_ + 1);
if(weight_type_ == CV_32F)
{
mask.convertTo(weight_map, CV_32F, 1./255.);
}
else// weight_type_ == CV_16S
{
mask.convertTo(weight_map, CV_16S);
add(weight_map, 1, weight_map, mask != 0);
}
copyMakeBorder(weight_map, weight_pyr_gauss[0], top, bottom, left, right, BORDER_CONSTANT);
for (int i = 0; i < num_bands_; ++i)
pyrDown(weight_pyr_gauss[i], weight_pyr_gauss[i + 1]);
int y_tl = tl_new.y - dst_roi_.y;
int y_br = br_new.y - dst_roi_.y;
int x_tl = tl_new.x - dst_roi_.x;
int x_br = br_new.x - dst_roi_.x;
// Add weighted layer of the source image to the final Laplacian pyramid layer
if(weight_type_ == CV_32F)
{
for (int i = 0; i <= num_bands_; ++i)
{
for (int y = y_tl; y < y_br; ++y)
{
int y_ = y - y_tl;
const Point3_<short>* src_row = src_pyr_laplace[i].ptr<Point3_<short> >(y_);
Point3_<short>* dst_row = dst_pyr_laplace_[i].ptr<Point3_<short> >(y);
const float* weight_row = weight_pyr_gauss[i].ptr<float>(y_);
float* dst_weight_row = dst_band_weights_[i].ptr<float>(y);
for (int x = x_tl; x < x_br; ++x)
{
int x_ = x - x_tl;
dst_row[x].x += static_cast<short>(src_row[x_].x * weight_row[x_]);
dst_row[x].y += static_cast<short>(src_row[x_].y * weight_row[x_]);
dst_row[x].z += static_cast<short>(src_row[x_].z * weight_row[x_]);
dst_weight_row[x] += weight_row[x_];
}
}
x_tl /= 2; y_tl /= 2;
x_br /= 2; y_br /= 2;
}
}
else// weight_type_ == CV_16S
{
for (int i = 0; i <= num_bands_; ++i)
{
for (int y = y_tl; y < y_br; ++y)
{
int y_ = y - y_tl;
const Point3_<short>* src_row = src_pyr_laplace[i].ptr<Point3_<short> >(y_);
Point3_<short>* dst_row = dst_pyr_laplace_[i].ptr<Point3_<short> >(y);
const short* weight_row = weight_pyr_gauss[i].ptr<short>(y_);
short* dst_weight_row = dst_band_weights_[i].ptr<short>(y);
for (int x = x_tl; x < x_br; ++x)
{
int x_ = x - x_tl;
dst_row[x].x += short((src_row[x_].x * weight_row[x_]) >> 8);
dst_row[x].y += short((src_row[x_].y * weight_row[x_]) >> 8);
dst_row[x].z += short((src_row[x_].z * weight_row[x_]) >> 8);
dst_weight_row[x] += weight_row[x_];
}
}
x_tl /= 2; y_tl /= 2;
x_br /= 2; y_br /= 2;
}
}
}
void MultiBandBlender::feed(InputArray _img, InputArray mask, Point tl)
{
#if ENABLE_LOG
int64 t = getTickCount();
#endif
UMat img = _img.getUMat();
CV_Assert(img.type() == CV_16SC3 || img.type() == CV_8UC3);
CV_Assert(mask.type() == CV_8U);
// Keep source image in memory with small border
int gap = 3 * (1 << num_bands_);
Point tl_new(std::max(dst_roi_.x, tl.x - gap),
std::max(dst_roi_.y, tl.y - gap));
Point br_new(std::min(dst_roi_.br().x, tl.x + img.cols + gap),
std::min(dst_roi_.br().y, tl.y + img.rows + gap));
// Ensure coordinates of top-left, bottom-right corners are divided by (1 << num_bands_).
// After that scale between layers is exactly 2.
//
// We do it to avoid interpolation problems when keeping sub-images only. There is no such problem when
// image is bordered to have size equal to the final image size, but this is too memory hungry approach.
tl_new.x = dst_roi_.x + (((tl_new.x - dst_roi_.x) >> num_bands_) << num_bands_);
tl_new.y = dst_roi_.y + (((tl_new.y - dst_roi_.y) >> num_bands_) << num_bands_);
int width = br_new.x - tl_new.x;
int height = br_new.y - tl_new.y;
width += ((1 << num_bands_) - width % (1 << num_bands_)) % (1 << num_bands_);
height += ((1 << num_bands_) - height % (1 << num_bands_)) % (1 << num_bands_);
br_new.x = tl_new.x + width;
br_new.y = tl_new.y + height;
int dy = std::max(br_new.y - dst_roi_.br().y, 0);
int dx = std::max(br_new.x - dst_roi_.br().x, 0);
tl_new.x -= dx; br_new.x -= dx;
tl_new.y -= dy; br_new.y -= dy;
int top = tl.y - tl_new.y;
int left = tl.x - tl_new.x;
int bottom = br_new.y - tl.y - img.rows;
int right = br_new.x - tl.x - img.cols;
// Create the source image Laplacian pyramid
UMat img_with_border;
copyMakeBorder(_img, img_with_border, top, bottom, left, right,
BORDER_REFLECT);
LOGLN(" Add border to the source image, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
#if ENABLE_LOG
t = getTickCount();
#endif
std::vector<UMat> src_pyr_laplace;
if (can_use_gpu_ && img_with_border.depth() == CV_16S)
createLaplacePyrGpu(img_with_border, num_bands_, src_pyr_laplace);
else
createLaplacePyr(img_with_border, num_bands_, src_pyr_laplace);
LOGLN(" Create the source image Laplacian pyramid, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
#if ENABLE_LOG
t = getTickCount();
#endif
// Create the weight map Gaussian pyramid
UMat weight_map;
std::vector<UMat> weight_pyr_gauss(num_bands_ + 1);
if(weight_type_ == CV_32F)
{
mask.getUMat().convertTo(weight_map, CV_32F, 1./255.);
}
else // weight_type_ == CV_16S
{
mask.getUMat().convertTo(weight_map, CV_16S);
UMat add_mask;
compare(mask, 0, add_mask, CMP_NE);
add(weight_map, Scalar::all(1), weight_map, add_mask);
}
copyMakeBorder(weight_map, weight_pyr_gauss[0], top, bottom, left, right, BORDER_CONSTANT);
for (int i = 0; i < num_bands_; ++i)
pyrDown(weight_pyr_gauss[i], weight_pyr_gauss[i + 1]);
LOGLN(" Create the weight map Gaussian pyramid, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
#if ENABLE_LOG
t = getTickCount();
#endif
int y_tl = tl_new.y - dst_roi_.y;
int y_br = br_new.y - dst_roi_.y;
int x_tl = tl_new.x - dst_roi_.x;
int x_br = br_new.x - dst_roi_.x;
// Add weighted layer of the source image to the final Laplacian pyramid layer
for (int i = 0; i <= num_bands_; ++i)
{
Rect rc(x_tl, y_tl, x_br - x_tl, y_br - y_tl);
#ifdef HAVE_OPENCL
if ( !cv::ocl::useOpenCL() ||
!ocl_MultiBandBlender_feed(src_pyr_laplace[i], weight_pyr_gauss[i],
dst_pyr_laplace_[i](rc), dst_band_weights_[i](rc)) )
#endif
{
Mat _src_pyr_laplace = src_pyr_laplace[i].getMat(ACCESS_READ);
Mat _dst_pyr_laplace = dst_pyr_laplace_[i](rc).getMat(ACCESS_RW);
Mat _weight_pyr_gauss = weight_pyr_gauss[i].getMat(ACCESS_READ);
Mat _dst_band_weights = dst_band_weights_[i](rc).getMat(ACCESS_RW);
if(weight_type_ == CV_32F)
{
for (int y = 0; y < rc.height; ++y)
{
const Point3_<short>* src_row = _src_pyr_laplace.ptr<Point3_<short> >(y);
Point3_<short>* dst_row = _dst_pyr_laplace.ptr<Point3_<short> >(y);
const float* weight_row = _weight_pyr_gauss.ptr<float>(y);
float* dst_weight_row = _dst_band_weights.ptr<float>(y);
for (int x = 0; x < rc.width; ++x)
{
dst_row[x].x += static_cast<short>(src_row[x].x * weight_row[x]);
dst_row[x].y += static_cast<short>(src_row[x].y * weight_row[x]);
dst_row[x].z += static_cast<short>(src_row[x].z * weight_row[x]);
dst_weight_row[x] += weight_row[x];
}
}
}
else // weight_type_ == CV_16S
{
for (int y = 0; y < y_br - y_tl; ++y)
{
const Point3_<short>* src_row = _src_pyr_laplace.ptr<Point3_<short> >(y);
Point3_<short>* dst_row = _dst_pyr_laplace.ptr<Point3_<short> >(y);
const short* weight_row = _weight_pyr_gauss.ptr<short>(y);
short* dst_weight_row = _dst_band_weights.ptr<short>(y);
for (int x = 0; x < x_br - x_tl; ++x)
{
dst_row[x].x += short((src_row[x].x * weight_row[x]) >> 8);
dst_row[x].y += short((src_row[x].y * weight_row[x]) >> 8);
dst_row[x].z += short((src_row[x].z * weight_row[x]) >> 8);
dst_weight_row[x] += weight_row[x];
}
}
}
}
x_tl /= 2; y_tl /= 2;
x_br /= 2; y_br /= 2;
}
