void paste_images_gallery(const std::vector<cv::Mat_<T> > & in,
cv::Mat_<T> & out,
int gallerycols, T background_color,
bool draw_borders = false, cv::Scalar border_color = CV_RGB(0,0,0)) {
if (in.size() == 0) {
out.create(0, 0);
return;
}
int cols1 = in[0].cols, rows1 = in[0].rows, nimgs = in.size();
// prepare output
int galleryrows = std::ceil(1. * nimgs / gallerycols);
out.create(rows1 * galleryrows, cols1 * gallerycols);
// printf("nimgs:%i, gallerycols:%i, galleryrows:%i\n", nimgs, gallerycols, galleryrows);
out.setTo(background_color);
// paste images
for (int img_idx = 0; img_idx < nimgs; ++img_idx) {
int galleryx = img_idx % gallerycols, galleryy = img_idx / gallerycols;
cv::Rect roi(galleryx * cols1, galleryy * rows1, cols1, rows1);
// printf("### out:%ix%i, roi %i:'%s'\n", out.cols, out.rows, img_idx, geometry_utils::print_rect(roi).c_str());
if (cols1 != in[img_idx].cols || rows1 != in[img_idx].rows) {
printf("Image %i of size (%ix%i), different from (%ix%i), skipping it.\n",
img_idx, in[img_idx].cols, in[img_idx].rows, cols1, rows1);
cv::line(out, roi.tl(), roi.br(), border_color, 2);
cv::line(out, roi.br(), roi.tl(), border_color, 2);
}
else
in[img_idx].copyTo( out(roi) );
if (draw_borders)
cv::rectangle(out, roi, border_color, 1);
} // end loop img_idx
} // end paste_images_gallery<_T>
//===========================================================================
// Get the 2D shape (in image space) from global and local parameters
void PDM::CalcShape2D(cv::Mat_<double>& out_shape, const cv::Mat_<double>& params_local, const cv::Vec6d& params_global) const
{
int n = this->NumberOfPoints();
double s = params_global[0]; // scaling factor
double tx = params_global[4]; // x offset
double ty = params_global[5]; // y offset
// get the rotation matrix from the euler angles
cv::Vec3d euler(params_global[1], params_global[2], params_global[3]);
cv::Matx33d currRot = Euler2RotationMatrix(euler);
// get the 3D shape of the object
cv::Mat_<double> Shape_3D = mean_shape + princ_comp * params_local;
// create the 2D shape matrix (if it has not been defined yet)
if((out_shape.rows != mean_shape.rows) || (out_shape.cols != 1))
{
out_shape.create(2*n,1);
}
// for every vertex
for(int i = 0; i < n; i++)
{
// Transform this using the weak-perspective mapping to 2D from 3D
out_shape.at<double>(i ,0) = s * ( currRot(0,0) * Shape_3D.at<double>(i, 0) + currRot(0,1) * Shape_3D.at<double>(i+n ,0) + currRot(0,2) * Shape_3D.at<double>(i+n*2,0) ) + tx;
out_shape.at<double>(i+n,0) = s * ( currRot(1,0) * Shape_3D.at<double>(i, 0) + currRot(1,1) * Shape_3D.at<double>(i+n ,0) + currRot(1,2) * Shape_3D.at<double>(i+n*2,0) ) + ty;
}
}
// static
cv::Mat_<float> CrossValidator::createCrossValidationMatrix( const vector< const vector<cv::Mat_<float> >* >& rowVectors,
cv::Mat_<int>& labels)
{
assert( !rowVectors.empty());
cv::Mat_<float> vecs;
labels.create( 0,1);
for ( int label = 0; label < rowVectors.size(); ++label)
{
assert( rowVectors[label] != 0);
const vector<cv::Mat_<float> >& rvecs = *rowVectors[label];
assert( !rvecs.empty());
const int colDim = rvecs[0].cols; // Should be the length of each row vector in this class
if ( vecs.empty())
vecs.create(0, colDim);
assert( colDim == vecs.cols); // Ensure this class's row vector length matches what's already stored
for ( int i = 0; i < rvecs.size(); ++i)
{
const cv::Mat_<float>& rv = rvecs[i];
if ( rv.rows != 1 || rv.cols != colDim)
{
std::cerr << "ERROR feature vector size: " << rv.size() << std::endl;
assert( rv.rows == 1 && rv.cols == colDim);
} // end if
vecs.push_back( rv); // Append the row vector to the bottom of the matrix
labels.push_back(label); // Set this vector's class label
} // end for
} // end for
labels = labels.t(); // Make row vector
return vecs;
} // end createCrossValidationMatrix
//===========================================================================
void Multi_SVR_patch_expert::Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response)
{
int response_height = area_of_interest.rows - height + 1;
int response_width = area_of_interest.cols - width + 1;
if(response.rows != response_height || response.cols != response_width)
{
response.create(response_height, response_width);
}
// For the purposes of the experiment only use the response of normal intensity, for fair comparison
if(svr_patch_experts.size() == 1)
{
svr_patch_experts[0].Response(area_of_interest, response);
}
else
{
// responses from multiple patch experts these can be gradients, LBPs etc.
response.setTo(1.0);
cv::Mat_<float> modality_resp(response_height, response_width);
for(size_t i = 0; i < svr_patch_experts.size(); i++)
{
svr_patch_experts[i].Response(area_of_interest, modality_resp);
response = response.mul(modality_resp);
}
}
}
void EncoderBoFSoft::encode(const cv::Mat_<float>& descriptors, cv::Mat_<float>& encoded)
{
int ndata = descriptors.rows;
int ndim = descriptors.cols;
if ( ndim != this->_m_nDim)
{
throw std::runtime_error("dimension not match when encode");
}
encoded.create(ndata,this->_m_nCode);
encoded.setTo(0.0f);
//encoded.zeros(ndata,this->_m_nCode);
#pragma omp parallel for
for(int i=0;i<ndata;i++)
{
Mat index,dist;
this->_m_pTree->findNearest(descriptors.row(i),_m_nz,INT_MAX,index,noArray(),dist);
Scalar mean,std;
cv::meanStdDev(dist,mean,std);
cv::divide(std(0),dist,dist);
for(int j=0;j<_m_nz;j++)
{
encoded(i,index.at<int>(j)) = dist.at<float>(j);
}
}
}
void serialize(Archive & ar, ::cv::Mat_<T>& m, const unsigned int version)
{
if(Archive::is_loading::value == true)
{
int cols, rows;
size_t elem_size, elem_type;
ar & cols;
ar & rows;
ar & elem_size;
ar & elem_type;
m.create(rows, cols);
size_t data_size = m.cols * m.rows * elem_size;
ar & boost::serialization::make_array(m.ptr(), data_size);
}
else
{
size_t elem_size = m.elemSize();
size_t elem_type = m.type();
ar & m.cols;
ar & m.rows;
ar & elem_size;
ar & elem_type;
const size_t data_size = m.cols * m.rows * elem_size;
ar & boost::serialization::make_array(m.ptr(), data_size);
}
}
void CameraGeometry::cameraPose ( cv::Mat_<double> &tvec )
{
tvec.create ( 3,1 );
tvec ( 0,0 ) = mInvExt ( 0,3 );
tvec ( 1,0 ) = mInvExt ( 1,3 );
tvec ( 2,0 ) = mInvExt ( 2,3 );
}
void DetectorLatentSVMOpencv::detectMultiScale(const cv::Mat& img, std::vector<cv::Rect>& boxes, cv::Mat_<float>& confidence, double scaleFactor
, double maxSize, double minSize)
{
double r = 1.0;
Mat imgBGR = img.clone();
if (img.cols > this->_m_intMaxSize)
{
r = double(_m_intMaxSize) / img.cols;
cv::resize(imgBGR,imgBGR,Size(),r,r);
}
LatentSvmDetector detector(this->_m_vecModelFilenames);
vector<LatentSvmDetector::ObjectDetection> detections;
detector.detect(imgBGR,detections);
vector<float> confs;
for(int i=0;i<(int)detections.size();i++)
{
if ( detections[i].score > this->_m_fltThresh)
{
Rect rect = detections[i].rect;
boxes.push_back(Rect(int(rect.x/r),int(rect.y/r),int(rect.width/r),int(rect.height/r)));
confs.push_back(detections[i].score);
}
}
confidence.create((int)confs.size(),1);
for(int i=0;i<confs.size();i++)
{
confidence(i) = confs[i];
}
}
void convertQImageToMat(QImage &img_qt, cv::Mat_<cv::Vec3b>& img_cv)
{
img_cv.create(img_qt.height(), img_qt.width());
img_qt.convertToFormat(QImage::Format_RGB32);
int lineNum = 0;
int height = img_qt.height();
int width = img_qt.width();
uchar *imgBits = img_qt.bits();
for (int i = 0; i < height; i++)
{
lineNum = i* width * 4;
for (int j = 0; j < width; j++)
{
img_cv(i, j)[2] = imgBits[lineNum + j * 4 + 2];
img_cv(i, j)[1] = imgBits[lineNum + j * 4 + 1];
img_cv(i, j)[0] = imgBits[lineNum + j * 4 + 0];
}
}
}
//===========================================================================
void CCNF_patch_expert::Response(cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response)
{
int response_height = area_of_interest.rows - height + 1;
int response_width = area_of_interest.cols - width + 1;
if(response.rows != response_height || response.cols != response_width)
{
response.create(response_height, response_width);
}
response.setTo(0);
// the placeholder for the DFT of the image, the integral image, and squared integral image so they don't get recalculated for every response
cv::Mat_<double> area_of_interest_dft;
cv::Mat integral_image, integral_image_sq;
cv::Mat_<float> neuron_response;
// responses from the neural layers
for(size_t i = 0; i < neurons.size(); i++)
{
// Do not bother with neuron response if the alpha is tiny and will not contribute much to overall result
if(neurons[i].alpha > 1e-4)
{
neurons[i].Response(area_of_interest, area_of_interest_dft, integral_image, integral_image_sq, neuron_response);
response = response + neuron_response;
}
}
int s_to_use = -1;
// Find the matching sigma
for(size_t i=0; i < window_sizes.size(); ++i)
{
if(window_sizes[i] == response_height)
{
// Found the correct sigma
s_to_use = i;
break;
}
}
cv::Mat_<float> resp_vec_f = response.reshape(1, response_height * response_width);
cv::Mat out = Sigmas[s_to_use] * resp_vec_f;
response = out.reshape(1, response_height);
// Making sure the response does not have negative numbers
double min;
minMaxIdx(response, &min, 0);
if(min < 0)
{
response = response - min;
}
}
void DistanceTransform<T>::compute(const cv::Mat_<T>& score_in, const PenaltyFunction& fx, const PenaltyFunction& fy, const cv::Point os, cv::Mat_<T>& score_out, cv::Mat_<int>& Ix, cv::Mat_<int>& Iy) const {
// get the dimensionality of the score
const size_t M = score_in.rows;
const size_t N = score_in.cols;
// allocate the output and working matrices
score_out.create(cv::Size(M, N));
Ix.create(cv::Size(N, M));
Iy.create(cv::Size(M, N));
cv::Mat_<T> score_tmp(cv::Size(N, M));
// compute the distance transform across the rows
for (size_t m = 0; m < M; ++m) {
computeRow(score_in[m], score_tmp[m], Ix[m], N, fx, os.x);
}
// transpose the intermediate matrices
transpose(score_tmp, score_tmp);
// compute the distance transform down the columns
for (size_t n = 0; n < N; ++n) {
computeRow(score_tmp[n], score_out[n], Iy[n], M, fy, os.y);
}
// transpose back to the original layout
transpose(score_out, score_out);
transpose(Iy, Iy);
// get argmins
cv::Mat_<int> row(cv::Size(N, 1));
int * const row_ptr = row[0];
for (size_t m = 0; m < M; ++m) {
int * const Iy_ptr = Iy[m];
int * const Ix_ptr = Ix[m];
for (size_t n = 0; n < N; ++n) {
row_ptr[n] = Iy_ptr[Ix_ptr[n]];
}
for (size_t n = 0; n < N; ++n) {
Iy_ptr[n] = row_ptr[n];
}
}
}
void SegmenterHumanSimple::getPixelProbability(const cv::Mat& img, cv::Mat_<float>& prob, vector<Rect> faces)
{
prob.create(img.rows,img.cols);
prob.setTo(0.3f);
for(int i=0;i<faces.size();i++)
{
Rect rect = faces[i];
rect = UtilsOpencv::ScaleRect(rect,Rect(0,0,img.cols,img.rows),1.2,1.5,1.2,1.2);
prob(Rect(rect.x,rect.y,rect.width,img.rows-rect.y)) = 0.7f;
}
}
void Multi_SVR_patch_expert::ResponseDepth(const cv::Mat_<float>& area_of_interest, cv::Mat_<float>& response)
{
int response_height = area_of_interest.rows - height + 1;
int response_width = area_of_interest.cols - width + 1;
if(response.rows != response_height || response.cols != response_width)
{
response.create(response_height, response_width);
}
// With depth patch experts only do raw data modality
svr_patch_experts[0].ResponseDepth(area_of_interest, response);
}
int CameraGeometry::quaterion ( cv::Mat_<double> &quat, bool update )
{
quat.create ( 4,1 );
if ( update ) computePoseMatrix4x4 ( mExt );
double w = mExt ( 0,0 ) + mExt ( 1,1 ) + mExt ( 2,2 ) + 1;
if ( w < 0.0 ) return 1;
w = sqrt ( w );
quat ( 0,0 ) = ( mExt ( 2,1 ) - mExt ( 1,2 ) ) / ( w*2.0 );
quat ( 1,0 ) = ( mExt ( 0,2 ) - mExt ( 2,0 ) ) / ( w*2.0 );
quat ( 2,0 ) = ( mExt ( 1,0 ) - mExt ( 0,1 ) ) / ( w*2.0 );
quat ( 3,0 ) = w / 2.0;
return 0;
}
void CostBoxFilter::TheBoxFilterArrayForm( const cv::Mat_<float> &bIn, cv::Mat_<float> &bOut, int radius )
{
int height, width, iy, ix;
height = bIn.rows;
width = bIn.cols;
bOut.create(height, width);
cv::Mat_<float> cumHorSum(height, width);
float *horSum = new float [width];
for (iy=0; iy<height; ++iy)
{
float s = 0.0f;
for (ix=0; ix<width; ++ix)
{
s += bIn[iy][ix];
horSum[ix] = s;
}
for (ix=0; ix<=radius; ++ix)
cumHorSum[iy][ix] = horSum[ix+radius];
for (; ix<width-radius; ++ix)
cumHorSum[iy][ix] = horSum[ix+radius]-horSum[ix-radius-1];
for (; ix<width; ++ix)
cumHorSum[iy][ix] = horSum[width-1]-horSum[ix-radius-1];
}
float *colSum = new float [height];
for (ix=0; ix<width; ++ix)
{
float s = 0.0f;
for (iy=0; iy<height; ++iy)
{
s += cumHorSum[iy][ix];
colSum[iy] = s;
}
for (iy=0; iy<=radius; ++iy)
bOut[iy][ix] = colSum[iy+radius];
for (; iy<height-radius; ++iy)
bOut[iy][ix] = colSum[iy+radius]-colSum[iy-radius-1];
for (; iy<height; ++iy)
bOut[iy][ix] = colSum[height-1]-colSum[iy-radius-1];
}
delete [] horSum;
delete [] colSum;
}
void CascadeTrainer::Classify(const Data& data, cv::Mat_<label_t>& labels) {
labels.create(data.rows, 1);
for (int i = 0; i < labels.rows; i++) labels(i, 0) = POSITIVE_LABEL;
for (int i = 0; i < data.rows; i++) {
for (uint j = 0; j < stages.size(); j++) {
Mat_<label_t> labels_inner;
stages[j]->Classify(data.row(i), labels_inner);
if (labels_inner(0, 0) == NEGATIVE_LABEL) {
labels(i, 0) = NEGATIVE_LABEL;
break;
}
}
}
}
int ExtractSubImageDaisyDescriptors(daisy *desc, cv::Mat_<float> &descOut, float py, float px, float step, int ori, int h, int w, float limH, float limW)
{
//printf("here 0 %d %d py = %f px = %f\n", w, h, py, px);
descOut.create(w*h, DAISY_FEATURE_LENGTH);
//printf("here 0-1\n");
descOut.setTo(cv::Scalar(0.0));
//printf("here 0-2\n");
int iy, ix;
float cy, cx, ssinOri, scosOri;
ssinOri = step*sin((float)ori/180.0*M_PI);
scosOri = step*cos((float)ori/180.0*M_PI);
float ry, rx; // the first pos of each row
ry = py; rx = px;
//printf("here 1\n");
//float *thor = new float [DAISY_FEATURE_LENGTH];
for (iy=0; iy<h; ++iy)
{
cy = ry; cx = rx;
float *ptr = (float *)(descOut.ptr(iy*w));
//printf("here 2 %f %f %f %f\n", cy, cx, ssinOri, scosOri);
for (ix=0; ix<w; ++ix)
{
//memset(thor, 0, sizeof(float)*desc->descriptor_size());
//desc->get_descriptor(std::min<double>(limH-1.0, std::max<double>(cy, 0.0)), std::min<double>(limW-1.0, std::max<double>(cx, 0.0)), ori, ptr);
desc->ExtractDescriptorFromNormalizedHistogram(ptr, std::min<double>(limH-1.0, std::max<double>(cy, 0.0)), std::min<double>(limW-1.0, std::max<double>(cx, 0.0)), ori);
ptr += DAISY_FEATURE_LENGTH;
//printf("here 2-1\n");
//memcpy(ptr, thor, sizeof(float)*DAISY_FEATURE_LENGTH);
//printf("here 2-2\n");
//printf("here 2-3\n");
cy = cy+ssinOri;
cx = cx+scosOri;
}
ry = ry+scosOri;
rx = rx-ssinOri;
//printf("here 2\n");
}
//delete [] thor;
//printf("here 3\n");
return 1;
}
void opticalFlow::ReadFlowFile( const char *flowFile, cv::Mat_<cv::Vec2f> &flowVec, int height, int width )
{
float *fBuffer = new float[height*width*2];
::ReadFlowFile(fBuffer, flowFile, height, width);
int iy, ix;
flowVec.create(height, width);
for (iy=0; iy<height; ++iy)
{
for (ix=0; ix<width; ++ix)
{
float tmp0, tmp1;
tmp0 = fBuffer[iy*2*width+2*ix];
tmp1 = fBuffer[iy*2*width+2*ix+1];
flowVec[iy][ix][0] = tmp0;
flowVec[iy][ix][1] = tmp1;
}
}
delete [] fBuffer;
}
void TemplateMatchCandidates::weakClassifiersForTemplate(
const cv::Mat &templ,
const cv::Mat &templMask,
const std::vector< cv::Rect > &rects,
cv::Mat_<int> &classifiers,
cv::Scalar &mean)
{
const int nChannels = templ.channels();
classifiers.create(nChannels, (int)rects.size());
// Note we use cv::mean here to make use of mask.
mean = cv::mean(templ, templMask);
for (int x = 0; x < (int)rects.size(); ++x) {
cv::Scalar blockMean = cv::mean(templ(rects[x]), templMask.empty() ? cv::noArray() : templMask(rects[x]));
for (int y = 0; y < nChannels; ++y) {
classifiers(y, x) = blockMean[y] > mean[y] ? 1 : -1;
}
}
}
void computeFlowField(const cv::Mat &image1, const cv::Mat &image2, std::unordered_map<std::string, parameter> ¶meters,
cv::Mat_<cv::Vec2d> &flowfield){
// convert images into 64 bit floating point images
cv::Mat i1, i2;
i1 = image1.clone();
i1.convertTo(i1, CV_64F);
i2 = image2.clone();
i2.convertTo(i2, CV_64F);
// Blurring
double sigma = (double)parameters.at("sigma").value/parameters.at("sigma").divfactor;
cv::GaussianBlur(i1, i1, cv::Size(0,0), sigma, sigma, cv::BORDER_REFLECT);
cv::GaussianBlur(i2, i2, cv::Size(0,0), sigma, sigma, cv::BORDER_REFLECT);
// compute Brightness and Gradient Tensors
double gamma = (double)parameters.at("gamma").value/parameters.at("gamma").divfactor;
cv::Mat_<cv::Vec6d> t = (1.0 - gamma) * ComputeBrightnessTensor(i1, i2, 1, 1) + gamma * ComputeGradientTensor(i1, i2, 1, 1);
// create flowfield
flowfield.create(i1.rows, i1.cols);
flowfield = cv::Vec2d(0,0);
cv::copyMakeBorder(flowfield, flowfield, 1, 1, 1 , 1, cv::BORDER_CONSTANT, 0);
// make sure all parameter exist
if (parameters.count("maxiter") == 0 || parameters.count("alpha") == 0 ||
parameters.count("omega") == 0 || parameters.count("gamma") == 0){
std::cout << "Parameter nicht gefunden" << std::endl;
std::exit(1);
}
// main loop
for (int i = 0; i < parameters.at("maxiter").value; i++){
HS_Stepfunction(t, flowfield, parameters);
}
flowfield = flowfield(cv::Rect(1,1,image1.cols, image1.rows));
}
void
gen_points_3d(std::vector<Plane>& planes_out, cv::Mat_<unsigned char> &plane_mask, cv::Mat& points3d, cv::Mat& normals,
int n_planes)
{
std::vector<Plane> planes;
for (int i = 0; i < n_planes; i++)
{
Plane px;
for (int j = 0; j < 1; j++)
{
px.set_d(rng.uniform(-3.f, -0.5f));
planes.push_back(px);
}
}
cv::Mat_ < cv::Vec3f > outp(H, W);
cv::Mat_ < cv::Vec3f > outn(H, W);
plane_mask.create(H, W);
// n ( r - r_0) = 0
// n * r_0 = d
//
// r_0 = (0,0,0)
// r[0]
for (int v = 0; v < H; v++)
{
for (int u = 0; u < W; u++)
{
unsigned int plane_index = (u / float(W)) * planes.size();
Plane plane = planes[plane_index];
outp(v, u) = plane.intersection(u, v, Kinv);
outn(v, u) = plane.n;
plane_mask(v, u) = plane_index;
}
}
planes_out = planes;
points3d = outp;
normals = outn;
}
//===========================================================================
// Calculate the PDM's Jacobian over rigid parameters (rotation, translation and scaling), the additional input W represents trust for each of the landmarks and is part of Non-Uniform RLMS
void PDM::ComputeRigidJacobian(const cv::Mat_<float>& p_local, const cv::Vec6d& params_global, cv::Mat_<float> &Jacob, const cv::Mat_<float> W, cv::Mat_<float> &Jacob_t_w)
{
// number of verts
int n = this->NumberOfPoints();
Jacob.create(n * 2, 6);
float X,Y,Z;
float s = (float)params_global[0];
cv::Mat_<double> shape_3D_d;
cv::Mat_<double> p_local_d;
p_local.convertTo(p_local_d, CV_64F);
this->CalcShape3D(shape_3D_d, p_local_d);
cv::Mat_<float> shape_3D;
shape_3D_d.convertTo(shape_3D, CV_32F);
// Get the rotation matrix
cv::Vec3d euler(params_global[1], params_global[2], params_global[3]);
cv::Matx33d currRot = Euler2RotationMatrix(euler);
float r11 = (float) currRot(0,0);
float r12 = (float) currRot(0,1);
float r13 = (float) currRot(0,2);
float r21 = (float) currRot(1,0);
float r22 = (float) currRot(1,1);
float r23 = (float) currRot(1,2);
float r31 = (float) currRot(2,0);
float r32 = (float) currRot(2,1);
float r33 = (float) currRot(2,2);
cv::MatIterator_<float> Jx = Jacob.begin();
cv::MatIterator_<float> Jy = Jx + n * 6;
for(int i = 0; i < n; i++)
{
X = shape_3D.at<float>(i,0);
Y = shape_3D.at<float>(i+n,0);
Z = shape_3D.at<float>(i+n*2,0);
// The rigid jacobian from the axis angle rotation matrix approximation using small angle assumption (R * R')
// where R' = [1, -wz, wy
// wz, 1, -wx
// -wy, wx, 1]
// And this is derived using the small angle assumption on the axis angle rotation matrix parametrisation
// scaling term
*Jx++ = (X * r11 + Y * r12 + Z * r13);
*Jy++ = (X * r21 + Y * r22 + Z * r23);
// rotation terms
*Jx++ = (s * (Y * r13 - Z * r12) );
*Jy++ = (s * (Y * r23 - Z * r22) );
*Jx++ = (-s * (X * r13 - Z * r11));
*Jy++ = (-s * (X * r23 - Z * r21));
*Jx++ = (s * (X * r12 - Y * r11) );
*Jy++ = (s * (X * r22 - Y * r21) );
// translation terms
*Jx++ = 1.0f;
*Jy++ = 0.0f;
*Jx++ = 0.0f;
*Jy++ = 1.0f;
}
cv::Mat Jacob_w = cv::Mat::zeros(Jacob.rows, Jacob.cols, Jacob.type());
Jx = Jacob.begin();
Jy = Jx + n*6;
cv::MatIterator_<float> Jx_w = Jacob_w.begin<float>();
cv::MatIterator_<float> Jy_w = Jx_w + n*6;
// Iterate over all Jacobian values and multiply them by the weight in diagonal of W
for(int i = 0; i < n; i++)
{
float w_x = W.at<float>(i, i);
float w_y = W.at<float>(i+n, i+n);
for(int j = 0; j < Jacob.cols; ++j)
{
*Jx_w++ = *Jx++ * w_x;
*Jy_w++ = *Jy++ * w_y;
}
}
Jacob_t_w = Jacob_w.t();
}
void CFFilter::GetCrossUsingSlidingWindow(const cv::Mat_<cv::Vec3b> &img, cv::Mat_<cv::Vec4b> &crMap, int maxL, int tau)
{
if ((img.data == NULL) || img.empty()) return;
////CalcTime ct;
//ct.Start();
int width, height;
width = img.cols;
height = img.rows;
crMap.create(height, width);
int iy, ix;
#pragma omp parallel for private(iy) schedule(guided, 1)
for (iy=0; iy<height; ++iy)
{
// to determine right most, cr[2]
cv::Vec3i sumColor;
int lp, rp;
rp = 0;
for (lp=0; lp<width; ++lp)
{
int diff = rp-lp;
if (diff == 0)
{
sumColor = img[iy][rp];
++rp;
++diff;
}
while (diff <= maxL)
{
if (rp >= width) break;
//if (abs(sumColor[0]/diff-img[iy][rp][0])>tau || abs(sumColor[1]/diff-img[iy][rp][1])>tau
// || abs(sumColor[2]/diff-img[iy][rp][2])>tau) break;
if (abs(sumColor[0]-img[iy][rp][0]*diff)>tau*diff
|| abs(sumColor[1]-img[iy][rp][1]*diff)>tau*diff
|| abs(sumColor[2]-img[iy][rp][2]*diff)>tau*diff) break;
sumColor += img[iy][rp];
++diff;
++rp;
}
sumColor -= img[iy][lp];
crMap[iy][lp][2] = diff-1;
}
// to determine left most, cr[0]
lp = width-1;
for (rp=width-1; rp>=0; --rp)
{
int diff = rp-lp;
if (diff == 0)
{
sumColor = img[iy][lp];
--lp;
++diff;
}
while (diff <= maxL)
{
if (lp < 0) break;
//if (abs(sumColor[0]/diff-img[iy][lp][0])>tau || abs(sumColor[1]/diff-img[iy][lp][1])>tau
// || abs(sumColor[2]/diff-img[iy][lp][2])>tau) break;
if (abs(sumColor[0]-img[iy][lp][0]*diff)>tau*diff
|| abs(sumColor[1]-img[iy][lp][1]*diff)>tau*diff
|| abs(sumColor[2]-img[iy][lp][2]*diff)>tau*diff) break;
sumColor += img[iy][lp];
++diff;
--lp;
}
sumColor -= img[iy][rp];
crMap[iy][rp][0] = diff-1;
}
}
// determine up and down arm length
#pragma omp parallel for private(ix) schedule(guided, 1)
for (ix=0; ix<width; ++ix)
{
// to determine down most, cr[3]
cv::Vec3i sumColor;
int up, dp;
dp = 0;
for (up=0; up<height; ++up)
{
int diff = dp-up;
if (diff == 0)
{
sumColor = img[dp][ix];
++dp;
++diff;
}
while (diff <= maxL)
{
if (dp >= height) break;
//if (abs(sumColor[0]/diff-img[dp][ix][0])>tau || abs(sumColor[1]/diff-img[dp][ix][1])>tau
// || abs(sumColor[2]/diff-img[dp][ix][2])>tau) break;
if (abs(sumColor[0]-img[dp][ix][0]*diff)>tau*diff
|| abs(sumColor[1]-img[dp][ix][1]*diff)>tau*diff
|| abs(sumColor[2]-img[dp][ix][2]*diff)>tau*diff) break;
sumColor += img[dp][ix];
++diff;
++dp;
}
sumColor -= img[up][ix];
crMap[up][ix][3] = diff-1;
}
// to determine up most, cr[1]
up = height-1;
for (dp=height-1; dp>=0; --dp)
{
int diff = dp-up;
if (diff == 0)
{
sumColor = img[up][ix];
--up;
++diff;
}
while (diff <= maxL)
{
if (up < 0) break;
//if (abs(sumColor[0]/diff-img[up][ix][0])>tau || abs(sumColor[1]/diff-img[up][ix][1])>tau
// || abs(sumColor[2]/diff-img[up][ix][2])>tau) break;
if (abs(sumColor[0]-img[up][ix][0]*diff)>tau*diff
|| abs(sumColor[1]-img[up][ix][1]*diff)>tau*diff
|| abs(sumColor[2]-img[up][ix][2]*diff)>tau*diff) break;
sumColor += img[up][ix];
++diff;
--up;
}
sumColor -= img[dp][ix];
crMap[dp][ix][1] = diff-1;
}
}
//ct.End("SlWin Construct CrossMap");
}
void SpmBowExtractor::extractSpm(const cv::Mat& image, const cv::Mat_<uchar>& mask, cv::Mat_<int>& spm_histogram, feature_type::T feature_types)
{
const std::size_t histogram_count = BowExtractor::getHistogramCount(pyramid_levels_);
const cv::Rect roi = cv::Rect(0,0, image.cols, image.rows);
const std::size_t histo_dim = compute_histogram_length(feature_types, histogram_count);
spm_histogram.create(1, histo_dim);
std::size_t last_offset = 0;
// extract surf
if (feature_types & feature_type::Surf)
{
cv::Mat_<int> surf_histo = getHistogramView(
spm_histogram,
0,
surf_.wordCount(),
histogram_count);
extract_feature(surf_extractor_, surf_, image, roi, mask, surf_histo);
last_offset += surf_histo.total();
}
// extract hog
if (feature_types & feature_type::Hog)
{
cv::Mat_<int> hog_histo = getHistogramView(
spm_histogram,
last_offset,
hog_.wordCount(),
histogram_count);
extract_feature(hog_extractor_, hog_, image, roi, mask, hog_histo);
last_offset += hog_histo.total();
}
// extract color
if (feature_types & feature_type::Color)
{
cv::Mat_<int> color_histo = getHistogramView(
spm_histogram,
last_offset,
color_.wordCount(),
histogram_count);
extract_feature(color_extractor_, color_, image, roi, mask, color_histo);
last_offset += color_histo.total();
}
// extract lbp
if (feature_types & feature_type::Lbp)
{
cv::Mat_<int> lbp_histo = getHistogramView(
spm_histogram,
last_offset,
lbp_.wordCount(),
histogram_count);
lbp_.sumPool(
image,
roi,
mask,
lbp_.wordCount(),
pyramid_levels_,
lbp_histo);
last_offset += lbp_histo.total();
}
// extract ssd
if ((feature_types & feature_type::Ssd) || (feature_types & feature_type::ColorSsd))
{
cv::Mat_<int> ssd_histo = getHistogramView(
spm_histogram,
last_offset,
ssd_.wordCount(),
histogram_count);
extract_feature(ssd_extractor_, ssd_, image, roi, mask, ssd_histo);
last_offset += ssd_histo.total();
}
// extract holbp
if (feature_types & feature_type::HoLbp)
{
cv::Mat_<int> holbp_histo = getHistogramView(
spm_histogram,
last_offset,
holbp_.wordCount(),
histogram_count);
extract_feature(holbp_extractor_, holbp_, image, roi, mask, holbp_histo);
last_offset += holbp_histo.total();
}
// extract dense_surf
if (feature_types & feature_type::DenseSurf)
{
cv::Mat_<int> dense_surf_histo = getHistogramView(
spm_histogram,
last_offset,
dense_surf_.wordCount(),
histogram_count);
extract_feature(dense_surf_extractor_, dense_surf_, image, roi, mask,
dense_surf_histo);
last_offset += dense_surf_histo.total();
}
}
//===========================================================================
// Calculate the PDM's Jacobian over all parameters (rigid and non-rigid), the additional input W represents trust for each of the landmarks and is part of Non-Uniform RLMS
void PDM::ComputeJacobian(const cv::Mat_<float>& params_local, const cv::Vec6d& params_global, cv::Mat_<float> &Jacobian, const cv::Mat_<float> W, cv::Mat_<float> &Jacob_t_w)
{
// number of vertices
int n = this->NumberOfPoints();
// number of non-rigid parameters
int m = this->NumberOfModes();
Jacobian.create(n * 2, 6 + m);
float X,Y,Z;
float s = (float) params_global[0];
cv::Mat_<double> shape_3D_d;
cv::Mat_<double> p_local_d;
params_local.convertTo(p_local_d, CV_64F);
this->CalcShape3D(shape_3D_d, p_local_d);
cv::Mat_<float> shape_3D;
shape_3D_d.convertTo(shape_3D, CV_32F);
cv::Vec3d euler(params_global[1], params_global[2], params_global[3]);
cv::Matx33d currRot = Euler2RotationMatrix(euler);
float r11 = (float) currRot(0,0);
float r12 = (float) currRot(0,1);
float r13 = (float) currRot(0,2);
float r21 = (float) currRot(1,0);
float r22 = (float) currRot(1,1);
float r23 = (float) currRot(1,2);
float r31 = (float) currRot(2,0);
float r32 = (float) currRot(2,1);
float r33 = (float) currRot(2,2);
cv::MatIterator_<float> Jx = Jacobian.begin();
cv::MatIterator_<float> Jy = Jx + n * (6 + m);
cv::MatConstIterator_<double> Vx = this->princ_comp.begin();
cv::MatConstIterator_<double> Vy = Vx + n*m;
cv::MatConstIterator_<double> Vz = Vy + n*m;
for(int i = 0; i < n; i++)
{
X = shape_3D.at<float>(i,0);
Y = shape_3D.at<float>(i+n,0);
Z = shape_3D.at<float>(i+n*2,0);
// The rigid jacobian from the axis angle rotation matrix approximation using small angle assumption (R * R')
// where R' = [1, -wz, wy
// wz, 1, -wx
// -wy, wx, 1]
// And this is derived using the small angle assumption on the axis angle rotation matrix parametrisation
// scaling term
*Jx++ = (X * r11 + Y * r12 + Z * r13);
*Jy++ = (X * r21 + Y * r22 + Z * r23);
// rotation terms
*Jx++ = (s * (Y * r13 - Z * r12) );
*Jy++ = (s * (Y * r23 - Z * r22) );
*Jx++ = (-s * (X * r13 - Z * r11));
*Jy++ = (-s * (X * r23 - Z * r21));
*Jx++ = (s * (X * r12 - Y * r11) );
*Jy++ = (s * (X * r22 - Y * r21) );
// translation terms
*Jx++ = 1.0f;
*Jy++ = 0.0f;
*Jx++ = 0.0f;
*Jy++ = 1.0f;
for(int j = 0; j < m; j++,++Vx,++Vy,++Vz)
{
// How much the change of the non-rigid parameters (when object is rotated) affect 2D motion
*Jx++ = (float) ( s*(r11*(*Vx) + r12*(*Vy) + r13*(*Vz)) );
*Jy++ = (float) ( s*(r21*(*Vx) + r22*(*Vy) + r23*(*Vz)) );
}
}
// Adding the weights here
cv::Mat Jacob_w = Jacobian.clone();
if(cv::trace(W)[0] != W.rows)
{
Jx = Jacobian.begin();
Jy = Jx + n*(6+m);
cv::MatIterator_<float> Jx_w = Jacob_w.begin<float>();
cv::MatIterator_<float> Jy_w = Jx_w + n*(6+m);
// Iterate over all Jacobian values and multiply them by the weight in diagonal of W
for(int i = 0; i < n; i++)
{
float w_x = W.at<float>(i, i);
float w_y = W.at<float>(i+n, i+n);
for(int j = 0; j < Jacobian.cols; ++j)
{
*Jx_w++ = *Jx++ * w_x;
*Jy_w++ = *Jy++ * w_y;
}
}
}
Jacob_t_w = Jacob_w.t();
}
//===========================================================================
void CCNF_neuron::Response(cv::Mat_<float> &im, cv::Mat_<double> &im_dft, cv::Mat &integral_img, cv::Mat &integral_img_sq, cv::Mat_<float> &resp)
{
int h = im.rows - weights.rows + 1;
int w = im.cols - weights.cols + 1;
// the patch area on which we will calculate reponses
cv::Mat_<float> I;
if(neuron_type == 3)
{
// Perform normalisation across whole patch (ignoring the invalid values indicated by <= 0
cv::Scalar mean;
cv::Scalar std;
// ignore missing values
cv::Mat_<uchar> mask = im > 0;
cv::meanStdDev(im, mean, std, mask);
// if all values the same don't divide by 0
if(std[0] != 0)
{
I = (im - mean[0]) / std[0];
}
else
{
I = (im - mean[0]);
}
I.setTo(0, mask == 0);
}
else
{
if(neuron_type == 0)
{
I = im;
}
else
{
printf("ERROR(%s,%d): Unsupported patch type %d!\n", __FILE__,__LINE__,neuron_type);
abort();
}
}
if(resp.empty())
{
resp.create(h, w);
}
// The response from neuron before activation
if(neuron_type == 3)
{
// In case of depth we use per area, rather than per patch normalisation
matchTemplate_m(I, im_dft, integral_img, integral_img_sq, weights, weights_dfts, resp, CV_TM_CCOEFF); // the linear multiplication, efficient calc of response
}
else
{
matchTemplate_m(I, im_dft, integral_img, integral_img_sq, weights, weights_dfts, resp, CV_TM_CCOEFF_NORMED); // the linear multiplication, efficient calc of response
}
cv::MatIterator_<float> p = resp.begin();
cv::MatIterator_<float> q1 = resp.begin(); // respone for each pixel
cv::MatIterator_<float> q2 = resp.end();
// the logistic function (sigmoid) applied to the response
while(q1 != q2)
{
*p++ = (2 * alpha) * 1.0 /(1.0 + exp( -(*q1++ * norm_weights + bias )));
}
}
//===========================================================================
// Compute the 3D representation of shape (in object space) using the local parameters
void PDM::CalcShape3D(cv::Mat_<double>& out_shape, const cv::Mat_<double>& p_local) const
{
out_shape.create(mean_shape.rows, mean_shape.cols);
out_shape = mean_shape + princ_comp*p_local;
}
void SVR_patch_expert::ResponseDepth(const Mat_<float>& area_of_interest, cv::Mat_<double> &response)
{
// How big the response map will be
int response_height = area_of_interest.rows - weights.rows + 1;
int response_width = area_of_interest.cols - weights.cols + 1;
// the patch area on which we will calculate reponses
Mat_<float> normalised_area_of_interest;
if(response.rows != response_height || response.cols != response_width)
{
response.create(response_height, response_width);
}
if(type == 0)
{
// Perform normalisation across whole patch
cv::Scalar mean;
cv::Scalar std;
// ignore missing values
cv::Mat_<uchar> mask = area_of_interest > 0;
cv::meanStdDev(area_of_interest, mean, std, mask);
// if all values the same don't divide by 0
if(std[0] == 0)
{
std[0] = 1;
}
normalised_area_of_interest = (area_of_interest - mean[0]) / std[0];
// Set the invalid pixels to 0
normalised_area_of_interest.setTo(0, mask == 0);
}
else
{
printf("ERROR(%s,%d): Unsupported patch type %d!\n", __FILE__,__LINE__,type);
abort();
}
Mat_<float> svr_response;
// The empty matrix as we don't pass precomputed dft's of image
Mat_<double> empty_matrix_0(0,0,0.0);
Mat_<float> empty_matrix_1(0,0,0.0);
Mat_<float> empty_matrix_2(0,0,0.0);
// Efficient calc of patch expert response across the area of interest
matchTemplate_m(normalised_area_of_interest, empty_matrix_0, empty_matrix_1, empty_matrix_2, weights, weights_dfts, svr_response, CV_TM_CCOEFF);
response.create(svr_response.size());
MatIterator_<double> p = response.begin();
cv::MatIterator_<float> q1 = svr_response.begin(); // respone for each pixel
cv::MatIterator_<float> q2 = svr_response.end();
while(q1 != q2)
{
// the SVR response passed through a logistic regressor
*p++ = 1.0/(1.0 + exp( -(*q1++ * scaling + bias )));
}
}
void CFFilter::FastCLMF0FloatFilterPointer( const cv::Mat_<cv::Vec4b> &crMap, const cv::Mat_<float> &src, cv::Mat_<float> &dst )
{
// qx_timer tt;
// tt.start();
int iy, ix, width, height;
width = crMap.cols;
height = crMap.rows;
cv::Mat_<float> cost = src;
// first iteration
cv::Mat_<float> crossHorSum(height, width);
cv::Mat_<int> sizeHorSum(height, width);
cv::Mat_<float> crossHorSumTranspose(width, height);
cv::Mat_<int> sizeHorSumTranspose(width, height);
cv::Mat_<cv::Vec4b> crMapTranspose(width, height);
cv::transpose(crMap, crMapTranspose);
float *horSum = new float [width+1];
float *rowSizeHorSum = new float [width+1];
float *verSum = new float [height+1];
int *colSizeVerSum = new int [height+1];
float *costPtr = (float *)(cost.ptr(0));
float *crossHorPtr = (float *)(crossHorSum.ptr(0));
int *sizeHorPtr = (int *)(sizeHorSum.ptr(0));
cv::Vec4b *crMapPtr = (cv::Vec4b *)(crMap.ptr(0));
for (iy=0; iy<height; ++iy)
{
float s = 0.0;
float *horPtr = horSum;
*horPtr++ = s;
for (ix=0; ix<width; ++ix)
{
s += *costPtr++;
*horPtr++ = s;
}
for (ix=0; ix<width; ++ix)
{
cv::Vec4b cross = *crMapPtr++;
*crossHorPtr++ = horSum[ix+cross[2]+1]-horSum[ix-cross[0]];
*sizeHorPtr++ = cross[2]+cross[0]+1;
}
}
cv::transpose(crossHorSum, crossHorSumTranspose);
cv::transpose(sizeHorSum, sizeHorSumTranspose);
crossHorPtr = (float *)(crossHorSumTranspose.ptr(0));
sizeHorPtr = (int *)(sizeHorSumTranspose.ptr(0));
cv::Mat_<float> crossVerSum(height, width);
cv::Mat_<int> sizeVerSum(height, width);
cv::Mat_<float> crossVerSumTranpose(width, height);
cv::Mat_<int> sizeVerSumTranpose(width, height);
float *crossVerPtr = (float *)(crossVerSumTranpose.ptr(0));
int *sizeVerPtr = (int *)(sizeVerSumTranpose.ptr(0));
crMapPtr = (cv::Vec4b *)(crMapTranspose.ptr(0));
const int W_FAC = width;
for (ix=0; ix<width; ++ix)
{
float s = 0.0;
int cs = 0;
float *verPtr = verSum;
int *colSizeVerPtr = colSizeVerSum;
*verPtr++ = s;
*colSizeVerPtr++ = cs;
for (iy=0; iy<height; ++iy)
{
s += *crossHorPtr++;
*verPtr++ =s;
cs += *sizeHorPtr++;
*colSizeVerPtr++ = cs;
}
for (iy=0; iy<height; ++iy)
{
cv::Vec4b cross = *crMapPtr++;
*crossVerPtr++ = verSum[iy+cross[3]+1]-verSum[iy-cross[1]];
*sizeVerPtr++ = colSizeVerSum[iy+cross[3]+1]-colSizeVerSum[iy-cross[1]];
}
}
cv::transpose(crossVerSumTranpose, crossVerSum);
cv::transpose(sizeVerSumTranpose, sizeVerSum);
// second iteration
crossVerPtr = (float *)(crossVerSum.ptr(0));
sizeVerPtr = (int *)(sizeVerSum.ptr(0));
crossHorPtr = (float *)(crossHorSum.ptr(0));
sizeHorPtr = (int *)(sizeHorSum.ptr(0));
crMapPtr = (cv::Vec4b *)(crMap.ptr(0));
for (iy=0; iy<height; ++iy)
{
float s = 0.0;
int rs = 0;
float *horPtr = horSum;
float *rowSizeHorPtr = rowSizeHorSum;
*horPtr++ = s;
*rowSizeHorPtr++ = rs;
for (ix=0; ix<width; ++ix)
{
s += *crossVerPtr++;
*horPtr++ = s;
rs += *sizeVerPtr++;
*rowSizeHorPtr++ = rs;
}
for (ix=0; ix<width; ++ix)
{
cv::Vec4b cross = *crMapPtr++;
*crossHorPtr++ = horSum[ix+cross[2]+1]-horSum[ix-cross[0]];
*sizeHorPtr++ = rowSizeHorSum[ix+cross[2]+1]-rowSizeHorSum[ix-cross[0]];
}
}
cv::transpose(crossHorSum, crossHorSumTranspose);
cv::transpose(sizeHorSum, sizeHorSumTranspose);
crossHorPtr = (float *)(crossHorSumTranspose.ptr(0));
sizeHorPtr = (int *)(sizeHorSumTranspose.ptr(0));
crossVerPtr = (float *)(crossVerSumTranpose.ptr(0));
sizeVerPtr = (int *)(sizeVerSumTranpose.ptr(0));
crMapPtr = (cv::Vec4b *)(crMapTranspose.ptr(0));
for (ix=0; ix<width; ++ix)
{
float s = 0.0;
int cs = 0;
float *verPtr = verSum;
int *colSizeVerPtr = colSizeVerSum;
*verPtr++ = s;
*colSizeVerPtr++ = cs;
for (iy=0; iy<height; ++iy)
{
s += *crossHorPtr++;
*verPtr++ = s;
cs += *sizeHorPtr++;
*colSizeVerPtr++ = cs;
}
for (iy=0; iy<height; ++iy)
{
cv::Vec4b cross = *crMapPtr++;
*crossVerPtr++ = verSum[iy+cross[3]+1]-verSum[iy-cross[1]];
*sizeVerPtr++ = colSizeVerSum[iy+cross[3]+1]-colSizeVerSum[iy-cross[1]];
}
}
//tt.time_display("Smoothing");
delete [] horSum;
delete [] rowSizeHorSum;
delete [] verSum;
delete [] colSizeVerSum;
cv::transpose(crossVerSumTranpose, crossVerSum);
cv::transpose(sizeVerSumTranpose, sizeVerSum);
dst.create(height, width);
cv::Mat_<float> tmpDst = dst;
float *pCrossVerSum = (float *)crossVerSum.ptr(0);
int *pSizeVerSum = (int *)sizeVerSum.ptr(0);
float *pTmpDst = (float *)tmpDst.ptr(0);
for (iy=0; iy<height*width; ++iy)
{
(*pTmpDst++) = (*pCrossVerSum++)/(*pSizeVerSum++);
}
}
void CostBoxFilter::TheBoxFilter( const cv::Mat_<float> &bIn, cv::Mat_<float> &bOut, int radius )
{
int height, width, iy, ix;
height = bIn.rows;
width = bIn.cols;
bOut.create(height, width);
cv::Mat_<float> cumHorSum(height, width);
float *horSum = new float [width];
for (iy=0; iy<height; ++iy)
{
float *bPtr = (float *)(bIn.ptr(iy));
float *hPtr = horSum;
float s = 0.0f;
for (ix=0; ix<width; ++ix)
{
//s += bIn[iy][ix];
//horSum[ix] = s;
s += *bPtr++;
*hPtr++ = s;
}
float *ptrR = horSum+radius;
float *dPtr = (float *)(cumHorSum.ptr(iy));
for (ix=0; ix<=radius; ++ix)
*dPtr++ = *ptrR++;
// cumHorSum[iy][ix] = horSum[ix+radius];
float *ptrL = horSum;
for (; ix<width-radius; ++ix)
*dPtr++ = *ptrR++-*ptrL++;
//cumHorSum[iy][ix] = horSum[ix+radius]-horSum[ix-radius-1];
--ptrR;
for (; ix<width; ++ix)
*dPtr++ = *ptrR-*ptrL++;
//cumHorSum[iy][ix] = horSum[width-1]-horSum[ix-radius-1];
}
const int W_FAC = width;
float *colSum = new float [height];
for (ix=0; ix<width; ++ix)
{
float s = 0.0f;
float *cuPtr = (float *)(cumHorSum.ptr(0))+ix;
float *coPtr = colSum;
for (iy=0; iy<height; ++iy, cuPtr+=W_FAC)
{
//s += cumHorSum[iy][ix];
//colSum[iy] = s;
s += *cuPtr;
*coPtr++ = s;
}
float *ptrD = colSum+radius;
float *dPtr = (float *)(bOut.ptr(0))+ix;
for (iy=0; iy<=radius; ++iy, dPtr+=W_FAC)
//bOut[iy][ix] = colSum[iy+radius];
*dPtr = *ptrD++;
float *ptrU = colSum;
for (; iy<height-radius; ++iy, dPtr+=W_FAC)
//bOut[iy][ix] = colSum[iy+radius]-colSum[iy-radius-1];
*dPtr = *ptrD++-*ptrU++;
--ptrD;
for (; iy<height; ++iy, dPtr+=W_FAC)
// bOut[iy][ix] = colSum[height-1]-colSum[iy-radius-1];
*dPtr = *ptrD-*ptrU++;
}
delete [] horSum;
delete [] colSum;
}