/*! Transform the grid with given homograhy and average colors over
* triangles.
*/
void LightCollector::averageImage(IplImage *im, CvMat *_homography)
{
if (avgChannels != im->nChannels) {
if (avgChannels < im->nChannels) {
delete[] avg;
avg = 0;
}
avgChannels = im->nChannels;
}
if (!avg) avg = new float[avgChannels*nbTri];
// apply the homography to every mesh vertex
if (_homography)
cvMatMul(_homography, vertices, transformed);
else
cvCopy(vertices, transformed);
CvMat r1,r2,r3;
cvGetRow(transformed, &r1, 0);
cvGetRow(transformed, &r2, 1);
cvGetRow(transformed, &r3, 2);
cvDiv(&r1,&r3,&r1);
cvDiv(&r2,&r3,&r2);
nbPix=0;
for (int t=0; t<nbTri;t++) {
int pts[3][2];
for (int i=0; i<3; i++) {
assert(triangles[t*3+i] < transformed->cols);
pts[i][0] = cvRound(CV_MAT_ELEM(*transformed, float, 0, triangles[t*3+i]));
pts[i][1] = cvRound(CV_MAT_ELEM(*transformed, float, 1, triangles[t*3+i]));
}
nbPix+=stat_triangle(im, pts, avg+t*avgChannels);
}
}
bool LightCollector::genGrid(float corners[4][2], int nx, int ny)
{
if (nx<1 || ny<1) return false;
if (avg) delete[] avg; avg=0;
if (vertices) cvReleaseMat(&vertices);
if (transformed) cvReleaseMat(&transformed);
// generate vertices
vertices = cvCreateMat(3, (nx+1)*(ny+1), CV_32FC1);
transformed = cvCreateMat(3, vertices->cols, CV_32FC1);
for (int y=0; y<(ny+1); ++y)
for (int x=0; x<(nx+1); ++x) {
CV_MAT_ELEM(*vertices, float, 0, y*(nx+1)+x) = float(x)/float(nx);
CV_MAT_ELEM(*vertices, float, 1, y*(nx+1)+x) = float(y)/float(ny);
CV_MAT_ELEM(*vertices, float, 2, y*(nx+1)+x) = 1;
}
// generate triangles
nbTri = nx*ny*2;
triangles = new int[nbTri*3];
int *tri = triangles;
for (int y=0; y<ny; ++y)
for (int x=0; x<nx; ++x) {
tri[0] = y*(nx+1)+x;
tri[1] = y*(nx+1)+x+1;
tri[2] = (y+1)*(nx+1)+x;
tri+=3;
tri[0] = y*(nx+1)+x+1;
tri[1] = (y+1)*(nx+1)+x+1;
tri[2] = (y+1)*(nx+1)+x;
tri+=3;
}
homography H;
if (!H.estimate(0, 0, corners[0][0], corners[0][1],
1, 0, corners[1][0], corners[1][1],
1, 1, corners[2][0], corners[2][1],
0, 1, corners[3][0], corners[3][1]))
return false;
cvMatMul(&H, vertices, transformed);
CvMat r1,r2,r3, d1, d2;
cvGetRow(transformed, &r1, 0);
cvGetRow(transformed, &r2, 1);
cvGetRow(transformed, &r3, 2);
cvGetRow(vertices, &d1, 0);
cvGetRow(vertices, &d2, 1);
cvDiv(&r1,&r3,&d1);
cvDiv(&r2,&r3,&d2);
return true;
}
double* ObtenerMaximo(IplImage* Imagen, STFrame* FrameData, CvRect Roi) {
// obtener matriz de distancias normalizadas al background
if (SHOW_VALIDATION_DATA == 1)
printf(" \n\n Busqueda del máximo umbral...");
IplImage* IDif = 0;
IplImage* peso = 0;
CvSize size = cvSize(Imagen->width, Imagen->height); // get current frame size
if (!IDif || IDif->width != size.width || IDif->height != size.height) {
cvReleaseImage(&IDif);
cvReleaseImage(&peso);
IDif = cvCreateImage(cvSize(FrameData->BGModel->width,
FrameData->BGModel->height), IPL_DEPTH_8U, 1); // imagen diferencia abs(I(pi)-u(p(i))
peso = cvCreateImage(cvSize(FrameData->BGModel->width,
FrameData->BGModel->height), IPL_DEPTH_32F, 1);//Imagen resultado wi ( pesos)
cvZero(IDif);
cvZero(peso);
}
// |I(p)-u(p)|/0(p)
cvAbsDiff(Imagen, FrameData->BGModel, IDif);
cvDiv(IDif, FrameData->IDesvf, peso);
// Buscar máximo
double* Maximo = 0;
cvMinMaxLoc(peso, Maximo, 0, 0, 0, FrameData->FG);
return Maximo;
}
static void node_composit_exec_cvDiv(void *data, bNode *node, bNodeStack **in, bNodeStack **out)
{
CvArr* dst;
CvArr* src1;
CvArr* src2;
CV_FUNCNAME( "cvDiv" );
//TODO: Use atach buffers
if(out[0]->hasoutput==0) return;
cvSetErrMode(1); //Parent mode error
__CV_BEGIN__;
if((in[0]->data)&&(in[1]->data)){
CV_CALL(src1 = in[0]->data);
CV_CALL(src2 = in[1]->data);
if(!BOCV_checkAreSameType(src1, src2))
CV_ERROR( CV_StsBadArg,"The source inputs are differents");
CV_CALL(dst=BOCV_CreateArrFrom(src2));
if(dst)
{
CV_CALL(cvDiv(src1, src2, dst,1));
CV_CALL(out[0]->data= dst);
}
}
__CV_END__;
}
void HarrisBuffer::OpticalFlowFromSMM()
{
// ref: Laptev et al. CVIU 2007, eq.(8)
cvMul(cxx, cyy, tmp1);
cvMul(cxy, cxy, tmp2);
cvSub(tmp1,tmp2,tmp5);
cvMul(cyy, cxt, tmp3);
cvMul(cxy, cyt, tmp4);
cvSub(tmp3,tmp4,tmp6);
cvDiv(tmp6,tmp5,OFx);
cvMul(cxx, cyt, tmp3);
cvMul(cxy, cxt, tmp4);
cvSub(tmp3,tmp4,tmp6);
cvDiv(tmp6,tmp5,OFy);
}
void BModel::wiener_filter_chanel(IplImage *channel, IplImage *kernel, const double sigma)
{
IplImage *fKernel = cvCreateImage(cvGetSize(kernel), IPL_DEPTH_64F, 2);
IplImage *fChannel = cvCreateImage(cvGetSize(channel), IPL_DEPTH_64F, 2);
IplImage *answ = cvCreateImage(cvGetSize(channel), IPL_DEPTH_64F, 2);
IplImage *reFKernel = cvCreateImage(cvGetSize(kernel), IPL_DEPTH_64F, 1);
IplImage *imFKernel = cvCreateImage(cvGetSize(kernel), IPL_DEPTH_64F, 1);
IplImage *reFChannel = cvCreateImage(cvGetSize(kernel), IPL_DEPTH_64F, 1);
IplImage *imFChannel = cvCreateImage(cvGetSize(kernel), IPL_DEPTH_64F, 1);
IplImage *reAnsw = cvCreateImage(cvGetSize(channel), IPL_DEPTH_64F, 1);
IplImage *imAnsw = cvCreateImage(cvGetSize(channel), IPL_DEPTH_64F, 1);
cvDFT(kernel, fKernel, CV_DXT_FORWARD, channel->height);
cvDFT(channel, fChannel, CV_DXT_FORWARD, channel->height);
cvMulSpectrums(fChannel, fKernel, answ, CV_DXT_MUL_CONJ);
cvSplit(answ, reAnsw, imAnsw, 0, 0 );
cvSplit(fKernel, reFKernel, imFKernel, 0, 0 );
cvPow(reFKernel, reFKernel, 2);
cvPow(imFKernel, imFKernel, 2);
cvAdd(reFKernel, imFKernel, reFKernel, 0);
cvAddS(reFKernel, cvScalarAll(sigma), reFKernel, 0);
cvDiv(reAnsw, reFKernel, reAnsw, 1);
cvDiv(imAnsw, reFKernel, imAnsw, 1);
cvMerge(reAnsw, imAnsw, NULL, NULL, answ);
cvDFT(answ, answ, CV_DXT_INV_SCALE, channel->height);
cvCopy(answ, channel);
cvReleaseImage(&fKernel);
cvReleaseImage(&fChannel);
cvReleaseImage(&answ);
cvReleaseImage(&reFKernel);
cvReleaseImage(&imFKernel);
cvReleaseImage(&reFChannel);
cvReleaseImage(&imFChannel);
cvReleaseImage(&reAnsw);
cvReleaseImage(&imAnsw);
}
void LightCollector::drawGrid(IplImage *im, CvMat *_homography)
{
// apply the homography to every mesh vertex
cvMatMul(_homography, vertices, transformed);
CvMat r1,r2,r3;
cvGetRow(transformed, &r1, 0);
cvGetRow(transformed, &r2, 1);
cvGetRow(transformed, &r3, 2);
cvDiv(&r1,&r3,&r1);
cvDiv(&r2,&r3,&r2);
for (int t=0; t<nbTri;t++) {
int pts[3][2];
for (int i=0; i<3; i++) {
pts[i][0] = cvRound(CV_MAT_ELEM(*transformed, float, 0, triangles[t*3+i]));
pts[i][1] = cvRound(CV_MAT_ELEM(*transformed, float, 1, triangles[t*3+i]));
}
cvLine(im, cvPoint(pts[0][0], pts[0][1]), cvPoint(pts[1][0], pts[1][1]), cvScalarAll(255), 1,4,0);
cvLine(im, cvPoint(pts[1][0], pts[1][1]), cvPoint(pts[2][0], pts[2][1]), cvScalarAll(255), 1,4,0);
cvLine(im, cvPoint(pts[2][0], pts[2][1]), cvPoint(pts[0][0], pts[0][1]), cvScalarAll(255), 1,4,0);
}
}
//! assuming row vectors (a row is a sample)
void cvSoftmax(CvMat * src, CvMat * dst){
CV_FUNCNAME("cvSoftmax");
__BEGIN__;
CV_ASSERT(cvCountNAN(src)<1);
cvExp(src,dst);
CV_ASSERT(cvCountNAN(dst)<1);
const int dtype = CV_MAT_TYPE(src->type);
CvMat * sum = cvCreateMat(src->rows,1,dtype);
CvMat * sum_repeat = cvCreateMat(src->rows,src->cols,dtype);
cvReduce(dst,sum,-1,CV_REDUCE_SUM);
CV_ASSERT(cvCountNAN(sum)<1);
cvRepeat(sum,sum_repeat);
cvDiv(dst,sum_repeat,dst);
cvReleaseMat(&sum);
cvReleaseMat(&sum_repeat);
__END__;
}
static void node_composit_exec_cvDiv(void *data, bNode *node, bNodeStack **in, bNodeStack **out) {
CvArr* dst;
CvArr* src1;
CvArr* src2;
CompBuf *dst_buf;
if (out[0]->hasoutput == 0) return;
if ((in[0]->data) && in[1]->data) {
//Get inuts
src1 = BOCV_IplImage_attach(in[0]->data);
src2 = BOCV_IplImage_attach(in[1]->data);
//Create output
dst_buf = dupalloc_compbuf(in[0]->data);
dst = BOCV_IplImage_attach(dst_buf);
//Check for inputs
if (!BOCV_checkAreSameType(src1, src2)) {
node->error = 1;
return;
}
if (!BOCV_checkSameNChannels(src1, src2)) {
node->error = 1;
return;
}
cvDiv(src1, src2, dst, in[2]->vec[0]);
//Output
out[0]->data = dst_buf;
//Release memory
BOCV_IplImage_detach(src1);
BOCV_IplImage_detach(src2);
BOCV_IplImage_detach(dst);
}
}
void FacePredict::CalcMeanTextureParams(const CvMat* GroupTextures, int group)
{
int nSamples = GroupTextures->rows;
CvMat mParams;
cvGetRow(__TextureParamGroups, &mParams, group); //resize the mParams
CvMat* lamda = cvCreateMat(1, __nTextureModes, CV_64FC1);
CvMat* oneTexture = cvCreateMat(1, GroupTextures->cols, CV_64FC1);
for (int i = 0; i < nSamples; i++) {
cvGetRow(GroupTextures, oneTexture, i);
__texture.CalcParams(oneTexture, lamda);
cvAdd(&mParams, lamda, &mParams);
}
CvMat * size = cvCreateMat(1, __nTextureModes, CV_64FC1);
for (int i = 0; i < __nTextureModes; i++)
cvmSet(size, 0, i, nSamples);
cvDiv(&mParams, size, &mParams);
cvReleaseMat(&lamda);
cvReleaseMat(&size);
cvReleaseMat(&oneTexture);
}
void FacePredict::CalcMeanShapeParams(const std::vector<AAM_Shape> &GroupShapes, int group)
{
int nSamples = GroupShapes.size();
CvMat mParams;
cvGetRow(__ShapeParamGroups, &mParams, group);
CvMat* p = cvCreateMat(1, __nShapeModes, CV_64FC1);
CvMat* pq = cvCreateMat(1, 4+__nShapeModes, CV_64FC1);
for (int i = 0; i < nSamples; i++) {
__shape.CalcParams(GroupShapes[i], pq);
cvGetCols(pq, p, 4, 4+__nShapeModes);
cvAdd(&mParams, p, &mParams);
}
CvMat * size = cvCreateMat(1, __nShapeModes, CV_64FC1);
for (int i = 0; i < __nShapeModes; i++)
cvmSet(size, 0, i, nSamples);
cvDiv(&mParams, size, &mParams);
cvReleaseMat(&p);
cvReleaseMat(&pq);
cvReleaseMat(&size);
}
/* log_weight_div_det[k] = -2*log(weights_k) + log(det(Sigma_k)))
covs[k] = cov_rotate_mats[k] * cov_eigen_values[k] * (cov_rotate_mats[k])'
cov_rotate_mats[k] are orthogonal matrices of eigenvectors and
cov_eigen_values[k] are diagonal matrices (represented by 1D vectors) of eigen values.
The <alpha_ik> is the probability of the vector x_i to belong to the k-th cluster:
<alpha_ik> ~ weights_k * exp{ -0.5[ln(det(Sigma_k)) + (x_i - mu_k)' Sigma_k^(-1) (x_i - mu_k)] }
We calculate these probabilities here by the equivalent formulae:
Denote
S_ik = -0.5(log(det(Sigma_k)) + (x_i - mu_k)' Sigma_k^(-1) (x_i - mu_k)) + log(weights_k),
M_i = max_k S_ik = S_qi, so that the q-th class is the one where maximum reaches. Then
alpha_ik = exp{ S_ik - M_i } / ( 1 + sum_j!=q exp{ S_ji - M_i })
*/
double CvEM::run_em( const CvVectors& train_data )
{
CvMat* centered_sample = 0;
CvMat* covs_item = 0;
CvMat* log_det = 0;
CvMat* log_weights = 0;
CvMat* cov_eigen_values = 0;
CvMat* samples = 0;
CvMat* sum_probs = 0;
log_likelihood = -DBL_MAX;
CV_FUNCNAME( "CvEM::run_em" );
__BEGIN__;
int nsamples = train_data.count, dims = train_data.dims, nclusters = params.nclusters;
double min_variation = FLT_EPSILON;
double min_det_value = MAX( DBL_MIN, pow( min_variation, dims ));
double likelihood_bias = -CV_LOG2PI * (double)nsamples * (double)dims / 2., _log_likelihood = -DBL_MAX;
int start_step = params.start_step;
int i, j, k, n;
int is_general = 0, is_diagonal = 0, is_spherical = 0;
double prev_log_likelihood = -DBL_MAX / 1000., det, d;
CvMat whdr, iwhdr, diag, *w, *iw;
double* w_data;
double* sp_data;
if( nclusters == 1 )
{
double log_weight;
CV_CALL( cvSet( probs, cvScalar(1.)) );
if( params.cov_mat_type == COV_MAT_SPHERICAL )
{
d = cvTrace(*covs).val[0]/dims;
d = MAX( d, FLT_EPSILON );
inv_eigen_values->data.db[0] = 1./d;
log_weight = pow( d, dims*0.5 );
}
else
{
w_data = inv_eigen_values->data.db;
if( params.cov_mat_type == COV_MAT_GENERIC )
cvSVD( *covs, inv_eigen_values, *cov_rotate_mats, 0, CV_SVD_U_T );
else
cvTranspose( cvGetDiag(*covs, &diag), inv_eigen_values );
cvMaxS( inv_eigen_values, FLT_EPSILON, inv_eigen_values );
for( j = 0, det = 1.; j < dims; j++ )
det *= w_data[j];
log_weight = sqrt(det);
cvDiv( 0, inv_eigen_values, inv_eigen_values );
}
log_weight_div_det->data.db[0] = -2*log(weights->data.db[0]/log_weight);
log_likelihood = DBL_MAX/1000.;
EXIT;
}
if( params.cov_mat_type == COV_MAT_GENERIC )
is_general = 1;
else if( params.cov_mat_type == COV_MAT_DIAGONAL )
is_diagonal = 1;
else if( params.cov_mat_type == COV_MAT_SPHERICAL )
is_spherical = 1;
/* In the case of <cov_mat_type> == COV_MAT_DIAGONAL, the k-th row of cov_eigen_values
contains the diagonal elements (variations). In the case of
<cov_mat_type> == COV_MAT_SPHERICAL - the 0-ths elements of the vectors cov_eigen_values[k]
are to be equal to the mean of the variations over all the dimensions. */
CV_CALL( log_det = cvCreateMat( 1, nclusters, CV_64FC1 ));
CV_CALL( log_weights = cvCreateMat( 1, nclusters, CV_64FC1 ));
CV_CALL( covs_item = cvCreateMat( dims, dims, CV_64FC1 ));
CV_CALL( centered_sample = cvCreateMat( 1, dims, CV_64FC1 ));
CV_CALL( cov_eigen_values = cvCreateMat( inv_eigen_values->rows, inv_eigen_values->cols, CV_64FC1 ));
CV_CALL( samples = cvCreateMat( nsamples, dims, CV_64FC1 ));
CV_CALL( sum_probs = cvCreateMat( 1, nclusters, CV_64FC1 ));
sp_data = sum_probs->data.db;
// copy the training data into double-precision matrix
for( i = 0; i < nsamples; i++ )
{
const float* src = train_data.data.fl[i];
double* dst = (double*)(samples->data.ptr + samples->step*i);
for( j = 0; j < dims; j++ )
dst[j] = src[j];
}
if( start_step != START_M_STEP )
{
for( k = 0; k < nclusters; k++ )
{
if( is_general || is_diagonal )
{
w = cvGetRow( cov_eigen_values, &whdr, k );
if( is_general )
cvSVD( covs[k], w, cov_rotate_mats[k], 0, CV_SVD_U_T );
else
cvTranspose( cvGetDiag( covs[k], &diag ), w );
w_data = w->data.db;
for( j = 0, det = 1.; j < dims; j++ )
det *= w_data[j];
if( det < min_det_value )
{
if( start_step == START_AUTO_STEP )
det = min_det_value;
else
EXIT;
}
log_det->data.db[k] = det;
}
else
{
d = cvTrace(covs[k]).val[0]/(double)dims;
if( d < min_variation )
{
if( start_step == START_AUTO_STEP )
d = min_variation;
else
EXIT;
}
cov_eigen_values->data.db[k] = d;
log_det->data.db[k] = d;
}
}
cvLog( log_det, log_det );
if( is_spherical )
cvScale( log_det, log_det, dims );
}
for( n = 0; n < params.term_crit.max_iter; n++ )
{
if( n > 0 || start_step != START_M_STEP )
{
// e-step: compute probs_ik from means_k, covs_k and weights_k.
CV_CALL(cvLog( weights, log_weights ));
// S_ik = -0.5[log(det(Sigma_k)) + (x_i - mu_k)' Sigma_k^(-1) (x_i - mu_k)] + log(weights_k)
for( k = 0; k < nclusters; k++ )
{
CvMat* u = cov_rotate_mats[k];
const double* mean = (double*)(means->data.ptr + means->step*k);
w = cvGetRow( cov_eigen_values, &whdr, k );
iw = cvGetRow( inv_eigen_values, &iwhdr, k );
cvDiv( 0, w, iw );
w_data = (double*)(inv_eigen_values->data.ptr + inv_eigen_values->step*k);
for( i = 0; i < nsamples; i++ )
{
double *csample = centered_sample->data.db, p = log_det->data.db[k];
const double* sample = (double*)(samples->data.ptr + samples->step*i);
double* pp = (double*)(probs->data.ptr + probs->step*i);
for( j = 0; j < dims; j++ )
csample[j] = sample[j] - mean[j];
if( is_general )
cvGEMM( centered_sample, u, 1, 0, 0, centered_sample, CV_GEMM_B_T );
for( j = 0; j < dims; j++ )
p += csample[j]*csample[j]*w_data[is_spherical ? 0 : j];
pp[k] = -0.5*p + log_weights->data.db[k];
// S_ik <- S_ik - max_j S_ij
if( k == nclusters - 1 )
{
double max_val = 0;
for( j = 0; j < nclusters; j++ )
max_val = MAX( max_val, pp[j] );
for( j = 0; j < nclusters; j++ )
pp[j] -= max_val;
}
}
}
CV_CALL(cvExp( probs, probs )); // exp( S_ik )
cvZero( sum_probs );
// alpha_ik = exp( S_ik ) / sum_j exp( S_ij ),
// log_likelihood = sum_i log (sum_j exp(S_ij))
for( i = 0, _log_likelihood = likelihood_bias; i < nsamples; i++ )
{
double* pp = (double*)(probs->data.ptr + probs->step*i), sum = 0;
for( j = 0; j < nclusters; j++ )
sum += pp[j];
sum = 1./MAX( sum, DBL_EPSILON );
for( j = 0; j < nclusters; j++ )
{
double p = pp[j] *= sum;
sp_data[j] += p;
}
_log_likelihood -= log( sum );
}
// check termination criteria
if( fabs( (_log_likelihood - prev_log_likelihood) / prev_log_likelihood ) < params.term_crit.epsilon )
break;
prev_log_likelihood = _log_likelihood;
}
// m-step: update means_k, covs_k and weights_k from probs_ik
cvGEMM( probs, samples, 1, 0, 0, means, CV_GEMM_A_T );
for( k = 0; k < nclusters; k++ )
{
double sum = sp_data[k], inv_sum = 1./sum;
CvMat* cov = covs[k], _mean, _sample;
w = cvGetRow( cov_eigen_values, &whdr, k );
w_data = w->data.db;
cvGetRow( means, &_mean, k );
cvGetRow( samples, &_sample, k );
// update weights_k
weights->data.db[k] = sum;
// update means_k
cvScale( &_mean, &_mean, inv_sum );
// compute covs_k
cvZero( cov );
cvZero( w );
for( i = 0; i < nsamples; i++ )
{
double p = probs->data.db[i*nclusters + k]*inv_sum;
_sample.data.db = (double*)(samples->data.ptr + samples->step*i);
if( is_general )
{
cvMulTransposed( &_sample, covs_item, 1, &_mean );
cvScaleAdd( covs_item, cvRealScalar(p), cov, cov );
}
else
for( j = 0; j < dims; j++ )
{
double val = _sample.data.db[j] - _mean.data.db[j];
w_data[is_spherical ? 0 : j] += p*val*val;
}
}
if( is_spherical )
{
d = w_data[0]/(double)dims;
d = MAX( d, min_variation );
w->data.db[0] = d;
log_det->data.db[k] = d;
}
else
{
if( is_general )
cvSVD( cov, w, cov_rotate_mats[k], 0, CV_SVD_U_T );
cvMaxS( w, min_variation, w );
for( j = 0, det = 1.; j < dims; j++ )
det *= w_data[j];
log_det->data.db[k] = det;
}
}
cvConvertScale( weights, weights, 1./(double)nsamples, 0 );
cvMaxS( weights, DBL_MIN, weights );
cvLog( log_det, log_det );
if( is_spherical )
cvScale( log_det, log_det, dims );
} // end of iteration process
//log_weight_div_det[k] = -2*log(weights_k/det(Sigma_k))^0.5) = -2*log(weights_k) + log(det(Sigma_k)))
if( log_weight_div_det )
{
cvScale( log_weights, log_weight_div_det, -2 );
cvAdd( log_weight_div_det, log_det, log_weight_div_det );
}
/* Now finalize all the covariation matrices:
1) if <cov_mat_type> == COV_MAT_DIAGONAL we used array of <w> as diagonals.
Now w[k] should be copied back to the diagonals of covs[k];
2) if <cov_mat_type> == COV_MAT_SPHERICAL we used the 0-th element of w[k]
as an average variation in each cluster. The value of the 0-th element of w[k]
should be copied to the all of the diagonal elements of covs[k]. */
if( is_spherical )
{
for( k = 0; k < nclusters; k++ )
cvSetIdentity( covs[k], cvScalar(cov_eigen_values->data.db[k]));
}
else if( is_diagonal )
{
for( k = 0; k < nclusters; k++ )
cvTranspose( cvGetRow( cov_eigen_values, &whdr, k ),
cvGetDiag( covs[k], &diag ));
}
cvDiv( 0, cov_eigen_values, inv_eigen_values );
log_likelihood = _log_likelihood;
__END__;
cvReleaseMat( &log_det );
cvReleaseMat( &log_weights );
cvReleaseMat( &covs_item );
cvReleaseMat( ¢ered_sample );
cvReleaseMat( &cov_eigen_values );
cvReleaseMat( &samples );
cvReleaseMat( &sum_probs );
return log_likelihood;
}
int main(int argc, char ** argv)
{
int height,width,step,channels;
uchar* data;
uchar* data1;
int i,j,k;
float s;
CvMat *dft_A;
CvMat *dft_B;
CvMat *dft_C;
IplImage* im;
IplImage* im1;
IplImage* image_ReB;
IplImage* image_ImB;
IplImage* image_ReC;
IplImage* image_ImC;
IplImage* complex_ImC;
IplImage* image_ReDen;
IplImage* image_ImDen;
FILE *fp;
fp = fopen("test.txt","w+");
int dft_M,dft_N;
int dft_M1,dft_N1;
CvMat* cvShowDFT();
void cvShowInvDFT();
im1 = cvLoadImage( "kutty-1.jpg",1 );
cvNamedWindow("original-color", 0);
cvShowImage("original-color", im1);
im = cvLoadImage( "kutty-1.jpg", CV_LOAD_IMAGE_GRAYSCALE );
if( !im )
return -1;
cvNamedWindow("original-gray", 0);
cvShowImage("original-gray", im);
// Create blur kernel (non-blind)
//float vals[]={.000625,.000625,.000625,.003125,.003125,.003125,.000625,.000625,.000625};
//float vals[]={-0.167,0.333,0.167,-0.167,.333,.167,-0.167,.333,.167};
float vals[]={.055,.055,.055,.222,.222,.222,.055,.055,.055};
CvMat kernel = cvMat(3, // number of rows
3, // number of columns
CV_32FC1, // matrix data type
vals);
IplImage* k_image_hdr;
IplImage* k_image;
k_image_hdr = cvCreateImageHeader(cvSize(3,3),IPL_DEPTH_64F,2);
k_image = cvCreateImage(cvSize(3,3),IPL_DEPTH_64F,1);
k_image = cvGetImage(&kernel,k_image_hdr);
/*IplImage* k_image;
k_image = cvLoadImage( "kernel4.bmp",0 );*/
cvNamedWindow("blur kernel", 0);
height = k_image->height;
width = k_image->width;
step = k_image->widthStep;
channels = k_image->nChannels;
//data1 = (float *)(k_image->imageData);
data1 = (uchar *)(k_image->imageData);
cvShowImage("blur kernel", k_image);
dft_M = cvGetOptimalDFTSize( im->height - 1 );
dft_N = cvGetOptimalDFTSize( im->width - 1 );
//dft_M1 = cvGetOptimalDFTSize( im->height+99 - 1 );
//dft_N1 = cvGetOptimalDFTSize( im->width+99 - 1 );
dft_M1 = cvGetOptimalDFTSize( im->height+3 - 1 );
dft_N1 = cvGetOptimalDFTSize( im->width+3 - 1 );
// Perform DFT of original image
dft_A = cvShowDFT(im, dft_M1, dft_N1,"original");
//Perform inverse (check & comment out) - Commented as it overwrites dft_A
//cvShowInvDFT(im,dft_A,dft_M1,dft_N1,fp, "original");
// Perform DFT of kernel
dft_B = cvShowDFT(k_image,dft_M1,dft_N1,"kernel");
//Perform inverse of kernel (check & comment out) - commented as it overwrites dft_B
//cvShowInvDFT(k_image,dft_B,dft_M1,dft_N1,fp, "kernel");
// Multiply numerator with complex conjugate
dft_C = cvCreateMat( dft_M1, dft_N1, CV_64FC2 );
printf("%d %d %d %d\n",dft_M,dft_N,dft_M1,dft_N1);
// Multiply DFT(blurred image) * complex conjugate of blur kernel
cvMulSpectrums(dft_A,dft_B,dft_C,CV_DXT_MUL_CONJ);
// Split Fourier in real and imaginary parts
image_ReC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
image_ImC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
complex_ImC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 2);
printf("%d %d %d %d\n",dft_M,dft_N,dft_M1,dft_N1);
//cvSplit( dft_C, image_ReC, image_ImC, 0, 0 );
cvSplit( dft_C, image_ReC, image_ImC, 0, 0 );
// Compute A^2 + B^2 of denominator or blur kernel
image_ReB = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
image_ImB = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
// Split Real and imaginary parts
cvSplit( dft_B, image_ReB, image_ImB, 0, 0 );
cvPow( image_ReB, image_ReB, 2.0);
cvPow( image_ImB, image_ImB, 2.0);
cvAdd(image_ReB, image_ImB, image_ReB,0);
//Divide Numerator/A^2 + B^2
cvDiv(image_ReC, image_ReB, image_ReC, 1.0);
cvDiv(image_ImC, image_ReB, image_ImC, 1.0);
// Merge Real and complex parts
cvMerge(image_ReC, image_ImC, NULL, NULL, complex_ImC);
// Perform Inverse
cvShowInvDFT(im, complex_ImC,dft_M1,dft_N1,fp,"deblur");
cvWaitKey(-1);
return 0;
}
//------------------------------------------------------------------------------
//------------------------------------------------------------------------------
CvMat *tgso (CvMat &tmap, int ntex, double sigma, double theta, CvMat &tsim, int useChi2) {
CvMat *roundTmap=cvCreateMat(tmap.rows,tmap.cols,CV_32FC1);
CvMat *comp=cvCreateMat(tmap.rows,tmap.cols,CV_32FC1);
for (int i=0;i<tmap.rows;i++)
for (int j=0;j<tmap.cols;j++)
cvSetReal2D(roundTmap,i,j,cvRound(cvGetReal2D(&tmap,i,j)));
cvSub(&tmap,roundTmap,comp);
if (cvCountNonZero(comp)) {
printf("texton labels not integral");
cvReleaseMat(&roundTmap);
cvReleaseMat(&comp);
exit(1);
}
double min,max;
cvMinMaxLoc(&tmap,&min,&max);
if (min<1 && max>ntex) {
char *msg=new char[50];
printf(msg,"texton labels out of range [1,%d]",ntex);
cvReleaseMat(&roundTmap);
cvReleaseMat(&comp);
exit(1);
}
cvReleaseMat(&roundTmap);
cvReleaseMat(&comp);
double wr=floor(sigma); //sigma=radius (Leo)
CvMat *x=cvCreateMat(1,wr-(-wr)+1, CV_64FC1);
CvMat *y=cvCreateMat(wr-(-wr)+1,1, CV_64FC1);
CvMat *u=cvCreateMat(wr-(-wr)+1,wr-(-wr)+1, CV_64FC1);
CvMat *v=cvCreateMat(wr-(-wr)+1,wr-(-wr)+1, CV_64FC1);
CvMat *gamma=cvCreateMat(u->rows,v->rows, CV_64FC1);
// Set x,y directions
for (int j=-wr;j<=wr;j++) {
cvSetReal2D(x,0,(j+wr),j);
cvSetReal2D(y,(j+wr),0,j);
}
// Set u,v, meshgrids
for (int i=0;i<u->rows;i++) {
cvRepeat(x,u);
cvRepeat(y,v);
}
// Compute the gamma matrix from the grid
for (int i=0;i<u->rows;i++)
for (int j=0;j<u->cols;j++)
cvSetReal2D(gamma,i,j,atan2(cvGetReal2D(v,i,j),cvGetReal2D(u,i,j)));
cvReleaseMat(&x);
cvReleaseMat(&y);
CvMat *sum=cvCreateMat(u->rows,u->cols, CV_64FC1);
cvMul(u,u,u);
cvMul(v,v,v);
cvAdd(u,v,sum);
CvMat *mask=cvCreateMat(u->rows,u->cols, CV_8UC1);
cvCmpS(sum,sigma*sigma,mask,CV_CMP_LE);
cvConvertScale(mask,mask,1.0/255);
cvSetReal2D(mask,wr,wr,0);
int count=cvCountNonZero(mask);
cvReleaseMat(&u);
cvReleaseMat(&v);
cvReleaseMat(&sum);
CvMat *sub=cvCreateMat(mask->rows,mask->cols, CV_64FC1);
CvMat *side=cvCreateMat(mask->rows,mask->cols, CV_8UC1);
cvSubS(gamma,cvScalar(theta),sub);
cvReleaseMat(&gamma);
for (int i=0;i<mask->rows;i++){
for (int j=0;j<mask->cols;j++) {
double n=cvmGet(sub,i,j);
double n_mod = n-floor(n/(2*M_PI))*2*M_PI;
cvSetReal2D(side,i,j, 1 + int(n_mod < M_PI));
}
}
cvMul(side,mask,side);
cvReleaseMat(&sub);
cvReleaseMat(&mask);
CvMat *lmask=cvCreateMat(side->rows,side->cols, CV_8UC1);
CvMat *rmask=cvCreateMat(side->rows,side->cols, CV_8UC1);
cvCmpS(side,1,lmask,CV_CMP_EQ);
cvCmpS(side,2,rmask,CV_CMP_EQ);
int count1=cvCountNonZero(lmask), count2=cvCountNonZero(rmask);
if (count1 != count2) {
printf("Bug: imbalance\n");
}
CvMat *rlmask=cvCreateMat(side->rows,side->cols, CV_32FC1);
CvMat *rrmask=cvCreateMat(side->rows,side->cols, CV_32FC1);
cvConvertScale(lmask,rlmask,1.0/(255*count)*2);
cvConvertScale(rmask,rrmask,1.0/(255*count)*2);
cvReleaseMat(&lmask);
cvReleaseMat(&rmask);
cvReleaseMat(&side);
int h=tmap.rows;
int w=tmap.cols;
CvMat *d = cvCreateMat(h*w,ntex,CV_32FC1);
CvMat *coltemp = cvCreateMat(h*w,1,CV_32FC1);
CvMat *tgL = cvCreateMat(h,w, CV_32FC1);
CvMat *tgR = cvCreateMat(h,w, CV_32FC1);
CvMat *temp = cvCreateMat(h,w,CV_8UC1);
CvMat *im = cvCreateMat(h,w, CV_32FC1);
CvMat *sub2 = cvCreateMat(h,w,CV_32FC1);
CvMat *sub2t = cvCreateMat(w,h,CV_32FC1);
CvMat *prod = cvCreateMat(h*w,ntex,CV_32FC1);
CvMat reshapehdr,*reshape;
CvMat* tgL_pad = cvCreateMat(h+rlmask->rows-1,w+rlmask->cols-1,CV_32FC1);
CvMat* tgR_pad = cvCreateMat(h+rlmask->rows-1,w+rlmask->cols-1,CV_32FC1);
CvMat* im_pad = cvCreateMat(h+rlmask->rows-1,w+rlmask->cols-1,CV_32FC1);
CvMat *tg=cvCreateMat(h,w,CV_32FC1);
cvZero(tg);
if (useChi2 == 1){
CvMat* temp_add1 = cvCreateMat(h,w,CV_32FC1);
for (int i=0;i<ntex;i++) {
cvCmpS(&tmap,i+1,temp,CV_CMP_EQ);
cvConvertScale(temp,im,1.0/255);
cvCopyMakeBorder(tgL,tgL_pad,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2),IPL_BORDER_CONSTANT);
cvCopyMakeBorder(tgR,tgR_pad,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2),IPL_BORDER_CONSTANT);
cvCopyMakeBorder(im,im_pad,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2),IPL_BORDER_CONSTANT);
cvFilter2D(im_pad,tgL_pad,rlmask,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2));
cvFilter2D(im_pad,tgR_pad,rrmask,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2));
cvGetSubRect(tgL_pad,tgL,cvRect((rlmask->cols-1)/2,(rlmask->rows-1)/2,tgL->cols,tgL->rows));
cvGetSubRect(tgR_pad,tgR,cvRect((rlmask->cols-1)/2,(rlmask->rows-1)/2,tgR->cols,tgR->rows));
cvSub(tgL,tgR,sub2);
cvPow(sub2,sub2,2.0);
cvAdd(tgL,tgR,temp_add1);
cvAddS(temp_add1,cvScalar(0.0000000001),temp_add1);
cvDiv(sub2,temp_add1,sub2);
cvAdd(tg,sub2,tg);
}
cvScale(tg,tg,0.5);
cvReleaseMat(&temp_add1);
}
else{// if not chi^2
for (int i=0;i<ntex;i++) {
cvCmpS(&tmap,i+1,temp,CV_CMP_EQ);
cvConvertScale(temp,im,1.0/255);
cvCopyMakeBorder(tgL,tgL_pad,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2),IPL_BORDER_CONSTANT);
cvCopyMakeBorder(tgR,tgR_pad,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2),IPL_BORDER_CONSTANT);
cvCopyMakeBorder(im,im_pad,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2),IPL_BORDER_CONSTANT);
cvFilter2D(im_pad,tgL_pad,rlmask,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2));
cvFilter2D(im_pad,tgR_pad,rrmask,cvPoint((rlmask->cols-1)/2,(rlmask->rows-1)/2));
cvGetSubRect(tgL_pad,tgL,cvRect((rlmask->cols-1)/2,(rlmask->rows-1)/2,tgL->cols,tgL->rows));
cvGetSubRect(tgR_pad,tgR,cvRect((rlmask->cols-1)/2,(rlmask->rows-1)/2,tgR->cols,tgR->rows));
cvSub(tgL,tgR,sub2);
cvAbs(sub2,sub2);
cvTranspose(sub2,sub2t);
reshape=cvReshape(sub2t,&reshapehdr,0,h*w);
cvGetCol(d,coltemp,i);
cvCopy(reshape,coltemp);
}
cvMatMul(d,&tsim,prod);
cvMul(prod,d,prod);
CvMat *sumcols=cvCreateMat(h*w,1,CV_32FC1);
cvSetZero(sumcols);
for (int i=0;i<prod->cols;i++) {
cvGetCol(prod,coltemp,i);
cvAdd(sumcols,coltemp,sumcols);
}
reshape=cvReshape(sumcols,&reshapehdr,0,w);
cvTranspose(reshape,tg);
cvReleaseMat(&sumcols);
}
//Smooth the gradient now!!
tg=fitparab(*tg,sigma,sigma/4,theta);
cvMaxS(tg,0,tg);
cvReleaseMat(&im_pad);
cvReleaseMat(&tgL_pad);
cvReleaseMat(&tgR_pad);
cvReleaseMat(&rlmask);
cvReleaseMat(&rrmask);
cvReleaseMat(&im);
cvReleaseMat(&tgL);
cvReleaseMat(&tgR);
cvReleaseMat(&temp);
cvReleaseMat(&coltemp);
cvReleaseMat(&sub2);
cvReleaseMat(&sub2t);
cvReleaseMat(&d);
cvReleaseMat(&prod);
return tg;
}
gint compose_skin_matrix(IplImage* rgbin, IplImage* gray_out)
{
/*
int skin_under_seed = 0;
static IplImage* imageHSV = cvCreateImage( cvSize(rgbin->width, rgbin->height), IPL_DEPTH_8U, 3);
cvCvtColor(rgbin, imageHSV, CV_RGB2HSV);
static IplImage* planeH = cvCreateImage( cvGetSize(imageHSV), 8, 1); // Hue component.
///IplImage* planeH2= cvCreateImage( cvGetSize(imageHSV), 8, 1); // Hue component, 2nd threshold
static IplImage* planeS = cvCreateImage( cvGetSize(imageHSV), 8, 1); // Saturation component.
static IplImage* planeV = cvCreateImage( cvGetSize(imageHSV), 8, 1); // Brightness component.
cvCvtPixToPlane(imageHSV, planeH, planeS, planeV, 0); // Extract the 3 color components.
// Detect which pixels in each of the H, S and V channels are probably skin pixels.
// Assume that skin has a Hue between 0 to 18 (out of 180), and Saturation above 50, and Brightness above 80.
///cvThreshold(planeH , planeH2, 10, UCHAR_MAX, CV_THRESH_BINARY); //(hue > 10)
cvThreshold(planeH , planeH , 20, UCHAR_MAX, CV_THRESH_BINARY_INV); //(hue < 20)
cvThreshold(planeS , planeS , 48, UCHAR_MAX, CV_THRESH_BINARY); //(sat > 48)
cvThreshold(planeV , planeV , 80, UCHAR_MAX, CV_THRESH_BINARY); //(val > 80)
// erode the HUE to get rid of noise.
cvErode(planeH, planeH, NULL, 1);
// Combine all 3 thresholded color components, so that an output pixel will only
// be white (255) if the H, S and V pixels were also white.
// gray_out = (hue > 10) ^ (hue < 20) ^ (sat > 48) ^ (val > 80), where ^ mean pixels-wise AND
cvAnd(planeH , planeS , gray_out);
//cvAnd(gray_out, planeH2, gray_out);
cvAnd(gray_out, planeV , gray_out);
return(skin_under_seed);
*/
static IplImage* planeR = cvCreateImage( cvGetSize(rgbin), 8, 1); // R component.
static IplImage* planeG = cvCreateImage( cvGetSize(rgbin), 8, 1); // G component.
static IplImage* planeB = cvCreateImage( cvGetSize(rgbin), 8, 1); // B component.
static IplImage* planeAll = cvCreateImage( cvGetSize(rgbin), IPL_DEPTH_32F, 1); // (R+G+B) component.
static IplImage* planeR2 = cvCreateImage( cvGetSize(rgbin), IPL_DEPTH_32F, 1); // R component, 32bits
static IplImage* planeRp = cvCreateImage( cvGetSize(rgbin), IPL_DEPTH_32F, 1); // R' and >0.4
static IplImage* planeGp = cvCreateImage( cvGetSize(rgbin), IPL_DEPTH_32F, 1); // G' and > 0.28
static IplImage* planeRp2 = cvCreateImage( cvGetSize(rgbin), IPL_DEPTH_32F, 1); // R' <0.6
static IplImage* planeGp2 = cvCreateImage( cvGetSize(rgbin), IPL_DEPTH_32F, 1); // G' <0.4
cvCvtPixToPlane(rgbin, planeR, planeG, planeB, 0); // Extract the 3 color components.
cvAdd( planeR, planeG, planeAll, NULL);
cvAdd( planeB, planeAll, planeAll, NULL); // All = R + G + B
cvDiv( planeR, planeAll, planeRp, 1.0); // R' = R / ( R + G + B)
cvDiv( planeG, planeAll, planeGp, 1.0); // G' = G / ( R + G + B)
cvConvertScale( planeR, planeR2, 1.0, 0.0);
cvCopy(planeGp, planeGp2, NULL);
cvCopy(planeRp, planeRp2, NULL);
cvThreshold(planeR2 , planeR2, 60, UCHAR_MAX, CV_THRESH_BINARY); //(R > 60)
cvThreshold(planeRp , planeRp, 0.40, UCHAR_MAX, CV_THRESH_BINARY); //(R'> 0.4)
cvThreshold(planeRp2, planeRp2, 0.6, UCHAR_MAX, CV_THRESH_BINARY_INV); //(R'< 0.6)
cvThreshold(planeGp , planeGp, 0.28, UCHAR_MAX, CV_THRESH_BINARY); //(G'> 0.28)
cvThreshold(planeGp2, planeGp2, 0.4, UCHAR_MAX, CV_THRESH_BINARY_INV); //(G'< 0.4)
// R’ = R / (R+G+B), G’ = G / (R + G + B)
// Skin pixel if:
// R > 60 AND R’ > 0.4 AND R’ < 0.6 AND G’ > 0.28 and G’ < 0.4
static IplImage* imageSkinPixels = cvCreateImage( cvGetSize(rgbin), IPL_DEPTH_32F, 1);
cvAnd( planeR2 , planeRp , imageSkinPixels);
cvAnd( planeRp , imageSkinPixels , imageSkinPixels);
cvAnd( planeRp2, imageSkinPixels , imageSkinPixels);
cvAnd( planeGp , imageSkinPixels , imageSkinPixels);
cvAnd( planeGp2, imageSkinPixels , imageSkinPixels);
cvConvertScale( imageSkinPixels, gray_out, 1.0, 0.0);
return(0);
}
int main(int argc, char ** argv)
{
int height,width,step,channels,depth;
uchar* data1;
CvMat *dft_A;
CvMat *dft_B;
CvMat *dft_C;
IplImage* im;
IplImage* im1;
IplImage* image_ReB;
IplImage* image_ImB;
IplImage* image_ReC;
IplImage* image_ImC;
IplImage* complex_ImC;
CvScalar val;
IplImage* k_image_hdr;
int i,j,k;
FILE *fp;
fp = fopen("test.txt","w+");
int dft_M,dft_N;
int dft_M1,dft_N1;
CvMat* cvShowDFT1(IplImage*, int, int,char*);
void cvShowInvDFT1(IplImage*, CvMat*, int, int,char*);
im1 = cvLoadImage( "../homer.jpg",1 );
cvNamedWindow("Original-color", 0);
cvShowImage("Original-color", im1);
im = cvLoadImage( "../homer.jpg", CV_LOAD_IMAGE_GRAYSCALE );
if( !im )
return -1;
cvNamedWindow("Original-gray", 0);
cvShowImage("Original-gray", im);
// Create a random noise matrix
fp = fopen("test.txt","w+");
int val_noise[357*383];
for(i=0; i <im->height;i++){
for(j=0;j<im->width;j++){
fprintf(fp, "%d ",(383*i+j));
val_noise[383*i+j] = rand() % 128;
}
fprintf(fp, "/n");
}
CvMat noise = cvMat(im->height,im->width, CV_8UC1,val_noise);
// Add the random noise matric to the image
cvAdd(im,&noise,im, 0);
cvNamedWindow("Original + Noise", 0);
cvShowImage("Original + Noise", im);
cvSmooth( im, im, CV_GAUSSIAN, 7, 7, 0.5, 0.5 );
cvNamedWindow("Gaussian Smooth", 0);
cvShowImage("Gaussian Smooth", im);
// Create a blur kernel
IplImage* k_image;
float r = rad;
float radius=((int)(r)*2+1)/2.0;
int rowLength=(int)(2*radius);
printf("rowlength %d/n",rowLength);
float kernels[rowLength*rowLength];
printf("rowl: %i",rowLength);
int norm=0; //Normalization factor
int x,y;
CvMat kernel;
for(x = 0; x < rowLength; x++)
for (y = 0; y < rowLength; y++)
if (sqrt((x - (int)(radius) ) * (x - (int)(radius) ) + (y - (int)(radius))* (y - (int)(radius))) <= (int)(radius))
norm++;
// Populate matrix
for (y = 0; y < rowLength; y++) //populate array with values
{
for (x = 0; x < rowLength; x++) {
if (sqrt((x - (int)(radius) ) * (x - (int)(radius) ) + (y - (int)(radius))
* (y - (int)(radius))) <= (int)(radius)) {
//kernels[y * rowLength + x] = 255;
kernels[y * rowLength + x] =1.0/norm;
printf("%f ",1.0/norm);
}
else{
kernels[y * rowLength + x] =0;
}
}
}
kernel= cvMat(rowLength, // number of rows
rowLength, // number of columns
CV_32FC1, // matrix data type
&kernels);
k_image_hdr = cvCreateImageHeader( cvSize(rowLength,rowLength), IPL_DEPTH_32F,1);
k_image = cvGetImage(&kernel,k_image_hdr);
height = k_image->height;
width = k_image->width;
step = k_image->widthStep/sizeof(float);
depth = k_image->depth;
channels = k_image->nChannels;
//data1 = (float *)(k_image->imageData);
data1 = (uchar *)(k_image->imageData);
cvNamedWindow("blur kernel", 0);
cvShowImage("blur kernel", k_image);
dft_M = cvGetOptimalDFTSize( im->height - 1 );
dft_N = cvGetOptimalDFTSize( im->width - 1 );
//dft_M1 = cvGetOptimalDFTSize( im->height+99 - 1 );
//dft_N1 = cvGetOptimalDFTSize( im->width+99 - 1 );
dft_M1 = cvGetOptimalDFTSize( im->height+3 - 1 );
dft_N1 = cvGetOptimalDFTSize( im->width+3 - 1 );
printf("dft_N1=%d,dft_M1=%d/n",dft_N1,dft_M1);
// Perform DFT of original image
dft_A = cvShowDFT1(im, dft_M1, dft_N1,"original");
//Perform inverse (check)
//cvShowInvDFT1(im,dft_A,dft_M1,dft_N1, "original"); - Commented as it overwrites the DFT
// Perform DFT of kernel
dft_B = cvShowDFT1(k_image,dft_M1,dft_N1,"kernel");
//Perform inverse of kernel (check)
//cvShowInvDFT1(k_image,dft_B,dft_M1,dft_N1, "kernel");- Commented as it overwrites the DFT
// Multiply numerator with complex conjugate
dft_C = cvCreateMat( dft_M1, dft_N1, CV_64FC2 );
printf("%d %d %d %d/n",dft_M,dft_N,dft_M1,dft_N1);
// Multiply DFT(blurred image) * complex conjugate of blur kernel
cvMulSpectrums(dft_A,dft_B,dft_C,CV_DXT_MUL_CONJ);
//cvShowInvDFT1(im,dft_C,dft_M1,dft_N1,"blur1");
// Split Fourier in real and imaginary parts
image_ReC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
image_ImC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
complex_ImC = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 2);
printf("%d %d %d %d/n",dft_M,dft_N,dft_M1,dft_N1);
//cvSplit( dft_C, image_ReC, image_ImC, 0, 0 );
cvSplit( dft_C, image_ReC, image_ImC, 0, 0 );
// Compute A^2 + B^2 of denominator or blur kernel
image_ReB = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
image_ImB = cvCreateImage( cvSize(dft_N1, dft_M1), IPL_DEPTH_64F, 1);
// Split Real and imaginary parts
cvSplit( dft_B, image_ReB, image_ImB, 0, 0 );
cvPow( image_ReB, image_ReB, 2.0);
cvPow( image_ImB, image_ImB, 2.0);
cvAdd(image_ReB, image_ImB, image_ReB,0);
val = cvScalarAll(kappa);
cvAddS(image_ReB,val,image_ReB,0);
//Divide Numerator/A^2 + B^2
cvDiv(image_ReC, image_ReB, image_ReC, 1.0);
cvDiv(image_ImC, image_ReB, image_ImC, 1.0);
// Merge Real and complex parts
cvMerge(image_ReC, image_ImC, NULL, NULL, complex_ImC);
// Perform Inverse
cvShowInvDFT1(im, (CvMat *)complex_ImC,dft_M1,dft_N1,"O/p Wiener k=1 rad=2");
cvWaitKey(-1);
return 0;
}
gint gstskin_find_skin_center_of_mass2(struct _GstSkin *skin, gint display)
{
int skin_under_seed = 0;
IplImage* imageRGB = cvCreateImageHeader( cvSize(skin->width, skin->height), IPL_DEPTH_8U, 3);
imageRGB->imageData = skin->cvRGB->imageData;
IplImage* planeR = cvCreateImage( cvGetSize(imageRGB), 8, 1); // R component.
IplImage* planeG = cvCreateImage( cvGetSize(imageRGB), 8, 1); // G component.
IplImage* planeB = cvCreateImage( cvGetSize(imageRGB), 8, 1); // B component.
IplImage* planeAll = cvCreateImage( cvGetSize(imageRGB), IPL_DEPTH_32F, 1); // (R+G+B) component.
IplImage* planeR2 = cvCreateImage( cvGetSize(imageRGB), IPL_DEPTH_32F, 1); // R component, 32bits
IplImage* planeRp = cvCreateImage( cvGetSize(imageRGB), IPL_DEPTH_32F, 1); // R' and >0.4
IplImage* planeGp = cvCreateImage( cvGetSize(imageRGB), IPL_DEPTH_32F, 1); // G' and > 0.28
IplImage* planeRp2 = cvCreateImage( cvGetSize(imageRGB), IPL_DEPTH_32F, 1); // R' <0.6
IplImage* planeGp2 = cvCreateImage( cvGetSize(imageRGB), IPL_DEPTH_32F, 1); // G' <0.4
cvCvtPixToPlane(imageRGB, planeR, planeG, planeB, 0); // Extract the 3 color components.
cvAdd( planeR, planeG, planeAll, NULL);
cvAdd( planeB, planeAll, planeAll, NULL); // All = R + G + B
cvDiv( planeR, planeAll, planeRp, 1.0); // R' = R / ( R + G + B)
cvDiv( planeG, planeAll, planeGp, 1.0); // G' = G / ( R + G + B)
cvConvertScale( planeR, planeR2, 1.0, 0.0);
cvCopy(planeGp, planeGp2, NULL);
cvCopy(planeRp, planeRp2, NULL);
cvThreshold(planeR2 , planeR2, 60, UCHAR_MAX, CV_THRESH_BINARY); //(R > 60)
cvThreshold(planeRp , planeRp, 0.42, UCHAR_MAX, CV_THRESH_BINARY); //(R'> 0.4)
cvThreshold(planeRp2, planeRp2, 0.6, UCHAR_MAX, CV_THRESH_BINARY_INV); //(R'< 0.6)
cvThreshold(planeGp , planeGp, 0.28, UCHAR_MAX, CV_THRESH_BINARY); //(G'> 0.28)
cvThreshold(planeGp2, planeGp2, 0.4, UCHAR_MAX, CV_THRESH_BINARY_INV); //(G'< 0.4)
// Combine all 3 thresholded color components, so that an output pixel will only
// be white (255) if the H, S and V pixels were also white.
IplImage* imageSkinPixels = cvCreateImage( cvGetSize(imageRGB), IPL_DEPTH_32F, 1); // Greyscale output image.
cvAnd( planeR2 , planeRp , imageSkinPixels);
cvAnd( planeRp , imageSkinPixels , imageSkinPixels);
cvAnd( planeRp2, imageSkinPixels , imageSkinPixels);
cvAnd( planeGp , imageSkinPixels , imageSkinPixels);
cvAnd( planeGp2, imageSkinPixels , imageSkinPixels);
IplImage* draft = cvCreateImage( cvGetSize(imageRGB), IPL_DEPTH_8U , 1); // Greyscale output image.
if(display){
if( skin->showH )
cvConvertScale( planeRp, draft, 1.0, 0.0);
else if( skin->showS )
cvConvertScale( planeG, draft, 1.0, 0.0);
else if( skin->showV )
cvConvertScale( planeB, draft, 1.0, 0.0);
else
cvConvertScale( imageSkinPixels, draft, 1.0, 0.0);
cvCvtColor(draft, imageRGB, CV_GRAY2RGB);
}
cvReleaseImage( &imageSkinPixels );
cvReleaseImage( &planeR );
cvReleaseImage( &planeG );
cvReleaseImage( &planeB );
cvReleaseImage( &planeAll );
cvReleaseImage( &planeR2 );
cvReleaseImage( &planeRp );
cvReleaseImage( &planeGp );
cvReleaseImage( &planeRp2 );
cvReleaseImage( &planeGp2 );
cvReleaseImage( &draft );
return(skin_under_seed);
}
static GstFlowReturn
gst_skin_detect_transform (GstOpencvVideoFilter * base, GstBuffer * buf,
IplImage * img, GstBuffer * outbuf, IplImage * outimg)
{
GstSkinDetect *filter = GST_SKIN_DETECT (base);
filter->cvRGB->imageData = (char *) img->imageData;
filter->cvSkin->imageData = (char *) outimg->imageData;
/* SKIN COLOUR BLOB DETECTION */
if (HSV == filter->method) {
cvCvtColor (filter->cvRGB, filter->cvHSV, CV_RGB2HSV);
cvCvtPixToPlane (filter->cvHSV, filter->cvH, filter->cvS, filter->cvV, 0); /* Extract the 3 color components. */
/* Detect which pixels in each of the H, S and V channels are probably skin pixels.
Assume that skin has a Hue between 0 to 18 (out of 180), and Saturation above 50, and Brightness above 80. */
cvThreshold (filter->cvH, filter->cvH2, 10, UCHAR_MAX, CV_THRESH_BINARY); /* (hue > 10) */
cvThreshold (filter->cvH, filter->cvH, 20, UCHAR_MAX, CV_THRESH_BINARY_INV); /* (hue < 20) */
cvThreshold (filter->cvS, filter->cvS, 48, UCHAR_MAX, CV_THRESH_BINARY); /* (sat > 48) */
cvThreshold (filter->cvV, filter->cvV, 80, UCHAR_MAX, CV_THRESH_BINARY); /* (val > 80) */
/* erode the HUE to get rid of noise. */
cvErode (filter->cvH, filter->cvH, NULL, 1);
/* Combine all 3 thresholded color components, so that an output pixel will only
be white (255) if the H, S and V pixels were also white.
imageSkin = (hue > 10) ^ (hue < 20) ^ (sat > 48) ^ (val > 80), where ^ mean pixels-wise AND */
cvAnd (filter->cvH, filter->cvS, filter->cvSkinPixels1, NULL);
cvAnd (filter->cvSkinPixels1, filter->cvH2, filter->cvSkinPixels1, NULL);
cvAnd (filter->cvSkinPixels1, filter->cvV, filter->cvSkinPixels1, NULL);
cvCvtColor (filter->cvSkinPixels1, filter->cvRGB, CV_GRAY2RGB);
} else if (RGB == filter->method) {
cvCvtPixToPlane (filter->cvRGB, filter->cvR, filter->cvG, filter->cvB, 0); /* Extract the 3 color components. */
cvAdd (filter->cvR, filter->cvG, filter->cvAll, NULL);
cvAdd (filter->cvB, filter->cvAll, filter->cvAll, NULL); /* All = R + G + B */
cvDiv (filter->cvR, filter->cvAll, filter->cvRp, 1.0); /* R' = R / ( R + G + B) */
cvDiv (filter->cvG, filter->cvAll, filter->cvGp, 1.0); /* G' = G / ( R + G + B) */
cvConvertScale (filter->cvR, filter->cvR2, 1.0, 0.0);
cvCopy (filter->cvGp, filter->cvGp2, NULL);
cvCopy (filter->cvRp, filter->cvRp2, NULL);
cvThreshold (filter->cvR2, filter->cvR2, 60, UCHAR_MAX, CV_THRESH_BINARY); /* (R > 60) */
cvThreshold (filter->cvRp, filter->cvRp, 0.42, UCHAR_MAX, CV_THRESH_BINARY); /* (R'> 0.4) */
cvThreshold (filter->cvRp2, filter->cvRp2, 0.6, UCHAR_MAX, CV_THRESH_BINARY_INV); /* (R'< 0.6) */
cvThreshold (filter->cvGp, filter->cvGp, 0.28, UCHAR_MAX, CV_THRESH_BINARY); /* (G'> 0.28) */
cvThreshold (filter->cvGp2, filter->cvGp2, 0.4, UCHAR_MAX, CV_THRESH_BINARY_INV); /* (G'< 0.4) */
/* Combine all 3 thresholded color components, so that an output pixel will only
be white (255) if the H, S and V pixels were also white. */
cvAnd (filter->cvR2, filter->cvRp, filter->cvSkinPixels2, NULL);
cvAnd (filter->cvRp, filter->cvSkinPixels2, filter->cvSkinPixels2, NULL);
cvAnd (filter->cvRp2, filter->cvSkinPixels2, filter->cvSkinPixels2, NULL);
cvAnd (filter->cvGp, filter->cvSkinPixels2, filter->cvSkinPixels2, NULL);
cvAnd (filter->cvGp2, filter->cvSkinPixels2, filter->cvSkinPixels2, NULL);
cvConvertScale (filter->cvSkinPixels2, filter->cvdraft, 1.0, 0.0);
cvCvtColor (filter->cvdraft, filter->cvRGB, CV_GRAY2RGB);
}
/* After this we have a RGB Black and white image with the skin, in
filter->cvRGB. We can postprocess by applying 1 erode-dilate and 1
dilate-erode, or alternatively 1 opening-closing all together, with
the goal of removing small (spurious) skin spots and creating large
connected areas */
if (filter->postprocess) {
cvSplit (filter->cvRGB, filter->cvChA, NULL, NULL, NULL);
cvErode (filter->cvChA, filter->cvChA,
cvCreateStructuringElementEx (3, 3, 1, 1, CV_SHAPE_RECT, NULL), 1);
cvDilate (filter->cvChA, filter->cvChA,
cvCreateStructuringElementEx (3, 3, 1, 1, CV_SHAPE_RECT, NULL), 2);
cvErode (filter->cvChA, filter->cvChA,
cvCreateStructuringElementEx (3, 3, 1, 1, CV_SHAPE_RECT, NULL), 1);
cvCvtColor (filter->cvChA, filter->cvRGB, CV_GRAY2RGB);
}
cvCopy (filter->cvRGB, filter->cvSkin, NULL);
return GST_FLOW_OK;
}
void prefilt(const IplImage* img,IplImage* prefedImg,int pad,IplImage* filter2D)
{
int imagesize=img->height;
int padImgSize=imagesize+2*pad;
IplImage* temp1=cvCreateImage(cvSize(imagesize,imagesize),IPL_DEPTH_32F,3);
/*put 8 bits image into 32 bits float image*/
cvConvertScale(img,temp1,1,1);
int i;
//use ptr to get access to data blocks/pixels,each block/pixel has three float numbers
IplImage* temp2=cvCreateImage(cvSize(imagesize,imagesize),IPL_DEPTH_32F,3);
float* ptr=NULL;
cvLog(temp1,temp2);
cvReleaseImage(&temp1);
/*pad the image to reduce boundary artifacts*/
IplImage* temp5=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,3);
IplImage* pad1=cvCreateImage(cvSize(imagesize,imagesize),IPL_DEPTH_32F,1);
IplImage* pad2=cvCreateImage(cvSize(imagesize,imagesize),IPL_DEPTH_32F,1);
IplImage* pad3=cvCreateImage(cvSize(imagesize,imagesize),IPL_DEPTH_32F,1);
cvSplit(temp2,pad1,pad2,pad3,NULL);
IplImage* C1=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
IplImage* C2=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
IplImage* C3=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
padarraySym(pad1,C1,pad);/* pad each channel */
padarraySym(pad2,C2,pad);
padarraySym(pad3,C3,pad);
cvMerge(C1,C2,C3,NULL,temp5);
cvReleaseImage(&pad1);
cvReleaseImage(&pad2);
cvReleaseImage(&pad3);
cvReleaseImage(&C1);
cvReleaseImage(&C2);
cvReleaseImage(&C3);
/* FFT */
/* split 3-channel image to single channel images */
IplImage* splitImgs[3];
IplImage* dftOutput[3];
IplImage* splitImgsC2[3];
for(i=0;i<3;i++)
{
splitImgs[i]=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
splitImgsC2[i]=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,2);
dftOutput[i]=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,2);
cvSetZero(splitImgs[i]);
cvSetZero(splitImgsC2[i]);
cvSetZero(dftOutput[i]);
}
cvSplit(temp5,splitImgs[0],splitImgs[1],splitImgs[2],NULL);
IplImage* mergSrc=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
cvSetZero(mergSrc);
/* merge to double channel images for input of FFT */
for(i=0;i<3;i++)
{
cvMerge(splitImgs[i],mergSrc,NULL,NULL,splitImgsC2[i]);
cvReleaseImage(&splitImgs[i]);
}
/* FFT */
for(i=0;i<3;i++)
{
cvDFT(splitImgsC2[i],dftOutput[i],CV_DXT_FORWARD);
cvReleaseImage(&splitImgsC2[i]);
}
/* apply prefilt to three channels */
IplImage* output_temp[3];
IplImage* outputReal[3];
IplImage* outputImaginary[3];
IplImage* ifftOutput[3];
float temp=0;
int j=0;
float* ptr1=NULL;
for(i=0;i<3;i++)
{
output_temp[i]=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,2);
cvSetZero(output_temp[i]);
cvMul(filter2D,dftOutput[i],output_temp[i]);
cvReleaseImage(&dftOutput[i]);
outputReal[i]=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
cvSetZero(outputReal[i]);
outputImaginary[i]=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
cvSetZero(outputImaginary[i]);
ifftOutput[i]=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,2);
cvSetZero(ifftOutput[i]);
cvDFT(output_temp[i],ifftOutput[i],CV_DXT_INVERSE_SCALE);
cvSplit(ifftOutput[i],outputReal[i],outputImaginary[i],NULL,NULL);
cvReleaseImage(&outputImaginary[i]);
cvReleaseImage(&ifftOutput[i]);
}
/* merge real parts to a 3-channel image */
IplImage* temp3=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,3);
cvMerge(outputReal[0],outputReal[1],outputReal[2],NULL,temp3);
cvReleaseImage(&outputReal[0]);
cvReleaseImage(&outputReal[1]);
cvReleaseImage(&outputReal[2]);
IplImage* prefiltOutput=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,3);
cvSub(temp5,temp3,prefiltOutput);
cvReleaseImage(&temp3);
cvReleaseImage(&temp5);
/* local contrast normalization */
/* mean of values in different channels of each pixel */
IplImage* mean=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
IplImage* mean2D=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,2);
ptr=(float*) mean->imageData;
ptr1=(float*) prefiltOutput->imageData;
for(i=0;i<(padImgSize)*(padImgSize)*3;i++)
{
if((i+1)%3==0)
{
temp=(*(ptr1+i)+*(ptr1+i-1)+*(ptr1+i-2))/3;
*(ptr+(i+1)/3-1)=temp*temp;
}
}
/* FFT */
IplImage* fftRes=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,2);
cvMerge(mean,mergSrc,NULL,NULL,mean2D);
cvReleaseImage(&mergSrc);
cvDFT(mean2D,fftRes,CV_DXT_FORWARD);
cvReleaseImage(&mean);
IplImage* temp4=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,2);
cvMul(filter2D,fftRes,temp4);
/* Inverse FFT */
IplImage* ifftRes=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,2);
IplImage* lccstTemp=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,1);
ptr1=(float*) lccstTemp->imageData;
cvFFT(temp4,ifftRes,CV_DXT_INVERSE_SCALE);
/* caculate the abs of complex number matrix */
ptr=(float*)ifftRes->imageData;
float realV=0;
float imgV=0;
for(i=0;i<(padImgSize)*(padImgSize);i++)
{
realV=*(ptr+2*i);
imgV=*(ptr+2*i+1);
*(ptr1+i)=sqrt(sqrt(realV*realV+fabs(imgV*imgV)))+0.2;
}
IplImage* lccst=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,3);
cvMerge(lccstTemp,lccstTemp,lccstTemp,NULL,lccst);
IplImage* outputTemp=cvCreateImage(cvSize(padImgSize,padImgSize),IPL_DEPTH_32F,3);
cvDiv(prefiltOutput,lccst,outputTemp);
cvReleaseImage(&mean2D);
cvReleaseImage(&fftRes);
cvReleaseImage(&ifftRes);
cvReleaseImage(&lccstTemp);
cvReleaseImage(&lccst);
cvReleaseImage(&prefiltOutput);
cvReleaseImage(&temp2);
cvReleaseImage(&temp4);
cvReleaseImage(&output_temp[0]);
cvReleaseImage(&output_temp[1]);
cvReleaseImage(&output_temp[2]);
IplImage* temp6 = cvGetSubImage(outputTemp,cvRect(pad,pad,imagesize,imagesize));
cvCopy(temp6,prefedImg);
cvReleaseImage(&outputTemp);
cvReleaseImage(&temp6);
}
void icvConvertPointsHomogenious( const CvMat* src, CvMat* dst )
{
CvMat* temp = 0;
CvMat* denom = 0;
CV_FUNCNAME( "cvConvertPointsHomogenious" );
__BEGIN__;
int i, s_count, s_dims, d_count, d_dims;
CvMat _src, _dst, _ones;
CvMat* ones = 0;
if( !CV_IS_MAT(src) )
CV_ERROR( !src ? CV_StsNullPtr : CV_StsBadArg,
"The input parameter is not a valid matrix" );
if( !CV_IS_MAT(dst) )
CV_ERROR( !dst ? CV_StsNullPtr : CV_StsBadArg,
"The output parameter is not a valid matrix" );
if( src == dst || src->data.ptr == dst->data.ptr )
{
if( src != dst && (!CV_ARE_TYPES_EQ(src, dst) ||
!CV_ARE_SIZES_EQ(src,dst)) )
CV_ERROR( CV_StsBadArg, "Invalid inplace operation" );
EXIT;
}
if( src->rows > src->cols )
{
if( !((src->cols > 1) ^ (CV_MAT_CN(src->type) > 1)) )
CV_ERROR( CV_StsBadSize,
"Either the number of channels or columns or "
"rows must be =1" );
s_dims = CV_MAT_CN(src->type)*src->cols;
s_count = src->rows;
}
else
{
if( !((src->rows > 1) ^ (CV_MAT_CN(src->type) > 1)) )
CV_ERROR( CV_StsBadSize,
"Either the number of channels or columns or "
"rows must be =1" );
s_dims = CV_MAT_CN(src->type)*src->rows;
s_count = src->cols;
}
if( src->rows == 1 || src->cols == 1 )
src = cvReshape( src, &_src, 1, s_count );
if( dst->rows > dst->cols )
{
if( !((dst->cols > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
CV_ERROR( CV_StsBadSize,
"Either the number of channels or columns or "
"rows in the input matrix must be =1" );
d_dims = CV_MAT_CN(dst->type)*dst->cols;
d_count = dst->rows;
}
else
{
if( !((dst->rows > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
CV_ERROR( CV_StsBadSize,
"Either the number of channels or columns or "
"rows in the output matrix must be =1" );
d_dims = CV_MAT_CN(dst->type)*dst->rows;
d_count = dst->cols;
}
if( dst->rows == 1 || dst->cols == 1 )
dst = cvReshape( dst, &_dst, 1, d_count );
if( s_count != d_count )
CV_ERROR( CV_StsUnmatchedSizes, "Both matrices must have the "
"same number of points" );
if( CV_MAT_DEPTH(src->type) < CV_32F || CV_MAT_DEPTH(dst->type) < CV_32F )
CV_ERROR( CV_StsUnsupportedFormat,
"Both matrices must be floating-point "
"(single or double precision)" );
if( s_dims < 2 || s_dims > 4 || d_dims < 2 || d_dims > 4 )
CV_ERROR( CV_StsOutOfRange,
"Both input and output point dimensionality "
"must be 2, 3 or 4" );
if( s_dims < d_dims - 1 || s_dims > d_dims + 1 )
CV_ERROR( CV_StsUnmatchedSizes,
"The dimensionalities of input and output "
"point sets differ too much" );
if( s_dims == d_dims - 1 )
{
if( d_count == dst->rows )
{
ones = cvGetSubRect( dst, &_ones, cvRect( s_dims, 0, 1, d_count ));
dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, s_dims, d_count ));
}
else
{
ones = cvGetSubRect( dst, &_ones, cvRect( 0, s_dims, d_count, 1 ));
dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, d_count, s_dims ));
}
}
if( s_dims <= d_dims )
{
if( src->rows == dst->rows && src->cols == dst->cols )
{
if( CV_ARE_TYPES_EQ( src, dst ) )
cvCopy( src, dst );
else
cvConvert( src, dst );
}
else
{
if( !CV_ARE_TYPES_EQ( src, dst ))
{
CV_CALL( temp = cvCreateMat( src->rows, src->cols, dst->type ));
cvConvert( src, temp );
src = temp;
}
cvTranspose( src, dst );
}
if( ones )
cvSet( ones, cvRealScalar(1.) );
}
else
{
int s_plane_stride, s_stride, d_plane_stride, d_stride, elem_size;
if( !CV_ARE_TYPES_EQ( src, dst ))
{
CV_CALL( temp = cvCreateMat( src->rows, src->cols, dst->type ));
cvConvert( src, temp );
src = temp;
}
elem_size = CV_ELEM_SIZE(src->type);
if( s_count == src->cols )
s_plane_stride = src->step / elem_size, s_stride = 1;
else
s_stride = src->step / elem_size, s_plane_stride = 1;
if( d_count == dst->cols )
d_plane_stride = dst->step / elem_size, d_stride = 1;
else
d_stride = dst->step / elem_size, d_plane_stride = 1;
CV_CALL( denom = cvCreateMat( 1, d_count, dst->type ));
if( CV_MAT_DEPTH(dst->type) == CV_32F )
{
const float* xs = src->data.fl;
const float* ys = xs + s_plane_stride;
const float* zs = 0;
const float* ws = xs + (s_dims - 1)*s_plane_stride;
float* iw = denom->data.fl;
float* xd = dst->data.fl;
float* yd = xd + d_plane_stride;
float* zd = 0;
if( d_dims == 3 )
{
zs = ys + s_plane_stride;
zd = yd + d_plane_stride;
}
for( i = 0; i < d_count; i++, ws += s_stride )
{
float t = *ws;
iw[i] = t ? t : 1.f;
}
cvDiv( 0, denom, denom );
if( d_dims == 3 )
for( i = 0; i < d_count; i++ )
{
float w = iw[i];
float x = *xs * w, y = *ys * w, z = *zs * w;
xs += s_stride; ys += s_stride; zs += s_stride;
*xd = x; *yd = y; *zd = z;
xd += d_stride; yd += d_stride; zd += d_stride;
}
else
for( i = 0; i < d_count; i++ )
{
float w = iw[i];
float x = *xs * w, y = *ys * w;
xs += s_stride; ys += s_stride;
*xd = x; *yd = y;
xd += d_stride; yd += d_stride;
}
}
else
{
const double* xs = src->data.db;
const double* ys = xs + s_plane_stride;
const double* zs = 0;
const double* ws = xs + (s_dims - 1)*s_plane_stride;
double* iw = denom->data.db;
double* xd = dst->data.db;
double* yd = xd + d_plane_stride;
double* zd = 0;
if( d_dims == 3 )
{
zs = ys + s_plane_stride;
zd = yd + d_plane_stride;
}
for( i = 0; i < d_count; i++, ws += s_stride )
{
double t = *ws;
iw[i] = t ? t : 1.;
}
cvDiv( 0, denom, denom );
if( d_dims == 3 )
for( i = 0; i < d_count; i++ )
{
double w = iw[i];
double x = *xs * w, y = *ys * w, z = *zs * w;
xs += s_stride; ys += s_stride; zs += s_stride;
*xd = x; *yd = y; *zd = z;
xd += d_stride; yd += d_stride; zd += d_stride;
}
else
for( i = 0; i < d_count; i++ )
{
double w = iw[i];
double x = *xs * w, y = *ys * w;
xs += s_stride; ys += s_stride;
*xd = x; *yd = y;
xd += d_stride; yd += d_stride;
}
}
}
__END__;
cvReleaseMat( &denom );
cvReleaseMat( &temp );
}
CvScalar calcSSIM :: compare(IplImage *source1, IplImage *source2, Colorspace space)
{
IplImage *src1, *src2;
src1 = colorspaceConversion(source1, space);
src2 = colorspaceConversion(source2, space);
int x = source1->width, y = source1->height;
// default settings
const double C1 = (K1 * L) * (K1 * L);
const double C2 = (K2 * L) * (K2 * L);
int nChan = src1->nChannels;
int d = IPL_DEPTH_32F;
CvSize size = cvSize(x, y);
//creating FLOAT type images of src1 and src2
IplImage *img1 = cvCreateImage(size, d, nChan);
IplImage *img2 = cvCreateImage(size, d, nChan);
//Image squares
IplImage *img1_sq = cvCreateImage(size, d, nChan);
IplImage *img2_sq = cvCreateImage(size, d, nChan);
IplImage *img1_img2 = cvCreateImage(size, d, nChan);
cvConvert(src1, img1);
cvConvert(src2, img2);
//Squaring the images thus created
cvPow(img1, img1_sq, 2);
cvPow(img2, img2_sq, 2);
cvMul(img1, img2, img1_img2, 1);
IplImage *mu1 = cvCreateImage(size, d, nChan);
IplImage *mu2 = cvCreateImage(size, d, nChan);
IplImage *mu1_sq = cvCreateImage(size, d, nChan);
IplImage *mu2_sq = cvCreateImage(size, d, nChan);
IplImage *mu1_mu2 = cvCreateImage(size, d, nChan);
IplImage *sigma1_sq = cvCreateImage(size, d, nChan);
IplImage *sigma2_sq = cvCreateImage(size, d, nChan);
IplImage *sigma12 = cvCreateImage(size, d, nChan);
//PRELIMINARY COMPUTING
//gaussian smoothing is performed
cvSmooth(img1, mu1, CV_GAUSSIAN, gaussian_window, gaussian_window, gaussian_sigma);
cvSmooth(img2, mu2, CV_GAUSSIAN, gaussian_window, gaussian_window, gaussian_sigma);
//gettting mu, mu_sq, mu1_mu2
cvPow(mu1, mu1_sq, 2);
cvPow(mu2, mu2_sq, 2);
cvMul(mu1, mu2, mu1_mu2, 1);
//calculating sigma1, sigma2, sigma12
cvSmooth(img1_sq, sigma1_sq, CV_GAUSSIAN, gaussian_window, gaussian_window, gaussian_sigma);
cvSub(sigma1_sq, mu1_sq, sigma1_sq);
cvSmooth(img2_sq, sigma2_sq, CV_GAUSSIAN, gaussian_window, gaussian_window, gaussian_sigma);
cvSub(sigma2_sq, mu2_sq, sigma2_sq);
cvSmooth(img1_img2, sigma12, CV_GAUSSIAN, gaussian_window, gaussian_window, gaussian_sigma);
cvSub(sigma12, mu1_mu2, sigma12);
//releasing some junk buffers
cvReleaseImage(&img1);
cvReleaseImage(&img2);
cvReleaseImage(&img1_sq);
cvReleaseImage(&img2_sq);
cvReleaseImage(&img1_img2);
cvReleaseImage(&mu1);
cvReleaseImage(&mu2);
// creating buffers for numerator and denominator
IplImage *numerator1 = cvCreateImage(size, d, nChan);
IplImage *numerator2 = cvCreateImage(size, d, nChan);
IplImage *numerator = cvCreateImage(size, d, nChan);
IplImage *denominator1 = cvCreateImage(size, d, nChan);
IplImage *denominator2 = cvCreateImage(size, d, nChan);
IplImage *denominator = cvCreateImage(size, d, nChan);
// FORMULA to calculate SSIM
// (2*mu1_mu2 + C1)
cvScale(mu1_mu2, numerator1, 2);
cvAddS(numerator1, cvScalarAll(C1), numerator1);
// (2*sigma12 + C2)
cvScale(sigma12, numerator2, 2);
cvAddS(numerator2, cvScalarAll(C2), numerator2);
// ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
cvMul(numerator1, numerator2, numerator, 1);
// (mu1_sq + mu2_sq + C1)
cvAdd(mu1_sq, mu2_sq, denominator1);
cvAddS(denominator1, cvScalarAll(C1), denominator1);
// (sigma1_sq + sigma2_sq + C2) >>>
cvAdd(sigma1_sq, sigma2_sq, denominator2);
cvAddS(denominator2, cvScalarAll(C2),denominator2);
// ((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2))
cvMul(denominator1, denominator2, denominator, 1);
//Release some junk buffers
cvReleaseImage(&numerator1);
cvReleaseImage(&denominator1);
cvReleaseImage(&mu1_sq);
cvReleaseImage(&mu2_sq);
cvReleaseImage(&mu1_mu2);
cvReleaseImage(&sigma1_sq);
cvReleaseImage(&sigma2_sq);
cvReleaseImage(&sigma12);
//ssim map and cs_map
ssim_map = cvCreateImage(size, d, nChan);
cs_map = cvCreateImage(size, d, nChan);
// SSIM_INDEX map
// ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2))
cvDiv(numerator, denominator, ssim_map, 1);
// Contrast Structure CS_index map
// (2*sigma12 + C2)./(sigma1_sq + sigma2_sq + C2)
cvDiv(numerator2, denominator2, cs_map, 1);
// average is taken for both SSIM_map and CS_map
mssim_value = cvAvg(ssim_map);
mean_cs_value = cvAvg(cs_map);
//Release images
cvReleaseImage(&numerator);
cvReleaseImage(&denominator);
cvReleaseImage(&numerator2);
cvReleaseImage(&denominator2);
cvReleaseImage(&src1);
cvReleaseImage(&src2);
return mssim_value;
}
//function definitions
void ComputeBrisqueFeature(IplImage *orig, vector<double>& featurevector)
{
IplImage *orig_bw_int = cvCreateImage(cvGetSize(orig), orig->depth, 1);
cvCvtColor(orig, orig_bw_int, CV_RGB2GRAY);
IplImage *orig_bw = cvCreateImage(cvGetSize(orig_bw_int), IPL_DEPTH_64F, 1);
cvConvertScale(orig_bw_int, orig_bw, 1.0/255);
cvReleaseImage(&orig_bw_int);
//orig_bw now contains the grayscale image normalized to the range 0,1
int scalenum = 2;
for (int itr_scale = 1; itr_scale<=scalenum; itr_scale++)
{
IplImage *imdist_scaled = cvCreateImage(cvSize(orig_bw->width/pow((double)2,itr_scale-1), orig_bw->height/pow((double)2,itr_scale-1)), IPL_DEPTH_64F, 1);
cvResize(orig_bw, imdist_scaled,CV_INTER_CUBIC);
//compute mu and mu squared
IplImage* mu = cvCreateImage(cvGetSize(imdist_scaled), IPL_DEPTH_64F, 1);
cvSmooth( imdist_scaled, mu, CV_GAUSSIAN, 7, 7, 1.16666 );
IplImage* mu_sq = cvCreateImage(cvGetSize(imdist_scaled), IPL_DEPTH_64F, 1);
cvMul(mu, mu, mu_sq);
//compute sigma
IplImage* sigma = cvCreateImage(cvGetSize(imdist_scaled), IPL_DEPTH_64F, 1);
cvMul(imdist_scaled, imdist_scaled, sigma);
cvSmooth(sigma, sigma, CV_GAUSSIAN, 7, 7, 1.16666 );
cvSub(sigma, mu_sq, sigma);
cvPow(sigma, sigma, 0.5);
//compute structdis = (x-mu)/sigma
cvAddS(sigma, cvScalar(1.0/255), sigma);
IplImage* structdis = cvCreateImage(cvGetSize(imdist_scaled), IPL_DEPTH_64F, 1);
cvSub(imdist_scaled, mu, structdis);
cvDiv(structdis, sigma, structdis);
//Compute AGGD fit
double lsigma_best, rsigma_best, gamma_best;
AGGDfit(structdis, lsigma_best, rsigma_best, gamma_best);
featurevector.push_back(gamma_best);
featurevector.push_back((lsigma_best*lsigma_best + rsigma_best*rsigma_best)/2);
//Compute paired product images
int shifts[4][2]={{0,1},{1,0},{1,1},{-1,1}};
for(int itr_shift=1; itr_shift<=4; itr_shift++)
{
int* reqshift = shifts[itr_shift-1];
IplImage* shifted_structdis = cvCreateImage(cvGetSize(imdist_scaled), IPL_DEPTH_64F, 1);
BwImage OrigArr(structdis);
BwImage ShiftArr(shifted_structdis);
for(int i=0; i<structdis->height; i++)
{
for(int j=0; j<structdis->width; j++)
{
if(i+reqshift[0]>=0 && i+reqshift[0]<structdis->height && j+reqshift[1]>=0 && j+reqshift[1]<structdis->width)
{
ShiftArr[i][j]=OrigArr[i+reqshift[0]][j+reqshift[1]];
}
else
{
ShiftArr[i][j]=0;
}
}
}
//computing correlation
cvMul(structdis, shifted_structdis, shifted_structdis);
AGGDfit(shifted_structdis, lsigma_best, rsigma_best, gamma_best);
double constant = sqrt(tgamma(1/gamma_best))/sqrt(tgamma(3/gamma_best));
double meanparam = (rsigma_best-lsigma_best)*(tgamma(2/gamma_best)/tgamma(1/gamma_best))*constant;
featurevector.push_back(gamma_best);
featurevector.push_back(meanparam);
featurevector.push_back(pow(lsigma_best,2));
featurevector.push_back(pow(rsigma_best,2));
cvReleaseImage(&shifted_structdis);
}
cvReleaseImage(&mu);
cvReleaseImage(&mu_sq);
cvReleaseImage(&sigma);
cvReleaseImage(&structdis);
cvReleaseImage(&imdist_scaled);
}
}
/*
* Parameters : complete path to the two image to be compared
* The file format must be supported by your OpenCV build
*/
int main(int argc, char** argv)
{
if(argc!=3)
return -1;
// default settings
double C1 = 6.5025, C2 = 58.5225;
IplImage
*img1=NULL, *img2=NULL, *img1_img2=NULL,
*img1_temp=NULL, *img2_temp=NULL,
*img1_sq=NULL, *img2_sq=NULL,
*mu1=NULL, *mu2=NULL,
*mu1_sq=NULL, *mu2_sq=NULL, *mu1_mu2=NULL,
*sigma1_sq=NULL, *sigma2_sq=NULL, *sigma12=NULL,
*ssim_map=NULL, *temp1=NULL, *temp2=NULL, *temp3=NULL;
/***************************** INITS **********************************/
img1_temp = cvLoadImage(argv[1]);
img2_temp = cvLoadImage(argv[2]);
if(img1_temp==NULL || img2_temp==NULL)
return -1;
int x=img1_temp->width, y=img1_temp->height;
int nChan=img1_temp->nChannels, d=IPL_DEPTH_32F;
CvSize size = cvSize(x, y);
img1 = cvCreateImage( size, d, nChan);
img2 = cvCreateImage( size, d, nChan);
cvConvert(img1_temp, img1);
cvConvert(img2_temp, img2);
cvReleaseImage(&img1_temp);
cvReleaseImage(&img2_temp);
img1_sq = cvCreateImage( size, d, nChan);
img2_sq = cvCreateImage( size, d, nChan);
img1_img2 = cvCreateImage( size, d, nChan);
cvPow( img1, img1_sq, 2 );
cvPow( img2, img2_sq, 2 );
cvMul( img1, img2, img1_img2, 1 );
mu1 = cvCreateImage( size, d, nChan);
mu2 = cvCreateImage( size, d, nChan);
mu1_sq = cvCreateImage( size, d, nChan);
mu2_sq = cvCreateImage( size, d, nChan);
mu1_mu2 = cvCreateImage( size, d, nChan);
sigma1_sq = cvCreateImage( size, d, nChan);
sigma2_sq = cvCreateImage( size, d, nChan);
sigma12 = cvCreateImage( size, d, nChan);
temp1 = cvCreateImage( size, d, nChan);
temp2 = cvCreateImage( size, d, nChan);
temp3 = cvCreateImage( size, d, nChan);
ssim_map = cvCreateImage( size, d, nChan);
/*************************** END INITS **********************************/
//////////////////////////////////////////////////////////////////////////
// PRELIMINARY COMPUTING
cvSmooth( img1, mu1, CV_GAUSSIAN, 11, 11, 1.5 );
cvSmooth( img2, mu2, CV_GAUSSIAN, 11, 11, 1.5 );
cvPow( mu1, mu1_sq, 2 );
cvPow( mu2, mu2_sq, 2 );
cvMul( mu1, mu2, mu1_mu2, 1 );
cvSmooth( img1_sq, sigma1_sq, CV_GAUSSIAN, 11, 11, 1.5 );
cvAddWeighted( sigma1_sq, 1, mu1_sq, -1, 0, sigma1_sq );
cvSmooth( img2_sq, sigma2_sq, CV_GAUSSIAN, 11, 11, 1.5 );
cvAddWeighted( sigma2_sq, 1, mu2_sq, -1, 0, sigma2_sq );
cvSmooth( img1_img2, sigma12, CV_GAUSSIAN, 11, 11, 1.5 );
cvAddWeighted( sigma12, 1, mu1_mu2, -1, 0, sigma12 );
//////////////////////////////////////////////////////////////////////////
// FORMULA
// (2*mu1_mu2 + C1)
cvScale( mu1_mu2, temp1, 2 );
cvAddS( temp1, cvScalarAll(C1), temp1 );
// (2*sigma12 + C2)
cvScale( sigma12, temp2, 2 );
cvAddS( temp2, cvScalarAll(C2), temp2 );
// ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
cvMul( temp1, temp2, temp3, 1 );
// (mu1_sq + mu2_sq + C1)
cvAdd( mu1_sq, mu2_sq, temp1 );
cvAddS( temp1, cvScalarAll(C1), temp1 );
// (sigma1_sq + sigma2_sq + C2)
cvAdd( sigma1_sq, sigma2_sq, temp2 );
cvAddS( temp2, cvScalarAll(C2), temp2 );
// ((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2))
cvMul( temp1, temp2, temp1, 1 );
// ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2))
cvDiv( temp3, temp1, ssim_map, 1 );
CvScalar index_scalar = cvAvg( ssim_map );
// through observation, there is approximately
// 1% error max with the original matlab program
cout << "(R, G & B SSIM index)" << endl ;
cout << index_scalar.val[2] * 100 << "%" << endl ;
cout << index_scalar.val[1] * 100 << "%" << endl ;
cout << index_scalar.val[0] * 100 << "%" << endl ;
// if you use this code within a program
// don't forget to release the IplImages
return 0;
}
CV_IMPL void cvDrlse_edge(CvArr * srcphi,
CvArr * srcgrad,
CvArr * dstarr,
double lambda,
double mu,
double alfa,
double epsilon,
int timestep,
int iter)
{
CV_FUNCNAME( "cvDrlse_edge" );
__BEGIN__;
CvMat sstub1, sstub2, *phi, *grad;
CvMat dstub, *dst;
CvMat *gradx=0, *grady=0, *phi_0=0, *phix=0, *phiy=0;
CvMat *s=0, *Nx=0, *Ny=0, *curvature=0, *distRegTerm=0;
CvMat *diracPhi=0, *areaTerm=0, *edgeTerm=0;
CvMat *temp1=0, *temp2=0, *temp3=0, *ones=0;
CvSize size;
int i;
CV_CALL( phi = cvGetMat(srcphi, &sstub1 ));
CV_CALL( grad = cvGetMat(srcgrad, &sstub2 ));
CV_CALL( dst = cvGetMat(dstarr, &dstub));
if( CV_MAT_TYPE(phi->type) != CV_32FC1)
CV_ERROR( CV_StsUnsupportedFormat, "Only-32bit, 1-channel input images are supported" );
if( CV_MAT_TYPE(grad->type) != CV_32FC1)
CV_ERROR( CV_StsUnsupportedFormat, "Only-32bit, 1-channel input images are supported" );
if( !CV_ARE_SIZES_EQ( phi, grad ))
CV_ERROR( CV_StsUnmatchedSizes, "The input images must have the same size" );
size = cvGetMatSize( phi );
//Initialization
gradx = cvCreateMat(size.height, size.width, CV_32FC1 );
grady = cvCreateMat(size.height, size.width, CV_32FC1 );
phi_0 = cvCreateMat(size.height, size.width, CV_32FC1 );
phix = cvCreateMat(size.height, size.width, CV_32FC1 );
phiy = cvCreateMat(size.height, size.width, CV_32FC1 );
Nx = cvCreateMat(size.height, size.width, CV_32FC1 );
Ny = cvCreateMat(size.height, size.width, CV_32FC1 );
s = cvCreateMat(size.height, size.width, CV_32FC1 );
curvature= cvCreateMat(size.height, size.width, CV_32FC1 );
distRegTerm= cvCreateMat(size.height, size.width, CV_32FC1 );
diracPhi = cvCreateMat(size.height, size.width, CV_32FC1 );
areaTerm = cvCreateMat(size.height, size.width, CV_32FC1 );
edgeTerm = cvCreateMat(size.height, size.width, CV_32FC1 );
temp1 = cvCreateMat(size.height, size.width, CV_32FC1 );
temp2 = cvCreateMat(size.height, size.width, CV_32FC1 );
temp3 = cvCreateMat(size.height, size.width, CV_32FC1 );
ones = cvCreateMat(size.height, size.width, CV_32FC1 );
cvSetZero(gradx);
cvSetZero(grady);
cvSetZero(phix);
cvSetZero(phiy);
cvSetZero(Nx);
cvSetZero(Ny);
cvSetZero(s);
cvSetZero(curvature);
cvSetZero(distRegTerm);
cvSetZero(diracPhi);
cvSetZero(areaTerm);
cvSetZero(edgeTerm);
cvSetZero(temp1);
cvSetZero(temp2);
cvSetZero(temp3);
cvSet(ones, cvScalar(1.0f));
//--------------BEGIN----------------------
cvSobel(grad, gradx, 1, 0, 1);
cvSobel(grad, grady, 0, 1, 1);
cvMul(gradx, ones, gradx, 0.25f);
cvMul(grady, ones, grady, 0.25f);
cvCopy(phi, dst);
for(i=0; i<iter; i++){
cvNeumannBoundCond(dst, dst);
cvSobel(dst, phix, 1, 0, 1);
cvSobel(dst, phiy, 0, 1, 1);
cvCalS(dst,s);
cvDiv(phix, s, Nx, 0.25f);
cvDiv(phiy, s, Ny, 0.25f);
cvCurvature(Nx, Ny, curvature);
cvDistReg(dst, distRegTerm);
cvDirac(dst, diracPhi, epsilon); //Compute driacPhi;
cvMul(diracPhi, grad, areaTerm); //Compute areaTerm
cvMul(gradx, Nx, gradx); //------------------//
cvMul(grady, Ny, grady); // Computing //
cvAdd(gradx, grady, temp1); // //
cvMul(diracPhi, temp1, temp2); // edgeTerm //
cvMul(areaTerm, curvature, temp3); // //
cvAdd(temp2, temp3, edgeTerm); //------------------//
cvMul(distRegTerm, ones, distRegTerm, mu); // distRegTerm = mu * distRegTerm
cvMul(edgeTerm, ones, edgeTerm, lambda); // edgeTerm = lambda * edgeTerm
cvMul(areaTerm, ones, areaTerm, alfa); // areaTerm = alfa * areaTerm
cvAdd(distRegTerm, edgeTerm, temp1);
cvAdd(temp1, areaTerm, temp2); // (distRegTerm + edgeTerm + areaTerm)
cvMul(temp2, ones, temp2, double(timestep)); // timestep * (distRegTerm + edgeTerm + areaTerm)
cvAdd(dst, temp2, dst); // phi = phi + timestep * (distRegTerm + edgeTerm + areaTerm)
}
//----------------END------------------------
// Clean up
cvReleaseMat(&ones);
cvReleaseMat(&phi_0);
cvReleaseMat(&gradx);
cvReleaseMat(&grady);
cvReleaseMat(&phix);
cvReleaseMat(&phiy);
cvReleaseMat(&Nx);
cvReleaseMat(&Ny);
cvReleaseMat(&s);
cvReleaseMat(&curvature);
cvReleaseMat(&distRegTerm);
cvReleaseMat(&diracPhi);
cvReleaseMat(&areaTerm);
cvReleaseMat(&edgeTerm);
cvReleaseMat(&temp1);
cvReleaseMat(&temp2);
cvReleaseMat(&temp3);
__END__;
}
CvPoint*
cvDRLSE(const CvArr * image,
const CvArr * mask,
int *length,
double lambda,
double alfa,
double epsilon,
int timestep,
int ITER_ext,
int ITER_int,
int flag)
{
IplImage sstub1, sstub2, *msk, *img, *marker, *levelset;
CvMat *ones, *Ix, *Iy, *phi, *f, *g;
CvSize size;
int comp_count =0, iStep;
float* fPtr;
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* contours = 0;
CvPoint pt= cvPoint(0,0), *point=NULL;
double mu = 0.2f/double(timestep);
char c;
CV_FUNCNAME( "cvDRLSE" );
__BEGIN__;
CV_CALL( msk = cvGetImage(mask, &sstub1 ));
CV_CALL( img = cvGetImage(image, &sstub2 ));
cvSmooth(img, img, CV_GAUSSIAN, 5, 5, 1.5f);
size = cvGetSize(img);
levelset = cvCreateImage(size, IPL_DEPTH_8U, 1);
ones = cvCreateMat(size.height, size.width, CV_32FC1);
Ix = cvCreateMat(size.height, size.width, CV_32FC1);
Iy = cvCreateMat(size.height, size.width, CV_32FC1);
phi = cvCreateMat(size.height, size.width, CV_32FC1);
f = cvCreateMat(size.height, size.width, CV_32FC1);
g = cvCreateMat(size.height, size.width, CV_32FC1);
marker = cvCreateImage(size, IPL_DEPTH_32S, 1);
cvSet(ones, cvScalar(1.0f));
cvSobel(img, Ix, 1, 0, 1);
cvSobel(img, Iy, 0, 1, 1);
cvMul(Ix, Ix, Ix, 0.25f*0.25f);
cvMul(Iy, Iy, Iy, 0.25f*0.25f);
cvAdd(Ix, Iy, f);
cvAdd(f, ones, f);
cvDiv(NULL, f, g, 1.0f);
cvFindContours( msk, storage, &contours, sizeof(CvContour),CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE );
cvZero(marker);
for( ; contours != 0; contours = contours->h_next, comp_count++ )
{
cvDrawContours( marker, contours, cvScalarAll(255),cvScalarAll(255), -1, -1, 8);
}
iStep = phi->step/sizeof(fPtr[0]);
fPtr = phi->data.fl;
for (int j=0; j<size.height; j++)
for (int i=0; i<size.width; i++) {
int idx = CV_IMAGE_ELEM( marker, int, j, i );
if (idx >0 )
if (flag == CV_LSE_SHR)
fPtr[i+iStep*j]=-2.0f;
else
fPtr[i+iStep*j]=2.0f;
else
if (flag == CV_LSE_SHR)
fPtr[i+iStep*j]=2.0f;
else
fPtr[i+iStep*j]=-2.0f;
}
for (int i=0; i<ITER_ext; i++) {
cvDrlse_edge(phi, g, phi, lambda, mu, alfa, epsilon, timestep, ITER_int);
loadBar(i+1, ITER_ext, 50);
}
cvDrlse_edge(phi, g, phi, lambda, mu, 0.0f, epsilon, timestep, ITER_int);
cvZero(msk);
if (flag == CV_LSE_SHR)
cvThreshold(phi, msk, 0.0f, 255, CV_THRESH_BINARY_INV);
else
cvThreshold(phi, msk, 0.0f, 255, CV_THRESH_BINARY);
cvFindContours(msk, storage, &contours, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
if(!contours) return 0;
*length = contours->total;
//if(*length<10) return 0;
point = new CvPoint[*length];
CvSeqReader reader;
cvStartReadSeq(contours, &reader);
for (int i = 0; i < *length; i++){
CV_READ_SEQ_ELEM(pt, reader);
point[i]=pt;
}
//clean up
cvReleaseMemStorage(&storage);
cvReleaseImage(&marker);
cvReleaseMat(&Ix);
cvReleaseMat(&Iy);
cvReleaseMat(&f);
cvReleaseMat(&g);
cvReleaseMat(&phi);
cvReleaseMat(&ones);
return point;
__END__;
}
