void run(cv::Mat& image,cv::Mat& outImage,cv::Mat& outMinTrace,cv::Mat& outDeletedLine)
{
cv::Mat image_gray(image.rows,image.cols,CV_8U,cv::Scalar(0));
cv::cvtColor(image,image_gray,CV_BGR2GRAY); //彩色图像转换为灰度图像
cv::Mat gradiant_H(image.rows,image.cols,CV_32F,cv::Scalar(0));//水平梯度矩阵
cv::Mat gradiant_V(image.rows,image.cols,CV_32F,cv::Scalar(0));//垂直梯度矩阵
cv::Mat kernel_H = (cv::Mat_<float>(3,3) << 0, 0, 0, 0, 1, -1, 0, 0, 0); //求水平梯度所使用的卷积核（赋初始值）
cv::Mat kernel_V = (cv::Mat_<float>(3,3) << 0, 0, 0, 0, 1, 0, 0, -1, 0); //求垂直梯度所使用的卷积核（赋初始值）
cv::filter2D(image_gray,gradiant_H,gradiant_H.depth(),kernel_H);
cv::filter2D(image_gray,gradiant_V,gradiant_V.depth(),kernel_V);
cv::Mat gradMag_mat(image.rows,image.rows,CV_32F,cv::Scalar(0));
cv::add(cv::abs(gradiant_H),cv::abs(gradiant_V),gradMag_mat);//水平与垂直滤波结果的绝对值相加，可以得到近似梯度大小
////如果要显示梯度大小这个图，因为gradMag_mat深度是CV_32F，所以需要先转换为CV_8U
//cv::Mat testMat;
//gradMag_mat.convertTo(testMat,CV_8U,1,0);
//cv::imshow("Image Show Window2",testMat);
//计算能量线
cv::Mat energyMat(image.rows,image.cols,CV_32F,cv::Scalar(0));//累计能量矩阵
cv::Mat traceMat(image.rows,image.cols,CV_32F,cv::Scalar(0));//能量最小轨迹矩阵
calculateEnergy(gradMag_mat,energyMat,traceMat);
//找出最小能量线
cv::Mat minTrace(image.rows,1,CV_32F,cv::Scalar(0));//能量最小轨迹矩阵中的最小的一条的轨迹
getMinEnergyTrace(energyMat,traceMat,minTrace);
//显示最小能量线
cv::Mat tmpImage(image.rows,image.cols,image.type());
image.copyTo(tmpImage);
for (int i = 0;i < image.rows;i++)
{
int k = minTrace.at<float>(i,0);
tmpImage.at<cv::Vec3b>(i,k)[0] = 0;
tmpImage.at<cv::Vec3b>(i,k)[1] = 0;
tmpImage.at<cv::Vec3b>(i,k)[2] = 255;
}
cv::imshow("Image Show Window (缩小)",tmpImage);
//删除一列
cv::Mat image2(image.rows,image.cols-1,image.type());
cv::Mat beDeletedLine(image.rows,1,CV_8UC3);//记录被删掉的那一列的值
delOneCol(image,image2,minTrace,beDeletedLine);
cv::imshow("Image Show Window",image2);
image2.copyTo(outImage);
minTrace.copyTo(outMinTrace);
beDeletedLine.copyTo(outDeletedLine);
}
static PyObject *UtilsC_expectation_A(PyObject *self, PyObject *args){
PyObject *Y,*y_tilde,*m_A,*m_H,*X,*Gamma,*Sigma_A,*sigma_epsilone,*Sigma_H,*PL,*mu_MK,*sigma_MK,*q_Z;
PyArrayObject *q_Zarray,*mu_MKarray,*sigma_MKarray,*PLarray,*Yarray,*y_tildearray,*m_Aarray,*m_Harray,*sigma_epsilonearray,*Sigma_Harray,*Xarray,*Gammaarray,*Sigma_Aarray;
int j,J,m,m2,d,D,nCond,Nrep,nr,K,k;
PyArg_ParseTuple(args, "OOOOOOOOOOOOOiiiii",&q_Z,&mu_MK,&sigma_MK,&PL,&sigma_epsilone,&Gamma,&Sigma_H,&Y,&y_tilde,&m_A,&m_H,&Sigma_A,&X,&J,&D,&nCond,&Nrep,&K);
Xarray = (PyArrayObject *) PyArray_ContiguousFromObject(X,PyArray_INT,3,3);
Sigma_Harray = (PyArrayObject *) PyArray_ContiguousFromObject(Sigma_H,PyArray_FLOAT64,2,2);
m_Aarray = (PyArrayObject *) PyArray_ContiguousFromObject(m_A,PyArray_FLOAT64,2,2);
mu_MKarray = (PyArrayObject *) PyArray_ContiguousFromObject(mu_MK,PyArray_FLOAT64,2,2);
sigma_MKarray = (PyArrayObject *) PyArray_ContiguousFromObject(sigma_MK,PyArray_FLOAT64,2,2);
PLarray = (PyArrayObject *) PyArray_ContiguousFromObject(PL,PyArray_FLOAT64,2,2);
m_Harray = (PyArrayObject *) PyArray_ContiguousFromObject(m_H,PyArray_FLOAT64,1,1);
y_tildearray = (PyArrayObject *) PyArray_ContiguousFromObject(y_tilde,PyArray_FLOAT64,2,2);
sigma_epsilonearray = (PyArrayObject *) PyArray_ContiguousFromObject(sigma_epsilone,PyArray_FLOAT64,1,1);
Yarray = (PyArrayObject *) PyArray_ContiguousFromObject(Y,PyArray_FLOAT64,2,2);
Gammaarray = (PyArrayObject *) PyArray_ContiguousFromObject(Gamma,PyArray_FLOAT64,2,2);
Sigma_Aarray = (PyArrayObject *) PyArray_ContiguousFromObject(Sigma_A,PyArray_FLOAT64,3,3);
q_Zarray = (PyArrayObject *) PyArray_ContiguousFromObject(q_Z,PyArray_FLOAT64,3,3);
npy_float64 *H,*tmp,*tmpT,*GGamma,*tmp2,*X_tilde,*Sigma_Aj,*Delta,*tmpDD,*tmpD,*tmpNrep,*tmpNrep2,*tmpnCond,*tmpnCond2;
npy_float64 *yy_tilde,*SSigma_H,*Sigma_A0;
int *XX,*XXT;
tmpnCond = malloc(sizeof(npy_float64)*nCond);
tmpnCond2 = malloc(sizeof(npy_float64)*nCond);
SSigma_H = malloc(sizeof(npy_float64)*D*D);
Delta = malloc(sizeof(npy_float64)*nCond*nCond);
Sigma_A0 = malloc(sizeof(npy_float64)*nCond*nCond);
Sigma_Aj = malloc(sizeof(npy_float64)*nCond*nCond);
X_tilde = malloc(sizeof(npy_float64)*nCond*D);
H = malloc(sizeof(npy_float64)*D);
tmp = malloc(sizeof(npy_float64)*Nrep*D);
tmpD = malloc(sizeof(npy_float64)*D);
tmpDD = malloc(sizeof(npy_float64)*D*D);
tmpNrep = malloc(sizeof(npy_float64)*Nrep);
tmpNrep2 = malloc(sizeof(npy_float64)*Nrep);
tmpT = malloc(sizeof(npy_float64)*Nrep*D);
GGamma = malloc(sizeof(npy_float64)*Nrep*Nrep);
tmp2 = malloc(sizeof(npy_float64)*Nrep*D);
yy_tilde = malloc(sizeof(npy_float64)*Nrep);
XX = malloc(sizeof(int)*D*Nrep);
XXT = malloc(sizeof(int)*D*Nrep);
for (d=0;d<Nrep;d++){
for (nr=0;nr<Nrep;nr++){
GGamma[d*Nrep + nr] = GetValue(Gammaarray,nr,d);
}
}
for (d=0;d<D;d++){
H[d] = GetValue1D(m_Harray,d);
for (nr=0;nr<D;nr++){
SSigma_H[d*D+nr] = GetValue(Sigma_Harray,d,nr);
}
}
for (m=0;m<nCond;m++){
for (m2=0;m2<nCond;m2++){
for (d=0;d<D;d++){
for (nr=0;nr<Nrep;nr++){
XX[nr*D + d] = GetValue3DInt(Xarray,m2,nr,d);
XXT[d*Nrep + nr] = GetValue3DInt(Xarray,m,nr,d);
}
}
prodMat4(H,XXT,tmpNrep,1,Nrep,D);
prodMat2(tmpNrep,GGamma,tmpNrep2,1,Nrep,Nrep);
prodMat4(tmpNrep2,XX,tmpD,1, D, Nrep);
Sigma_A0[m*nCond + m2] = prodMatScal(tmpD,H,D);
prodMat4(SSigma_H,XXT,tmp,D,Nrep,D);
prodMat2(tmp,GGamma,tmpT,D,Nrep,Nrep);
prodMat4(tmpT,XX,tmpDD,D, D, Nrep);
Sigma_A0[m*nCond + m2] += traceMat(tmpDD,D);
Delta[m*nCond+m2] = 0;
}
}
for (j=0;j<J;j++){
if (GetValue1D(sigma_epsilonearray,j) < eps) GetValue1D(sigma_epsilonearray,j) = eps;
for (nr=0;nr<Nrep;nr++){
yy_tilde[nr] = GetValue(y_tildearray,nr,j);
}
for (m=0;m<nCond;m++){
for (d=0;d<D;d++){
for (nr=0;nr<Nrep;nr++){
XX[nr*D + d] = GetValue3DInt(Xarray,m,nr,d);
}
}
prodMat2(GGamma,yy_tilde,tmpNrep,Nrep,1,Nrep);
prodMat4(tmpNrep,XX,tmpD,1,D,Nrep);
for (d=0;d<D;d++){
X_tilde[m*D+d] = tmpD[d] / GetValue1D(sigma_epsilonearray,j);
}
for (m2=0;m2<nCond;m2++){
Sigma_Aj[m*nCond + m2] = Sigma_A0[m*nCond + m2] / GetValue1D(sigma_epsilonearray,j);
}
}
prodMat2(X_tilde,H,tmpnCond,nCond,1,D);
for (k=0;k<K;k++){
for (m=0;m<nCond;m++){
// Delta[m*nCond+m] = GetValue3D(q_Zarray,m,k,j) / (GetValue(sigma_MKarray,m,k) + eps);
Delta[m*nCond+m] = GetValue3D(q_Zarray,m,k,j) / GetValue(sigma_MKarray,m,k);
tmpnCond2[m] = GetValue(mu_MKarray,m,k);
}
prodaddMat(Delta,tmpnCond2,tmpnCond,nCond,1,nCond);
addMatfast(Delta,Sigma_Aj,nCond,nCond);
}
invMat(Sigma_Aj,nCond);
for (m=0;m<nCond;m++){
for (m2=0;m2<nCond;m2++){
GetValue3D(Sigma_Aarray,m,m2,j) = Sigma_Aj[m*nCond + m2];
}
}
prodMat2(Sigma_Aj,tmpnCond,tmpnCond2,nCond,1,nCond);
for (m=0;m<nCond;m++){
GetValue(m_Aarray,j,m) = tmpnCond2[m];
}
}
free(X_tilde);
free(Sigma_Aj);
free(Sigma_A0);
free(Delta);
free(tmpDD);
free(tmpD);
free(tmpNrep);
free(tmpNrep2);
free(tmpnCond);
free(tmpnCond2);
free(H);
free(tmp);
free(tmpT);
free(GGamma);
free(tmp2);
free(yy_tilde);
free(XX);
free(XXT);
Py_DECREF(q_Zarray);
Py_DECREF(Sigma_Harray);
Py_DECREF(Sigma_Aarray);
Py_DECREF(m_Aarray);
Py_DECREF(m_Harray);
Py_DECREF(Xarray);
Py_DECREF(Yarray);
Py_DECREF(sigma_MKarray);
Py_DECREF(mu_MKarray);
Py_DECREF(PLarray);
Py_DECREF(y_tildearray);
Py_DECREF(Gammaarray);
Py_DECREF(sigma_epsilonearray);
Py_INCREF(Py_None);
return Py_None;
}
static PyObject *UtilsC_maximization_sigma_noise(PyObject *self, PyObject *args){
PyObject *Y,*m_A,*m_H,*X,*Sigma_A,*sigma_epsilone,*Sigma_H,*PL,*Gamma;
PyArrayObject *Yarray,*m_Aarray,*m_Harray,*sigma_epsilonearray,*Sigma_Harray,*Xarray,*Sigma_Aarray,*PLarray,*Gammaarray;
int j,J,m,m2,d,D,nCond,Nrep,nr;
PyArg_ParseTuple(args, "OOOOOOOOOiiii",&Gamma,&PL,&sigma_epsilone,&Sigma_H,&Y,&m_A,&m_H,&Sigma_A,&X,&J,&D,&nCond,&Nrep);
Xarray = (PyArrayObject *) PyArray_ContiguousFromObject(X,PyArray_INT,3,3);
m_Aarray = (PyArrayObject *) PyArray_ContiguousFromObject(m_A,PyArray_FLOAT64,2,2);
PLarray = (PyArrayObject *) PyArray_ContiguousFromObject(PL,PyArray_FLOAT64,2,2);
m_Harray = (PyArrayObject *) PyArray_ContiguousFromObject(m_H,PyArray_FLOAT64,1,1);
sigma_epsilonearray = (PyArrayObject *) PyArray_ContiguousFromObject(sigma_epsilone,PyArray_FLOAT64,1,1);
Yarray = (PyArrayObject *) PyArray_ContiguousFromObject(Y,PyArray_FLOAT64,2,2);
Sigma_Harray = (PyArrayObject *) PyArray_ContiguousFromObject(Sigma_H,PyArray_FLOAT64,2,2);
Sigma_Aarray = (PyArrayObject *) PyArray_ContiguousFromObject(Sigma_A,PyArray_FLOAT64,3,3);
Gammaarray = (PyArrayObject *) PyArray_ContiguousFromObject(Gamma,PyArray_FLOAT64,2,2);
npy_float64 *S,*H,*Htilde,*Yj,*PLj,*Sigma_A0,*m_Aj,*tmpnCondnCond,*tmpnCond,*tmpD,*tmp,*tmpDD,*SSigma_H,*GGamma;
int *XX,*XXT;
Sigma_A0 = malloc(sizeof(npy_float64)*nCond*nCond);
H = malloc(sizeof(npy_float64)*D);
m_Aj = malloc(sizeof(npy_float64)*nCond);
Yj = malloc(sizeof(npy_float64)*Nrep);
PLj = malloc(sizeof(npy_float64)*Nrep);
S = malloc(sizeof(npy_float64)*Nrep);
tmpnCondnCond = malloc(sizeof(npy_float64)*nCond*nCond);
tmpnCond = malloc(sizeof(npy_float64)*nCond);
tmpD = malloc(sizeof(npy_float64)*D);
tmpDD = malloc(sizeof(npy_float64)*D*D);
tmp = malloc(sizeof(npy_float64)*D*Nrep);
Htilde = malloc(sizeof(npy_float64)*nCond*nCond);
XX = malloc(sizeof(int)*D*Nrep);
XXT = malloc(sizeof(int)*D*Nrep);
SSigma_H = malloc(sizeof(npy_float64)*D*D);
GGamma = malloc(sizeof(npy_float64)*Nrep*Nrep);
npy_float64 *SQ, *YGamma;
SQ = malloc(sizeof(npy_float64)*Nrep);
YGamma = malloc(sizeof(npy_float64)*Nrep);
for (d=0;d<Nrep;d++){
for (nr=0;nr<Nrep;nr++){
GGamma[d*Nrep + nr] = GetValue(Gammaarray,nr,d);
}
}
for (d=0;d<D;d++){
H[d] = GetValue1D(m_Harray,d);
for (nr=0;nr<D;nr++){
SSigma_H[d*D+nr] = GetValue(Sigma_Harray,d,nr);
}
}
for (m=0;m<nCond;m++){
for (m2=0;m2<nCond;m2++){
for (d=0;d<D;d++){
for (nr=0;nr<Nrep;nr++){
XX[nr*D + d] = GetValue3DInt(Xarray,m2,nr,d);
XXT[d*Nrep + nr] = GetValue3DInt(Xarray,m,nr,d);
}
}
prodMat4(H,XXT,S,1,Nrep,D);
prodMat4(S,XX,tmpD,1, D, Nrep);
Htilde[m*nCond + m2] = prodMatScal(tmpD,H,D);
prodMat4(SSigma_H,XXT,tmp,D,Nrep,D);
prodMat4(tmp,XX,tmpDD,D, D, Nrep);
Htilde[m*nCond + m2] += traceMat(tmpDD,D);
}
}
for (j=0;j<J;j++){
for (nr=0;nr<Nrep;nr++){
S[nr] = 0;
Yj[nr] = GetValue(Yarray,nr,j);
PLj[nr] = GetValue(PLarray,nr,j);
// printf("Yj[%d] = %f\n",nr, Yj[nr]);
// printf("PLj[%d] = %f\n",nr, PLj[nr]);
// getchar();
}
for (m=0;m<nCond;m++){
for (d=0;d<D;d++){
for (nr=0;nr<Nrep;nr++){
XX[nr*D + d] = GetValue3DInt(Xarray,m,nr,d);
}
}
prodaddMatAlpha(XX,H,S,Nrep,1,D,GetValue(m_Aarray,j,m));
prodMat2(S,GGamma,SQ,1,Nrep,Nrep);
for (m2=0;m2<nCond;m2++){
Sigma_A0[m*nCond+m2] = GetValue3D(Sigma_Aarray,m,m2,j);
}
m_Aj[m] = GetValue(m_Aarray,j,m);
}
SubMatfastVect(PLj,Yj,Nrep);
// for (nr=0;nr<Nrep;nr++){
// printf("Yj[%d] = %f\n",nr, Yj[nr]);
// getchar();
// }
GetValue1D(sigma_epsilonearray,j) = -2*prodMatScal(SQ,Yj,Nrep);
prodMat2(Sigma_A0,Htilde,tmpnCondnCond,nCond,nCond,nCond);
GetValue1D(sigma_epsilonearray,j) += traceMat(tmpnCondnCond,nCond);
prodMat2(m_Aj,Htilde,tmpnCond,1,nCond,nCond);
GetValue1D(sigma_epsilonearray,j) += prodMatScal(tmpnCond,m_Aj,nCond);
prodMat2(Yj,GGamma,YGamma,1,Nrep,Nrep);
GetValue1D(sigma_epsilonearray,j) += prodMatScal(YGamma,Yj,Nrep);
GetValue1D(sigma_epsilonearray,j) /= Nrep;
// printf("sigma_epsilonearray[%d] = %f\n",j, GetValue1D(sigma_epsilonearray,j));
// getchar();
}
free(Sigma_A0);
free(H);
free(m_Aj);
free(Yj);
free(PLj);
free(S);
free(tmpnCondnCond);
free(tmpnCond);
free(tmpD);
free(tmpDD);
free(tmp);
free(Htilde);
free(XX);
free(XXT);
free(SSigma_H);
free(GGamma);
free(SQ);
free(YGamma);
Py_DECREF(Yarray);
Py_DECREF(m_Aarray);
Py_DECREF(m_Harray);
Py_DECREF(sigma_epsilonearray);
Py_DECREF(Sigma_Harray);
Py_DECREF(Xarray);
Py_DECREF(Sigma_Aarray);
Py_DECREF(PLarray);
Py_DECREF(Gammaarray);
Py_INCREF(Py_None);
return Py_None;
}
