void CmSaliencyRC::SmoothSaliency(CMat &colorNum1i, Mat &sal1f, float delta, const vector<vector<CostfIdx>> &similar)
{
if (sal1f.cols < 2)
return;
CV_Assert(sal1f.rows == 1 && sal1f.type() == CV_32FC1);
CV_Assert(colorNum1i.size() == sal1f.size() && colorNum1i.type() == CV_32SC1);
int binN = sal1f.cols;
Mat newSal1d= Mat::zeros(1, binN, CV_64FC1);
float *sal = (float*)(sal1f.data);
double *newSal = (double*)(newSal1d.data);
int *pW = (int*)(colorNum1i.data);
// Distance based smooth
int n = max(cvRound(binN * delta), 2);
vecD dist(n, 0), val(n), w(n);
for (int i = 0; i < binN; i++){
const vector<CostfIdx> &similari = similar[i];
double totalDist = 0, totoalWeight = 0;
for (int j = 0; j < n; j++){
int ithIdx =similari[j].second;
dist[j] = similari[j].first;
val[j] = sal[ithIdx];
w[j] = pW[ithIdx];
totalDist += dist[j];
totoalWeight += w[j];
}
double valCrnt = 0;
for (int j = 0; j < n; j++)
valCrnt += val[j] * (totalDist - dist[j]) * w[j];
newSal[i] = valCrnt / (totalDist * totoalWeight);
}
normalize(newSal1d, sal1f, 0, 1, NORM_MINMAX, CV_32FC1);
}
void CmShow::Pseudocolor(CMat& matfd1, CStr& title)
{
Mat hsvMat[3], hsvM;
matfd1.convertTo(hsvMat[0], CV_32FC1, -240, 240);
hsvMat[1] = hsvMat[2] = Mat::ones(matfd1.size(), CV_32F);
merge(hsvMat, 3, hsvM);
cvtColor(hsvM, hsvM, CV_HSV2BGR);
SaveShow(hsvM, title);
}
void BenchMarkLatex::printMatTransport(CMat &mat1di, CStr texFile, const char* rowDes[], bool *descendOrder)
{
FILE* f = fopen(_S(texFile), "w");
if (f == NULL){
printf("Can't open file %s\n", _S(texFile));
return;
}
CV_Assert(mat1di.cols == _numMethod);
const int MAX_COL = min(20, _numMethod);
const int NUM_BLOCK = (_numMethod + MAX_COL - 1) / MAX_COL;
string strAlign = "|l||";
for (int i = 0; i < MAX_COL; i++)
strAlign += "c|";
const char* rankCommand[3] = {"\\first", "\\second", "\\third"};
Mat mat1d = mat1di.t();
mat1d.convertTo(mat1d, CV_64F);
Mat rnk1i = getRankIdx(mat1d, true).t();
Mat rnkInv = getRankIdx(mat1d, false).t();
for (int i = 0; i < mat1di.rows; i++)
if (!descendOrder[i])
rnkInv.row(i).copyTo(rnk1i.row(i)); // Revert ordering for row i
for (int b = 0; b < NUM_BLOCK; b++){
fprintf(f, "\\begin{tabular}{%s} \\hline\n\tMethod ", _S(strAlign));
for (int i = 0; i < MAX_COL; i++){
int colIdx = i + b * MAX_COL;
fprintf(f, "& %4s", colIdx < _numMethod ? _S(_methodNames[colIdx]) : "");
}
fprintf(f, "\\\\\\hline\n");
for (int i = 0; i < mat1di.rows; i++){
fprintf(f, "\t%-5s ", rowDes[i]);
for (int j = 0; j < MAX_COL; j++){
int colIdx = j + b * MAX_COL;
if (colIdx >= _numMethod){
fprintf(f, "& ");
continue;
}
int idx = rnk1i.at<int>(i, colIdx);
if (mat1di.type() == CV_32S){
if (idx < 3)
fprintf(f, "& %s{%d} ", rankCommand[idx], mat1di.at<int>(i, colIdx));
else
fprintf(f, "& %d ", mat1di.at<int>(i, colIdx));
}
else{// CV_64F
if (idx < 3)
fprintf(f, "& %s{%5.3f} ", rankCommand[idx], mat1di.at<double>(i, colIdx));
else
fprintf(f, "& %5.3f ", mat1di.at<double>(i, colIdx));
}
}
fprintf(f, "\\\\\n");
}
fprintf(f, "\\hline\n\\end{tabular}\n");
}
fclose(f);
}
void CmSaliencyRC::SmoothByHist(CMat &img3f, Mat &sal1f, float delta)
{
//imshow("Before", sal1f); imshow("Src", img3f);
// Quantize colors
CV_Assert(img3f.size() == sal1f.size() && img3f.type() == CV_32FC3 && sal1f.type() == CV_32FC1);
Mat idx1i, binColor3f, colorNums1i;
int binN = Quantize(img3f, idx1i, binColor3f, colorNums1i);
//CmShow::HistBins(binColor3f, colorNums1i, "Frequency");
// Get initial color saliency
Mat _colorSal = Mat::zeros(1, binN, CV_64FC1);
int rows = img3f.rows, cols = img3f.cols;{
double* colorSal = (double*)_colorSal.data;
if (img3f.isContinuous() && sal1f.isContinuous())
cols *= img3f.rows, rows = 1;
for (int y = 0; y < rows; y++){
const int* idx = idx1i.ptr<int>(y);
const float* initialS = sal1f.ptr<float>(y);
for (int x = 0; x < cols; x++)
colorSal[idx[x]] += initialS[x];
}
const int *colorNum = (int*)(colorNums1i.data);
for (int i = 0; i < binN; i++)
colorSal[i] /= colorNum[i];
normalize(_colorSal, _colorSal, 0, 1, NORM_MINMAX, CV_32F);
}
// Find similar colors & Smooth saliency value for color bins
vector<vector<CostfIdx>> similar(binN); // Similar color: how similar and their index
Vec3f* color = (Vec3f*)(binColor3f.data);
cvtColor(binColor3f, binColor3f, CV_BGR2Lab);
for (int i = 0; i < binN; i++){
vector<CostfIdx> &similari = similar[i];
similari.push_back(make_pair(0.f, i));
for (int j = 0; j < binN; j++)
if (i != j)
similari.push_back(make_pair(vecDist<float, 3>(color[i], color[j]), j));
sort(similari.begin(), similari.end());
}
cvtColor(binColor3f, binColor3f, CV_Lab2BGR);
//CmShow::HistBins(binColor3f, _colorSal, "BeforeSmooth", true);
SmoothSaliency(colorNums1i, _colorSal, delta, similar);
//CmShow::HistBins(binColor3f, _colorSal, "AfterSmooth", true);
// Reassign pixel saliency values
float* colorSal = (float*)(_colorSal.data);
for (int y = 0; y < rows; y++){
const int* idx = idx1i.ptr<int>(y);
float* resSal = sal1f.ptr<float>(y);
for (int x = 0; x < cols; x++)
resSal[x] = colorSal[idx[x]];
}
//imshow("After", sal1f);
//waitKey(0);
}
void Objectness::meanStdDev(CMat &data1f, Mat &mean1f, Mat &stdDev1f)
{
const int DIM = data1f.cols, NUM = data1f.rows;
mean1f = Mat::zeros(1, DIM, CV_32F), stdDev1f = Mat::zeros(1, DIM, CV_32F);
for (int i = 0; i < NUM; i++)
mean1f += data1f.row(i);
mean1f /= NUM;
for (int i = 0; i < NUM; i++) {
Mat tmp;
pow(data1f.row(i) - mean1f, 2, tmp);
stdDev1f += tmp;
}
pow(stdDev1f/NUM, 0.5, stdDev1f);
}
int main(int argc, char **argv){
DEFAULT Default;
for(int i = 1 ; i < argc ; i++){
if(string(argv[i]) == string("-i")) {
Default.INPUT() = string(argv[i+1]);
++i;
} else if (string(argv[i]) == string("-o")) {
Default.OUTPUT() = string(argv[i+1]);
++i;
} else if (string(argv[i]) == string("-mo")) {
Default.MASK_OUTPUT() = string(argv[i+1]);
++i;
} else if (string(argv[i]) == string("-l")) {
Default.LONELY_START() = string2int(string(argv[i+1]));
Default.LONELY_END() = string2int(string(argv[i+2]));
Default.LONELY_TIMES() = string2int(string(argv[i+3]));
i+=3;
} else if (string(argv[i]) == string("-bw")) {
Default.BLACK_WHITE() = string2int(string(argv[i+1]));
++i;
} else if (string(argv[i]) == string("-m")) {
Default.MARGIN() = string2int(string(argv[i+1]));
++i;
} else if (string(argv[i]) == string("-s")) {
Default.TOTAL_SOURCES() = string2int(string(argv[i+1]));
++i;
Default.source.pop_back();
for(int a=0;a<Default.TOTAL_SOURCES();a++){
++i;
string input_point = string(argv[i]);
int sep=input_point.find(",");
int x=string2int(input_point.substr(0,sep));
int y=string2int(input_point.substr(sep+1,input_point.length()-1));
Default.source.push_back(pair<int,int>(x,y));
}
} else if (string(argv[i]) == string("help") || string(argv[i]) == string("-h")) {
cout << "[Help]" << endl;
cout << "[-i input] [-o output] [-mo mask_output]" << endl;
cout << "[-l lonely_start lonely_end lonely_times]" << endl;
cout << "[-s $total_sources \"a1,b1\" \"a2,b2\" ... source]" << endl;
cout << "[-bw black_white] [-m margin] [-h | help]" << endl;
return 0;
} else {
return 0;
}
}
Default.m_default.print_tree();
CMat pic;
CMat mask = pic.set_in_file_path( Default.INPUT() ).set_out_file_path( Default.OUTPUT() ).open();
mask.guass().cvt_color().soble().guass().black_white( Default.BLACK_WHITE() );
for(int i = 0 ; i < Default.LONELY_TIMES() ; i++){
mask.lonely( Default.LONELY_START() + i, Default.LONELY_END() - i);
}
mask.set_out_file_path( Default.MASK_OUTPUT() ).save();
pic.kill_pos_color(mask.flood(Default.source), Default.MARGIN()).save();
return 0;
}
Mat CmSaliencyRC::GetFT(CMat &img3f)
{
CV_Assert(img3f.data != NULL && img3f.type() == CV_32FC3);
Mat sal(img3f.size(), CV_32F), tImg;
GaussianBlur(img3f, tImg, Size(3, 3), 0);
cvtColor(tImg, tImg, CV_BGR2Lab);
Scalar colorM = mean(tImg);
for (int r = 0; r < tImg.rows; r++) {
float *s = sal.ptr<float>(r);
float *lab = tImg.ptr<float>(r);
for (int c = 0; c < tImg.cols; c++, lab += 3)
s[c] = (float)(sqr(colorM[0] - lab[0]) + sqr(colorM[1] - lab[1]) + sqr(colorM[2] - lab[2]));
}
normalize(sal, sal, 0, 1, NORM_MINMAX);
return sal;
}
// Show a label map. labelNum: how many number of random colors used for show, use default colors if is -1
Mat CmShow::Label(CMat& label1i, CStr& title, int labelNum, bool showIdx)
{
bool useRandom = labelNum > 0;
labelNum = useRandom ? labelNum : COLOR_NU_NO_GRAY;
vector<Vec3b> colors(labelNum);
if (useRandom)
for (size_t i = 0; i < colors.size(); i++)
colors[i] = RandomColor();
else
for (size_t i = 0; i < colors.size(); i++)
colors[i] = gColors[i];
Mat showImg = Mat::zeros(label1i.size(), CV_8UC3);
for (int y = 0; y < label1i.rows; y++) {
Vec3b* showD = showImg.ptr<Vec3b>(y);
const int* label = label1i.ptr<int>(y);
for (int x = 0; x < label1i.cols; x++)
if (label[x] >= 0) {
showD[x] = colors[label[x] % labelNum];
if (showIdx)
showD[x][2] = (byte)(label[x]);
}
}
SaveShow(showImg, title);
return showImg;
}
void BenchMarkLatex::printModelRanking(CStr outName, CMat &meanF1d, CMat &maxF1d, CMat &cutAdp1d, CMat &cutSC1d, CMat &auc1d, CMat &mae1d)
{
CV_Assert(meanF1d.size == cutAdp1d.size && cutAdp1d.size == cutSC1d.size);
CV_Assert(meanF1d.size() == Size(_numDb, _numMethod));
Mat RnkMean1i = getRankIdx(meanF1d), RnkAdp1i = getRankIdx(cutAdp1d), RnkSc1i = getRankIdx(cutSC1d);
Mat RnkMax1i = getRankIdx(maxF1d), RnkAuc1i = getRankIdx(auc1d), RnkMae1i = getRankIdx(mae1d, false);
const int NUM_ROW = 5; // Number of rows in the table
const char* rowDes[NUM_ROW] = { "Max", "AUC", "MAE", "AdpT", "SCut" };
bool decentOrder[NUM_ROW];
memset(decentOrder, 0, sizeof(bool) * NUM_ROW);
Mat dbTable = Mat::zeros(NUM_ROW, _numMethod, CV_64F);
for (int m = 0; m < _numMethod; m++){
for (int d = 0; d < _numDb; d++) {
dbTable.at<double>(0, m) += RnkMax1i.at<int>(m, d) + 1;
dbTable.at<double>(1, m) += RnkAuc1i.at<int>(m, d) + 1;
dbTable.at<double>(2, m) += RnkMae1i.at<int>(m, d) + 1;
dbTable.at<double>(3, m) += RnkAdp1i.at<int>(m, d) + 1;
dbTable.at<double>(4, m) += RnkSc1i.at<int>(m, d) + 1;
}
}
printMatTransport(dbTable, outName + "D.tex", rowDes, decentOrder);
Mat rnkDb = getRankIdx(dbTable.t(), false).t() + 1;
printMatTransport(rnkDb, outName + ".tex", rowDes, decentOrder);
}
void CmSaliencyRC::GetCmplx(CMat& mag32F, CMat& ang32F, Mat& cmplx32FC2)
{
CV_Assert(mag32F.type() == CV_32FC1 && ang32F.type() == CV_32FC1 && mag32F.size() == ang32F.size());
cmplx32FC2.create(mag32F.size(), CV_32FC2);
for (int y = 0; y < mag32F.rows; y++)
{
float* cmpD = cmplx32FC2.ptr<float>(y);
const float* dataA = ang32F.ptr<float>(y);
const float* dataM = mag32F.ptr<float>(y);
for (int x = 0; x < mag32F.cols; x++, cmpD += 2)
{
cmpD[0] = dataM[x] * cos(dataA[x]);
cmpD[1] = dataM[x] * sin(dataA[x]);
}
}
}
Mat CmSaliencyRC::GetSR(CMat &img3f)
{
Size sz(64, 64);
Mat img1f[2], sr1f, cmplxSrc2f, cmplxDst2f;
cvtColor(img3f, img1f[1], CV_BGR2GRAY);
resize(img1f[1], img1f[0], sz, 0, 0, CV_INTER_AREA);
img1f[1] = Mat::zeros(sz, CV_32F);
merge(img1f, 2, cmplxSrc2f);
dft(cmplxSrc2f, cmplxDst2f);
AbsAngle(cmplxDst2f, img1f[0], img1f[1]);
log(img1f[0], img1f[0]);
blur(img1f[0], sr1f, Size(3, 3));
sr1f = img1f[0] - sr1f;
exp(sr1f, sr1f);
GetCmplx(sr1f, img1f[1], cmplxDst2f);
dft(cmplxDst2f, cmplxSrc2f, DFT_INVERSE | DFT_SCALE);
split(cmplxSrc2f, img1f);
pow(img1f[0], 2, img1f[0]);
pow(img1f[1], 2, img1f[1]);
img1f[0] += img1f[1];
GaussianBlur(img1f[0], img1f[0], Size(3, 3), 0);
normalize(img1f[0], img1f[0], 0, 1, NORM_MINMAX);
resize(img1f[0], img1f[1], img3f.size(), 0, 0, INTER_CUBIC);
return img1f[1];
}
void GrabCutMF::illuProb(CMat sampleDf, CmGMM &bGMM, CmGMM &fGMM, CStr &nameNE)
{
vector<Mat> pciF, pciB;
Mat pI = Mat::zeros(sampleDf.size(), CV_32F);
fGMM.GetProbsWN(sampleDf, pciF);
bGMM.GetProbsWN(sampleDf, pciB);
int fK = fGMM.K(), bK = bGMM.K();
assert(fGMM.maxK() == bGMM.maxK());
for (int i = 0; i < fK; i++)
add(pciF[i], pI, pI);
for (int i = 0; i < bK; i++)
add(pciB[i], pI, pI);
Mat tmpShow;
for (int i = 0; i < fK; i++){
divide(pciF[i], pI, pciF[i]);
imwrite(nameNE + format("_F%d.png", i), pciF[i]*255);
}
for (int i = 0; i < bK; i++){
divide(pciB[i], pI, pciB[i]);
imwrite(nameNE + format("_B%d.png", i), pciB[i]*255);
}
for (int i = fK; i < fGMM.maxK(); i++)
CmFile::WriteNullFile(nameNE + format("_F%d.nul", i));
for (int i = bK; i < bGMM.maxK(); i++)
CmFile::WriteNullFile(nameNE + format("_B%d.nul", i));
}
Mat CmSaliencyRC::GetLC(CMat &img3f)
{
Mat img;
cvtColor(img3f, img, CV_BGR2GRAY);
img.convertTo(img, CV_8U, 255);
double f[256], s[256];
memset(f, 0, 256*sizeof(double));
memset(s, 0, 256*sizeof(double));
for (int r = 0; r < img.rows; r++){
byte* data = img.ptr<byte>(r);
for (int c = 0; c < img.cols; c++)
f[data[c]] += 1;
}
for (int i = 0; i < 256; i++)
for (int j = 0; j < 256; j++)
s[i] += abs(i - j) * f[j];
Mat sal1f(img3f.size(), CV_64F);
for (int r = 0; r < img.rows; r++){
byte* data = img.ptr<byte>(r);
double* sal = sal1f.ptr<double>(r);
for (int c = 0; c < img.cols; c++)
sal[c] = s[data[c]];
}
normalize(sal1f, sal1f, 0, 1, NORM_MINMAX, CV_32F);
return sal1f;
}
// Training SVM with feature vector X and label Y.
// Each row of X is a feature vector, with corresponding label in Y.
// Return a CV_32F weight Mat
Mat Objectness::trainSVM(CMat &X1f, const vecI &Y, int sT, double C, double bias, double eps)
{
// Set SVM parameters
parameter param; {
param.solver_type = sT; // L2R_L2LOSS_SVC_DUAL;
param.C = C;
param.eps = eps; // see setting below
param.p = 0.1;
param.nr_weight = 0;
param.weight_label = NULL;
param.weight = NULL;
set_print_string_function(print_null);
CV_Assert(X1f.rows == Y.size() && X1f.type() == CV_32F);
}
// Initialize a problem
feature_node *x_space = NULL;
problem prob;{
prob.l = X1f.rows;
prob.bias = bias;
prob.y = Malloc(double, prob.l);
prob.x = Malloc(feature_node*, prob.l);
const int DIM_FEA = X1f.cols;
prob.n = DIM_FEA + (bias >= 0 ? 1 : 0);
x_space = Malloc(feature_node, (prob.n + 1) * prob.l);
int j = 0;
for (int i = 0; i < prob.l; i++){
prob.y[i] = Y[i];
prob.x[i] = &x_space[j];
const float* xData = X1f.ptr<float>(i);
for (int k = 0; k < DIM_FEA; k++){
x_space[j].index = k + 1;
x_space[j++].value = xData[k];
}
if (bias >= 0){
x_space[j].index = prob.n;
x_space[j++].value = bias;
}
x_space[j++].index = -1;
}
CV_Assert(j == (prob.n + 1) * prob.l);
}
// Training SVM for current problem
const char* error_msg = check_parameter(&prob, ¶m);
if(error_msg){
fprintf(stderr,"ERROR: %s\n",error_msg);
exit(1);
}
model *svmModel = train(&prob, ¶m);
Mat wMat(1, prob.n, CV_64F, svmModel->w);
wMat.convertTo(wMat, CV_32F);
free_and_destroy_model(&svmModel);
destroy_param(¶m);
free(prob.y);
free(prob.x);
free(x_space);
return wMat;
}
Mat CmShow::Complex(CMat& _cmplx, CStr& title, float minMagShowAng, int flag)
{
CV_Assert(_cmplx.channels() == 2 && _cmplx.data != NULL);
Mat ang(_cmplx.size(), CV_32FC1), mag(_cmplx.size(), CV_32FC1), cmplx;
_cmplx.convertTo(cmplx, CV_32F);
for (int y = 0; y < cmplx.rows; y++) {
float* cpV = cmplx.ptr<float>(y);
float* angV = ang.ptr<float>(y);
float* magV = mag.ptr<float>(y);
for (int x = 0; x < cmplx.cols; x++, cpV+=2) {
magV[x] = sqrt(cpV[0] * cpV[0] + cpV[1] * cpV[1]);
angV[x] = cvFastArctan(cpV[1], cpV[0]);
}
}
return Complex(ang, mag, title, minMagShowAng, flag);
}
void CmSaliencyRC::AbsAngle(CMat& cmplx32FC2, Mat& mag32FC1, Mat& ang32FC1)
{
CV_Assert(cmplx32FC2.type() == CV_32FC2);
mag32FC1.create(cmplx32FC2.size(), CV_32FC1);
ang32FC1.create(cmplx32FC2.size(), CV_32FC1);
for (int y = 0; y < cmplx32FC2.rows; y++)
{
const float* cmpD = cmplx32FC2.ptr<float>(y);
float* dataA = ang32FC1.ptr<float>(y);
float* dataM = mag32FC1.ptr<float>(y);
for (int x = 0; x < cmplx32FC2.cols; x++, cmpD += 2)
{
dataA[x] = atan2(cmpD[1], cmpD[0]);
dataM[x] = sqrt(cmpD[0] * cmpD[0] + cmpD[1] * cmpD[1]);
}
}
}
Mat CmSaliencyRC::GetBorderReg(CMat &idx1i, int regNum, double ratio, double thr)
{
// Variance of x and y
vecD vX(regNum), vY(regNum);
int w = idx1i.cols, h = idx1i.rows;{
vecD mX(regNum), mY(regNum), n(regNum); // Mean value of x and y, pixel number of region
for (int y = 0; y < idx1i.rows; y++){
const int *idx = idx1i.ptr<int>(y);
for (int x = 0; x < idx1i.cols; x++, idx++)
mX[*idx] += x, mY[*idx] += y, n[*idx]++;
}
for (int i = 0; i < regNum; i++)
mX[i] /= n[i], mY[i] /= n[i];
for (int y = 0; y < idx1i.rows; y++){
const int *idx = idx1i.ptr<int>(y);
for (int x = 0; x < idx1i.cols; x++, idx++)
vX[*idx] += abs(x - mX[*idx]), vY[*idx] += abs(y - mY[*idx]);
}
for (int i = 0; i < regNum; i++)
vX[i] = vX[i]/n[i] + EPS, vY[i] = vY[i]/n[i] + EPS;
}
// Number of border pixels in x and y border region
vecI xbNum(regNum), ybNum(regNum);
int wGap = cvRound(w * ratio), hGap = cvRound(h * ratio);
vector<Point> bPnts; {
ForPoints2(pnt, 0, 0, w, hGap) // Top region
ybNum[idx1i.at<int>(pnt)]++, bPnts.push_back(pnt);
ForPoints2(pnt, 0, h - hGap, w, h) // Bottom region
ybNum[idx1i.at<int>(pnt)]++, bPnts.push_back(pnt);
ForPoints2(pnt, 0, 0, wGap, h) // Left region
xbNum[idx1i.at<int>(pnt)]++, bPnts.push_back(pnt);
ForPoints2(pnt, w - wGap, 0, w, h)
xbNum[idx1i.at<int>(pnt)]++, bPnts.push_back(pnt);
}
Mat bReg1u(idx1i.size(), CV_8U);{ // likelihood map of border region
double xR = 1.0/(4*hGap), yR = 1.0/(4*wGap);
vector<byte> regL(regNum); // likelihood of each region belongs to border background
for (int i = 0; i < regNum; i++) {
double lk = xbNum[i] * xR / vY[i] + ybNum[i] * yR / vX[i];
regL[i] = lk/thr > 1 ? 255 : 0; //saturate_cast<byte>(255 * lk / thr);
}
for (int r = 0; r < h; r++) {
const int *idx = idx1i.ptr<int>(r);
byte* maskData = bReg1u.ptr<byte>(r);
for (int c = 0; c < w; c++, idx++)
maskData[c] = regL[*idx];
}
}
for (size_t i = 0; i < bPnts.size(); i++)
bReg1u.at<byte>(bPnts[i]) = 255;
return bReg1u;
}
Mat CmSaliencyRC::Get(CMat &img3f, GET_SAL_FUNC fun, int _wkSize)
{
Mat sal;
double ratio = _wkSize * 1.0 / max(img3f.rows, img3f.cols);
Size wkSize(cvRound(img3f.cols * ratio), cvRound(img3f.rows * ratio));
Mat wkImg, wkSal;
resize(img3f, wkImg, wkSize, 0, 0, INTER_AREA);
wkSal = fun(wkImg);
resize(wkSal, sal, img3f.size(), 0, 0, INTER_CUBIC);
return sal;
}
int SegmentImage(CMat &_src3f, Mat &pImgInd, float sigma, float c, int min_size)
{
CV_Assert(_src3f.type() == CV_32FC3);
int width(_src3f.cols), height(_src3f.rows);
pImgInd.create(height, width, CV_32S);
image<RGB_f> *im = new image<RGB_f>(width, height, _src3f.data);
image<int> *regIdx = new image<int>(width, height, pImgInd.data);
int regNum = SegmentImage(im, regIdx, sigma, c, min_size);
im->data = NULL; regIdx->data = NULL;
delete im; delete regIdx;
return regNum;
}
void CmShow::SaveShow(CMat& img, CStr& title)
{
if (title.size() == 0)
return;
int mDepth = CV_MAT_DEPTH(img.type());
double scale = (mDepth == CV_32F || mDepth == CV_64F ? 255 : 1);
if (title.size() > 4 && title[title.size() - 4] == '.')
imwrite(title, img*scale);
else if (title.size())
imshow(title, img);
}
// Write matrix to binary file
bool CmFile::matWrite(CStr& filename, CMat& _M){
Mat M;
_M.copyTo(M);
FILE* file = fopen(_S(filename), "wb");
if (file == NULL || M.empty())
return false;
fwrite("CmMat", sizeof(char), 5, file);
int headData[3] = {M.cols, M.rows, M.type()};
fwrite(headData, sizeof(int), 3, file);
fwrite(M.data, sizeof(char), M.step * M.rows, file);
fclose(file);
return true;
}
void BenchMarkLatex::saveData2Csv(CMat &_value1d, CStr csvFile, vecS &rowStart)
{
Mat value1d = _value1d.reshape(1);
FILE *f = fopen(_S(csvFile), "w");
CV_Assert(f != NULL && value1d.type() == CV_64FC1);
for (int r = 0; r < value1d.rows; r++) {
const double *v = value1d.ptr<double>(r);
fprintf(f, "\n%s", _S(rowStart[r]));
for (int c = 0; c < value1d.cols; c++)
fprintf(f, ", %5.3f", v[c]);
}
fclose(f);
}
// src3f are BGR, color3f are 1xBinDim matrix represent color fore each histogram bin
int CmColorQua::S_BinInf(CMat& idx1i, Mat &color3f, vecI &colorNum, int method, CMat &src3f)
{
int totalBinNum = 0;
CV_Assert(idx1i.data != NULL && idx1i.type() == CV_32S && method >= 0 && method < S_Q_NUM);
// Find colors for each bin
color3f = Mat::zeros(1, binNum[method], CV_32FC3);
Vec3f* color = (Vec3f*)(color3f.data);
vector<Vec3d> colorD(color3f.cols, 0);
colorNum.resize(color3f.cols, 0);
if (src3f.size() != Size() && src3f.data != NULL) {
for (int r = 0; r < idx1i.rows; r++) {
const int *idx = idx1i.ptr<int>(r);
const Vec3f *src = src3f.ptr<Vec3f>(r);
for (int c = 0; c < idx1i.cols; c++) {
colorD[idx[c]] += src[c];
colorNum[idx[c]] ++;
}
}
}
S_RECOVER_FUNC SR_Function = srFuns[method];
for (int i = 0; i < color3f.cols; i++) {
if (colorNum[i] == 0)
(*SR_Function)(i, color[i]);
else
totalBinNum += colorNum[i];
}
if (method == 1)
cvtColor(color3f, color3f, CV_HSV2BGR);
else if (method == 2)
cvtColor(color3f, color3f, CV_Lab2BGR);
for (int i = 0; i < color3f.cols; i++)
if (colorNum[i] > 0)
color[i] = Vec3f((float)(colorD[i][0]/colorNum[i]), (float)(colorD[i][1]/colorNum[i]), (float)(colorD[i][2]/colorNum[i]));
return totalBinNum;
}
Mat GrabCutMF::getGrabMask(CMat &img3u, Rect rect)
{
// Initialize flood fill
queue<Point> selectedPnts;
const int _h = img3u.rows, _w = img3u.cols, BW = 5;
{// If not connected to image border, expand selection border unless stopped by edges
Point rowT(rect.x, rect.y), rowB(rect.x, rect.y + rect.height - 1);
Point colL(rect.x, rect.y), colR(rect.x + rect.width - 1, rect.y);
if (rect.x >= BW) // Expand left edge
for (int y = 0; y < rect.height; y++, colL.y++) selectedPnts.push(colL);
else
rect.x = BW;
if (rect.y >= BW) // Expand top edge
for (int x = 0; x < rect.width; x++, rowT.x++) selectedPnts.push(rowT);
else
rect.y = BW;
if (rect.x + rect.width + BW <= _w) // Expand right edge
for (int y = 0; y < rect.height; y++, colR.y++) selectedPnts.push(colR);
else
rect.width = _w - rect.x - BW;
if (rect.y + rect.height + BW <= _h) // Expand bottom edge
for (int x = 0; x < rect.width; x++, rowB.x++) selectedPnts.push(rowB);
else
rect.height = _h - rect.y - BW;
}
Mat mask1u(img3u.size(), CV_8U);
memset(mask1u.data, 255, mask1u.step.p[0] * mask1u.rows);
mask1u(rect) = Scalar(0);
Mat edge1u;
CmCv::CannySimpleRGB(img3u, edge1u, 120, 1200, 5);
dilate(edge1u, edge1u, Mat(), Point(-1, -1), 3);
//rectangle(edge1u, rect, Scalar(128));
//imwrite(sameNameNE + "_Selection.png", edge1u);
// Flood fill
while (!selectedPnts.empty()){
Point crntPnt = selectedPnts.front();
mask1u.at<byte>(crntPnt) = 255;
selectedPnts.pop();
for (int i = 0; i < 4; i++){
Point nbrPnt = crntPnt + DIRECTION4[i];
if (CHK_IND(nbrPnt) && mask1u.at<byte>(nbrPnt) == 0 && edge1u.at<byte>(nbrPnt) == 0)
mask1u.at<byte>(nbrPnt) = 255, selectedPnts.push(nbrPnt);
}
}
cv::Mat temp(mask1u(Rect(rect.x + 1, rect.y + 1, rect.width - 2, rect.height - 2)));
CmCv::rubustifyBorderMask(temp);
return mask1u;
}
Mat CmSaliencyRC::GetRC(CMat &img3f, CMat ®Idx1i, int regNum, double sigmaDist)
{
Mat colorIdx1i, regSal1v, tmp, color3fv;
int QuatizeNum = Quantize(img3f, colorIdx1i, color3fv, tmp);
if (QuatizeNum == 2){
printf("QuatizeNum == 2, %d: %s\n", __LINE__, __FILE__);
Mat sal;
compare(colorIdx1i, 1, sal, CMP_EQ);
sal.convertTo(sal, CV_32F, 1.0/255);
return sal;
}
if (QuatizeNum <= 2) // Color quantization
return Mat::zeros(img3f.size(), CV_32F);
cvtColor(color3fv, color3fv, CV_BGR2Lab);
vector<Region> regs(regNum);
BuildRegions(regIdx1i, regs, colorIdx1i, color3fv.cols);
RegionContrast(regs, color3fv, regSal1v, sigmaDist);
Mat sal1f = Mat::zeros(img3f.size(), CV_32F);
cv::normalize(regSal1v, regSal1v, 0, 1, NORM_MINMAX, CV_32F);
float* regSal = (float*)regSal1v.data;
for (int r = 0; r < img3f.rows; r++){
const int* regIdx = regIdx1i.ptr<int>(r);
float* sal = sal1f.ptr<float>(r);
for (int c = 0; c < img3f.cols; c++)
sal[c] = regSal[regIdx[c]];
}
Mat bdReg1u = GetBorderReg(regIdx1i, regNum, 0.02, 0.4);
sal1f.setTo(0, bdReg1u);
SmoothByHist(img3f, sal1f, 0.1f);
SmoothByRegion(sal1f, regIdx1i, regNum);
sal1f.setTo(0, bdReg1u);
GaussianBlur(sal1f, sal1f, Size(3, 3), 0);
return sal1f;
}
void BenchMarkLatex::printMat(CMat &_mat1d, CStr texFile, bool descendOrder)
{
FILE* f = fopen(_S(texFile), "w");
if (f == NULL){
printf("Can't open file %s\n", _S(texFile));
return;
}
CV_Assert(_mat1d.rows == _numMethod);
Mat mat1d = _mat1d.reshape(1);
int dataWidth = mat1d.cols;
string strAlign = "|l||";
for (int i = 0; i < _mat1d.cols; i++){
for (int c = 0; c < _mat1d.channels(); c++)
strAlign += "c";
strAlign += "|";
}
fprintf(f, "\\begin{tabular}{%s} \\hline\n\t\\tabTitle \\\\", _S(strAlign));
const char* rankCommand[3] = {"\\first", "\\second", "\\third"};
Mat rnk1i = getRankIdx(mat1d, descendOrder);
for (int i = 0; i < _numMethod; i++){
if (find(_subNumbers.begin(), _subNumbers.end(), i) != _subNumbers.end())
fprintf(f, "\t\\hline \\hline\n");
fprintf(f, "\t\\textbf{%-5s ", _S(_methodNames[i] + "}"));
for (int j = 0; j < dataWidth; j++){
int idx = rnk1i.at<int>(i, j);
if (idx < 3)
fprintf(f, "& %s{%5.3f} ", rankCommand[idx], mat1d.at<double>(i, j));
else
fprintf(f, "& %5.3f ", mat1d.at<double>(i, j));
}
fprintf(f, "\\\\\n");
}
fprintf(f, "\\hline\n\\end{tabular}\n");
fclose(f);
}
// img3f and src3f are BGR
void CmColorQua::S_Recover(CMat& idx1i, Mat& img3f, int method, CMat &src3f)
{
Mat color3f;
vecI colorNum;
S_BinInf(idx1i, color3f, colorNum, method, src3f);
Vec3f* color = (Vec3f*)(color3f.data);
img3f.create(idx1i.size(), CV_32FC3);
for (int r = 0; r < idx1i.rows; r++) {
const int *idx = idx1i.ptr<int>(r);
Vec3f *img = img3f.ptr<Vec3f>(r);
for (int c = 0; c < idx1i.cols; c++)
img[c] = color[idx[c]];
}
}
void CmColorQua::D_Recover(CMat& idx1i, Mat &img3f, CMat &color3f)
{
CV_Assert(idx1i.data != NULL);
img3f.create(idx1i.size(), CV_32FC3);
Vec3f* color = (Vec3f*)(color3f.data);
for (int y = 0; y < idx1i.rows; y++) {
Vec3f* imgData = img3f.ptr<Vec3f>(y);
const int* idx = idx1i.ptr<int>(y);
for (int x = 0; x < idx1i.cols; x++) {
imgData[x] = color[idx[x]];
CV_Assert(idx[x] < color3f.cols);
}
}
}
Mat CmShow::HistBins(CMat& color3f, CMat& val, CStr& title, bool descendShow, CMat &with)
{
// Prepare data
int H = 300, spaceH = 6, barH = 10, n = color3f.cols;
CV_Assert(color3f.size() == val.size() && color3f.rows == 1);
Mat binVal1i, binColor3b, width1i;
if (with.size() == val.size())
with.convertTo(width1i, CV_32S, 400/sum(with).val[0]); // Default shown width
else
width1i = Mat(1, n, CV_32S, Scalar(600.0/(val.cols*val.rows))); // Default bin width = 10
int W = cvRound(sum(width1i).val[0]);
color3f.convertTo(binColor3b, CV_8UC3, 255);
double maxVal, minVal;
minMaxLoc(val, &minVal, &maxVal);
val.convertTo(binVal1i, CV_32S, H/max(maxVal, -minVal));
Size szShow(W, H + spaceH + barH);
szShow.height += minVal < 0 && !descendShow ? H + spaceH : 0;
Mat showImg3b(szShow, CV_8UC3, WHITE);
int* binH = (int*)(binVal1i.data);
Vec3b* binColor = (Vec3b*)(binColor3b.data);
int* binW = (int*)(width1i.data);
vector<CostiIdx> costIdx(n);
if (descendShow) {
for (int i = 0; i < n; i++)
costIdx[i] = make_pair(binH[i], i);
sort(costIdx.begin(), costIdx.end(), std::greater<CostiIdx>());
}
// Show image
for (int i = 0, x = 0; i < n; i++) {
int idx = descendShow ? costIdx[i].second : i;
int h = descendShow ? abs(binH[idx]) : binH[idx];
Scalar color(binColor[idx]);
Rect reg(x, H + spaceH, binW[idx], barH);
showImg3b(reg) = color; // Draw bar
rectangle(showImg3b, reg, BLACK);
reg.height = abs(h);
reg.y = h >= 0 ? H - h : H + 2 * spaceH + barH;
showImg3b(reg) = color;
rectangle(showImg3b, reg, BLACK);
x += binW[idx];
}
putText(showImg3b, format("Min = %g, Max = %g", minVal, maxVal), Point(5, 20), CV_FONT_HERSHEY_PLAIN, 1, CV_RGB(255,0,0));
SaveShow(showImg3b, title);
return showImg3b;
}
// Write matrix to binary file
bool Objectness::matWrite(CStr& filename, CMat& _M) {
Mat M;
_M.copyTo(M);
FILE* file = fopen(_S(filename), "wb");
if (file == NULL || M.empty())
return false;
fwrite("CmMat", sizeof(char), 5, file);
int headData[3] = {M.cols, M.rows, M.type()};
fwrite(headData, sizeof(int), 3, file);
fwrite(M.data, sizeof(char), M.step * M.rows, file);
fclose(file);
FileStorage fs(filename + ".yml.gz", FileStorage::WRITE);
fs << removeExtension(basename(filename)) << M;
fs.release();
return true;
}
