void Saliency::Evaluate(const string& resultW, const string >ImgW, vecD &precision, vecD &recall)
{
vecS names;
string inDir;
int imgNum = CmFile::GetNames(resultW, names, inDir);
int COLOR_NUM = 256;
precision.resize(COLOR_NUM, 0);
recall.resize(COLOR_NUM, 0);
string truthDir = CmFile::GetFolder(gtImgW);
size_t pathSize = gtImgW.size(), nPos = gtImgW.find_last_of('.');
string ext = gtImgW.substr(nPos, pathSize - nPos);
for (int i = 0; i < imgNum; i++)
{
CmLog::LogProgress("Processing %-40s\r", names[i].c_str());
Mat resS = imread(inDir + names[i], CV_LOAD_IMAGE_GRAYSCALE);
CV_Assert_(resS.data != NULL, ("Can't load saliency map: %s\n", names[i].c_str()));
names[i].resize(names[i].find_last_of("_"));
Mat truM = imread(truthDir + CmFile::GetNameNE(names[i]) + ext, CV_LOAD_IMAGE_GRAYSCALE);
compare(truM, 128, truM, CMP_GE);
if (truM.data == NULL)
{
CmLog::LogLine("Evaluation stopped due to missing ground truth results\n");
exit(1);
}
CV_Assert_(resS.size() == truM.size(), ("Saliency map and ground truth image size mismatch\n"));
double groundTruth = sum(truM).val[0];
#pragma omp parallel for
for (int thr = 0; thr < COLOR_NUM; thr++)
{
Mat resM;
compare(resS, thr, resM, CMP_GE);
double res = sum(resM).val[0];
bitwise_and(resM, truM, resM);
double common = sum(resM).val[0];
precision[thr] += common/(res + 1e-8);
recall[thr] += common/(groundTruth + 1e-8);
}
}
for (int thr = 0; thr < COLOR_NUM; thr++)
{
precision[thr] /= imgNum;
recall[thr] /= imgNum;
}
}
// Load mask image and threshold thus noisy by compression can be removed
Mat CmFile::LoadMask(CStr& fileName)
{
Mat mask = imread(fileName, CV_LOAD_IMAGE_GRAYSCALE);
CV_Assert_(mask.data != NULL, ("Can't find mask image: %s", _S(fileName)));
compare(mask, 128, mask, CV_CMP_GT);
return mask;
}
// Get potential bounding boxes, each of which is represented by a Vec4i for (minX, minY, maxX, maxY).
// The trained model should be prepared before calling this function: loadTrainedModel() or trainStageI() + trainStageII().
// Use numDet to control the final number of proposed bounding boxes, and number of per size (scale and aspect ratio)
void Objectness::getObjBndBoxes(CMat &img3u, ValStructVec<float, Vec4i> &valBoxes, int numDetPerSize)
{
CV_Assert_(filtersLoaded() , ("SVM filters should be initialized before getting object proposals\n"));
vecI sz;
predictBBoxSI(img3u, valBoxes, sz, numDetPerSize, false);
predictBBoxSII(valBoxes, sz);
return;
}
void DataSetVOC::loadBox(const FileNode &fn, vector<Vec4i> &boxes, vecI &clsIdx){
string isDifficult;
fn["difficult"]>>isDifficult;
if (isDifficult == "1")
return;
string strXmin, strYmin, strXmax, strYmax;
fn["bndbox"]["xmin"] >> strXmin;
fn["bndbox"]["ymin"] >> strYmin;
fn["bndbox"]["xmax"] >> strXmax;
fn["bndbox"]["ymax"] >> strYmax;
boxes.push_back(Vec4i(atoi(_S(strXmin)), atoi(_S(strYmin)), atoi(_S(strXmax)), atoi(_S(strYmax))));
string clsName;
fn["name"]>>clsName;
clsIdx.push_back(findFromList(clsName, classNames));
CV_Assert_(clsIdx[clsIdx.size() - 1] >= 0, ("Invalidate class name\n"));
}
vecD BenchMarkLatex::readVectorFromMatlabeFile(CStr &fileName, CStr &vecName)
{
ifstream fin(fileName);
CV_Assert(fin.is_open());
string lineStr, token = vecName + " = [";
while (getline(fin, lineStr))
if (strncmp(_S(lineStr), _S(token), token.size() - 1) == 0)
break;
CV_Assert_(lineStr.size(), ("Can't load vector '%s' from: %s\n", _S(vecName), _S(fileName)));
lineStr = lineStr.substr(token.size());
istringstream sIn(lineStr);
vecD scores;
double s;
while (sIn>>s)
scores.push_back(s);
return scores;
}
void CmEvaluation::MeanAbsoluteError(CStr inDir, CStr salDir, vecS des, bool zeroMapIfMissing)
{
vecS namesNE;
int imgNum = CmFile::GetNamesNE(inDir + "*.jpg", namesNE), count = 0, methodNum = des.size();
vecD costs(des.size());
for (int i = 0; i < imgNum; i++){
Mat gt = imread(inDir + namesNE[i] + ".png", CV_LOAD_IMAGE_GRAYSCALE);
if (gt.empty()) {
if (zeroMapIfMissing){
Mat img = imread(inDir + namesNE[i] + ".jpg");
gt = Mat::zeros(img.size(), CV_8U);
}
else
continue;
}
//printf("%s.jpg ", _S(namesNE[i]));
gt.convertTo(gt, CV_32F, 1.0/255);
#pragma omp parallel for
for (int j = 0; j < methodNum; j++){
Mat res = imread(salDir + namesNE[i] + "_" + des[j] + ".png", CV_LOAD_IMAGE_GRAYSCALE);
CV_Assert_(res.data != NULL, ("Can't load file %s\n", _S(namesNE[i] + "_" + des[j] + ".png")));
if (res.size != gt.size){
printf("size don't match %s\n", _S(namesNE[i] + "_" + des[j] + ".png"));
resize(res, res, gt.size());
CmFile::MkDir(salDir + "Out/");
imwrite(salDir + "Out/" + namesNE[i] + "_" + des[j] + ".png", res);
}
res.convertTo(res, CV_32F, 1.0/255);
cv::absdiff(gt, res, res);
costs[j] += sum(res).val[0] / (gt.rows * gt.cols);
}
count++;
}
for (size_t j = 0; j < des.size(); j++)
printf("%4s:%5.3f ", _S(des[j]), costs[j]/count);
printf("\n");
}
// Return mean absolute error (MAE)
double CmEvaluation::Evaluate_(CStr >ImgW, CStr &inDir, CStr& resExt, vecD &precision, vecD &recall, vecD &tpr, vecD &fpr)
{
vecS names;
string truthDir, gtExt;
int imgNum = CmFile::GetNamesNE(gtImgW, names, truthDir, gtExt);
precision.resize(NUM_THRESHOLD, 0);
recall.resize(NUM_THRESHOLD, 0);
tpr.resize(NUM_THRESHOLD, 0);
fpr.resize(NUM_THRESHOLD, 0);
if (imgNum == 0){
printf("Can't load ground truth images %s\n", _S(gtImgW));
return 10^20;
}
else
printf("Evaluating %d saliency maps ... \r", imgNum);
double mea = 0;
for (int i = 0; i < imgNum; i++){
if(i % 100 == 0)
printf("Evaluating %03d/%d %-40s\r", i, imgNum, _S(names[i] + resExt));
Mat resS = imread(inDir + names[i] + resExt, CV_LOAD_IMAGE_GRAYSCALE);
CV_Assert_(resS.data != NULL, ("Can't load saliency map: %s\n", _S(names[i] + resExt)));
normalize(resS, resS, 0, 255, NORM_MINMAX);
Mat gtFM = imread(truthDir + names[i] + gtExt, CV_LOAD_IMAGE_GRAYSCALE), gtBM;
if (gtFM.data == NULL)
continue;
CV_Assert_(resS.size() == gtFM.size(), ("Saliency map and ground truth image size mismatch: %s\n", _S(names[i])));
compare(gtFM, 128, gtFM, CMP_GT);
bitwise_not(gtFM, gtBM);
double gtF = sum(gtFM).val[0];
double gtB = resS.cols * resS.rows * 255 - gtF;
#pragma omp parallel for
for (int thr = 0; thr < NUM_THRESHOLD; thr++){
Mat resM, tpM, fpM;
compare(resS, thr * STEP, resM, CMP_GE);
bitwise_and(resM, gtFM, tpM);
bitwise_and(resM, gtBM, fpM);
double tp = sum(tpM).val[0];
double fp = sum(fpM).val[0];
//double fn = gtF - tp;
//double tn = gtB - fp;
recall[thr] += tp/(gtF+EPS);
double total = EPS + tp + fp;
precision[thr] += (tp+EPS)/total;
tpr[thr] += (tp + EPS) / (gtF + EPS);
fpr[thr] += (fp + EPS) / (gtB + EPS);
}
gtFM.convertTo(gtFM, CV_32F, 1.0/255);
resS.convertTo(resS, CV_32F, 1.0/255);
cv::absdiff(gtFM, resS, resS);
mea += sum(resS).val[0] / (gtFM.rows * gtFM.cols);
}
int thrS = 0, thrE = NUM_THRESHOLD, thrD = 1;
for (int thr = thrS; thr != thrE; thr += thrD){
precision[thr] /= imgNum;
recall[thr] /= imgNum;
tpr[thr] /= imgNum;
fpr[thr] /= imgNum;
}
return mea/imgNum;
}