double dist_directional_swc_1_2(V3DLONG & nseg1, V3DLONG & nseg1big, double & sum1big, const NeuronTree *p1, const NeuronTree *p2)
{
if (!p1 || !p2) return -1;
V3DLONG p1sz = p1->listNeuron.size(), p2sz = p2->listNeuron.size();
if (p1sz<2 || p2sz<2) return -1;
NeuronSWC *tp1, *tp2;
V3DLONG i, j;
double sum1=0;
nseg1=0;
nseg1big=0;
sum1big=0;
QHash<int, int> h1 = generate_neuron_swc_hash(p1); //generate a hash lookup table from a neuron swc graph
for (i=0;i<p1->listNeuron.size();i++)
{
//first find the two ends of a line seg
tp1 = (NeuronSWC *)(&(p1->listNeuron.at(i)));
if (tp1->pn < 0 || tp1->pn >= p1sz)
continue;
tp2 = (NeuronSWC *)(&(p1->listNeuron.at(h1.value(tp1->pn)))); //use hash table
//qDebug() << "i="<< i << " pn="<<tp1->pn - 1;
//now produce a series of points for the line seg
double len=dist_L2(XYZ(tp1->x,tp1->y,tp1->z), XYZ(tp2->x,tp2->y,tp2->z));
int N = int(1+len+0.5);
XYZ ptdiff;
if (N<=1)
{
qDebug() << "detect one very short segment, len=" << len;
ptdiff = XYZ(0,0,0);
}
else
{
double N1=1.0/(N-1);
ptdiff = XYZ(N1,N1,N1) * XYZ(tp2->x-tp1->x, tp2->y-tp1->y, tp2->z-tp1->z);
}
qDebug() << "N="<<N << "len=" <<len << "xd="<<ptdiff.x << " yd=" << ptdiff.y << " zd=" << ptdiff.z << " ";
for (j=0;j<N;j++)
{
XYZ curpt(tp1->x + ptdiff.x*j, tp1->y + ptdiff.y*j, tp1->z + ptdiff.z*j);
double cur_d = dist_pt_to_swc(curpt, p2);
sum1 += cur_d;
nseg1++;
if (cur_d>=2)
{
sum1big += cur_d;
nseg1big++;
}
}
}
qDebug() << "end directional neuronal distance computing";
return sum1;
}
bool GenericObjectDetector::RunVOC()
{
FileInfos imglist;
db_man.Init(DB_VOC07);
if( !db_man.GetImageList(imglist) )
return false;
for(size_t id=0; id<10; id++)
{
double start_t = getTickCount();
img = imread(imglist[id].filepath);
// preprocessing
Preprocess(img);
segmentor.DoSegmentation(img);
const vector<visualsearch::SuperPixel>& sps = segmentor.superPixels;
vector<ImgWin> det_wins;
// loop all segment
for (size_t sel_id=0; sel_id<sps.size(); sel_id++)
{
// test all window settings
for(size_t win_id=0; win_id<winconfs.size(); win_id++)
{
// compute adjust range
Point minpt, maxpt;
if( !WinLocRange(sps[sel_id].box, winconfs[win_id], minpt, maxpt) )
continue;
Point curpt(sps[sel_id].box.x+sps[sel_id].box.width/2, sps[sel_id].box.y+sps[sel_id].box.height/2);
double bestscore = 0;
ImgWin bestWin;
// do shifting
for(int i=0; i<10; i++)
{
// generate locations
vector<ImgWin> wins;
SampleWinLocs(curpt, winconfs[win_id], minpt, maxpt, 6, wins);
if(wins.empty())
continue;
for(size_t j=0; j<wins.size(); j++)
wins[j].score = ComputeCenterSurroundMeanColorDiff(wins[j]);
// sort
sort(wins.begin(), wins.end());
const ImgWin selWin = wins[wins.size()-1];
//cout<<"Best score: "<<selWin.score<<endl;
if(selWin.score > bestscore)
{
// shift to max point
curpt.x = selWin.x + selWin.width/2;
curpt.y = selWin.y + selWin.height/2;
// update
bestWin = selWin;
}
}
det_wins.push_back(bestWin);
}
}
sort(det_wins.begin(), det_wins.end());
cout<<"Time for image "<<id<<": "<<(getTickCount()-start_t) / getTickFrequency()<<"s"<<endl;
// visualize final results
reverse(det_wins.begin(), det_wins.end());
visualsearch::ImgVisualizer::DrawImgWins("final", img, det_wins);
waitKey(0);
}
return true;
}
bool GenericObjectDetector::test()
{
// test oversegmentation
// get one image and depth map
FileInfos imglist;
db_man.Init(DB_VOC07);
if( !db_man.GetImageList(imglist) )
return false;
map<string, vector<ImgWin>> gt_wins;
db_man.LoadGTWins(imglist, gt_wins);
img = imread(imglist[58].filepath);
//db_man.GetDepthmapList(imglist);
//db_man.LoadDepthData(imglist[0].filepath, depthMap);
vector<ImgWin> wins;
a9win.GenerateBlocks(img, wins);
visualsearch::ImgVisualizer::DrawImgWins("detection", img, wins);
WindowEvaluator wineval;
vector<ImgWin> bestWins;
wineval.FindBestWins(wins, gt_wins[imglist[58].filename], bestWins);
visualsearch::ImgVisualizer::DrawImgWins("matches", img, bestWins);
return true;
// preprocessing
Preprocess(img);
segmentor.DoSegmentation(img);
const vector<visualsearch::SuperPixel>& sps = segmentor.superPixels;
int cnt = 10;
while(cnt >= 0)
{
// select a segment
srand(time(NULL));
int sel_id = rand()%sps.size();
vector<ImgWin> imgwins;
// place window at center
ImgWin curwin;
curwin.x = sps[sel_id].box.x+sps[sel_id].box.width/2 - winconfs[0].width/2;
curwin.y = sps[sel_id].box.y+sps[sel_id].box.height/2 - winconfs[0].height/2;
curwin.width = winconfs[0].width;
curwin.height = winconfs[0].height;
imgwins.push_back(curwin);
curwin = ImgWin(sps[sel_id].box.x, sps[sel_id].box.y, sps[sel_id].box.width, sps[sel_id].box.height);
imgwins.push_back(curwin);
visualsearch::ImgVisualizer::DrawImgWins("img", img, imgwins);
visualsearch::ImgVisualizer::DrawImgWins("seg", segmentor.m_segImg, imgwins);
visualsearch::ImgVisualizer::DrawImgWins("meancolor", segmentor.m_mean_img, imgwins);
// compute adjust range
Point minpt, maxpt;
if( !WinLocRange(sps[sel_id].box, winconfs[0], minpt, maxpt) )
continue;
// check range
//ImgWin minWin(minpt.x-winconfs[0].width/2, minpt.y-winconfs[0].height/2, winconfs[])
Point curpt(sps[sel_id].box.x+sps[sel_id].box.width/2, sps[sel_id].box.y+sps[sel_id].box.height/2);
double bestscore = 0;
ImgWin bestWin;
// do shifting
for(int i=0; i<10; i++)
{
// generate locations
vector<ImgWin> wins;
SampleWinLocs(curpt, winconfs[0], minpt, maxpt, 6, wins);
if(wins.empty())
continue;
for(size_t j=0; j<wins.size(); j++)
wins[j].score = ComputeCenterSurroundMeanColorDiff(wins[j]);
// sort
sort(wins.begin(), wins.end());
const ImgWin selWin = wins[wins.size()-1];
cout<<"Best score: "<<selWin.score<<endl;
if(selWin.score > bestscore)
{
// shift to max point
curpt.x = selWin.x + selWin.width/2;
curpt.y = selWin.y + selWin.height/2;
// update
bestWin = selWin;
// visualize
vector<ImgWin> imgwins2;
ImgWin spwin = ImgWin(sps[sel_id].box.x, sps[sel_id].box.y, sps[sel_id].box.width, sps[sel_id].box.height);
imgwins2.push_back(spwin);
imgwins2.push_back(bestWin);
visualsearch::ImgVisualizer::DrawImgWins("shift", img, imgwins2);
cv::waitKey(0);
cv::destroyWindow("shift");
cout<<"Updated best score."<<endl;
}
}
cnt--;
}
return true;
}
