I am working on a project using OPENCV for some image processing. I been learning from examples, tutorials but I am new on opencv so I have a question. I have attached pictures (1) is original input (2) output for better understanding all of you. I have used dbscan algorithm for segmentation. My purpose is to detect multiple objects in an image and draw rectangle around them and give center point of each rectangle to track or know the position.
But after some image processing filters , effects, Background subtraction, Findcontours ... i have found center of all contours. and pass to merged_contour_points vector. But it draw rectangle around all contours. I Hope you understand.
Here are my codes;
#include "stdafx.h"
#include <iostream>
#include "opencv2/opencv.hpp"
using namespace cv;
using namespace std;
#include <map>
#include <sstream>
template <class T>
inline std::string to_string (const T& t)
{
std::stringstream ss;
ss << t;
return ss.str();
}
class DbScan
{
public:
std::map<int, int> labels;
vector<Rect>& data;
int C;
double eps;
int mnpts;
double* dp;
//memoization table in case of complex dist functions
#define DP(i,j) dp[(data.size()*i)+j]
DbScan(vector<Rect>& _data,double _eps,int _mnpts):data(_data)
{
C=-1;
for(int i=0;i<data.size();i++)
{
labels[i]=-99;
}
eps=_eps;
mnpts=_mnpts;
}
void run()
{
dp = new double[data.size()*data.size()];
for(int i=0;i<data.size();i++)
{
for(int j=0;j<data.size();j++)
{
if(i==j)
DP(i,j)=0;
else
DP(i,j)=-1;
}
}
for(int i=0;i<data.size();i++)
{
if(!isVisited(i))
{
vector<int> neighbours = regionQuery(i);
if(neighbours.size()<mnpts)
{
labels[i]=-1;//noise
}else
{
C++;
expandCluster(i,neighbours);
}
}
}
delete [] dp;
}
void expandCluster(int p,vector<int> neighbours)
{
labels[p]=C;
for(int i=0;i<neighbours.size();i++)
{
if(!isVisited(neighbours[i]))
{
labels[neighbours[i]]=C;
vector<int> neighbours_p = regionQuery(neighbours[i]);
if (neighbours_p.size() >= mnpts)
{
expandCluster(neighbours[i],neighbours_p);
}
}
}
}
bool isVisited(int i)
{
return labels[i]!=-99;
}
vector<int> regionQuery(int p)
{
vector<int> res;
for(int i=0;i<data.size();i++)
{
if(distanceFunc(p,i)<=eps)
{
res.push_back(i);
}
}
return res;
}
double dist2d(Point2d a,Point2d b)
{
return sqrt(pow(a.x-b.x,2) + pow(a.y-b.y,2));
}
double distanceFunc(int ai,int bi)
{
if(DP(ai,bi)!=-1)
return DP(ai,bi);
Rect a = data[ai];
Rect b = data[bi];
/*
Point2d cena= Point2d(a.x+a.width/2,
a.y+a.height/2);
Point2d cenb = Point2d(b.x+b.width/2,
b.y+b.height/2);
double dist = sqrt(pow(cena.x-cenb.x,2) + pow(cena.y-cenb.y,2));
DP(ai,bi)=dist;
DP(bi,ai)=dist;*/
Point2d tla =Point2d(a.x,a.y);
Point2d tra =Point2d(a.x+a.width,a.y);
Point2d bla =Point2d(a.x,a.y+a.height);
Point2d bra =Point2d(a.x+a.width,a.y+a.height);
Point2d tlb =Point2d(b.x,b.y);
Point2d trb =Point2d(b.x+b.width,b.y);
Point2d blb =Point2d(b.x,b.y+b.height);
Point2d brb =Point2d(b.x+b.width,b.y+b.height);
double minDist = 9999999;
minDist = min(minDist,dist2d(tla,tlb));
minDist = min(minDist,dist2d(tla,trb));
minDist = min(minDist,dist2d(tla,blb));
minDist = min(minDist,dist2d(tla,brb));
minDist = min(minDist,dist2d(tra,tlb));
minDist = min(minDist,dist2d(tra,trb));
minDist = min(minDist,dist2d(tra,blb));
minDist = min(minDist,dist2d(tra,brb));
minDist = min(minDist,dist2d(bla,tlb));
minDist = min(minDist,dist2d(bla,trb));
minDist = min(minDist,dist2d(bla,blb));
minDist = min(minDist,dist2d(bla,brb));
minDist = min(minDist,dist2d(bra,tlb));
minDist = min(minDist,dist2d(bra,trb));
minDist = min(minDist,dist2d(bra,blb));
minDist = min(minDist,dist2d(bra,brb));
DP(ai,bi)=minDist;
DP(bi,ai)=minDist;
return DP(ai,bi);
}
vector<vector<Rect> > getGroups()
{
vector<vector<Rect> > ret;
for(int i=0;i<=C;i++)
{
ret.push_back(vector<Rect>());
for(int j=0;j<data.size();j++)
{
if(labels[j]==i)
{
ret[ret.size()-1].push_back(data[j]);
}
}
}
return ret;
}
};
cv::Scalar HSVtoRGBcvScalar(int H, int S, int V) {
int bH = H; // H component
int bS = S; // S component
int bV = V; // V component
double fH, fS, fV;
double fR, fG, fB;
const double double_TO_BYTE = 255.0f;
const double BYTE_TO_double = 1.0f / double_TO_BYTE;
// Convert from 8-bit integers to doubles
fH = (double)bH * BYTE_TO_double;
fS = (double)bS * BYTE_TO_double;
fV = (double)bV * BYTE_TO_double;
// Convert from HSV to RGB, using double ranges 0.0 to 1.0
int iI;
double fI, fF, p, q, t;
if( bS == 0 ) {
// achromatic (grey)
fR = fG = fB = fV;
}
else {
// If Hue == 1.0, then wrap it around the circle to 0.0
if (fH>= 1.0f)
fH = 0.0f;
fH *= 6.0; // sector 0 to 5
fI = floor( fH ); // integer part of h (0,1,2,3,4,5 or 6)
iI = (int) fH; // " " " "
fF = fH - fI; // factorial part of h (0 to 1)
p = fV * ( 1.0f - fS );
q = fV * ( 1.0f - fS * fF );
t = fV * ( 1.0f - fS * ( 1.0f - fF ) );
switch( iI ) {
case 0:
fR = fV;
fG = t;
fB = p;
break;
case 1:
fR = q;
fG = fV;
fB = p;
break;
case 2:
fR = p;
fG = fV;
fB = t;
break;
case 3:
fR = p;
fG = q;
fB = fV;
break;
case 4:
fR = t;
fG = p;
fB = fV;
break;
default: // case 5 (or 6):
fR = fV;
fG = p;
fB = q;
break;
}
}
// Convert from doubles to 8-bit integers
int bR = (int)(fR * double_TO_BYTE);
int bG = (int)(fG * double_TO_BYTE);
int bB = (int)(fB * double_TO_BYTE);
// Clip the values to make sure it fits within the 8bits.
if (bR > 255)
bR = 255;
if (bR < 0)
bR = 0;
if (bG >255)
bG = 255;
if (bG < 0)
bG = 0;
if (bB > 255)
bB = 255;
if (bB < 0)
bB = 0;
// Set the RGB cvScalar with G B R, you can use this values as you want too..
return cv::Scalar(bB,bG,bR); // R component
}
int main(int argc,char** argv )
{
Mat im = imread("c:/data/testpic.png",0);
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
std::vector<cv::Point> merged_contour_points;
std::vector<cv::Point> merged_contour_points1;
std::vector<cv::Point> pointsForSearch;
Mat grouped = Mat::zeros(im.size(),CV_8UC3);
vector<Scalar> colors;
RNG rng(3);
int largest_area=0;
int largest_contour_index=0;
int label;
findContours(im.clone(), contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_NONE);
/// Get the moments
vector<Moments> mu(contours.size());
for (int i = 0; i < contours.size(); i++)
{
mu[i] = moments(contours[i], false);
}
/// Get the mass centers:
vector<Point2f> centroids(contours.size());
for (int i = 0; i < contours.size(); i++) {
centroids[i] = Point2f(mu[i].m10 / mu[i].m00, mu[i].m01 / mu[i].m00);
pointsForSearch.push_back(centroids[i]);
circle(grouped,centroids[i],2,Scalar(0,255,0),-1,CV_AA);
cout << "size: " << pointsForSearch[i] << endl;
}
vector<Rect> boxes;
for(size_t i = 0; i < contours.size(); i++)
{
Rect r = boundingRect(contours[i]);
boxes.push_back(r);
}
DbScan dbscan(boxes,20,2);
dbscan.run();
//done, perform display
for(int i=0;i<=dbscan.C;i++)
{
colors.push_back(HSVtoRGBcvScalar(rng(255),255,255));
}
cout << "dbscan data size: " << dbscan.data.size() << endl;
// line(grouped,Point(0,140),Point(600,140),Scalar(0,0,255),2,CV_AA);
for(int i=0;i<dbscan.data.size();i++)
{
Scalar color;
if(dbscan.labels[i]==-1)
{
color=Scalar(128,128,128);
}else
{
label=dbscan.labels[i];
color=colors[label];
for (int j = 0; j < contours[i].size(); j++) {
merged_contour_points1.push_back(contours[i][j]);
}
}
putText(grouped,to_string(dbscan.labels[i]),dbscan.data[i].tl(), FONT_HERSHEY_COMPLEX,.5,color,1);
// drawContours(grouped,contours,i,color,-1);
}
cv::RotatedRect rotated_bounding_rect1 = minAreaRect(merged_contour_points1);
Rect brect1 = rotated_bounding_rect1.boundingRect();
rectangle(grouped, brect1, Scalar(255,255,0));
imshow("grouped",grouped);
imwrite("c:/data/grouped.jpg",grouped);
waitKey(0);
}
