Main.cpp

/*
This code is intended for academic use only.
You are free to use and modify the code, at your own risk.

If you use this code, or find it useful, please refer to the paper:

Michele Fornaciari, Andrea Prati, Rita Cucchiara,
A fast and effective ellipse detector for embedded vision applications
Pattern Recognition, Volume 47, Issue 11, November 2014, Pages 3693-3708, ISSN 0031-3203,
http://dx.doi.org/10.1016/j.patcog.2014.05.012.
(http://www.sciencedirect.com/science/article/pii/S0031320314001976)


The comments in the code refer to the abovementioned paper.
If you need further details about the code or the algorithm, please contact me at:

michele.fornaciari@unimore.it

last update: 23/12/2014
*/

//#include <cv.hpp>
#include <cv.h>
#include <highgui.h>

#include "EllipseDetectorYaed.h"
#include <fstream>


using namespace std;
using namespace cv;



// Should be checked
void SaveEllipses(const string& workingDir, const string& imgName, const vector<Ellipse>& ellipses /*, const vector<double>& times*/)
{
	string path(workingDir + "/" + imgName + ".txt");
	ofstream out(path, ofstream::out | ofstream::trunc);
	if (!out.good())
	{
		cout << "Error saving: " << path << endl;
		return;
	}

	// Save execution time
	//out << times[0] << "\t" << times[1] << "\t" << times[2] << "\t" << times[3] << "\t" << times[4] << "\t" << times[5] << "\t" << "\n";

	unsigned n = ellipses.size();
	// Save number of ellipses
	out << n << "\n";

	// Save ellipses
	for (unsigned i = 0; i < n; ++i)
	{
		const Ellipse& e = ellipses[i];
		out << e._xc << "\t" << e._yc << "\t" << e._a << "\t" << e._b << "\t" << e._rad << "\t" << e._score << "\n";
	}
	out.close();
}

// Should be checked
bool LoadTest(vector<Ellipse>& ellipses, const string& sTestFileName, vector<double>& times, bool bIsAngleInRadians = true)
{
	ifstream in(sTestFileName);
	if (!in.good())
	{
		cout << "Error opening: " << sTestFileName << endl;
		return false;
	}

	times.resize(6);
	in >> times[0] >> times[1] >> times[2] >> times[3] >> times[4] >> times[5];

	unsigned n;
	in >> n;

	ellipses.clear();

	if (n == 0) return true;

	ellipses.reserve(n);

	while (in.good() && n--)
	{
		Ellipse e;
		in >> e._xc >> e._yc >> e._a >> e._b >> e._rad >> e._score;

		if (!bIsAngleInRadians)
		{
			e._rad = e._rad * float(CV_PI / 180.0);
		}

		e._rad = fmod(float(e._rad + 2.0*CV_PI), float(CV_PI));

		if ((e._a > 0) && (e._b > 0) && (e._rad >= 0))
		{
			ellipses.push_back(e);
		}
	}
	in.close();

	// Sort ellipses by decreasing score
	sort(ellipses.begin(), ellipses.end());

	return true;
}


void LoadGT(vector<Ellipse>& gt, const string& sGtFileName, bool bIsAngleInRadians = true)
{
	ifstream in(sGtFileName);
	if (!in.good())
	{
		cout << "Error opening: " << sGtFileName << endl;
		return;
	}

	unsigned n;
	in >> n;

	gt.clear();
	gt.reserve(n);

	while (in.good() && n--)
	{
		Ellipse e;
		in >> e._xc >> e._yc >> e._a >> e._b >> e._rad;

		if (!bIsAngleInRadians)
		{
			// convert to radians
			e._rad = float(e._rad * CV_PI / 180.0);
		}

		if (e._a < e._b)
		{
			float temp = e._a;
			e._a = e._b;
			e._b = temp;

			e._rad = e._rad + float(0.5*CV_PI);
		}

		e._rad = fmod(float(e._rad + 2.f*CV_PI), float(CV_PI));
		e._score = 1.f;
		gt.push_back(e);
	}
	in.close();
}

bool TestOverlap(const Mat1b& gt, const Mat1b& test, float th)
{
	float fAND = float(countNonZero(gt & test));
	float fOR = float(countNonZero(gt | test));
	float fsim = fAND / fOR;

	return (fsim >= th);
}

int Count(const vector<bool> v)
{
	int counter = 0;
	for (unsigned i = 0; i < v.size(); ++i)
	{
		if (v[i]) { ++counter; }
	}
	return counter;
}


// Should be checked !!!!!
std::tuple<float, float, float> Evaluate(const vector<Ellipse>& ellGT, const vector<Ellipse>& ellTest, const float th_score, const Mat3b& img)
{
	float threshold_overlap = 0.8f;
	//float threshold = 0.95f;

	unsigned sz_gt = ellGT.size();
	unsigned size_test = ellTest.size();

	unsigned sz_test = unsigned(min(1000, int(size_test)));

	vector<Mat1b> gts(sz_gt);
	vector<Mat1b> tests(sz_test);

	for (unsigned i = 0; i < sz_gt; ++i)
	{
		const Ellipse& e = ellGT[i];

		Mat1b tmp(img.rows, img.cols, uchar(0));
		ellipse(tmp, Point(e._xc, e._yc), Size(e._a, e._b), e._rad * 180.0 / CV_PI, 0.0, 360.0, Scalar(255), -1);
		gts[i] = tmp;
	}

	for (unsigned i = 0; i < sz_test; ++i)
	{
		const Ellipse& e = ellTest[i];

		Mat1b tmp(img.rows, img.cols, uchar(0));
		ellipse(tmp, Point(e._xc, e._yc), Size(e._a, e._b), e._rad * 180.0 / CV_PI, 0.0, 360.0, Scalar(255), -1);
		tests[i] = tmp;
	}

	Mat1b overlap(sz_gt, sz_test, uchar(0));
	for (int r = 0; r < overlap.rows; ++r)
	{
		for (int c = 0; c < overlap.cols; ++c)
		{
			overlap(r, c) = TestOverlap(gts[r], tests[c], threshold_overlap) ? uchar(255) : uchar(0);
		}
	}

	int counter = 0;

	vector<bool> vec_gt(sz_gt, false);

	for (int i = 0; i < sz_test; ++i)
	{
		const Ellipse& e = ellTest[i];
		for (int j = 0; j < sz_gt; ++j)
		{
			if (vec_gt[j]) { continue; }

			bool bTest = overlap(j, i) != 0;

			if (bTest)
			{
				vec_gt[j] = true;
				break;
			}
		}
	}

	int tp = Count(vec_gt);
	int fn = int(sz_gt) - tp;
	int fp = size_test - tp; // !!!!

	float pr(0.f);
	float re(0.f);
	float fmeasure(0.f);

	if (tp == 0)
	{
		if (fp == 0)
		{
			pr = 1.f;
			re = 0.f;
			fmeasure = (2.f * pr * re) / (pr + re);
		}
		else
		{
			pr = 0.f;
			re = 0.f;
			fmeasure = 0.f;
		}
	}
	else
	{
		pr = float(tp) / float(tp + fp);
		re = float(tp) / float(tp + fn);
		fmeasure = (2.f * pr * re) / (pr + re);
	}

	return make_tuple(pr, re, fmeasure);
}

void OnImage()
{
	string sWorkingDir = "/home/itv/Desktop/ellipse_detect";
	string imagename = "1.jpg";

	string filename = sWorkingDir + "/images/" + imagename;

	// Read image
	Mat3b image = imread(filename);
	Size sz = image.size();

	// Convert to grayscale
	Mat1b gray;
	cvtColor(image, gray, CV_BGR2GRAY);


	// Parameters Settings (Sect. 4.2)
	int		iThLength = 16;
	float	fThObb = 3.0f;
	float	fThPos = 1.0f;
	float	fTaoCenters = 0.05f;
	int 	iNs = 16;
	float	fMaxCenterDistance = sqrt(float(sz.width*sz.width + sz.height*sz.height)) * fTaoCenters;

	float	fThScoreScore = 0.4f;

	// Other constant parameters settings. 

	// Gaussian filter parameters, in pre-processing
	Size	szPreProcessingGaussKernelSize = Size(5, 5);
	double	dPreProcessingGaussSigma = 1.0;

	float	fDistanceToEllipseContour = 0.1f;	// (Sect. 3.3.1 - Validation)
	float	fMinReliability = 0.4f;	// Const parameters to discard bad ellipses


	// Initialize Detector with selected parameters
	CEllipseDetectorYaed* yaed = new CEllipseDetectorYaed();
	yaed->SetParameters(szPreProcessingGaussKernelSize,
		dPreProcessingGaussSigma,
		fThPos,
		fMaxCenterDistance,
		iThLength,
		fThObb,
		fDistanceToEllipseContour,
		fThScoreScore,
		fMinReliability,
		iNs
		);


	// Detect
	vector<Ellipse> ellsYaed;
	Mat1b gray2 = gray.clone();
	yaed->Detect(gray2, ellsYaed);

	vector<double> times = yaed->GetTimes();
	cout << "--------------------------------" << endl;
	cout << "Execution Time: " << endl;
	cout << "Edge Detection: \t" << times[0] << endl;
	cout << "Pre processing: \t" << times[1] << endl;
	cout << "Grouping:       \t" << times[2] << endl;
	cout << "Estimation:     \t" << times[3] << endl;
	cout << "Validation:     \t" << times[4] << endl;
	cout << "Clustering:     \t" << times[5] << endl;
	cout << "--------------------------------" << endl;
	cout << "Total:	         \t" << yaed->GetExecTime() << endl;
	cout << "--------------------------------" << endl;


	vector<Ellipse> gt;
	LoadGT(gt, sWorkingDir + "/gt/" + "gt_" + imagename + ".txt", true); // Prasad is in radians

	Mat3b resultImage = image.clone();

	// Draw GT ellipses
	for (unsigned i = 0; i < gt.size(); ++i)
	{
		Ellipse& e = gt[i];
		Scalar color(0, 0, 255);
		ellipse(resultImage, Point(cvRound(e._xc), cvRound(e._yc)), Size(cvRound(e._a), cvRound(e._b)), e._rad*180.0 / CV_PI, 0.0, 360.0, color, 3);
	}

	yaed->DrawDetectedEllipses(resultImage, ellsYaed);


	Mat3b res = image.clone();

	Evaluate(gt, ellsYaed, fThScoreScore, res);


	imshow("Yaed", resultImage);
	waitKey();
}

void OnVideo()
{

	string sWorkingDir = "/home/itv/Desktop/ellipse_detect";
	string imagename = "1.jpg";

	string filename = sWorkingDir + "/images/" + imagename;
	
	VideoCapture cap(0);
	if(!cap.isOpened()) return;

	int width = 800;
	int height = 600;

	// Parameters Settings (Sect. 4.2)
	int		iThLength = 16;
	float	fThObb = 3.0f;
	float	fThPos = 1.0f;
	float	fTaoCenters = 0.05f;
	int 	iNs = 16;
	float	fMaxCenterDistance = sqrt(float(width*width + height*height)) * fTaoCenters;

	float	fThScoreScore = 0.4f;

	// Other constant parameters settings. 

	// Gaussian filter parameters, in pre-processing
	Size	szPreProcessingGaussKernelSize = Size(5, 5);
	double	dPreProcessingGaussSigma = 1.0;

	float	fDistanceToEllipseContour = 0.1f;	// (Sect. 3.3.1 - Validation)
	float	fMinReliability = 0.4f;	// Const parameters to discard bad ellipses


	// Initialize Detector with selected parameters
	CEllipseDetectorYaed* yaed = new CEllipseDetectorYaed();
	yaed->SetParameters(szPreProcessingGaussKernelSize,
		dPreProcessingGaussSigma,
		fThPos,
		fMaxCenterDistance,
		iThLength,
		fThObb,
		fDistanceToEllipseContour,
		fThScoreScore,
		fMinReliability,
		iNs
		);

	Mat1b gray;
	while(true)
	{	
		Mat3b image;
		cap >> image;
		cvtColor(image, gray, CV_BGR2GRAY);	

			vector<Ellipse> ellsYaed;
		Mat1b gray2 = gray.clone();
		yaed->Detect(gray2, ellsYaed);

		vector<double> times = yaed->GetTimes();
		cout << "--------------------------------" << endl;
		cout << "Execution Time: " << endl;
		cout << "Edge Detection: \t" << times[0] << endl;
		cout << "Pre processing: \t" << times[1] << endl;
		cout << "Grouping:       \t" << times[2] << endl;
		cout << "Estimation:     \t" << times[3] << endl;
		cout << "Validation:     \t" << times[4] << endl;
		cout << "Clustering:     \t" << times[5] << endl;
		cout << "--------------------------------" << endl;
		cout << "Total:	         \t" << yaed->GetExecTime() << endl;
		cout << "--------------------------------" << endl;


		vector<Ellipse> gt;
		LoadGT(gt, sWorkingDir + "/gt/" + "gt_" + imagename + ".txt", true); // Prasad is in radians

		Mat3b resultImage = image.clone();

		// Draw GT ellipses
		for (unsigned i = 0; i < gt.size(); ++i)
		{
			Ellipse& e = gt[i];
			Scalar color(0, 0, 255);
			ellipse(resultImage, Point(cvRound(e._xc), cvRound(e._yc)), Size(cvRound(e._a), cvRound(e._b)), e._rad*180.0 / CV_PI, 0.0, 360.0, color, 3);
		}

		yaed->DrawDetectedEllipses(resultImage, ellsYaed);


		Mat3b res = image.clone();

		Evaluate(gt, ellsYaed, fThScoreScore, res);


		imshow("Yaed", resultImage);

			
		if(waitKey(10) >= 0) break;
	}
}

void OnDataset()
{
	string sWorkingDir = "D:\\data\\ellipse_dataset\\Random Images - Dataset #1\\";
	//string sWorkingDir = "D:\\data\\ellipse_dataset\\Prasad Images - Dataset Prasad\\";
	string out_folder = "D:\\data\\ellipse_dataset\\";

	vector<string> names;

	vector<float> prs;
	vector<float> res;
	vector<float> fms;
	vector<double> tms;

	glob(sWorkingDir + "images\\" + "*.*", names);

	int counter = 0;
	for (const auto& image_name : names)
	{
		cout << double(counter++) / names.size() << "\n";

		string name_ext = image_name.substr(image_name.find_last_of("\\") + 1);
		string name = name_ext.substr(0, name_ext.find_last_of("."));

		Mat3b image = imread(image_name);
		Size sz = image.size();

		// Convert to grayscale
		Mat1b gray;
		cvtColor(image, gray, CV_BGR2GRAY);

		// Parameters Settings (Sect. 4.2)
		int		iThLength = 16;
		float	fThObb = 3.0f;
		float	fThPos = 1.0f;
		float	fTaoCenters = 0.05f;
		int 	iNs = 16;
		float	fMaxCenterDistance = sqrt(float(sz.width*sz.width + sz.height*sz.height)) * fTaoCenters;

		float	fThScoreScore = 0.72f;

		// Other constant parameters settings. 

		// Gaussian filter parameters, in pre-processing
		Size	szPreProcessingGaussKernelSize = Size(5, 5);
		double	dPreProcessingGaussSigma = 1.0;

		float	fDistanceToEllipseContour = 0.1f;	// (Sect. 3.3.1 - Validation)
		float	fMinReliability = 0.4;	// Const parameters to discard bad ellipses


		// Initialize Detector with selected parameters
		CEllipseDetectorYaed* yaed = new CEllipseDetectorYaed();
		yaed->SetParameters(szPreProcessingGaussKernelSize,
			dPreProcessingGaussSigma,
			fThPos,
			fMaxCenterDistance,
			iThLength,
			fThObb,
			fDistanceToEllipseContour,
			fThScoreScore,
			fMinReliability,
			iNs
			);


		// Detect
		vector<Ellipse> ellsYaed;
		Mat1b gray2 = gray.clone();
		yaed->Detect(gray2, ellsYaed);

		/*vector<double> times = yaed.GetTimes();
		cout << "--------------------------------" << endl;
		cout << "Execution Time: " << endl;
		cout << "Edge Detection: \t" << times[0] << endl;
		cout << "Pre processing: \t" << times[1] << endl;
		cout << "Grouping:       \t" << times[2] << endl;
		cout << "Estimation:     \t" << times[3] << endl;
		cout << "Validation:     \t" << times[4] << endl;
		cout << "Clustering:     \t" << times[5] << endl;
		cout << "--------------------------------" << endl;
		cout << "Total:	         \t" << yaed.GetExecTime() << endl;
		cout << "--------------------------------" << endl;*/

		tms.push_back(yaed->GetExecTime());


		vector<Ellipse> gt;
		LoadGT(gt, sWorkingDir + "gt\\" + "gt_" + name_ext + ".txt", false); // Prasad is in radians,set to true


		float pr, re, fm;
		std::tie(pr, re, fm) = Evaluate(gt, ellsYaed, fThScoreScore, image);

		prs.push_back(pr);
		res.push_back(re);
		fms.push_back(fm);

		Mat3b resultImage = image.clone();

		// Draw GT ellipses
		for (unsigned i = 0; i < gt.size(); ++i)
		{
			Ellipse& e = gt[i];
			Scalar color(0, 0, 255);
			ellipse(resultImage, Point(cvRound(e._xc), cvRound(e._yc)), Size(cvRound(e._a), cvRound(e._b)), e._rad*180.0 / CV_PI, 0.0, 360.0, color, 3);
		}

		yaed->DrawDetectedEllipses(resultImage, ellsYaed);

		//imwrite(out_folder + name + ".png", resultImage);
		//imshow("Yaed", resultImage);
		//waitKey();


		int dbg = 0;
	}

	float N = float(prs.size());
	float sumPR = accumulate(prs.begin(), prs.end(), 0.f);
	float sumRE = accumulate(res.begin(), res.end(), 0.f);
	float sumFM = accumulate(fms.begin(), fms.end(), 0.f);
	double sumTM = accumulate(tms.begin(), tms.end(), 0.0);

	float meanPR = sumPR / N;
	float meanRE = sumRE / N;
	float meanFM = sumFM / N;
	double meanTM = sumTM / N;

	float finalFM = (2.f * meanPR * meanRE) / (meanPR + meanRE);

	cout << "F-measure : " << finalFM << endl;
	cout << "Exec time : " << meanTM << endl;

	getchar();
}


int main(int argc, char** argv)
{
	OnVideo();
	//OnImage();
	//OnDataset();

	return 0;
}

// Test on single image
int main2()
{
	string images_folder = "D:\\SO\\img\\";
	string out_folder = "D:\\SO\\img\\";
	vector<string> names;

	glob(images_folder + "Lo3my4.*", names);

	for (const auto& image_name : names)
	{
		string name = image_name.substr(image_name.find_last_of("\\") + 1);
		name = name.substr(0, name.find_last_of("."));

		Mat3b image = imread(image_name);
		Size sz = image.size();
		
		// Convert to grayscale
		Mat1b gray;
		cvtColor(image, gray, CV_BGR2GRAY);
		
		// Parameters Settings (Sect. 4.2)
		int		iThLength = 16;
		float	fThObb = 3.0f;
		float	fThPos = 1.0f;
		float	fTaoCenters = 0.05f;
		int 	iNs = 16;
		float	fMaxCenterDistance = sqrt(float(sz.width*sz.width + sz.height*sz.height)) * fTaoCenters;

		float	fThScoreScore = 0.7f;

		// Other constant parameters settings. 

		// Gaussian filter parameters, in pre-processing
		Size	szPreProcessingGaussKernelSize = Size(5, 5);
		double	dPreProcessingGaussSigma = 1.0;

		float	fDistanceToEllipseContour = 0.1f;	// (Sect. 3.3.1 - Validation)
		float	fMinReliability = 0.5;	// Const parameters to discard bad ellipses


		CEllipseDetectorYaed* yaed = new CEllipseDetectorYaed();
		yaed->SetParameters(szPreProcessingGaussKernelSize,
			dPreProcessingGaussSigma,
			fThPos,
			fMaxCenterDistance,
			iThLength,
			fThObb,
			fDistanceToEllipseContour,
			fThScoreScore,
			fMinReliability,
			iNs
			);


		// Detect
		vector<Ellipse> ellsYaed;
		Mat1b gray2 = gray.clone();
		yaed->Detect(gray2, ellsYaed);

		vector<double> times = yaed->GetTimes();
		cout << "--------------------------------" << endl;
		cout << "Execution Time: " << endl;
		cout << "Edge Detection: \t" << times[0] << endl;
		cout << "Pre processing: \t" << times[1] << endl;
		cout << "Grouping:       \t" << times[2] << endl;
		cout << "Estimation:     \t" << times[3] << endl;
		cout << "Validation:     \t" << times[4] << endl;
		cout << "Clustering:     \t" << times[5] << endl;
		cout << "--------------------------------" << endl;
		cout << "Total:	         \t" << yaed->GetExecTime() << endl;
		cout << "--------------------------------" << endl;


		
		Mat3b resultImage = image.clone();
		yaed->DrawDetectedEllipses(resultImage, ellsYaed);
		
		imwrite(out_folder + name + ".png", resultImage);

		imshow("Yaed", resultImage);
		waitKey();


		int yghds = 0;
	}





	return 0;
}