//----------------------------------------------------------------------------
//???20014.11.26?by?Charles?

/*	circle_found為找圓結(jié)果的結(jié)構(gòu)
	FindCircles為最終對(duì)外的接口，其他都是內(nèi)部函數(shù)
//優(yōu)化處理后打包好的HoughCircles接口，cv核心算法的優(yōu)化補(bǔ)充在該函數(shù)中添加如下：
//1、添加cvCanny的低閾值low_threshold，參數(shù)控制可以更靈活
//2、可選擇性的手動(dòng)輸入輪廓圖代替cv核心算法內(nèi)自動(dòng)調(diào)用的canny，可以更有效的進(jìn)行預(yù)處理
//3、添加分?jǐn)?shù)過(guò)濾，將找出的圓與實(shí)際輪廓對(duì)比重合率（如果輸入了輪廓圖則與輪廓圖比，否則用算法內(nèi)部自動(dòng)生成的輪廓圖對(duì)比），
//		按照分?jǐn)?shù)高低排序并過(guò)濾掉分?jǐn)?shù)過(guò)低的圓
*/
//----------------------------------------------------------------------------

#include?“ExtendHoughCircle.h“



bool?SortCircleFound（?const?circle_found?&v1?const?circle_found?&v2）//注意：本函數(shù)的參數(shù)的類(lèi)型一定要與vector中元素的類(lèi)型一致??
{??
	return?v1.score?>?v2.score;//降序排列??
}??

const?int?STORAGE_SIZE?=?1?<
#define?hough_cmp_gt（l1l2）?（aux[l1]?>?aux[l2]）

#define?cvSobel_Core?5
#define?overlap_check_brush?2

enum?{?XY_SHIFT?=?16?XY_ONE?=?1?<
static?CV_IMPLEMENT_QSORT_EX（?icvHoughSortDescent32s?int?hough_cmp_gt?const?int*?）

static?void?seqToMat（const?CvSeq*?seq?cv::OutputArray?_arr）
{
	if（?seq?&&?seq->total?>?0?）
	{
		_arr.create（1?seq->total?seq->flags?-1?true）;
		cv::Mat?arr?=?_arr.getMat（）;
		cvCvtSeqToArray（seq?arr.data）;
	}
	else
		_arr.release（）;
}

static?void?icvFindCirclesGradient（?CvMat*?img?cv::Mat?&contour_img?float?dp?float?min_distint?min_radius?int?max_radius
	int?low_threshold?int?high_thresholdint?acc_thresholdCvSeq*?circles?int?circles_max?）
{
	const?int?SHIFT?=?10?ONE?=?1?<	cv::Ptr?dx?dy;
	cv::Ptr?edges?accum?dist_buf;
	std::vector?sort_buf;
	cv::Ptr?storage;

	int?x?y?i?j?k?center_count?nz_count;
	float?min_radius2?=?（float）min_radius*min_radius;
	float?max_radius2?=?（float）max_radius*max_radius;
	int?rows?cols?arows?acols;
	int?astep?*adata;
	float*?ddata;
	CvSeq?*nz?*centers;
	float?idp?dr;
	CvSeqReader?reader;

	//如果輸入的輪廓圖尺寸與輸入的源圖完全相同，則使用輸入的輪廓圖，否則cvCanny提取輪廓圖，可以更靈活的預(yù)處理圖像
	if?（?contour_img.cols==img->cols?&&?contour_img.rows==img->rows?）
	{
		edges?=?cvCloneMat（&CvMat（contour_img））;
	}else
	{
		edges?=?cvCreateMat（?img->rows?img->cols?CV_8UC1?）;
		cvCanny（?img?edges?low_threshold?high_threshold?3?）;	//添加cvCanny的低閾值low_threshold，參數(shù)控制可以更靈活
		contour_img=cv::Mat（edges）+0;
	}

	dx?=?cvCreateMat（?img->rows?img->cols?CV_16SC1?）;
	dy?=?cvCreateMat（?img->rows?img->cols?CV_16SC1?）;
	///////////////////////////////////計(jì)算方向?qū)?shù)的Sobel核大小，重要參數(shù)////////////////////////////////////////
	cvSobel（?img?dx?1?0?cvSobel_Core?）;
	cvSobel（?img?dy?0?1?cvSobel_Core?）;

	if（?dp?		dp?=?1.f;
	idp?=?1.f/dp;
	accum?=?cvCreateMat（?cvCeil（img->rows*idp）+2?cvCeil（img->cols*idp）+2?CV_32SC1?）;
	cvZero（accum）;

	storage?=?cvCreateMemStorage（）;
	nz?=?cvCreateSeq（?CV_32SC2?sizeof（CvSeq）?sizeof（CvPoint）?storage?）;
	centers?=?cvCreateSeq（?CV_32SC1?sizeof（CvSeq）?sizeof（int）?storage?）;

	rows?=?img->rows;
	cols?=?img->cols;
	arows?=?accum->rows

?屬性????????????大小?????日期????時(shí)間???名稱(chēng)
-----------?---------??----------?-----??----

?????文件????????547??2017-01-12?16:33??Main.cpp

?????文件????????965??2017-01-12?16:33??ExtendHoughCircle.h

?????文件??????14129??2017-01-12?16:33??ExtendHoughCircle.cpp

-----------?---------??----------?-----??----

????????????????15641????????????????????3