轮廓跟踪

这个算法也是看到有的的，不算复杂，但算法让是个好算法。while(!FindPoint)是关键，它保证了在循环里面找到的点是边界点而不是内部点。

上代码

void CImageColorProcess::TraceContour(LPBYTE lpSrc, LPBYTE lpDst, LPBYTE lpDst_, int nSrcCount, int nW, int nH)
{
	this->OSTUThreshold(lpSrc, lpDst_, nSrcCount, nW, nH);//大律法二值化
	bool bFindStartPoint;//是否找到起始点及回到起始点
	bool bFindPoint;//是否找到一个边界点
	int Direction[8][2] = { { -1, 1 }, { 0, 1 }, { 1, 1 }, { 1, 0 },
	{ 1, -1 }, { 0, -1 }, { -1, -1 }, { -1, 0 } };//八个方向和起始扫描方向
	int BeginDirect;
	Point StartPoint, CurrentPoint;//起始点及当前边界点
	for (int i = 0; i < nH; i++)// 目标图像设定初始值为背景255
	{
		for (int j = 0; j < nW; j++)
		{
			lpDst[i*nW + j] = 255;
		}
	}
	bFindStartPoint = false;
	for (int j = 0; j < nH&&!bFindStartPoint; j++)
	{
		for (int i = 0; i < nW&&!bFindStartPoint; i++)
		{
			if (lpDst_[(nH-1-j)*nW + i] == 0)// 指向源图像倒数第j行，第i个象素的指针	
			{
				bFindStartPoint = true;
				StartPoint.m_row = j;
				StartPoint.m_col = i;
				lpDst[(nH - 1 - j)*nW + i] = 0;
			}
		}
	}
	//由于起始点是在左下方，故起始扫描沿左上方向
	BeginDirect = 0;
	bFindStartPoint = false;
	//从初始点开始扫描
	CurrentPoint.m_row = StartPoint.m_row;
	CurrentPoint.m_col = StartPoint.m_col;
	while (!bFindStartPoint)
	{
		bFindPoint = false;
		while (!bFindPoint)
		{
			//沿扫描方向查看一个像素
			if (lpDst_[(nH-1-(CurrentPoint.m_row + Direction[BeginDirect][1]))*nW + CurrentPoint.m_col
				+ Direction[BeginDirect][0]] == 0)
			{
				bFindPoint = true;
				CurrentPoint.m_row = CurrentPoint.m_row + Direction[BeginDirect][1];
				CurrentPoint.m_col = CurrentPoint.m_col + Direction[BeginDirect][0];
				if (CurrentPoint.m_row == StartPoint.m_row&&CurrentPoint.m_col == StartPoint.m_col)
				{
					bFindStartPoint = true;
				}
				lpDst[(nH-1-CurrentPoint.m_row)*nW + CurrentPoint.m_col] = 0;
				//扫描的方向逆时针旋转两格
				BeginDirect--;
				if (BeginDirect == -1)
					BeginDirect = 7;
				BeginDirect--;
				if (BeginDirect == -1)
					BeginDirect = 7;
			}
			else
			{
				//扫描方向顺时针旋转一格
				BeginDirect++;
				if (BeginDirect == 8)
					BeginDirect = 0;
			}
		}
	}
}

经测试发现该算法对整个相连的轮廓检测较好，如果遇到了琐碎的小轮廓它可能找到的就是这个小轮廓，导致大轮廓检测失败。上一组测试图片：

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