图像练习-计算平行线距离opencv(03)

原图 

//对输入图像进行细化
cv::Mat ThinLine(const cv::Mat& matsrc, const int& iterations)
{
	//CvSize size = cvGetSize(src);
	cv::Mat dst = matsrc.clone();//拷贝一个数组给另一个数组
	int _iwidth = dst.cols;
	int _iheight = dst.rows;
	int n = 0, i = 0, j = 0;
	for (n = 0; n < iterations; n++)
	{
		cv::Mat temp = dst.clone();
		for (i = 0; i < temp.rows; i++)
		{
			for (int j = 0; j < temp.cols; j++)
			{
				if ((*temp.ptr(i, j)) == 1)
				{
					int ap = 0;
					int p2 = (i == 0) ? 0 : (*temp.ptr(i - 1, j));
					int p3 = (i == 0 || j == _iwidth - 1) ? 0 : (*temp.ptr(i - 1, j + 1));
					if (p2 == 0 && p3 == 1)
					{
						ap++;
					}
					int p4 = (j == _iwidth - 1) ? 0 : (*temp.ptr(i, j + 1));
					if (p3 == 0 && p4 == 1)
					{
						ap++;
					}
					int p5 = (i == _iwidth - 1 || j == _iwidth - 1) ? 0 : (*temp.ptr(i + 1, j + 1));
					if (p4 == 0 && p5 == 1)
					{
						ap++;
					}
					int p6 = (i == _iwidth - 1) ? 0 : (*temp.ptr(i + 1, j));
					if (p5 == 0 && p6 == 1)
					{
						ap++;
					}
					int p7 = (i == _iwidth - 1 || j == 0) ? 0 : (*temp.ptr(i + 1, j - 1));
					if (p6 == 0 && p7 == 1)
					{
						ap++;
					}
					int p8 = (j == 0) ? 0 : (*temp.ptr(i, j - 1));
					if (p7 == 0 && p8 == 1)
					{
						ap++;
					}
					int p9 = (i == 0 || j == 0) ? 0 : (*temp.ptr(i - 1, j - 1));
					if (p8 == 0 && p9 == 1)
					{
						ap++;
					}
					if (p9 == 0 && p2 == 1)
					{
						ap++;
					}
					if ((p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) > 1 && (p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) < 7)
					{
						if (ap == 1)
						{
							if (p2 * p4 * p8 == 0)
							{
								if (p2 * p6 * p8 == 0)
								{
									(*dst.ptr(i, j)) = 0;
								}
							}
						}
					}
				}
 
			}
 
		}
	}
 
	//将二值图像转换成灰度，以便显示
	for (i = 0; i < dst.rows; i++)
	{
		for (j = 0; j < dst.cols; j++)
		{
			if ((*dst.ptr(i, j)) == 1)
			{
				(*dst.ptr(i, j)) = 255;
			}
			else
			{
				(*dst.ptr(i, j)) = 0;
			}
		}
	}
 
	return dst;
}
 
void LineDistance()
{
	cv::Mat src = cv::imread("平行线求距离.png", cv::IMREAD_COLOR);
 
	cv::Mat gray;
	cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY);
 
	cv::Mat thre;
	cv::threshold(gray, thre, 128, 1, cv::THRESH_BINARY_INV);
 
	//第一步 对图像中的直线进行细化
	cv::Mat line = ThinLine(thre, 50);
 
	//第二步  提取直线的轮廓坐标
	std::vector> contours;
	cv::findContours(line, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE);
 
	cv::Mat show = src.clone();
	cv::drawContours(show, contours, -1, cv::Scalar(255));
 
	//直线拟合 	
   //拟合结果为一个四元素的容器，比如Vec4f - (vx, vy, x0, y0)
   //其中(vx, vy) 是直线的方向向量 (x0, y0) 是直线上的一个点
	cv::Vec4d l0;
	cv::fitLine(contours[0], l0, cv::DIST_L2, 0, 0.01, 0.01);//拟合方法采用最小二乘法
 
	double cos_theta0 = l0[0];
	double sin_theta0 = l0[1];
	double x0 = l0[2], y0 = l0[3];
 
	double k0 = sin_theta0 / cos_theta0;
	double b0 = y0 - k0 * x0;
	printf("line0: y = %f * x + %f \n", k0 * 180.0 / CV_PI, b0);
 
	double x = 0;
	double y = k0 * x + b0;
 
	cv::Vec4d l1;
	//拟合方法采用最小二乘法
	cv::fitLine(contours[1], l1, cv::DIST_L2, 0, 0.01, 0.01);
	double cos_theta1 = l1[0];
	double sin_theta1 = l1[1];
	double x1 = l1[2], y1 = l1[3];
 
	double k1 = sin_theta1 / cos_theta1;
	double b1 = y1 - k1 * x1;
	printf("line1: y = %f * x + %f \n", k1 * 180.0 / CV_PI, b1);
 
	//第四部  计算两条直线之间的距离 公式是: |b1 - b0| / sqrt(A * A + B * B)
	double A = k0;
	double B = 1.0;
	double 	dis = abs(b1 - b0) / sqrt(A * A + B * B);
	printf("dis is %f \n", dis);
}