OpenCV实现基于Zernike矩的亚像素边缘检测

在做物体检测时，由于成本和应用场合的限制，不能够一味地增加相机的分辨率，或者已经用了分辨率很高的相机，但是视野范围很大，仍然无法实现很高的精度，这时就要考虑亚像素技术，亚像素技术就是在两个像素点之间进行进一步的细分，从而得到亚像素级别的边缘点的坐标（也就是float类型的坐标），一般来说，现有的技术可以做到2细分、4细分，甚至很牛的能做到更高，通过亚像素边缘检测技术的使用，可以节约成本，提高识别精度。

    常见的亚像素技术包括灰度矩、Hu矩、空间矩、Zernike矩等，通过查阅多篇论文，综合比较精度、时间和实现难度，选择Zernike矩作为项目中的亚像素边缘检测算法。基于Zernike矩的边缘检测原理，下一篇文章详细再总结一下，这里大体把步骤说一下，并使用OpenCV实现。

    大体步骤就是，首先确定使用的模板大小，然后根据Zernike矩的公式求出各个模板系数，例如我选择的是7*7模板（模板越大精度越高，但是运算时间越长），要计算M00，M11R，M11I，M20，M31R，M31I，M40七个Zernike模板。第二步是对待检测图像进行预处理，包括滤波二值化等，也可以在进行一次Canny边缘检测。第三步是将预处理的图像跟7个Zernike矩模板分别进行卷积，得到7个Zernike矩。第四步是把上一步的矩乘上角度校正系数（这是因为利用Zernike的旋转不变性，Zernike模型把边缘都简化成了x=n的直线，这里要调整回来）。第五步是计算距离参数l和灰度差参数k，根据k和l判断该点是否为边缘点。以下是基于OpenCV的实现。

#include 
#include 
 
using namespace std;
using namespace cv;
 
const double PI = 3.14159265358979323846;
const int g_N = 7;
 
Mat M00 = (Mat_(7, 7) <<
	0, 0.0287, 0.0686, 0.0807, 0.0686, 0.0287, 0,
	0.0287, 0.0815, 0.0816, 0.0816, 0.0816, 0.0815, 0.0287,
	0.0686, 0.0816, 0.0816, 0.0816, 0.0816, 0.0816, 0.0686,
	0.0807, 0.0816, 0.0816, 0.0816, 0.0816, 0.0816, 0.0807,
	0.0686, 0.0816, 0.0816, 0.0816, 0.0816, 0.0816, 0.0686,
	0.0287, 0.0815, 0.0816, 0.0816, 0.0816, 0.0815, 0.0287,
	0, 0.0287, 0.0686, 0.0807, 0.0686, 0.0287, 0);
 
Mat M11R = (Mat_(7, 7) <<
	0, -0.015, -0.019, 0, 0.019, 0.015, 0,
	-0.0224, -0.0466, -0.0233, 0, 0.0233, 0.0466, 0.0224,
	-0.0573, -0.0466, -0.0233, 0, 0.0233, 0.0466, 0.0573,
	-0.069, -0.0466, -0.0233, 0, 0.0233, 0.0466, 0.069,
	-0.0573, -0.0466, -0.0233, 0, 0.0233, 0.0466, 0.0573,
	-0.0224, -0.0466, -0.0233, 0, 0.0233, 0.0466, 0.0224,
	0, -0.015, -0.019, 0, 0.019, 0.015, 0);
 
Mat M11I = (Mat_(7, 7) <<
	0, -0.0224, -0.0573, -0.069, -0.0573, -0.0224, 0,
	-0.015, -0.0466, -0.0466, -0.0466, -0.0466, -0.0466, -0.015,
	-0.019, -0.0233, -0.0233, -0.0233, -0.0233, -0.0233, -0.019,
	0, 0, 0, 0, 0, 0, 0,
	0.019, 0.0233, 0.0233, 0.0233, 0.0233, 0.0233, 0.019,
	0.015, 0.0466, 0.0466, 0.0466, 0.0466, 0.0466, 0.015,
	0, 0.0224, 0.0573, 0.069, 0.0573, 0.0224, 0);
 
Mat M20 = (Mat_(7, 7) <<
	0, 0.0225, 0.0394, 0.0396, 0.0394, 0.0225, 0,
	0.0225, 0.0271, -0.0128, -0.0261, -0.0128, 0.0271, 0.0225,
	0.0394, -0.0128, -0.0528, -0.0661, -0.0528, -0.0128, 0.0394,
	0.0396, -0.0261, -0.0661, -0.0794, -0.0661, -0.0261, 0.0396,
	0.0394, -0.0128, -0.0528, -0.0661, -0.0528, -0.0128, 0.0394,
	0.0225, 0.0271, -0.0128, -0.0261, -0.0128, 0.0271, 0.0225,
	0, 0.0225, 0.0394, 0.0396, 0.0394, 0.0225, 0);
 
Mat M31R = (Mat_(7, 7) <<
	0, -0.0103, -0.0073, 0, 0.0073, 0.0103, 0,
	-0.0153, -0.0018, 0.0162, 0, -0.0162, 0.0018, 0.0153,
	-0.0223, 0.0324, 0.0333, 0, -0.0333, -0.0324, 0.0223,
	-0.0190, 0.0438, 0.0390, 0, -0.0390, -0.0438, 0.0190,
	-0.0223, 0.0324, 0.0333, 0, -0.0333, -0.0324, 0.0223,
	-0.0153, -0.0018, 0.0162, 0, -0.0162, 0.0018, 0.0153,
	0, -0.0103, -0.0073, 0, 0.0073, 0.0103, 0);
 
Mat M31I = (Mat_(7, 7) <<
	0, -0.0153, -0.0223, -0.019, -0.0223, -0.0153, 0,
	-0.0103, -0.0018, 0.0324, 0.0438, 0.0324, -0.0018, -0.0103,
	-0.0073, 0.0162, 0.0333, 0.039, 0.0333, 0.0162, -0.0073,
	0, 0, 0, 0, 0, 0, 0,
	0.0073, -0.0162, -0.0333, -0.039, -0.0333, -0.0162, 0.0073,
	0.0103, 0.0018, -0.0324, -0.0438, -0.0324, 0.0018, 0.0103,
	0, 0.0153, 0.0223, 0.0190, 0.0223, 0.0153, 0);
 
Mat M40 = (Mat_(7, 7) <<
	0, 0.013, 0.0056, -0.0018, 0.0056, 0.013, 0,
	0.0130, -0.0186, -0.0323, -0.0239, -0.0323, -0.0186, 0.0130,
	0.0056, -0.0323, 0.0125, 0.0406, 0.0125, -0.0323, 0.0056,
	-0.0018, -0.0239, 0.0406, 0.0751, 0.0406, -0.0239, -0.0018,
	0.0056, -0.0323, 0.0125, 0.0406, 0.0125, -0.0323, 0.0056,
	0.0130, -0.0186, -0.0323, -0.0239, -0.0323, -0.0186, 0.0130,
	0, 0.013, 0.0056, -0.0018, 0.0056, 0.013, 0);
int main()
{
	Mat OriginalImage = imread("original.png", 0);
	Mat SubImage = OriginalImage;
	Mat NewSmoothImage;
	medianBlur(OriginalImage, NewSmoothImage, 13);
	Mat NewAdaThresImage;
	adaptiveThreshold(NewSmoothImage, NewAdaThresImage, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY_INV, 7, 4);
	vector SubEdgePoints;
 
	Mat ZerImageM00;
	filter2D(NewAdaThresImage, ZerImageM00, CV_64F, M00, Point(-1, -1), 0, BORDER_DEFAULT);
	//////////filter2D( cannyImage, zerImageM00, CV_64F, M00, Point(-1,-1), 0, BORDER_DEFAULT);
 
	Mat ZerImageM11R;
	filter2D(NewAdaThresImage, ZerImageM11R, CV_64F, M11R, Point(-1, -1), 0, BORDER_DEFAULT);
	//////////filter2D( cannyImage, zerImageM11R, CV_64F, M11R, Point(-1, -1), 0, BORDER_DEFAULT);
 
	Mat ZerImageM11I;
	filter2D(NewAdaThresImage, ZerImageM11I, CV_64F, M11I, Point(-1, -1), 0, BORDER_DEFAULT);
	//////////filter2D( cannyImage, zerImageM11I, CV_64F, M11I, Point(-1, -1), 0, BORDER_DEFAULT);
 
	Mat ZerImageM20;
	filter2D(NewAdaThresImage, ZerImageM20, CV_64F, M20, Point(-1, -1), 0, BORDER_DEFAULT);
	//////////filter2D( cannyImage, zerImageM20, CV_64F, M20, Point(-1, -1), 0, BORDER_DEFAULT);
 
	Mat ZerImageM31R;
	filter2D(NewAdaThresImage, ZerImageM31R, CV_64F, M31R, Point(-1, -1), 0, BORDER_DEFAULT);
	//////////filter2D(cannyImage, zerImageM31R, CV_64F, M31R, Point(-1, -1), 0, BORDER_DEFAULT);
 
	Mat ZerImageM31I;
	filter2D(NewAdaThresImage, ZerImageM31I, CV_64F, M31I, Point(-1, -1), 0, BORDER_DEFAULT);
	//////////filter2D(cannyImage, zerImageM31I, CV_64F, M31I, Point(-1, -1), 0, BORDER_DEFAULT);
 
	Mat ZerImageM40;
	filter2D(NewAdaThresImage, ZerImageM40, CV_64F, M40, Point(-1, -1), 0, BORDER_DEFAULT);
	//////////filter2D(cannyImage, zerImageM40, CV_64F, M40, Point(-1, -1), 0, BORDER_DEFAULT);
 
 
 
	int row_number = NewAdaThresImage.rows;
	int col_number = NewAdaThresImage.cols;
	//使用7个的7*7的Zernike模板（其本质是个矩阵）M00、M11R、M11I、M20、M31R、M31I、M40，分别与图像进行卷积，得到每个像素点的七个Zernike矩Z00、Z11R、Z11I、Z20、Z31R、Z31I、Z40
	//对于每个点，根据它的七个Zernike矩，求得距离参数l和灰度差参数k，当l和k都满足设定的条件时，则判读该点为边缘点，并进一步利用上述7个Zernike矩求出该点的亚像素级坐标
	//如果l或k不满足设定的条件，则该点不是边缘点，转到下一个点求解距离参数l和灰度差参数k
	for (int i = 0; i < row_number; i++)
	{
		for (int j = 0; j (i, j) == 0)
			{
				continue;
			}
 
			//compute theta
			//vector > theta2(0);
			double theta_temporary = atan2(ZerImageM31I.at(i, j), ZerImageM31R.at(i, j));
			//theta2[i].push_back(thetaTem);
 
			//compute Z11'/Z31'
			double rotated_z11 = 0.0;
			rotated_z11 = sin(theta_temporary)*(ZerImageM11I.at(i, j)) + cos(theta_temporary)*(ZerImageM11R.at(i, j));
			double rotated_z31 = 0.0;
			rotated_z31 = sin(theta_temporary)*(ZerImageM31I.at(i, j)) + cos(theta_temporary)*(ZerImageM31R.at(i, j));
 
			//compute l
			double l_method1 = sqrt((5 * ZerImageM40.at(i, j) + 3 * ZerImageM20.at(i, j)) / (8 * ZerImageM20.at(i, j)));
			double l_method2 = sqrt((5 * rotated_z31 + rotated_z11) / (6 * rotated_z11));
			double l = (l_method1 + l_method2) / 2;
			//compute k/h
			double k, h;
		
			k = 3 * rotated_z11 / 2 / pow((1 - l_method2*l_method2), 1.5);
			h = (ZerImageM00.at(i, j) - k*PI / 2 + k*asin(l_method2) + k*l_method2*sqrt(1 - l_method2*l_method2)) / PI;
 
			//judge the edge
			double k_value = 20.0;
		
			double l_value = sqrt(2) / g_N;
		
			double absl = abs(l_method2 - l_method1);
			if (k >= k_value && absl <= l_value)
			{
				Point2d point_temporary;
				point_temporary.x = j + g_N*l*cos(theta_temporary) / 2;
				point_temporary.y = i + g_N*l*sin(theta_temporary) / 2;
				SubEdgePoints.push_back(point_temporary);
			}
			else
			{
				continue;
			}
		}
	}
	//显示所检测到的亚像素边缘
	for (size_t i = 0; i < SubEdgePoints.size(); i++)
	{
	    Point center_forshow(cvRound(SubEdgePoints[i].x), cvRound(SubEdgePoints[i].y));
		circle(OriginalImage, center_forshow, 1, Scalar(0, 97, 255), 1, 8, 0);
	}
	imshow("亚像素边缘", OriginalImage);
	
	waitKey(0);
	return 0;
}

 

 

---------------------------------------7.28更新分割线-------------------------------------------------

1.因为留言要源代码的朋友比较多，所以我把上面的代码补完整了，新的代码能够实现基本的过程，但是针对不同的应用，肯定不是最优的效果，可能需要根据不同的应用场合进行调整；

2.评论里提到的双层轮廓，我能想到的是因为对线进行边缘检测操作，因为线的两侧相当于都是前景和背景的边缘，所以两侧各会有一层边缘，不管是Canny（事实上不少Zernike论文都建议先用canny再用Zernike）还是二值化，只要送入Zernike的是线而不是区域，就会有双层轮廓，其他的原因我暂时还没想到，欢迎大家赐教；

3.双层轮廓并不一定是坏事，比如做圆检测，最后是要用这些边缘点来拟合圆，那么两层轮廓拟合出来的圆放大了说应该是处于两层轮廓中间，这样的结果其实更符合实际情况，但是如果不是规则轮廓，可能会对边缘点的表达和拟合有些问题；

转载自：https://blog.csdn.net/zilanpotou182/article/details/69848610