canny算子实现

本文主要是介绍canny算子实现，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

原理：
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实现：

/*** @description:	计算阶乘* @param n			自然数* @return			阶乘*/
int factorial(int n)
{int fac = 1;if (n == 0)	return fac;for (int i = 1; i <= n; ++i)	fac *= i;return fac;
}/*** @description:    获得Sobel平滑算子* @param size		掩膜大小* @return			Sobel平滑算子*/
cv::Mat getSobelSmooth(int size)
{int n = size - 1;cv::Mat SobelSmoothoper = cv::Mat::zeros(size, 1, CV_32F);for (int k = 0; k <= n; k++){float *pt = SobelSmoothoper.ptr<float>(0);pt[k] = factorial(n) / (factorial(k)*factorial(n - k));}return SobelSmoothoper;
}/*** @description:   	获得Sobel差分算子* @param size		掩膜大小* @return			Sobel差分算子*/
cv::Mat getSobeldiff(int size)
{cv::Mat Sobeldiffoper = cv::Mat::zeros(cv::Size(size, 1), CV_32F);cv::Mat SobelSmooth = getSobelSmooth(size - 1);for (int k = 0; k < size; k++){if (k == 0)Sobeldiffoper.at<float>(0, k) = 1;else if (k == size - 1)Sobeldiffoper.at<float>(0, k) = -1;elseSobeldiffoper.at<float>(0, k) = SobelSmooth.at<float>(0, k) - SobelSmooth.at<float>(0, k - 1);}return Sobeldiffoper;
}/*** @description:	卷积实现* @param src		输入图像	* @param dst		输出图像* @param kernel	卷积核*/
void conv2D(cv::Mat& src, cv::Mat& dst, cv::Mat kernel)
{cv::flip(kernel, kernel, -1);cv::filter2D(src, dst, CV_32F, kernel);
}/*** @description:  	可分离卷积———先水平方向卷积，后垂直方向卷积* @param src		输入图像* @param dst		输出图像* @param kernel_X	x方向卷积* @param kernel_Y	y方向卷积*/
void sepConv2D_X_Y(cv::Mat& src, cv::Mat& dst, cv::Mat kernel_X, cv::Mat kernel_Y)
{cv::Mat dst_kernel_X;conv2D(src, dst_kernel_X, kernel_X); conv2D(dst_kernel_X, dst, kernel_Y); 
}/*** @description:  	可分离卷积———先垂直方向卷积，后水平方向卷积* @param src		输入图像* @param dst		输出图像* @param kernel_Y	y方向卷积* @param kernel_X	x方向卷积*/
void sepConv2D_Y_X(cv::Mat& src, cv::Mat& dst, cv::Mat kernel_Y, cv::Mat kernel_X)
{cv::Mat dst_kernel_Y;conv2D(src, dst_kernel_Y, kernel_Y);conv2D(dst_kernel_Y, dst, kernel_X); 
}/*** @description:	Sobel算子边缘检测* @param src		输入图像* @param dst		输出图像* @param dst_X		x方向边缘* @param dst_Y		y方向边缘* @param size		掩膜大小*/
void sobel(cv::Mat& src, cv::Mat& dst, cv::Mat& dst_X, cv::Mat& dst_Y, int size)
{cv::Mat SobelSmoothoper = getSobelSmooth(size);cv::Mat Sobeldiffoper = getSobeldiff(size);    sepConv2D_X_Y(src, dst_Y, SobelSmoothoper, Sobeldiffoper.t()); sepConv2D_Y_X(src, dst_X, SobelSmoothoper.t(), Sobeldiffoper); dst = abs(dst_X) + abs(dst_Y);convertScaleAbs(dst, dst);
}/*** @description:  	确定一个点的坐标是否在图像内* @param r			点的行坐标* @param c			点的列坐标* @param rows		图像行数* @param cols		图像列数* @return			点的坐标是否在图像内*/
bool checkInRange(int r, int c, int rows, int cols) 
{if (r >= 0 && r < rows && c >= 0 && c < cols)return true;elsereturn false;
}/*** @description:			从确定边缘点出发，延长边缘* @param edgeMag_noMaxsup	未经过极大值抑制的边缘强度* @param edge				图像边缘* @param Th				灰度阈值* @param r					点的行坐标* @param c					点的列坐标* @param rows				图像行数* @param cols				图像列数*/
void trace(cv::Mat &edgeMag_noMaxsup, cv::Mat &edge, float Th, int r, int c, int rows, int cols)
{if (edge.at<uchar>(r, c) == 0){for (int i = -1; i <= 1; ++i){for (int j = -1; j <= 1; ++j){if (checkInRange(r + i, c + j, rows, cols) && edgeMag_noMaxsup.at<float>(r + i, c + j) > Th)edge.at<uchar>(r, c) = 255;}}}
}/*** @description:		Canny边缘检测* @param src			输入图像* @param dst			输出图像* @param Tl			低灰度阈值* @param Th			高灰度阈值* @param ksize			sobel算子掩膜大小* @param L2graydient	是否使用L2灰度梯度*/
void canny(cv::Mat &src, cv::Mat &dst, float Tl, float Th, int ksize = 3, bool L2graydient = false)
{cv::GaussianBlur(src, src, cv::Size(3, 3), 0);cv::Mat dx, dy, sobel_dst;sobel(src, sobel_dst, dx, dy, ksize);cv::Mat edgeMag;if (L2graydient)magnitude(dx, dy, edgeMag);  elseedgeMag = abs(dx) + abs(dy); cv::Mat edgeMag_noMaxsup = cv::Mat::zeros(src.size(), CV_32F);for (int i = 1; i < src.rows - 1; ++i){for (int j = 1; j < src.cols - 1; ++j) {float angle =  atan2f(dy.at<float>(i, j), dx.at<float>(i, j)) / CV_PI * 180; float cur = edgeMag.at<float>(i, j);  if (abs(angle) < 22.5 || abs(angle) > 157.5){float left = edgeMag.at<float>(i, j - 1);float right = edgeMag.at<float>(i, j + 1);if (cur >= left && cur >= right)edgeMag_noMaxsup.at<float>(i, j) = cur;}if ((angle >= 67.5 && angle <= 112.5) || (angle >= -112.5 && angle <= -67.5)) {float top = edgeMag.at<float>(i - 1, j);float down = edgeMag.at<float>(i + 1, j);if (cur >= top && cur >= down)edgeMag_noMaxsup.at<float>(i, j) = cur;}if ((angle>112.5 && angle <= 157.5) || (angle>-67.5 && angle <= -22.5)) {float right_top = edgeMag.at<float>(i - 1, j + 1);float left_down = edgeMag.at<float>(i + 1, j - 1);if (cur >= right_top && cur >= left_down)edgeMag_noMaxsup.at<float>(i, j) = cur;}if ((angle >= 22.5 && angle < 67.5) || (angle >= -157.5 && angle < -112.5)) {float left_top = edgeMag.at<float>(i - 1, j - 1);float right_down = edgeMag.at<float>(i + 1, j + 1);if (cur >= left_top && cur >= right_down)edgeMag_noMaxsup.at<float>(i, j) = cur;}}}dst = cv::Mat::zeros(src.size(), CV_8U);for (int i = 1; i < src.rows - 1; ++i) {for (int j = 1; j < src.cols - 1; ++j) {float mag = edgeMag_noMaxsup.at<float>(i, j);if (mag > Th)dst.at<uchar>(i, j) = 255;else if (mag < Tl)dst.at<uchar>(i, j) = 0;elsetrace(edgeMag_noMaxsup, dst, Th, i, j, src.rows, src.cols);}}
}