在x y的梯度上再求一次導數后相加便是散度。注意不要使用convertScaleAbs

int main(int argc, char*argv[])
{cv::Mat IMG = cv::imread("./img.jpg", 1);cv::Mat k = getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3), cv::Point(-1, -1));cv::Mat graySrc, grad_x, grad_y;cv::Mat abs_grad_x, abs_grad_y;cv::cvtColor(IMG, graySrc, cv::COLOR_BGR2GRAY);//cv::GaussianBlur(graySrc, graySrc, cv::Size(3,3), 0, 0, 4);// 計算x方向的散度cv::Mat Sobel_x = (Mat_<char>(3, 3) << -1, 0, 1, -2, 0, 2, -1, 0, 1);filter2D(graySrc, grad_x, CV_64FC1, Sobel_x, cv::Point(-1, -1), 0, 0);filter2D(grad_x, grad_x, CV_64FC1, Sobel_x, cv::Point(-1, -1), 0, 0);
//	Sobel(grad_x, grad_x, CV_32FC1, 1, 0, 3, 1, 0, cv::BORDER_DEFAULT);//convertScaleAbs(grad_x, abs_grad_x);// 計算y方向的散度cv::Mat Sobel_y = (Mat_<char>(3, 3) << 1, 2, 1, 0, 0, 0, -1, -2, -1);filter2D(graySrc, grad_y, CV_64FC1, Sobel_y, cv::Point(-1, -1), 0, 0);filter2D(grad_y, grad_y, CV_64FC1, Sobel_y, cv::Point(-1, -1), 0, 0);
//	Sobel(grad_y, grad_y, CV_32FC1, 0, 1, 3, 1, 0, cv::BORDER_DEFAULT);//convertScaleAbs(grad_y, abs_grad_y);// 合并散度cv::Mat divImage;addWeighted(grad_x, 0.5, grad_y, 0.5, 0, divImage);//morphologyEx(divImage, divImage, cv::MORPH_OPEN, k);morphologyEx(divImage, divImage, cv::MORPH_CLOSE, k);cv::imwrite("dstGradientImage.jpg", divImage);}

總結

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